Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

PubMed

Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

2015-09-01

National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

PubMed

Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

2016-08-01

Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk. The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.

Moving closer to understanding the risks of living kidney donation.

PubMed

Steiner, Robert W

2016-01-01

Recent studies from the United States and Norway have suggested an unexpected 8- to 11-fold relative risk of ESRD after kidney donation, but a low long-term absolute risk. Abundant renal epidemiologic data predict that these studies have underestimated long-term risk. The 1% lifetime post-donation risk in the US study requires medical screening to predict ESRD in 96 of 100 candidates. This is particularly unlikely in the 30-35% of candidates under age 35, half of whose lifetime ESRD will occur after age 64. Many experts have attributed the increased relative risks in these studies to loss of GFR at donation, which ultimately means that high-normal pre-donation GFRs will reduce absolute post-donation risks. The 8- to 11-fold relative risks predict implausible risks of uninephrectomy in the general population, but lower estimates still result in very high risks for black donors. Young vs. older age, low vs. high-normal pre-donation GFRs, black race, and an increased relative risk of donation all predict highly variable individual risks, not a single "low" or "1%" risk as these studies suggest. A uniform, ethically defensible donor selection protocol would accept older donors with many minor medical abnormalities but protect from donation many currently acceptable younger, black, and/or low GFR candidates. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Community-based Cross-sectional Study of Cardiovascular Risk in a Rural Community of Puducherry.

PubMed

Shrivastava, Saurabh R; Ghorpade, Arun G; Shrivastava, Prateek S

2015-01-01

The World Health Organization (WHO) / International Society of Hypertension (ISH) risk prediction chart can predict the risk of cardiovascular events in any population. To assess the prevalence of cardiovascular risk factors and to estimate the cardiovascular risk using the WHO/ISH risk charts. A cross-sectional study was done from November 2011 to January 2012 in a rural area of Puducherry. Method of sampling was a single stage cluster random sampling, and subjects were enrolled depending on their suitability with the inclusion and exclusion criteria. The data collection tool was a piloted and semi-structured questionnaire, while WHO/ISH cardiovascular risk prediction charts for the South-East Asian region was used to predict the cardiovascular risk. Institutional Ethics committee permission was obtained before the start of the study. Statistical analysis was done using SPSS version 16 and appropriate statistical tests were applied. The mean age in years was 54.2 (±11.1) years with 46.7% of the participants being male. On application of the WHO/ISH risk prediction charts, almost 17% of the study subjects had moderate or high risk for a cardiovascular event. Additionally, high salt diet, alcohol use and low HDL levels, were identified as the major CVD risk factors. To conclude, stratification of people on the basis of risk prediction chart is a major step to have a clear idea about the magnitude of the problem. The findings of the current study revealed that there is a high burden of CVD risk in the rural Puducherry.

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

PubMed

Bernecker, Samantha L; Rosellini, Anthony J; Nock, Matthew K; Chiu, Wai Tat; Gutierrez, Peter M; Hwang, Irving; Joiner, Thomas E; Naifeh, James A; Sampson, Nancy A; Zaslavsky, Alan M; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2018-04-03

High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

PubMed Central

Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

2015-01-01

Introduction National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the ‘high risk’ patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Methods Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien–Dindo classification. Results The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien–Dindo grade 2–3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4–5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Discussion Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. Conclusions In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the ‘high-risk’ patient. PMID:26468369

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

PubMed Central

2012-01-01

Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting. PMID:22417403

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

PubMed

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

Construction of a model predicting the risk of tube feeding intolerance after gastrectomy for gastric cancer based on 225 cases from a single Chinese center

PubMed Central

Xiaoyong, Wu; Xuzhao, Li; Deliang, Yu; Pengfei, Yu; Zhenning, Hang; Bin, Bai; zhengyan, Li; Fangning, Pang; Shiqi, Wang; Qingchuan, Zhao

2017-01-01

Identifying patients at high risk of tube feeding intolerance (TFI) after gastric cancer surgery may prevent the occurrence of TFI; however, a predictive model is lacking. We therefore analyzed the incidence of TFI and its associated risk factors after gastric cancer surgery in 225 gastric cancer patients divided into without-TFI (n = 114) and with-TFI (n = 111) groups. A total of 49.3% of patients experienced TFI after gastric cancer. Multivariate analysis identified a history of functional constipation (FC), a preoperative American Society of Anesthesiologists (ASA) score of III, a high pain score at 6-hour postoperation, and a high white blood cell (WBC) count on the first day after surgery as independent risk factors for TFI. The area under the curve (AUC) was 0.756, with an optimal cut-off value of 0.5410. In order to identify patients at high risk of TFI after gastric cancer surgery, we constructed a predictive nomogram model based on the selected independent risk factors to indicate the probability of developing TFI. Use of our predictive nomogram model in screening, if a probability > 0.5410, indicated a high-risk patients would with a 70.1% likelihood of developing TFI. These high-risk individuals should take measures to prevent TFI before feeding with enteral nutrition. PMID:29245951

Development and Validation of a Clinic-Based Prediction Tool to Identify Female Athletes at High Risk for Anterior Cruciate Ligament Injury

PubMed Central

Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy E.

2012-01-01

Background Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at high risk for anterior cruciate ligament injury. Laboratory-based measurements demonstrate 90% accuracy in prediction of high KAM. Clinic-based prediction algorithms that employ correlates derived from laboratory-based measurements also demonstrate high accuracy for prediction of high KAM mechanics during landing. Hypotheses Clinic-based measures derived from highly predictive laboratory-based models are valid for the accurate prediction of high KAM status, and simultaneous measurements using laboratory-based and clinic-based techniques highly correlate. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods One hundred female athletes (basketball, soccer, volleyball players) were tested using laboratory-based measures to confirm the validity of identified laboratory-based correlate variables to clinic-based measures included in a prediction algorithm to determine high KAM status. To analyze selected clinic-based surrogate predictors, another cohort of 20 female athletes was simultaneously tested with both clinic-based and laboratory-based measures. Results The prediction model (odds ratio: 95% confidence interval), derived from laboratory-based surrogates including (1) knee valgus motion (1.59: 1.17-2.16 cm), (2) knee flexion range of motion (0.94: 0.89°-1.00°), (3) body mass (0.98: 0.94-1.03 kg), (4) tibia length (1.55: 1.20-2.07 cm), and (5) quadriceps-to-hamstrings ratio (1.70: 0.48%-6.0%), predicted high KAM status with 84% sensitivity and 67% specificity (P < .001). Clinic-based techniques that used a calibrated physician’s scale, a standard measuring tape, standard camcorder, ImageJ software, and an isokinetic dynamometer showed high correlation (knee valgus motion, r = .87; knee flexion range of motion, r = .95; and tibia length, r = .98) to simultaneous laboratory-based measurements. Body mass and quadriceps-to-hamstrings ratio were included in both methodologies and therefore had r values of 1.0. Conclusion Clinically obtainable measures of increased knee valgus, knee flexion range of motion, body mass, tibia length, and quadriceps-to-hamstrings ratio predict high KAM status in female athletes with high sensitivity and specificity. Female athletes who demonstrate high KAM landing mechanics are at increased risk for anterior cruciate ligament injury and are more likely to benefit from neuromuscular training targeted to this risk factor. Use of the developed clinic-based assessment tool may facilitate high-risk athletes’ entry into appropriate interventions that will have greater potential to reduce their injury risk. PMID:20595554

Risk assessment models to predict caries recurrence after oral rehabilitation under general anaesthesia: a pilot study.

PubMed

Lin, Yai-Tin; Kalhan, Ashish Chetan; Lin, Yng-Tzer Joseph; Kalhan, Tosha Ashish; Chou, Chein-Chin; Gao, Xiao Li; Hsu, Chin-Ying Stephen

2018-05-08

Oral rehabilitation under general anaesthesia (GA), commonly employed to treat high caries-risk children, has been associated with high economic and individual/family burden, besides high post-GA caries recurrence rates. As there is no caries prediction model available for paediatric GA patients, this study was performed to build caries risk assessment/prediction models using pre-GA data and to explore mid-term prognostic factors for early identification of high-risk children prone to caries relapse post-GA oral rehabilitation. Ninety-two children were identified and recruited with parental consent before oral rehabilitation under GA. Biopsychosocial data collection at baseline and the 6-month follow-up were conducted using questionnaire (Q), microbiological assessment (M) and clinical examination (C). The prediction models constructed using data collected from Q, Q + M and Q + M + C demonstrated an accuracy of 72%, 78% and 82%, respectively. Furthermore, of the 83 (90.2%) patients recalled 6 months after GA intervention, recurrent caries was identified in 54.2%, together with reduced bacterial counts, lower plaque index and increased percentage of children toothbrushing for themselves (all P < 0.05). Additionally, meal-time and toothbrushing duration were shown, through bivariate analyses, to be significant prognostic determinants for caries recurrence (both P < 0.05). Risk assessment/prediction models built using pre-GA data may be promising in identifying high-risk children prone to post-GA caries recurrence, although future internal and external validation of predictive models is warranted. © 2018 FDI World Dental Federation.

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

PubMed Central

Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O.

2018-01-01

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping. PMID:29617333

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999-2012.

PubMed

Stefanoff, Pawel; Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O; Rosinska, Magdalena

2018-04-04

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

Prediction of Cardiovascular Disease by the Framingham-REGICOR Equation in the High-Risk PREDIMED Cohort: Impact of the Mediterranean Diet Across Different Risk Strata.

PubMed

Amor, Antonio J; Serra-Mir, Mercè; Martínez-González, Miguel A; Corella, Dolores; Salas-Salvadó, Jordi; Fitó, Montserrat; Estruch, Ramón; Serra-Majem, Lluis; Arós, Fernando; Babio, Nancy; Ros, Emilio; Ortega, Emilio

2017-03-13

The usefulness of cardiovascular disease (CVD) predictive equations in different populations is debatable. We assessed the efficacy of the Framingham-REGICOR scale, validated for the Spanish population, to identify future CVD in participants, who were predefined as being at high-risk in the PREvención con DIeta MEDiterránea (PREDIMED) study-a nutrition-intervention primary prevention trial-and the impact of adherence to the Mediterranean diet on CVD across risk categories. In a post hoc analysis, we assessed the CVD predictive value of baseline estimated risk in 5966 PREDIMED participants (aged 55-74 years, 57% women; 48% with type 2 diabetes mellitus). Major CVD events, the primary PREDIMED end point, were an aggregate of myocardial infarction, stroke, and cardiovascular death. Multivariate-adjusted Cox regression was used to calculate hazard ratios for major CVD events and effect modification from the Mediterranean diet intervention across risk strata (low, moderate, high, very high). The Framingham-REGICOR classification of PREDIMED participants was 25.1% low risk, 44.5% moderate risk, and 30.4% high or very high risk. During 6-year follow-up, 188 major CVD events occurred. Hazard ratios for major CVD events increased in parallel with estimated risk (2.68, 4.24, and 6.60 for moderate, high, and very high risk), particularly in men (7.60, 13.16, and 15.85, respectively, versus 2.16, 2.28, and 3.51, respectively, in women). Yet among those with low or moderate risk, 32.2% and 74.3% of major CVD events occurred in men and women, respectively. Mediterranean diet adherence was associated with CVD risk reduction regardless of risk strata ( P >0.4 for interaction). Incident CVD increased in parallel with estimated risk in the PREDIMED cohort, but most events occurred in non-high-risk categories, particularly in women. Until predictive tools are improved, promotion of the Mediterranean diet might be useful to reduce CVD independent of baseline risk. URL: http://www.Controlled-trials.com. Unique identifier: ISRCTN35739639. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Cancer Risk Prediction and Assessment

Cancer.gov

Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

Predict value of adiponectin for coronary atherosclerosis plaques according to computed tomography angiography in an asymptomatic population.

PubMed

Gan, Lu; Yang, Li; Yan, Guangtao

2018-05-25

The association between serum adiponectin levels and coronary atherosclerosis plaque characteristics in asymptomatic populations is unclear. To examine the predictive value of serum adiponectin levels for coronary high risk plaques as detected by computed tomography angiography (CTA). This was a cross-sectional study. All patients were divided into high risk plaque group and non high risk plaque group. The FRS was calculated for each patient. CTA was performed for each patient. Adiponectin levels were measured by flow fluorescence immunmicrobead assay (FFIA). Receiver-operating characteristic (ROC) curves and multivariate analysis was used to determine the predictive value of adiponectin for high risk plaques. The high risk plaque group showed lower adiponectin levels than non high risk plaque group (median, 7.27 vs. 8.51 μg/ml, P = 0.003). The multivariate analysis showed that age (OR = 2.62, 95%CI: 1.51-4.56, P = 0.001), hyperlipidemia (OR = 1.89, 95%CI: 1.07-3.36, P = 0.029), high-density lipoprotein cholesterol (HDL-C) (OR = 0.46, 95%CI: 0.24-0.87, P = 0.02), the ratio of total cholesterol to high-density lipoproteincholesterol (TC/HDL-C) (OR = 0.69, 95%CI: 0.50-0.94, P = 0.02), apolipoprotein B (apoB) (OR = 3.08, 95%CI: 1.50-6.32, P = 0.002), and adiponectin (OR = 0.37, 95%CI: 0.19-0.74, P = 0.005) were independently associated with the presence of high risk plaques. AUC of the multivariate model for high-risk plaques was 0.728 (95%CI: 0.627-0.783). Sensitivity was 74.9%, specificity was 60.2%, the positive predictive value was 65.3%, and the negative predictive value was 70.6%. Decreased adiponectin levels were associated with the presence of high-risk plaques in asymptomatic populations at low to intermediate FRS. Adiponectin can play an important role in plaque screening before coronary CTA. Copyright © 2018 Elsevier Inc. All rights reserved.

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

PubMed

Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

2016-10-01

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

Two unconventional risk factors for major adverse cardiovascular events in subjects with sexual dysfunction: low education and reported partner's hypoactive sexual desire in comparison with conventional risk factors.

PubMed

Rastrelli, Giulia; Corona, Giovanni; Fisher, Alessandra D; Silverii, Antonio; Mannucci, Edoardo; Maggi, Mario

2012-12-01

The classification of subjects as low or high cardiovascular (CV) risk is usually performed by risk engines, based upon multivariate prediction algorithms. However, their accuracy in predicting major adverse CV events (MACEs) is lower in high-risk populations as they take into account only conventional risk factors. To evaluate the accuracy of Progetto Cuore risk engine in predicting MACE in subjects with erectile dysfunction (ED) and to test the role of unconventional CV risk factors, specifically identified for ED. A consecutive series of 1,233 men (mean age 53.33 ± 9.08 years) attending our outpatient clinic for sexual dysfunction was longitudinally studied for a mean period of 4.4 ± 2.6 years. Several clinical, biochemical, and instrumental parameters were evaluated. Subjects were classified as high or low risk, according to previously reported ED-specific risk factors. In the overall population, Progetto Cuore-predicted population survival was not significantly different from the observed one (P = 0.545). Accordingly, receiver operating characteristic (ROC) analysis shows that Progetto Cuore has an accuracy of 0.697 ± 0.037 (P < 0.001) in predicting MACE. Considering subjects at high risk according to ED-specific risk factors, the observed incidence of MACE was significantly higher than the expected for both low educated and patients reporting partner's hypoactive sexual desire (HSD, both <0.05), but not for other described factors. The area under ROC curves of Progetto Cuore for MACE in subjects with low education and reported partner's HSD were 0.659 ± 0.053 (P = 0.008) and 0.550 ± 0.076 (P = 0.570), respectively. Overall, Progetto Cuore is a proper instrument for evaluating CV risk in ED subjects. However, in ED, other factors such as low education and partner's HSD concur to risk profile. At variance with low education, Progetto Cuore is not accurate enough to predict MACE in subjects with partner's HSD, suggesting that the latter effect is not mediated by conventional risk factors included in the algorithm. © 2012 International Society for Sexual Medicine.

Psychosis prediction and clinical utility in familial high-risk studies: Selective review, synthesis, and implications for early detection and intervention

PubMed Central

Shah, Jai L.; Tandon, Neeraj; Keshavan, Matcheri S.

2016-01-01

Aim Accurate prediction of which individuals will go on to develop psychosis would assist early intervention and prevention paradigms. We sought to review investigations of prospective psychosis prediction based on markers and variables examined in longitudinal familial high-risk (FHR) studies. Methods We performed literature searches in MedLine, PubMed and PsycINFO for articles assessing performance characteristics of predictive clinical tests in FHR studies of psychosis. Studies were included if they reported one or more predictive variables in subjects at FHR for psychosis. We complemented this search strategy with references drawn from articles, reviews, book chapters and monographs. Results Across generations of familial high-risk projects, predictive studies have investigated behavioral, cognitive, psychometric, clinical, neuroimaging, and other markers. Recent analyses have incorporated multivariate and multi-domain approaches to risk ascertainment, although with still generally modest results. Conclusions While a broad range of risk factors has been identified, no individual marker or combination of markers can at this time enable accurate prospective prediction of emerging psychosis for individuals at FHR. We outline the complex and multi-level nature of psychotic illness, the myriad of factors influencing its development, and methodological hurdles to accurate and reliable prediction. Prospects and challenges for future generations of FHR studies are discussed in the context of early detection and intervention strategies. PMID:23693118

Problematic Dichotomization of Risk for Intensive Care Unit (ICU)-Acquired Invasive Candidiasis: Results Using a Risk-Predictive Model to Categorize 3 Levels of Risk From a Multicenter Prospective Cohort of Australian ICU Patients.

PubMed

Playford, E Geoffrey; Lipman, Jeffrey; Jones, Michael; Lau, Anna F; Kabir, Masrura; Chen, Sharon C-A; Marriott, Deborah J; Seppelt, Ian; Gottlieb, Thomas; Cheung, Winston; Iredell, Jonathan R; McBryde, Emma S; Sorrell, Tania C

2016-12-01

 Delayed antifungal therapy for invasive candidiasis (IC) contributes to poor outcomes. Predictive risk models may allow targeted antifungal prophylaxis to those at greatest risk.  A prospective cohort study of 6685 consecutive nonneutropenic patients admitted to 7 Australian intensive care units (ICUs) for ≥72 hours was performed. Clinical risk factors for IC occurring prior to and following ICU admission, colonization with Candida species on surveillance cultures from 3 sites assessed twice weekly, and the occurrence of IC ≥72 hours following ICU admission or ≤72 hours following ICU discharge were measured. From these parameters, a risk-predictive model for the development of ICU-acquired IC was then derived.  Ninety-six patients (1.43%) developed ICU-acquired IC. A simple summation risk-predictive model using the 10 independently significant variables associated with IC demonstrated overall moderate accuracy (area under the receiver operating characteristic curve = 0.82). No single threshold score could categorize patients into clinically useful high- and low-risk groups. However, using 2 threshold scores, 3 patient cohorts could be identified: those at high risk (score ≥6, 4.8% of total cohort, positive predictive value [PPV] 11.7%), those at low risk (score ≤2, 43.1% of total cohort, PPV 0.24%), and those at intermediate risk (score 3-5, 52.1% of total cohort, PPV 1.46%).  Dichotomization of ICU patients into high- and low-risk groups for IC risk is problematic. Categorizing patients into high-, intermediate-, and low-risk groups may more efficiently target early antifungal strategies and utilization of newer diagnostic tests. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

A utility/cost analysis of breast cancer risk prediction algorithms

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

2016-03-01

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Assessing risk of reoffending in adolescents who have committed a sexual offense: the accuracy of clinical judgments after completion of risk assessment instruments.

PubMed

Elkovitch, Natasha; Viljoen, Jodi L; Scalora, Mario J; Ullman, Daniel

2008-01-01

As courts often rely on clinicians when differentiating between sexually abusive youth at a low versus high risk of reoffense, understanding factors that contribute to accuracy in assessment of risk is imperative. The present study built on existing research by examining (1) the accuracy of clinical judgments of risk made after completing risk assessment instruments, (2) whether instrument-informed clinical judgments made with a high degree of confidence are associated with greater accuracy, and (3) the risk assessment instruments and subscales most predictive of clinical judgments. Raters assessed each youth's (n = 166) risk of reoffending after completing the SAVRY and J-SOAP-II. Raters were not able to predict detected cases of either sexual recidivism or nonsexual violent recidivism above chance, and a high degree of rater confidence was not associated with higher levels of accuracy. Total scores on the J-SOAP-II were predictive of instrument-informed clinical judgments of sexual risk, and total scores on the SAVRY of nonsexual risk.

Association of Lipid Accumulation Product with Cardio-Metabolic Risk Factors in Postmenopausal Women.

PubMed

Namazi Shabestari, Alireza; Asadi, Mojgan; Jouyandeh, Zahra; Qorbani, Mostafa; Kelishadi, Roya

2016-06-01

The lipid accumulation product is a novel, safe and inexpensive index of central lipid over accumulation based on waist circumference and fasting concentration of circulating triglycerides. This study was designed to investigate the ability of lipid accumulation product to predict Cardio-metabolic risk factors in postmenopausal women. In this Cross-sectional study, 264 postmenopausal women by using convenience sampling method were selected from menopause clinic in Tehran. Cardio-metabolic risk factors were measured, and lipid accumulation product (waist-58×triglycerides [nmol/L]) was calculated. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was estimated by ROC (Receiver-operating characteristic) curve analysis. Metabolic syndrome was diagnosed in 41.2% of subjects. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was 47.63 (sensitivity:75%; specificity:77.9%). High lipid accumulation product increases risk of all Cardio-metabolic risk factors except overweight, high Total Cholesterol, high Low Density Lipoprotein Cholesterol and high Fasting Blood Sugar in postmenopausal women. Our findings show that lipid accumulation product is associated with metabolic syndrome and some Cardio-metabolic risk factors Also lipid accumulation product may have been a useful tool for predicting cardiovascular disease and metabolic syndrome risk in postmenopausal women.

Mortality determinants and prediction of outcome in high risk newborns.

PubMed

Dalvi, R; Dalvi, B V; Birewar, N; Chari, G; Fernandez, A R

1990-06-01

The aim of this study was to determine independent patient-related predictors of mortality in high risk newborns admitted at our centre. The study population comprised 100 consecutive newborns each, from the premature unit (PU) and sick baby care unit (SBCU), respectively. Thirteen high risk factors (variables) for each of the two units, were entered into a multivariate regression analysis. Variables with independent predictive value for poor outcome (i.e., death) in PU were, weight less than 1 kg, hyaline membrane disease, neurologic problems, and intravenous therapy. High risk factors in SBCU included, blood gas abnormality, bleeding phenomena, recurrent convulsions, apnea, and congenital anomalies. Identification of these factors guided us in defining priority areas for improvement in our system of neonatal care. Also, based on these variables a simple predictive score for outcome was constructed. The prediction equation and the score were cross-validated by applying them to a 'test-set' of 100 newborns each for PU and SBCU. Results showed a comparable sensitivity, specificity and error rate.

Cardiovascular risk of patients with gout seen at rheumatology clinics following a structured assessment.

PubMed

Andrés, Mariano; Bernal, José Antonio; Sivera, Francisca; Quilis, Neus; Carmona, Loreto; Vela, Paloma; Pascual, Eliseo

2017-07-01

Gout-associated cardiovascular (CV) risk relates to comorbidities and crystal-led inflammation. The aim was to estimate the CV risk by prediction tools in new patients with gout and to assess whether ultrasonographic carotid changes are present in patients without high CV risk. Cross-sectional study. Consecutive new patients with crystal-proven gout underwent a structured CV consultation, including CV events, risk factors and two risk prediction tools-the Systematic COronary Evaluation (SCORE) and the Framingham Heart Study (FHS). CV risk was stratified according to current European guidelines. Carotid ultrasound (cUS) was performed in patients with less than very high CV risk. The presence of carotid plaques was studied depending on the SCORE and FHS by the area under the curve (AUC) of receiver operating curves. 237 new patients with gout were recruited. CV stratification by scores showed a predominance of very high (95 patients, 40.1%) and moderate (72 patients, 30.5%) risk levels. cUS was performed in 142 patients, finding atheroma plaques in 66 (46.5%, 95% CI 37.8 to 54.2). Following cUS findings, patients classified as very high risk increased from 40.1% up to 67.9% (161/237 patients). SCORE and FHS predicted moderately (AUC 0.711 and 0.683, respectively) the presence of atheroma plaques at cUS. The majority of patients presenting with gout may be at very high CV risk, indicating the need for initiating optimal prevention strategies at this stage. Risk prediction tools appear to underestimate the presence of carotid plaque in patients with gout. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Microalbuminuria could improve risk stratification in patients with TIA and minor stroke.

PubMed

Elyas, Salim; Shore, Angela C; Kingwell, Hayley; Keenan, Samantha; Boxall, Leigh; Stewart, Jane; James, Martin A; Strain, William David

2016-09-01

Transient ischemic attacks (TIA) and minor strokes are important risk factors for recurrent strokes. Current stroke risk prediction scores such as ABCD2, although widely used, lack optimal sensitivity and specificity. Elevated urinary albumin excretion predicts cardiovascular disease, stroke, and mortality. We explored the role of microalbuminuria (using albumin creatinine ratio (ACR)) in predicting recurrence risk in patients with TIA and minor stroke. Urinary ACR was measured on a spot sample in 150 patients attending a daily stroke clinic with TIA or minor stroke. Patients were followed up at day 7, 30, and 90 to determine recurrent stroke, cardiovascular events, or death. Eligible patients had a carotid ultrasound Doppler investigation. High-risk patients were defined as those who had an event within 90 days or had >50% internal carotid artery (ICA) stenosis. Fourteen (9.8%) recurrent events were reported by day 90 including two deaths. Fifteen patients had severe ICA stenosis. In total, 26 patients were identified as high risk. These patients had a higher frequency of previous stroke or hypercholesterolemia compared to low-risk patients (P = 0.04). ACR was higher in high-risk patients (3.4 [95% CI 2.2-5.2] vs. 1.7 [1.5-2.1] mg/mmol, P = 0.004), independent of age, sex, blood pressure, diabetes, and previous stroke. An ACR greater than 1.5 mg/mmol predicted high-risk patients (Cox proportional hazard ratio 3.5 (95% CI 1.3-9.5, P = 0.01). After TIA or minor stroke, a higher ACR predicted recurrent events and significant ICA stenosis. Incorporation of urinary ACR from a spot sample in the acute setting could improve risk stratification in patients with TIA and minor stroke.

A Latent Class Analysis of Maternal Responsiveness and Autonomy-Granting in Early Adolescence: Prediction to Later Adolescent Sexual Risk-Taking

PubMed Central

Lanza, H. Isabella; Huang, David Y. C.; Murphy, Debra A.; Hser, Yih-Ing

2013-01-01

The present study sought to extend empirical inquiry related to the role of parenting on adolescent sexual risk-taking by using latent class analysis (LCA) to identify patterns of adolescent-reported mother responsiveness and autonomy-granting in early adolescence and examine associations with sexual risk-taking in mid- and late-adolescence. Utilizing a sample of 12- to 14-year-old adolescents (N = 4,743) from the 1997 National Longitudinal Survey of Youth (NLSY97), results identified a four-class model of maternal responsiveness and autonomy-granting: low responsiveness/high autonomy-granting, moderate responsiveness/moderate autonomy-granting, high responsiveness/low autonomy-granting, high responsiveness/moderate autonomy-granting. Membership in the low responsiveness/high autonomy-granting class predicted greater sexual risk-taking in mid- and late-adolescence compared to all other classes, and membership in the high responsiveness/ moderate autonomy-granting class predicted lower sexual risk-taking. Gender and ethnic differences in responsiveness and autonomy-granting class membership were also found, potentially informing gender and ethnic disparities of adolescent sexual risk-taking. PMID:23828712

Understanding Interrater Reliability and Validity of Risk Assessment Tools Used to Predict Adverse Clinical Events.

PubMed

Siedlecki, Sandra L; Albert, Nancy M

This article will describe how to assess interrater reliability and validity of risk assessment tools, using easy-to-follow formulas, and to provide calculations that demonstrate principles discussed. Clinical nurse specialists should be able to identify risk assessment tools that provide high-quality interrater reliability and the highest validity for predicting true events of importance to clinical settings. Making best practice recommendations for assessment tool use is critical to high-quality patient care and safe practices that impact patient outcomes and nursing resources. Optimal risk assessment tool selection requires knowledge about interrater reliability and tool validity. The clinical nurse specialist will understand the reliability and validity issues associated with risk assessment tools, and be able to evaluate tools using basic calculations. Risk assessment tools are developed to objectively predict quality and safety events and ultimately reduce the risk of event occurrence through preventive interventions. To ensure high-quality tool use, clinical nurse specialists must critically assess tool properties. The better the tool's ability to predict adverse events, the more likely that event risk is mediated. Interrater reliability and validity assessment is relatively an easy skill to master and will result in better decisions when selecting or making recommendations for risk assessment tool use.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

PubMed

Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J; Wernli, Karen J; Katki, Hormuzd A

2017-12-01

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Location of Food Stores Near Schools Does Not Predict the Weight Status of Maine High School Students

ERIC Educational Resources Information Center

Harris, David E.; Blum, Janet Whatley; Bampton, Matthew; O'Brien, Liam M.; Beaudoin, Christina M.; Polacsek, Michele; O'Rourke, Karen A.

2011-01-01

Objective: To examine the relationship between stores selling calorie-dense food near schools and student obesity risk, with the hypothesis that high availability predicts increased risk. Methods: Mail surveys determined height, weight, and calorie-dense food consumption for 552 students at 11 Maine high schools. Driving distance from all food…

Prospective evaluation of a screening protocol to exclude deep vein thrombosis on the basis of a combination of quantitative D-dimer testing and pretest clinical probability score.

PubMed

Yamaki, Takashi; Nozaki, Motohiro; Sakurai, Hiroyuki; Takeuchi, Masaki; Soejima, Kazutaka; Kono, Taro

2005-11-01

Clinical signs and symptoms such as swelling, pain, and redness are unreliable markers of deep vein thrombosis (DVT). Because of this venous duplex scanning (VDS) has been heavily used in DVT detection. The purpose of this study was to determine if a combination of D-dimer testing and pretest clinical score could reduce the use of VDS in symptomatic patients with suspected DVT. One hundred seventy-four consecutive patients with suspected DVT were prospectively evaluated using pretest clinical probability (PCP) score and D-dimer testing before VDS. After calculating clinical probability scores developed by Wells and associates, patients were divided into low risk (or=3 points) PCP. One hundred fifty-eight patients were enrolled. The prevalence of DVT in this study was 37%. Thirty-eight patients (24%) were classified as low risk, 64 (41%) as moderate risk, and 56 (35%) as high risk PCP. DVT was identified in only one patient (2.6%) with low risk PCP. In contrast, DVT was found in 22 (34%) with moderate risk, and 35 (63%) with high risk PCP. In the high and moderate risk PCP groups, positive scan patients had a markedly higher value of D-dimer assay than negative scan patients (p=0.0001 and p=0.0057, respectively). In the low risk PCP patients, D-dimer testing provided 100% sensitivity, 46% specificity, 4.8% positive predictive value, and 100% negative predictive value in the diagnosis of DVT. Similarly, in the moderate risk PCP, the D-dimer testing showed 100% sensitivity, 45% specificity, 49% positive predictive value, and 100% negative predictive value. In the high risk group, D-dimer testing achieved 100% sensitivity, 57% specificity, 80% positive predictive value, and 100% negative predictive value in the diagnosis of DVT. These results suggested that 36 of 158 patients who had a non-high PCP (low and moderate PCP) and a normal D-dimer concentration were considered to have no additional investigation, so VDS could have been reduced by 23% (36/158). A combination of D-dimer testing and clinical probability score may be effective in avoiding unnecessary VDS in suspected symptomatic DVT in the low and moderate PCP patients. The need for VDS could be reduced by 23% despite a relatively high prevalence of DVT.

Alternative evaluation metrics for risk adjustment methods.

PubMed

Park, Sungchul; Basu, Anirban

2018-06-01

Risk adjustment is instituted to counter risk selection by accurately equating payments with expected expenditures. Traditional risk-adjustment methods are designed to estimate accurate payments at the group level. However, this generates residual risks at the individual level, especially for high-expenditure individuals, thereby inducing health plans to avoid those with high residual risks. To identify an optimal risk-adjustment method, we perform a comprehensive comparison of prediction accuracies at the group level, at the tail distributions, and at the individual level across 19 estimators: 9 parametric regression, 7 machine learning, and 3 distributional estimators. Using the 2013-2014 MarketScan database, we find that no one estimator performs best in all prediction accuracies. Generally, machine learning and distribution-based estimators achieve higher group-level prediction accuracy than parametric regression estimators. However, parametric regression estimators show higher tail distribution prediction accuracy and individual-level prediction accuracy, especially at the tails of the distribution. This suggests that there is a trade-off in selecting an appropriate risk-adjustment method between estimating accurate payments at the group level and lower residual risks at the individual level. Our results indicate that an optimal method cannot be determined solely on the basis of statistical metrics but rather needs to account for simulating plans' risk selective behaviors. Copyright © 2018 John Wiley & Sons, Ltd.

Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

PubMed

Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

2015-11-01

To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting Financial Distress and Closure in Rural Hospitals.

PubMed

Holmes, George M; Kaufman, Brystana G; Pink, George H

2017-06-01

Annual rates of rural hospital closure have been increasing since 2010, and hospitals that close have poor financial performance relative to those that remain open. This study develops and validates a latent index of financial distress to forecast the probability of financial distress and closure within 2 years for rural hospitals. Hospital and community characteristics are used to predict the risk of financial distress 2 years in the future. Financial and community data were drawn for 2,466 rural hospitals from 2000 through 2013. We tested and validated a model predicting a latent index of financial distress (FDI), measured by unprofitability, equity decline, insolvency, and closure. Using the predicted FDI score, hospitals are assigned to high, medium-high, medium-low, and low risk of financial distress for use by practitioners. The FDI forecasts 8.01% of rural hospitals to be at high risk of financial distress in 2015, 16.3% as mid-high, 46.8% as mid-low, and 28.9% as low risk. The rate of closure for hospitals in the high-risk category is 4 times the rate in the mid-high category and 28 times that in the mid-low category. The ability of the FDI to discriminate hospitals experiencing financial distress is supported by a c-statistic of .74 in a validation sample. This methodology offers improved specificity and predictive power relative to existing measures of financial distress applied to rural hospitals. This risk assessment tool may inform programs at the federal, state, and local levels that provide funding or support to rural hospitals. © 2016 National Rural Health Association.

Delayed risk stratification, to include the response to initial treatment (surgery and radioiodine ablation), has better outcome predictivity in differentiated thyroid cancer patients.

PubMed

Castagna, Maria Grazia; Maino, Fabio; Cipri, Claudia; Belardini, Valentina; Theodoropoulou, Alexandra; Cevenini, Gabriele; Pacini, Furio

2011-09-01

After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed. To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8-12 months after initial treatment). We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8-12 months after initial treatment, patients were re-stratified according to their clinical status: DRS. Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively, P<0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS. Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

Techniques for predicting high-risk drivers for alcohol countermeasures. Volume 1, Technical report

DOT National Transportation Integrated Search

1979-05-01

This technical report, a companion to the Volume II User Manual by the same name describes the development and testing of predictive models for identifying individual with a high risk of alcohol/related (A/R) crash involvement. From a literature revi...

Development of a Risk Prediction Model and Clinical Risk Score for Isolated Tricuspid Valve Surgery.

PubMed

LaPar, Damien J; Likosky, Donald S; Zhang, Min; Theurer, Patty; Fonner, C Edwin; Kern, John A; Bolling, Stephen F; Drake, Daniel H; Speir, Alan M; Rich, Jeffrey B; Kron, Irving L; Prager, Richard L; Ailawadi, Gorav

2018-02-01

While tricuspid valve (TV) operations remain associated with high mortality (∼8-10%), no robust prediction models exist to support clinical decision-making. We developed a preoperative clinical risk model with an easily calculable clinical risk score (CRS) to predict mortality and major morbidity after isolated TV surgery. Multi-state Society of Thoracic Surgeons database records were evaluated for 2,050 isolated TV repair and replacement operations for any etiology performed at 50 hospitals (2002-2014). Parsimonious preoperative risk prediction models were developed using multi-level mixed effects regression to estimate mortality and composite major morbidity risk. Model results were utilized to establish a novel CRS for patients undergoing TV operations. Models were evaluated for discrimination and calibration. Operative mortality and composite major morbidity rates were 9% and 42%, respectively. Final regression models performed well (both P<0.001, AUC = 0.74 and 0.76) and included preoperative factors: age, gender, stroke, hemodialysis, ejection fraction, lung disease, NYHA class, reoperation and urgent or emergency status (all P<0.05). A simple CRS from 0-10+ was highly associated (P<0.001) with incremental increases in predicted mortality and major morbidity. Predicted mortality risk ranged from 2%-34% across CRS categories, while predicted major morbidity risk ranged from 13%-71%. Mortality and major morbidity after isolated TV surgery can be predicted using preoperative patient data from the STS Adult Cardiac Database. A simple clinical risk score predicts mortality and major morbidity after isolated TV surgery. This score may facilitate perioperative counseling and identification of suitable patients for TV surgery. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study.

PubMed

Parikh, Nisha I; Pencina, Michael J; Wang, Thomas J; Benjamin, Emelia J; Lanier, Katherine J; Levy, Daniel; D'Agostino, Ralph B; Kannel, William B; Vasan, Ramachandran S

2008-01-15

Studies suggest that targeting high-risk, nonhypertensive individuals for treatment may delay hypertension onset, thereby possibly mitigating vascular complications. Risk stratification may facilitate cost-effective approaches to management. To develop a simple risk score for predicting hypertension incidence by using measures readily obtained in the physician's office. Longitudinal cohort study. Framingham Heart Study, Framingham, Massachusetts. 1717 nonhypertensive white individuals 20 to 69 years of age (mean age, 42 years; 54% women), without diabetes and with both parents in the original cohort of the Framingham Heart Study, contributed 5814 person-examinations. Scores were developed for predicting the 1-, 2-, and 4-year risk for new-onset hypertension, and performance characteristics of the prediction algorithm were assessed by using calibration and discrimination measures. Parental hypertension was ascertained from examinations of the original cohort of the Framingham Heart Study. During follow-up (median time over all person-examinations, 3.8 years), 796 persons (52% women) developed new-onset hypertension. In multivariable analyses, age, sex, systolic and diastolic blood pressure, body mass index, parental hypertension, and cigarette smoking were significant predictors of hypertension. According to the risk score based on these factors, the 4-year risk for incident hypertension was classified as low (<5%) in 34% of participants, medium (5% to 10%) in 19%, and high (>10%) in 47%. The c-statistic for the prediction model was 0.788, and calibration was very good. The risk score findings may not be generalizable to persons of nonwhite race or ethnicity or to persons with diabetes. The risk score algorithm has not been validated in an independent cohort and is based on single measurements of risk factors and blood pressure. The hypertension risk prediction score can be used to estimate an individual's absolute risk for hypertension on short-term follow-up, and it represents a simple, office-based tool that may facilitate management of high-risk individuals with prehypertension.

The predictive validity of the HERO Scorecard in determining future health care cost and risk trends.

PubMed

Goetzel, Ron Z; Henke, Rachel Mosher; Benevent, Richele; Tabrizi, Maryam J; Kent, Karen B; Smith, Kristyn J; Roemer, Enid Chung; Grossmeier, Jessica; Mason, Shawn T; Gold, Daniel B; Noeldner, Steven P; Anderson, David R

2014-02-01

To determine the ability of the Health Enhancement Research Organization (HERO) Scorecard to predict changes in health care expenditures. Individual employee health care insurance claims data for 33 organizations completing the HERO Scorecard from 2009 to 2011 were linked to employer responses to the Scorecard. Organizations were dichotomized into "high" versus "low" scoring groups and health care cost trends were compared. A secondary analysis examined the tool's ability to predict health risk trends. "High" scorers experienced significant reductions in inflation-adjusted health care costs (averaging an annual trend of -1.6% over 3 years) compared with "low" scorers whose cost trend remained stable. The risk analysis was inconclusive because of the small number of employers scoring "low." The HERO Scorecard predicts health care cost trends among employers. More research is needed to determine how well it predicts health risk trends for employees.

The Costs and Risks of Social Activism: A Study of Sanctuary Movement Activism.

ERIC Educational Resources Information Center

Wiltfang, Gregory L.; McAdam, Doug

1991-01-01

Among 141 activists with varying levels of participation in the sanctuary movement, biographical availability factors--younger age and greater discretionary time--best predict high-cost activism (more hours devoted to the movement), whereas ideological socialization factors best predict high-risk activism (direct contact with refugees). Contains…

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu.

PubMed

Neyra, Javier A; Leaf, David E

2018-05-31

Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality. Among critically ill patients admitted to intensive care units (ICUs), the incidence of AKI is as high as 50% and is associated with dismal outcomes. Thus, the development and validation of clinical risk prediction tools that accurately identify patients at high risk for AKI in the ICU is of paramount importance. We provide a comprehensive review of 3 clinical risk prediction tools that have been developed for incident AKI occurring in the first few hours or days following admission to the ICU. We found substantial heterogeneity among the clinical variables that were examined and included as significant predictors of AKI in the final models. The area under the receiver operating characteristic curves was ∼0.8 for all 3 models, indicating satisfactory model performance, though positive predictive values ranged from only 23 to 38%. Hence, further research is needed to develop more accurate and reproducible clinical risk prediction tools. Strategies for improved assessment of AKI susceptibility in the ICU include the incorporation of dynamic (time-varying) clinical parameters, as well as biomarker, functional, imaging, and genomic data. © 2018 S. Karger AG, Basel.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

PubMed

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

Risk and protective factors as predictors of outcome in adolescents with psychiatric disorder and aggression.

PubMed

Vance, J Eric; Bowen, Natasha K; Fernandez, Gustavo; Thompson, Shealy

2002-01-01

To identify predictors of behavioral outcomes in high-risk adolescents with aggression and serious emotional disturbance (SED). Three hundred thirty-seven adolescents from a statewide North Carolina treatment program for aggressive youths with SED were followed between July 1995 and June 1999 from program entry (T1) to approximately 1 year later (T2). Historical and current psychosocial risk and protective factors as well as psychiatric symptom severity at T1 were tested as predictors of high and low behavioral functioning at T2. Behavioral functioning was a composite based on the frequency of risk-taking, self-injurious, threatening, and assaultive behavior. Eleven risk and protective factors were predictive of T2 behavioral functioning, while none of the measured T1 psychiatric symptoms was predictive. A history of aggression and negative parent-child relationships in childhood was predictive of worse T2 behavior, as was lower IQ. Better T2 behavioral outcomes were predicted by a history of consistent parental employment and positive parent-child relations, higher levels of current family support, contact with prosocial peers, higher reading level, good problem-solving abilities, and superior interpersonal skills. Among high-risk adolescents with aggression and SED, psychiatric symptom severity may be a less important predictor of behavioral outcomes than certain risk and protective factors. Several factors predictive of good behavioral functioning represent feasible intervention targets.

Eating in the absence of hunger during childhood predicts self-reported binge eating in adolescence.

PubMed

Balantekin, Katherine N; Birch, Leann L; Savage, Jennifer S

2017-01-01

The objectives of the current study were to examine whether eating in the absence of hunger (EAH) at age 7 predicted reports of self-reported binge eating at age 15 and to identify factors among girls with high-EAH that moderated risk of later binge eating. Subjects included 158 girls assessed at age 7 and age 15. Logistic regression was used to predict binge eating at age 15 from calories consumed during EAH at age 7. A series of logistic regressions were used to examine the odds of reporting binge eating given levels of risk factors (e.g., anxiety) among those with high-EAH in childhood. Girls' EAH intake predicted reports of binge eating at age 15; after adjusting for age 7 BMI, for each additional 100kcal consumed, girls were 1.7 times more likely to report binge eating in adolescence. Among those with high-EAH, BMI, anxiety, depression, dietary restraint, emotional disinhibition, and body dissatisfaction all predicted binge eating. EAH during childhood predicted reports of binge eating during adolescence; girls with elevated BMI, negative affect, and maladaptive eating- and weight-related cognitions were at increased risk. High-EAH in childhood may be useful for indicating those at risk for developing binge eating. Copyright © 2016 Elsevier Ltd. All rights reserved.

Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk

PubMed Central

Abdul-Ghani, Muhammad A.; Abdul-Ghani, Tamam; Stern, Michael P.; Karavic, Jasmina; Tuomi, Tiinamaija; Bo, Insoma; DeFronzo, Ralph A.; Groop, Leif

2011-01-01

OBJECTIVE To develop a model for the prediction of type 2 diabetes mellitus (T2DM) risk on the basis of a multivariate logistic model and 1-h plasma glucose concentration (1-h PG). RESEARCH DESIGN AND METHODS The model was developed in a cohort of 1,562 nondiabetic subjects from the San Antonio Heart Study (SAHS) and validated in 2,395 nondiabetic subjects in the Botnia Study. A risk score on the basis of anthropometric parameters, plasma glucose and lipid profile, and blood pressure was computed for each subject. Subjects with a risk score above a certain cut point were considered to represent high-risk individuals, and their 1-h PG concentration during the oral glucose tolerance test was used to further refine their future T2DM risk. RESULTS We used the San Antonio Diabetes Prediction Model (SADPM) to generate the initial risk score. A risk-score value of 0.065 was found to be an optimal cut point for initial screening and selection of high-risk individuals. A 1-h PG concentration >140 mg/dL in high-risk individuals (whose risk score was >0.065) was the optimal cut point for identification of subjects at increased risk. The two cut points had 77.8, 77.4, and 44.8% (for the SAHS) and 75.8, 71.6, and 11.9% (for the Botnia Study) sensitivity, specificity, and positive predictive value, respectively, in the SAHS and Botnia Study. CONCLUSIONS A two-step model, based on the combination of the SADPM and 1-h PG, is a useful tool for the identification of high-risk Mexican-American and Caucasian individuals. PMID:21788628

Gender differences in predicting high-risk drinking among undergraduate students.

PubMed

Wilke, Dina J; Siebert, Darcy Clay; Delva, Jorge; Smith, Michael P; Howell, Richard L

2005-01-01

The purpose of this study was to examine gender differences in college students' high-risk drinking as measured by an estimated blood alcohol concentration (eBAC) based on gender, height, weight, self-reported number of drinks, and hours spent drinking. Using a developmental/contextual framework, high-risk drinking is conceptualized as a function of relevant individual characteristics, interpersonal factors, and contextual factors regularly mentioned in the college drinking literature. Individual characteristics include race, gender, and age; interpersonal characteristics include number of sexual partners and having experienced forced sexual contact. Finally, contextual factors include Greek membership, living off-campus, and perception of peer drinking behavior. This study is a secondary data analysis of 1,422 students at a large university in the Southeast. Data were gathered from a probability sample of students through a mail survey. A three-step hierarchical logistic regression analysis showed gender differences in the pathway for high-risk drinking. For men, high-risk drinking was predicted by a combination of individual characteristics and contextual factors. For women, interpersonal factors, along with individual characteristics and contextual factors, predicted high-risk drinking, highlighting the importance of understanding female sexual relationships and raising questions about women's risk-taking behavior. Implications for prevention and assessment are discussed.

Dissociable effects of surprising rewards on learning and memory.

PubMed

Rouhani, Nina; Norman, Kenneth A; Niv, Yael

2018-03-19

Reward-prediction errors track the extent to which rewards deviate from expectations, and aid in learning. How do such errors in prediction interact with memory for the rewarding episode? Existing findings point to both cooperative and competitive interactions between learning and memory mechanisms. Here, we investigated whether learning about rewards in a high-risk context, with frequent, large prediction errors, would give rise to higher fidelity memory traces for rewarding events than learning in a low-risk context. Experiment 1 showed that recognition was better for items associated with larger absolute prediction errors during reward learning. Larger prediction errors also led to higher rates of learning about rewards. Interestingly we did not find a relationship between learning rate for reward and recognition-memory accuracy for items, suggesting that these two effects of prediction errors were caused by separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for items within the high-risk context. In Experiment 3, we controlled for the difficulty of reward learning in the risk environments, again replicating the previous results. Moreover, this control revealed that the high-risk context enhanced item-recognition memory beyond the effect of prediction errors. In summary, our results show that prediction errors boost both episodic item memory and incremental reward learning, but the two effects are likely mediated by distinct underlying systems. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

PubMed

Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

2013-04-01

Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

The Dose-Volume Relationship of Small Bowel Irradiation and Acute Grade 3 Diarrhea During Chemoradiotherapy for Rectal Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, John M.; Lockman, David; Yan Di

Purpose: Previous work has found a highly significant relationship between the irradiated small-bowel volume and development of Grade 3 small-bowel toxicity in patients with rectal cancer. This study tested the previously defined parameters in a much larger group of patients. Methods and Materials: A total of 96 consecutive patients receiving pelvic radiation therapy for rectal cancer had treatment planning computed tomographic scans with small-bowel contrast that allowed the small bowel to be outlined with calculation of a small-bowel dose-volume histogram for the initial intended pelvic treatment to 45 Gy. Patients with at least one parameter above the previously determined dose-volumemore » parameters were considered high risk, whereas those with all parameters below these levels were low risk. The grade of diarrhea and presence of liquid stool was determined prospectively. Results: There was a highly significant association with small-bowel dose-volume and Grade 3 diarrhea (p {<=} 0.008). The high-risk and low-risk parameters were predictive with Grade 3 diarrhea in 16 of 51 high-risk patients and in 4 of 45 low-risk patients (p = 0.01). Patients who had undergone irradiation preoperatively had a lower incidence of Grade 3 diarrhea than those treated postoperatively (18% vs. 28%; p = 0.31); however, the predictive ability of the high-risk/low-risk parameters was better for preoperatively (p = 0.03) than for postoperatively treated patients (p = 0.15). Revised risk parameters were derived that improved the overall predictive ability (p = 0.004). Conclusions: The highly significant dose-volume relationship and validity of the high-risk and low-risk parameters were confirmed in a large group of patients. The risk parameters provided better modeling for the preoperative patients than for the postoperative patients.« less

Predicting High Risk Adolescents' Substance Use over Time: The Role of Parental Monitoring

ERIC Educational Resources Information Center

Clark, Heddy Kovach; Shamblen, Stephen R.; Ringwalt, Chris L.; Hanley, Sean

2012-01-01

We examined whether parental monitoring at baseline predicted subsequent substance use in a high-risk youth population. Students in 14 alternative high schools in Washington State completed self-report surveys at three time points over the course of 2 years. Primary analyses included 1,423 students aged 14-20 who lived with at least one parent or…

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

PubMed Central

Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

2015-01-01

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

A microRNA-based prediction model for lymph node metastasis in hepatocellular carcinoma.

PubMed

Zhang, Li; Xiang, Zuo-Lin; Zeng, Zhao-Chong; Fan, Jia; Tang, Zhao-You; Zhao, Xiao-Mei

2016-01-19

We developed an efficient microRNA (miRNA) model that could predict the risk of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC). We first evaluated a training cohort of 192 HCC patients after hepatectomy and found five LNM associated predictive factors: vascular invasion, Barcelona Clinic Liver Cancer stage, miR-145, miR-31, and miR-92a. The five statistically independent factors were used to develop a predictive model. The predictive value of the miRNA-based model was confirmed in a validation cohort of 209 consecutive HCC patients. The prediction model was scored for LNM risk from 0 to 8. The cutoff value 4 was used to distinguish high-risk and low-risk groups. The model sensitivity and specificity was 69.6 and 80.2%, respectively, during 5 years in the validation cohort. And the area under the curve (AUC) for the miRNA-based prognostic model was 0.860. The 5-year positive and negative predictive values of the model in the validation cohort were 30.3 and 95.5%, respectively. Cox regression analysis revealed that the LNM hazard ratio of the high-risk versus low-risk groups was 11.751 (95% CI, 5.110-27.021; P < 0.001) in the validation cohort. In conclusion, the miRNA-based model is reliable and accurate for the early prediction of LNM in patients with HCC.

In extremely preterm infants, do the Movement Assessment of Infants and the Alberta Infant Motor Scale predict 18-month outcomes using the Bayley-III?

PubMed

Lefebvre, Francine; Gagnon, Marie-Michèle; Luu, Thuy Mai; Lupien, Geneviève; Dorval, Véronique

2016-03-01

Extremely preterm infants are at high-risk for neurodevelopmental disabilities. The Movement Assessment of Infants (MAI) and the Alberta Infant Motor Scale (AIMS) have been designed to predict outcome with modest accuracy with the Bayley-I or Bayley-II. To examine and compare the predictive validity of the MAI and AIMS in determining neurodevelopmental outcome with the Bayley-III. Retrospective cohort study of 160 infants born at ≤ 28 weeks gestation. At their corrected age, infants underwent the MAI at 4 months, the AIMS at 4 and 10-12 months, and the Bayley-III and neurological examination at 18 months. Sensitivity and specificity were calculated. Infants had a mean gestation of 26.3 ± 1.4 weeks and birth weight of 906 ± 207 g. A high-risk score (≥ 14) for adverse outcome was obtained by 57% of infants on the MAI. On the AIMS, a high-risk score (<5th percentile) was obtained by 56% at 4 months and 30% at 10-12 months. At 18 months, infants with low-risk scores on either the MAI or AIMS had higher cognitive, language, and motor Bayley-III scores than those with high-risk scores. They were less likely to have severe neurodevelopmental impairment. To predict Bayley-III scores <70, sensitivity and specificity were 91% and 49%, respectively, for the MAI and 78% and 48%, respectively, for the AIMS. Extremely preterm infants with low-risk MAI at 4 months or AIMS scores at 4 or 10-12 months had better outcomes than those with high-risk scores. However, both tests lack specificity to predict individual neurodevelopmental status at 18 months. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Applying new Magee equations for predicting the Oncotype Dx recurrence score.

PubMed

Sughayer, Maher; Alaaraj, Rolla; Alsughayer, Ahmad

2018-04-24

Breast cancer is one of the most prevalent cancers in women. Oncotype Dx is a multi-gene assay frequently used to predict the recurrence risk for estrogen receptor-positive early breast cancer, with values < 18 considered low risk; ≥ 18 and ≤ 30, intermediate risk; and > 30, high risk. Patients at a high risk for recurrence are more likely to benefit from chemotherapy treatment. In this study, clinicopathological parameters for 37 cases of early breast cancer with available Oncotype Dx results were used to estimate the recurrence score using the three new Magee equations. Correlation studies with Oncotype Dx results were performed. Applying the same cutoff points as Oncotype Dx, patients were categorized into low-, intermediate- and high-risk groups according to their estimated recurrence scores. Pearson correlation coefficient (R) values between estimated and actual recurrence score were 0.73, 0.66, and 0.70 for Magee equations 1, 2 and 3, respectively. The concordance values between actual and estimated recurrence scores were 57.6%, 52.9%, and 57.6% for Magee equations 1, 2 and 3, respectively. Using standard pathologic measures and immunohistochemistry scores in these three linear Magee equations, most low and high recurrence risk cases can be predicted with a strong positive correlation coefficient, high concordance and negligible two-step discordance. Magee equations are user-friendly and can be used to predict the recurrence score in early breast cancer cases.

Premorbid functional development and conversion to psychosis in clinical high-risk youths

PubMed Central

Tarbox, Sarah I.; Addington, Jean; Cadenhead, Kristin S.; Cannon, Tyrone D.; Cornblatt, Barbara A.; Perkins, Diana O.; Seidman, Larry J.; Tsuang, Ming T.; Walker, Elaine F.; Heinssen, Robert; Mcglashan, Thomas H.; Woods, Scott W.

2014-01-01

Deterioration in premorbid functioning is a common feature of schizophrenia, but sensitivity to psychosis conversion among clinical high-risk samples has not been examined. This study evaluates premorbid functioning as a predictor of psychosis conversion among a clinical high-risk sample, controlling for effects of prior developmental periods. Participants were 270 clinical high-risk individuals in the North American Prodrome Longitudinal Study—I, 78 of whom converted to psychosis over the next 2.5 years. Social, academic, and total maladjustment in childhood, early adolescence, and late adolescence were rated using the Cannon–Spoor Premorbid Adjustment Scale. Early adolescent social dysfunction significantly predicted conversion to psychosis (hazard ratio = 1.30, p = .014), independently of childhood social maladjustment and independently of severity of most baseline positive and negative prodromal symptoms. Baseline prodromal symptoms of disorganized communication, social anhedonia, suspiciousness, and diminished ideational richness mediated this association. Early adolescent social maladjustment and baseline suspiciousness together demonstrated moderate positive predictive power (59%) and high specificity (92.1%) in predicting conversion. Deterioration of academic and total functioning, although observed, did not predict conversion to psychosis. Results indicate early adolescent social dysfunction to be an important early predictor of conversion. As such, it may be a good candidate for inclusion in prediction algorithms and could represent an advantageous target for early intervention. PMID:24229556

Baseline Characteristics Predicting Very Good Outcome of Allogeneic Hematopoietic Cell Transplantation in Young Patients With High Cytogenetic Risk Chronic Lymphocytic Leukemia - A Retrospective Analysis From the Chronic Malignancies Working Party of the EBMT.

PubMed

van Gelder, Michel; Ziagkos, Dimitris; de Wreede, Liesbeth; van Biezen, Anja; Dreger, Peter; Gramatzki, Martin; Stelljes, Matthias; Andersen, Niels Smedegaard; Schaap, Nicolaas; Vitek, Antonin; Beelen, Dietrich; Lindström, Vesa; Finke, Jürgen; Passweg, Jacob; Eder, Matthias; Machaczka, Maciej; Delgado, Julio; Krüger, William; Raida, Luděk; Socié, Gerard; Jindra, Pavel; Afanasyev, Boris; Wagner, Eva; Chalandon, Yves; Henseler, Anja; Schoenland, Stefan; Kröger, Nicolaus; Schetelig, Johannes

2017-10-01

Patients with genetically high-risk relapsed/refractory chronic lymphocytic leukemia have shorter median progression-free survival (PFS) with kinase- and BCL2-inhibitors (KI, BCL2i). Allogeneic hematopoietic stem cell transplantation (alloHCT) may result in sustained PFS, especially in younger patients because of its age-dependent non-relapse mortality (NRM) risk, but outcome data are lacking for this population. Risk factors for 2-year NRM and 8-year PFS were identified in patients < 50 years in an updated European Society for Blood and Marrow Transplantation registry cohort (n = 197; median follow-up, 90.4 months) by Cox regression modeling, and predicted probabilities of NRM and PFS of 2 reference patients with favorable or unfavorable characteristics were plotted. Predictors for poor 8-year PFS were no remission at the time of alloHCT (hazard ratio [HR], 1.7; 95% confidence interval [CI], 1.1-2.5) and partially human leukocyte antigen (HLA)-mismatched unrelated donor (HR, 2.8; 95% CI, 1.5-5.2). The latter variable also predicted a higher risk of 2-year NRM (HR, 4.0; 95% CI, 1.4-11.6) compared with HLA-matched sibling donors. Predicted 2-year NRM and 8-year PFS of a high cytogenetic risk (del(17p) and/or del(11q)) patient in remission with a matched related donor were 12% (95% CI, 3%-22%) and 54% (95% CI, 38%-69%), and for an unresponsive patient with a female partially HLA-matched unrelated donor 37% (95% CI, 12%-62%) and 38% (95% CI, 13%-63%). Low predicted NRM and high 8-year PFS in favorable transplant high cytogenetic risk patients compares favorably with outcomes with KI or BCL2i. Taking into account the amount of uncertainty for predicting survival after alloHCT and after sequential administration of KI and BCL2i, alloHCT remains a valid option for younger patients with high cytogenetic risk chronic lymphocytic leukemia with a well-HLA-matched donor. Copyright © 2017 Elsevier Inc. All rights reserved.

Development and validation of a predictive score for perioperative transfusion in patients with hepatocellular carcinoma undergoing liver resection.

PubMed

Wang, Hai-Qing; Yang, Jian; Yang, Jia-Yin; Wang, Wen-Tao; Yan, Lu-Nan

2015-08-01

Liver resection is a major surgery requiring perioperative blood transfusion. Predicting the need for blood transfusion for patients undergoing liver resection is of great importance. The present study aimed to develop and validate a model for predicting transfusion requirement in HBV-related hepatocellular carcinoma patients undergoing liver resection. A total of 1543 consecutive liver resections were included in the study. Randomly selected sample set of 1080 cases (70% of the study cohort) were used to develop a predictive score for transfusion requirement and the remaining 30% (n=463) was used to validate the score. Based on the preoperative and predictable intraoperative parameters, logistic regression was used to identify risk factors and to create an integer score for the prediction of transfusion requirement. Extrahepatic procedure, major liver resection, hemoglobin level and platelets count were identified as independent predictors for transfusion requirement by logistic regression analysis. A score system integrating these 4 factors was stratified into three groups which could predict the risk of transfusion, with a rate of 11.4%, 24.7% and 57.4% for low, moderate and high risk, respectively. The prediction model appeared accurate with good discriminatory abilities, generating an area under the receiver operating characteristic curve of 0.736 in the development set and 0.709 in the validation set. We have developed and validated an integer-based risk score to predict perioperative transfusion for patients undergoing liver resection in a high-volume surgical center. This score allows identifying patients at a high risk and may alter transfusion practices.

Sensitivity and specificity of a brief personality screening instrument in predicting future substance use, emotional, and behavioral problems: 18-month predictive validity of the Substance Use Risk Profile Scale.

PubMed

Castellanos-Ryan, Natalie; O'Leary-Barrett, Maeve; Sully, Laura; Conrod, Patricia

2013-01-01

This study assessed the validity, sensitivity, and specificity of the Substance Use Risk Profile Scale (SURPS), a measure of personality risk factors for substance use and other behavioral problems in adolescence. The concurrent and predictive validity of the SURPS was tested in a sample of 1,162 adolescents (mean age: 13.7 years) using linear and logistic regressions, while its sensitivity and specificity were examined using the receiver operating characteristics curve analyses. Concurrent and predictive validity tests showed that all 4 brief scales-hopelessness (H), anxiety sensitivity (AS), impulsivity (IMP), and sensation seeking (SS)-were related, in theoretically expected ways, to measures of substance use and other behavioral and emotional problems. Results also showed that when using the 4 SURPS subscales to identify adolescents "at risk," one can identify a high number of those who developed problems (high sensitivity scores ranging from 72 to 91%). And, as predicted, because each scale is related to specific substance and mental health problems, good specificity was obtained when using the individual personality subscales (e.g., most adolescents identified at high risk by the IMP scale developed conduct or drug use problems within the next 18 months [a high specificity score of 70 to 80%]). The SURPS is a valuable tool for identifying adolescents at high risk for substance misuse and other emotional and behavioral problems. Implications of findings for the use of this measure in future research and prevention interventions are discussed. Copyright © 2012 by the Research Society on Alcoholism.

Framing effects and risk-sensitive decision making.

PubMed

Mishra, Sandeep; Gregson, Margaux; Lalumière, Martin L

2012-02-01

Prospect theory suggests that people are risk-averse when facing gains, but risk-prone when facing losses, a pattern known as the framing effect. Although framing effects have been widely demonstrated, few studies have investigated framing effects under conditions of need. Risk-sensitivity theory predicts that decision makers should prefer high-risk options in situations of high need, when lower risk options are unlikely to meet those needs. In two experiments, we examined (1) whether framing effects occurred in behavioural tasks involving risky decision making from description and decision making from experience, (2) whether participants' risky decision making conformed to the predictions of risk-sensitivity theory, and (3) whether decision framing interacted with conditions of need to influence decision making under risk. The results suggest that under all circumstances, risky decision making conformed to the predictions of risk-sensitivity theory. Framing effects were at least partially demonstrable under all experimental conditions. Finally, negative frames interacted with situations of high need to produce particularly elevated levels of risky choice. Together, the results suggest that risk-sensitivity theory can augment prospect theory to explain choice under conditions of need. ©2011 The British Psychological Society.

High Peer Popularity Longitudinally Predicts Adolescent Health Risk Behavior, or Does It?: An Examination of Linear and Quadratic Associations

PubMed Central

Choukas-Bradley, Sophia C.; Helms, Sarah W.; Brechwald, Whitney A.; Rancourt, Diana

2011-01-01

Objective In contrast to prior work, recent theory suggests that high, not low, levels of adolescent peer popularity may be associated with health risk behavior. This study examined (a) whether popularity may be uniquely associated with cigarette use, marijuana use, and sexual risk behavior, beyond the predictive effects of aggression; (b) whether the longitudinal association between popularity and health risk behavior may be curvilinear; and (c) gender moderation. Methods A total of 336 adolescents, initially in 10–11th grades, reported cigarette use, marijuana use, and number of sexual intercourse partners at two time points 18 months apart. Sociometric peer nominations were used to examine popularity and aggression. Results Longitudinal quadratic effects and gender moderation suggest that both high and low levels of popularity predict some, but not all, health risk behaviors. Conclusions New theoretical models can be useful for understanding the complex manner in which health risk behaviors may be reinforced within the peer context. PMID:21852342

High peer popularity longitudinally predicts adolescent health risk behavior, or does it?: an examination of linear and quadratic associations.

PubMed

Prinstein, Mitchell J; Choukas-Bradley, Sophia C; Helms, Sarah W; Brechwald, Whitney A; Rancourt, Diana

2011-10-01

In contrast to prior work, recent theory suggests that high, not low, levels of adolescent peer popularity may be associated with health risk behavior. This study examined (a) whether popularity may be uniquely associated with cigarette use, marijuana use, and sexual risk behavior, beyond the predictive effects of aggression; (b) whether the longitudinal association between popularity and health risk behavior may be curvilinear; and (c) gender moderation. A total of 336 adolescents, initially in 10-11th grades, reported cigarette use, marijuana use, and number of sexual intercourse partners at two time points 18 months apart. Sociometric peer nominations were used to examine popularity and aggression. Longitudinal quadratic effects and gender moderation suggest that both high and low levels of popularity predict some, but not all, health risk behaviors. New theoretical models can be useful for understanding the complex manner in which health risk behaviors may be reinforced within the peer context.

Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

PubMed

van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

2017-11-01

Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developmental interplay between children’s biobehavioral risk and the parenting environment from toddler to early school age: Prediction of socialization outcomes in preadolescence

PubMed Central

Kochanska, Grazyna; Boldt, Lea J.; Kim, Sanghag; Yoon, Jeung Eun; Philibert, Robert A.

2014-01-01

We followed 100 community families from toddler age to preadolescence. Each mother- and father-child dyad was observed at 25, 38, 52, 67, and 80 months (10 hours per child) to assess positive and power-assertive parenting. At age 10 (N=82), we obtained parent- and child-reported outcome measures of children’s acceptance of parental socialization: cooperation with parental monitoring, negative attitude toward substance use, internalization of adult values, and callous-unemotional (CU) tendencies. Children who carried a short 5-HTTLPR allele and were highly anger prone, based on anger observed in laboratory from 25 to 80 months, were classified as high in biobehavioral risk. The remaining children were classified as low in biobehavioral risk. Biobehavioral risk moderated links between parenting history and outcomes. For low-risk children, parenting measures were unrelated to outcomes. For children high in biobehavioral risk, variations in positive parenting predicted cooperation with monitoring and negative attitude toward substance use, and variations in power-assertive parenting predicted internalization of adult values and CU tendencies. Suboptimal parenting combined with high biobehavioral risk resulted in the poorest outcomes. The effect for attitude toward substance use supported differential susceptibility: Children high in biobehavioral risk who received optimal parenting had a more adaptive outcome than their low-risk peers. The remaining effects were consistent with diathesis-stress. PMID:25154427

Prostate cancer: predicting high-risk prostate cancer-a novel stratification tool.

PubMed

Buck, Jessica; Chughtai, Bilal

2014-05-01

Currently, numerous systems exist for the identification of high-risk prostate cancer, but few of these systems can guide treatment strategies. A new stratification tool that uses common diagnostic factors can help to predict outcomes after radical prostatectomy. The tool aids physicians in the identification of appropriate candidates for aggressive, local treatment.

A risk tertiles model for predicting mortality in patients with acute respiratory distress syndrome: age, plateau pressure, and P(aO(2))/F(IO(2)) at ARDS onset can predict mortality.

PubMed

Villar, Jesús; Pérez-Méndez, Lina; Basaldúa, Santiago; Blanco, Jesús; Aguilar, Gerardo; Toral, Darío; Zavala, Elizabeth; Romera, Miguel A; González-Díaz, Gumersindo; Nogal, Frutos Del; Santos-Bouza, Antonio; Ramos, Luís; Macías, Santiago; Kacmarek, Robert M

2011-04-01

Predicting mortality has become a necessary step for selecting patients for clinical trials and defining outcomes. We examined whether stratification by tertiles of respiratory and ventilatory variables at the onset of acute respiratory distress syndrome (ARDS) identifies patients with different risks of death in the intensive care unit. We performed a secondary analysis of data from 220 patients included in 2 multicenter prospective independent trials of ARDS patients mechanically ventilated with a lung-protective strategy. Using demographic, pulmonary, and ventilation data collected at ARDS onset, we derived and validated a simple prediction model based on a population-based stratification of variable values into low, middle, and high tertiles. The derivation cohort included 170 patients (all from one trial) and the validation cohort included 50 patients (all from a second trial). Tertile distribution for age, plateau airway pressure (P(plat)), and P(aO(2))/F(IO(2)) at ARDS onset identified subgroups with different mortalities, particularly for the highest-risk tertiles: age (> 62 years), P(plat) (> 29 cm H(2)O), and P(aO(2))/F(IO(2)) (< 112 mm Hg). Risk was defined by the number of coexisting high-risk tertiles: patients with no high-risk tertiles had a mortality of 12%, whereas patients with 3 high-risk tertiles had 90% mortality (P < .001). A prediction model based on tertiles of patient age, P(plat), and P(aO(2))/F(IO(2)) at the time the patient meets ARDS criteria identifies patients with the lowest and highest risk of intensive care unit death.

Using risk-adjustment models to identify high-cost risks.

PubMed

Meenan, Richard T; Goodman, Michael J; Fishman, Paul A; Hornbrook, Mark C; O'Keeffe-Rosetti, Maureen C; Bachman, Donald J

2003-11-01

We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.

A new look at the International Duration Evaluation of Adjuvant therapy (IDEA) classification-Defining novel predictive and prognostic markers in stage III colon cancer.

PubMed

Margalit, Ofer; Mamtani, Ronac; Yang, Yu-Xiao; Reiss, Kim A; Golan, Talia; Halpern, Naama; Aderka, Dan; Giantonio, Bruce; Shacham-Shmueli, Einat; Boursi, Ben

2018-04-23

The International Duration Evaluation of Adjuvant therapy (IDEA) pooled analysis compared 3 to 6 months of adjuvant chemotherapy for stage III colon cancer. The overarching goal was to reduce chemotherapy-related toxicity, mainly oxaliplatin-induced neuropathy. Patients were classified into low-risk and high-risk groups, suggesting that low-risk patients may be offered only 3 months of treatment. We aimed to evaluate the benefit of monotherapy versus doublet chemotherapy in low and high IDEA risk groups. Using the National Cancer Database (2004-2014), we identified 56,728 low-risk and 47,557 high-risk individuals with stage III colon cancer, according to the IDEA classification. We used multivariate Cox regression to evaluate the magnitude of survival differences between IDEA risk groups, according to treatment intensity (doublet versus monotherapy). In a secondary analysis, we examined the prognostic and predictive value of subgroups of age, tumour sidedness and lymph node ratio (LNR). Low and high IDEA risk groups derived similar benefit from doublet adjuvant chemotherapy as compared with monotherapy, with hazard ratios (HRs) of 0.83 (95% confidence interval [CI] 0.79-0.86) and 0.80 (95% CI 0.78-0.83), respectively. The only subpopulations that did not benefit from doublet chemotherapy were low-risk patients older than 72 years (HR = 0.95, 95% CI 0.90-1.01) and high-risk patients older than 85 years (HR = 0.90, 95% CI 0.77-1.05). LNR and tumour sidedness were shown as additional prognostic, but not predictive, factors within the IDEA risk groups. IDEA risk classification per se does not predict for treatment benefit from doublet chemotherapy in stage III colon cancer. However, omission of oxaliplatin can be considered in IDEA low-risk patients older than 72 years. Copyright © 2018 Elsevier Ltd. All rights reserved.

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys.

PubMed

Ueda, Peter; Woodward, Mark; Lu, Yuan; Hajifathalian, Kaveh; Al-Wotayan, Rihab; Aguilar-Salinas, Carlos A; Ahmadvand, Alireza; Azizi, Fereidoun; Bentham, James; Cifkova, Renata; Di Cesare, Mariachiara; Eriksen, Louise; Farzadfar, Farshad; Ferguson, Trevor S; Ikeda, Nayu; Khalili, Davood; Khang, Young-Ho; Lanska, Vera; León-Muñoz, Luz; Magliano, Dianna J; Margozzini, Paula; Msyamboza, Kelias P; Mutungi, Gerald; Oh, Kyungwon; Oum, Sophal; Rodríguez-Artalejo, Fernando; Rojas-Martinez, Rosalba; Valdivia, Gonzalo; Wilks, Rainford; Shaw, Jonathan E; Stevens, Gretchen A; Tolstrup, Janne S; Zhou, Bin; Salomon, Joshua A; Ezzati, Majid; Danaei, Goodarz

2017-03-01

Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years. Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40-64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40-64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. National Institutes of Health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

PubMed Central

Ueda, Peter; Woodward, Mark; Lu, Yuan; Hajifathalian, Kaveh; Al-Wotayan, Rihab; Aguilar-Salinas, Carlos A; Ahmadvand, Alireza; Azizi, Fereidoun; Bentham, James; Cifkova, Renata; Di Cesare, Mariachiara; Eriksen, Louise; Farzadfar, Farshad; Ferguson, Trevor S; Ikeda, Nayu; Khalili, Davood; Khang, Young-Ho; Lanska, Vera; León-Muñoz, Luz; Magliano, Dianna J; Margozzini, Paula; Msyamboza, Kelias P; Mutungi, Gerald; Oh, Kyungwon; Oum, Sophal; Rodríguez-Artalejo, Fernando; Rojas-Martinez, Rosalba; Valdivia, Gonzalo; Wilks, Rainford; Shaw, Jonathan E; Stevens, Gretchen A; Tolstrup, Janne S; Zhou, Bin; Salomon, Joshua A; Ezzati, Majid; Danaei, Goodarz

2017-01-01

Summary Background Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40–74 years. Methods Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40–64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Findings Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40–64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Interpretation Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. PMID:28126460

Climate change risk to forests in China associated with warming.

PubMed

Yin, Yunhe; Ma, Danyang; Wu, Shaohong

2018-01-11

Variations in forest net primary productivity (NPP) reflects the combined effects of key climate variables on ecosystem structure and function, especially on the carbon cycle. We performed risk analysis indicated by the magnitude of future negative anomalies in NPP in comparison with the natural interannual variability to investigate the impact of future climatic projections on forests in China. Results from the multi-model ensemble showed that climate change risk of decreases in forest NPP would be more significant in higher emission scenario in China. Under relatively low emission scenarios, the total area of risk was predicted to decline, while for RCP8.5, it was predicted to first decrease and then increase after the middle of 21st century. The rapid temperature increases predicted under the RCP8.5 scenario would be probably unfavorable for forest vegetation growth in the long term. High-level risk area was likely to increase except RCP2.6. The percentage area at high risk was predicted to increase from 5.39% (2021-2050) to 27.62% (2071-2099) under RCP8.5. Climate change risk to forests was mostly concentrated in southern subtropical and tropical regions, generally significant under high emission scenario of RCP8.5, which was mainly attributed to the intensified dryness in south China.

Preexposure Prophylaxis and Predicted Condom Use Among High-Risk Men Who Have Sex With Men

PubMed Central

Golub, Sarit A.; Kowalczyk, William; Weinberger, Corina L.; Parsons, Jeffrey T.

2010-01-01

Objectives Preexposure prophylaxis (PREP) is an emerging HIV prevention strategy; however, many fear it may lead to neglect of traditional risk reduction practices through behavioral disinhibition or risk compensation. Methods Participants were 180 HIV-negative high-risk men who have sex with men recruited in New York City, who completed an Audio Computer Assisted Self Interview-administered survey between September 2007 and July 2009. Bivariate and multivariate logistic regression models were used to predict intention to use PREP and perceptions that PREP would decrease condom use. Results Almost 70% (n = 124) of participants reported that they would be likely to use PREP if it were at least 80% effective in preventing HIV. Of those who would use PREP, over 35% reported that they would be likely to decrease condom use while on PREP. In multivariate analyses, arousal/pleasure barriers to condom use significantly predicted likelihood of PREP use (odds ratio = 1.71, P < 0.05) and risk perception motivations for condom use significantly predicted decreased condom use on PREP (odds ratio = 2.48, P < 0.05). Discussion These data provide support for both behavioral disinhibition and risk compensation models and underscore the importance of developing behavioral interventions to accompany any wide-scale provision of PREP to high-risk populations. PMID:20512046

How Do Alcohol and Relationship Type Affect Women’s Risk Judgment of Partners with Differing Risk Histories?

PubMed Central

Norris, Jeanette; Kiekel, Preston A.; Morrison, Diane M.; Davis, Kelly Cue; George, William H.; Zawacki, Tina; Abdallah, Devon Alisa; Jacques-Tiura, Angela J.; Stappenbeck, Cynthia A.

2013-01-01

Understanding how women judge male partners’ sexual risk is important to developing risk reduction programs. Applying a cognitive mediation model of sexual decision making, our study investigated effects of alcohol consumption (control, low dose, high dose) and relationship type (disrupted vs. new) on women’s risk judgments of a male sexual partner in three sexual risk conditions (low, unknown, high). After random assignment to an experimental condition, 328 participants projected themselves into a story depicting a sexual interaction. The story was paused to assess primary appraisals of sexual and relationship potential and secondary appraisals of pleasure, health, and relationship concerns, followed by sexual risk judgments. In all risk conditions, alcohol and disrupted relationship increased sexual potential whereas disrupted relationship increased relationship potential in the low- and high-risk conditions. In the unknown-risk condition, women in the no-alcohol, new relationship condition had the lowest primary sexual appraisals. In all conditions, sexual appraisals predicted all secondary appraisals, but primary relationship appraisals predicted only secondary relationship appraisals. Secondary health appraisals led to increased risk judgments whereas relationship appraisals predicted lower risk judgments. Possible intervention points include helping women to re-evaluate their safety beliefs about past partners, as well as to develop behavioral strategies for decreasing hazardous drinking. PMID:24003264

The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

PubMed

Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

2017-03-01

Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

The utility of the Historical Clinical Risk-20 Scale as a predictor of outcomes in decisions to transfer patients from high to lower levels of security--a UK perspective.

PubMed

Dolan, Mairead; Blattner, Regine

2010-09-29

Structured Professional Judgment (SPJ) approaches to violence risk assessment are increasingly being adopted into clinical practice in international forensic settings. The aim of this study was to examine the predictive validity of the Historical Clinical Risk -20 (HCR-20) violence risk assessment scale for outcome following transfers from high to medium security in a United Kingdom setting. The sample was predominately male and mentally ill and the majority of cases were detained under the criminal section of the Mental Health Act (1986). The HCR-20 was rated based on detailed case file information on 72 cases transferred from high to medium security. Outcomes were examined, independent of risk score, and cases were classed as "success or failure" based on established criteria. The mean length of follow up was 6 years. The total HCR-20 score was a robust predictor of failure at lower levels of security and return to high security. The Clinical and Risk management items contributed most to predictive accuracy. Although the HCR-20 was designed as a violence risk prediction tool our findings suggest it has potential utility in decisions to transfer patients from high to lower levels of security.

A model to predict the onset of non-alcoholic fatty liver disease within 2 years in elderly adults.

PubMed

Lin, Ya-Jie; Gao, Xi-Mei; Pan, Wei-Wei; Gao, Shuai; Yu, Zhen-Zhen; Xu, Ping; Fan, Xiao-Peng

2017-10-01

Non-alcoholic fatty liver disease (NAFLD) is a common cause of chronic hepatitis, which leads to cirrhosis and hepatocellular carcinoma. However, it is difficult to identify subjects at high risk for NAFLD onset. This study aims to construct a model to predict the onset of NAFLD within 2 years in elderly adults. This study included and followed 3378 initial NAFLD-free subjects aged 60 years or over for 2 years, which were randomly divided into a training set and a validation set. NAFLD was diagnosed on ultrasound. Clinical and laboratory data were recorded at baseline. A model was constructed in the training set to predict the onset of NAFLD and validated in the validation set. Body mass index, hemoglobin, fasting blood glucose, and triglycerides were identified as predictors for the onset of NAFLD. A risk score (R) was calculated by them. It classified the subjects into low-risk group (R ≤ -2.88), moderate-risk group (-2.88 < R ≤ -1.26), and high-risk group (R > -1.26). In the training set, 4.68% of the participants in the low-risk group, 11.59% of the participants in the moderate-risk group, and 31.02% of the participants in the high-risk group developed NAFLD. In the validation set, 5.84% of the participants in the low-risk group, 10.57% of the participants in the moderate-risk group, and 29.44% of the participants in the high-risk group developed NAFLD. This study developed a model to predict the onset of NAFLD in elderly adults, which might provide indications for intervention to these subjects. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

An analysis of high-impact, low-predictive skill severe weather events in the northeast U.S

NASA Astrophysics Data System (ADS)

Vaughan, Matthew T.

An objective evaluation of Storm Prediction Center slight risk convective outlooks, as well as a method to identify high-impact severe weather events with poor-predictive skill are presented in this study. The objectives are to assess severe weather forecast skill over the northeast U.S. relative to the continental U.S., build a climatology of high-impact, low-predictive skill events between 1980--2013, and investigate the dynamic and thermodynamic differences between severe weather events with low-predictive skill and high-predictive skill over the northeast U.S. Severe storm reports of hail, wind, and tornadoes are used to calculate skill scores including probability of detection (POD), false alarm ratio (FAR) and threat scores (TS) for each convective outlook. Low predictive skill events are binned into low POD (type 1) and high FAR (type 2) categories to assess temporal variability of low-predictive skill events. Type 1 events were found to occur in every year of the dataset with an average of 6 events per year. Type 2 events occur less frequently and are more common in the earlier half of the study period. An event-centered composite analysis is performed on the low-predictive skill database using the National Centers for Environmental Prediction Climate Forecast System Reanalysis 0.5° gridded dataset to analyze the dynamic and thermodynamic conditions prior to high-impact severe weather events with varying predictive skill. Deep-layer vertical shear between 1000--500 hPa is found to be a significant discriminator in slight risk forecast skill where high-impact events with less than 31-kt shear have lower threat scores than high-impact events with higher shear values. Case study analysis of type 1 events suggests the environment over which severe weather occurs is characterized by high downdraft convective available potential energy, steep low-level lapse rates, and high lifting condensation level heights that contribute to an elevated risk of severe wind.

An individual risk prediction model for lung cancer based on a study in a Chinese population.

PubMed

Wang, Xu; Ma, Kewei; Cui, Jiuwei; Chen, Xiao; Jin, Lina; Li, Wei

2015-01-01

Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk. We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point. Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively. The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

PubMed

Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

2017-05-01

A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P < .001) for both tools. In all, 43 (21%) cases had discordant GEP and AJCC classification (using 79% cutoff). Eleven of 13 (85%) deaths in that group were predicted as high risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

PubMed

Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

2017-07-01

OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently produced complication predictions that underestimated complication occurrence: 3.4% in the low-risk group (observed 12.6%), 5.9% in the medium-risk group (observed 34.5%), and 12.5% in the high-risk group (observed 38.8%). The RAT was more accurate than the ACS NSQIP calculator (p = 0.0018). CONCLUSIONS While the RAT and ACS NSQIP calculator were both able to identify patients more likely to experience complications following spine surgery, both have substantial room for improvement. Risk stratification is feasible in spine surgery procedures; currently used measures have low accuracy.

Enhanced risk prediction model for emergency department use and hospitalizations in patients in a primary care medical home.

PubMed

Takahashi, Paul Y; Heien, Herbert C; Sangaralingham, Lindsey R; Shah, Nilay D; Naessens, James M

2016-07-01

With the advent of healthcare payment reform, identifying high-risk populations has become more important to providers. Existing risk-prediction models often focus on chronic conditions. This study sought to better understand other factors to improve identification of the highest risk population. A retrospective cohort study of a paneled primary care population utilizing 2010 data to calibrate a risk prediction model of hospital and emergency department (ED) use in 2011. Data were randomly split into development and validation data sets. We compared the enhanced model containing the additional risk predictors with the Minnesota medical tiering model. The study was conducted in the primary care practice of an integrated delivery system at an academic medical center in Rochester, Minnesota. The study focus was primary care medical home patients in 2010 and 2011 (n = 84,752), with the primary outcome of subsequent hospitalization or ED visit. A total of 42,384 individuals derived the enhanced risk-prediction model and 42,368 individuals validated the model. Predictors included Adjusted Clinical Groups-based Minnesota medical tiering, patient demographics, insurance status, and prior year healthcare utilization. Additional variables included specific mental and medical conditions, use of high-risk medications, and body mass index. The area under the curve in the enhanced model was 0.705 (95% CI, 0.698-0.712) compared with 0.662 (95% CI, 0.656-0.669) in the Minnesota medical tiering-only model. New high-risk patients in the enhanced model were more likely to have lack of health insurance, presence of Medicaid, diagnosed depression, and prior ED utilization. An enhanced model including additional healthcare-related factors improved the prediction of risk of hospitalization or ED visit.

Recent ecological responses to climate change support predictions of high extinction risk

PubMed Central

Maclean, Ilya M. D.; Wilson, Robert J.

2011-01-01

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity. PMID:21746924

Recent ecological responses to climate change support predictions of high extinction risk.

PubMed

Maclean, Ilya M D; Wilson, Robert J

2011-07-26

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.

Joint Attention Initiation with and without Positive Affect: Risk Group Differences and Associations with ASD Symptoms

PubMed Central

Gangi, Devon N.; Ibañez, Lisa V.; Messinger, Daniel S.

2014-01-01

Infants at risk for Autism Spectrum Disorders (ASD) may have difficulty integrating smiles into initiating joint attention (IJA) bids. A specific IJA pattern, anticipatory smiling, may communicate preexisting positive affect when an infant smiles at an object and then turns the smile toward the social partner. We compared the development of anticipatory smiling at 8, 10, and 12 months in infant siblings of children with ASD (high-risk siblings) and without ASD (low-risk siblings). High-risk siblings produced less anticipatory smiling than low-risk siblings, suggesting early differences in communicating preexisting positive affect. While early anticipatory smiling distinguished the risk groups, IJA not accompanied by smiling best predicted later severity of ASD-related behavioral characteristics among high-risk siblings. High-risk infants appear to show lower levels of motivation to share positive affect with others. However, facility with initiating joint attention in the absence of a clear index of positive affective motivation appears to be central to the prediction of ASD symptoms. PMID:24281421

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

PubMed Central

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

Purpose The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Patients and methods Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Results Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. Conclusion It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones. PMID:23226004

Baseline placental growth factor levels for the prediction of benefit from early aspirin prophylaxis for preeclampsia prevention.

PubMed

Moore, Gaea S; Allshouse, Amanda A; Winn, Virginia D; Galan, Henry L; Heyborne, Kent D

2015-10-01

Placental growth factor (PlGF) levels early in pregnancy are lower in women who ultimately develop preeclampsia. Early initiation of low-dose aspirin reduces preeclampsia risk in some high risk women. We hypothesized that low PlGF levels may identify women at increased risk for preeclampsia who would benefit from aspirin. Secondary analysis of the MFMU High-Risk Aspirin study including singleton pregnancies randomized to aspirin 60mg/d (n=102) or placebo (n=72), with PlGF collected at 13w 0d-16w 6d. Within the placebo group, we estimated the probability of preeclampsia by PlGF level using logistic regression analysis, then determined a potential PlGF threshold for preeclampsia prediction using ROC analysis. We performed logistic regression modeling for potential confounders. ROC analysis indicated 87.71pg/ml as the threshold between high and low PlGF for preeclampsia-prediction. Within the placebo group high PlGF weakly predicted preeclampsia (AUC 0.653, sensitivity/specificity 63%/66%). We noted a 2.6-fold reduction in preeclampsia with aspirin in the high-PlGF group (12.15% aspirin vs 32.14% placebo, p=0.057), but no significant differences in preeclampsia in the low PlGF group (21.74% vs 15.91%, p=0.445). Unlike other studies, we found that high rather than low PlGF levels were associated with an increased preeclampsia risk. Low PlGF neither identified women at increased risk of preeclampsia nor women who benefitted from aspirin. Further research is needed to determine whether aspirin is beneficial in women with high PlGF, and whether the paradigm linking low PlGF and preeclampsia needs to be reevaluated. High-risk women with low baseline PlGF, a risk factor for preeclampsia, did not benefit from early initiation of low-dose aspirin. Copyright © 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

When does risk perception predict protection motivation for health threats? A person-by-situation analysis.

PubMed

Ferrer, Rebecca A; Klein, William M P; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

2018-01-01

Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation.

When does risk perception predict protection motivation for health threats? A person-by-situation analysis

PubMed Central

Klein, William M. P.; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

2018-01-01

Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation. PMID:29494705

Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures.

PubMed

Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D

2015-08-01

A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19-1.58) and 1.26 (95% CI: 1.12-1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that measures of frailty status may aid in fracture risk assessment and fracture prevention in the elderly. Further evidence from randomized controlled trials of osteoporosis medication interventions is needed to support the FI and FRAX as validated measures of fracture risk. Copyright © 2015 Elsevier Inc. All rights reserved.

Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures

PubMed Central

Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D.

2016-01-01

A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02–1.05) and 1.02 (95% CI: 1.01–1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19–1.58) and 1.26 (95% CI: 1.12–1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36–3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82–1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09–3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48–4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18 months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that measures of frailty status may aid in fracture risk assessment and fracture prevention in the elderly. Further evidence from randomized controlled trials of osteoporosis medication interventions is needed to support the FI and FRAX as validated measures of fracture risk. PMID:25916552

Using the theory of planned behavior to explain the drinking motivations of social, high-risk, and extreme drinkers on game day.

PubMed

Glassman, Tavis; Braun, Robert E; Dodd, Virginia; Miller, Jeffrey M; Miller, E Maureen

2010-04-01

This study assessed the extent to which the Theory of Planned Behavior (TPB) correctly predicted college student's motivation to consume alcohol on game day based on alcohol consumption rates. Three cohorts of 1,000 participants each (N = 3,000) were randomly selected and invited to complete an anonymous web-based survey the Monday following one of three designated college home football games. Path analyses were conducted to determine which of the TPB constructs were most effective in predicting Behavioral Intention and alcohol consumption among social, high-risk, and extreme drinkers. Social drinkers, high-risk, and those drinkers who engage in Extreme Ritualistic Alcohol Consumption (ERAC) were defined as males who consumed 1-4, 5-9, or 10 or more drinks on game day (1-3, 4-8, or nine or more drinks for females), respectively. Attitude Towards the Behavior and Subjective Norm constructs predicted participant's intentions to consume alcohol and corresponding behavior among all three classifications of drinkers; whereas the Perceived Behavioral Control (PBC) construct inconsistently predicted intention and alcohol consumption. Based on Behavioral Intention, the proportion of variance the TPB model explained decreased as participants alcohol consumption increased. It appears that the TPB constructs Attitude Toward the Behavior and Subjective Norm can effectively be utilized when designing universal prevention interventions targeting game day alcohol consumption among college students. However, the applicability of the PBC construct remains in question. While select constructs in the TPB appear to have predictive ability, the usefulness of the complete theoretical framework is limited when trying to predict high-risk drinking and ERAC. These findings suggest that other behavioral theories should be considered when addressing the needs of high-risk and extreme drinkers.

Framingham risk score for estimation of 10-years of cardiovascular diseases risk in patients with metabolic syndrome.

PubMed

Jahangiry, Leila; Farhangi, Mahdieh Abbasalizad; Rezaei, Fatemeh

2017-11-13

There are a few studies evaluating the predictive value of Framingham risk score (FRS) for cardiovascular disease (CVD) risk assessment in patients with metabolic syndrome in Iran. Because of the emerging high prevalence of CVD among Iranian population, it is important to predict its risk among populations with potential predictive tools. Therefore, the aim of the current study is to evaluate the FRS and its determinants in patients with metabolic syndrome. In the current cross-sectional study, 160 patients with metabolic syndrome diagnosed according to the National Cholesterol Education Adult Treatment Panel (ATP) III criteria were enrolled. The FRS was calculated using a computer program by a previously suggested algorithm. Totally, 77.5, 16.3, and 6.3% of patients with metabolic syndrome were at low, intermediate, and high risk of CVD according to FRS categorization. The highest prevalence of all of metabolic syndrome components were in low CVD risk according to the FRS grouping (P < 0.05), while the lowest prevalence of these components was in high CVD risk group (P < 0.05). According to multiple logistic regression analysis, high systolic blood pressure (SBP) and fasting serum glucose (FSG) were potent determinants of intermediate and high risk CVD risk of FRS scoring compared with low risk group (P < 0.05). In the current study, significant associations between components of metabolic syndrome and different FRS categorization among patients with metabolic syndrome were identified. High SBP and FSG were associated with meaningfully increased risk of CVD compared with other parameters. The study is not a trial; the registration number is not applicable.

SOX9 expression predicts relapse of stage II colon cancer patients.

PubMed

Marcker Espersen, Maiken Lise; Linnemann, Dorte; Christensen, Ib Jarle; Alamili, Mahdi; Troelsen, Jesper T; Høgdall, Estrid

2016-06-01

The aim of this study was to investigate if the protein expression of sex-determining region y-box 9 (SOX9) in primary tumors could predict relapse of stage II colon cancer patients. One hundred forty-four patients with stage II primary colon cancer were retrospectively enrolled in the study. SOX9 expression was evaluated by immunohistochemistry, and mismatch repair status was assessed by both immunohistochemistry and promoter hypermethylation assay. High SOX9 expression at the invasive front was significantly associated with lower risk of relapse when including the SOX9 expression as a continuous variable (from low to high expression) in univariate (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.56-0.94; P = .01) and multivariate Cox proportional hazards analyses (HR, 0.75; 95% CI, 0.58-0.96; P = .02), adjusting for mismatch repair deficiency and histopathologic risk factors. Conversely, low SOX9 expression at the invasive front was significantly associated with high risk of relapse, when including SOX9 expression as a dichotomous variable, in univariate (HR, 2.32; 95% CI, 1.14-4.69; P = .02) and multivariate analyses (HR, 2.32; 95% CI, 1.14-4.69; P = .02), adjusting for histopathologic risk factors and mismatch repair deficiency. In conclusion, high levels of SOX9 of primary stage II colon tumors predict low risk of relapse, whereas low levels of SOX9 predict high risk of relapse. SOX9 may have an important value as a biomarker when evaluating risk of relapse for personalized treatment. Copyright © 2016 Elsevier Inc. All rights reserved.

Drinking Status Between Ages 50 and 55 for Men From the San Diego Prospective Study Who Developed DSM-IV Alcohol Abuse or Dependence in Prior Follow-Ups.

PubMed

Gonçalves, Priscila Dib; Schuckit, Marc A; Smith, Tom L

2017-07-01

Although alcohol use disorders (AUDs) are prevalent among older individuals, few studies have examined the course and predictors of AUDs from their onset into the person's 50s. This study describes the AUD course from ages 50 to 55 in participants who developed AUDs according to criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), during the San Diego Prospective Study (SDPS). Among the 397 university students in the SDPS who were followed about every 5 years from age 20 (before AUD onset), 165 developed AUDs, 156 of whom were interviewed at age 55. Age 50-55 outcomes were compared regarding age 20-50 characteristics. Variables that differed significantly across outcome groups were evaluated using binary logistic regression analyses predicting each outcome type. Between ages 50 and 55, 16% had low-risk drinking, 36% had high-risk drinking, 38% met DSM-5 AUD criteria, and 10% were abstinent. Baseline predictors of outcome at ages 50-55 included earlier low levels of response to alcohol predicting DSM-5 AUDs and abstinence, higher drinking frequency predicting DSM-5 diagnoses and lower predicting low-risk drinking, higher participation in treatment and/or self-help groups predicting abstinence and lower predicting DSM-5 AUDs, later ages of AUD onset predicting high-risk drinking, and cannabis use disorders predicting abstinent outcomes. Despite the high functioning of these men, few were abstinent or maintained low-risk drinking during the recent 5 years, and 38% met DSM-5 AUD criteria. The data may be helpful to both clinicians and researchers predicting the future course of AUDs in their older patients and research participants.

A modified fall risk assessment tool that is specific to physical function predicts falls in community-dwelling elderly people.

PubMed

Hirase, Tatsuya; Inokuchi, Shigeru; Matsusaka, Nobuou; Nakahara, Kazumi; Okita, Minoru

2014-01-01

Developing a practical fall risk assessment tool to predict the occurrence of falls in the primary care setting is important because investigators have reported deterioration of physical function associated with falls. Researchers have used many performance tests to predict the occurrence of falls. These performance tests predict falls and also assess physical function and determine exercise interventions. However, the need for such specialists as physical therapists to accurately conduct these tests limits their use in the primary care setting. Questionnaires for fall prediction offer an easy way to identify high-risk fallers without requiring specialists. Using an existing fall assessment questionnaire, this study aimed to identify items specific to physical function and determine whether those items were able to predict falls and estimate physical function of high-risk fallers. The analysis consisted of both retrospective and prospective studies and used 2 different samples (retrospective, n = 1871; prospective, n = 292). The retrospective study and 3-month prospective study comprised community-dwelling individuals aged 65 years or older and older adults using community day centers. The number of falls, risk factors for falls (15 risk factors on the questionnaire), and physical function determined by chair standing test (CST) and Timed Up and Go Test (TUGT) were assessed. The retrospective study selected fall risk factors related to physical function. The prospective study investigated whether the number of selected risk factors could predict falls. The predictive power was determined using the area under the receiver operating characteristic curve. Seven of the 15 risk factors were related to physical function. The area under the receiver operating characteristic curve for the sum of the selected risk factors of previous falls plus the other risk factors was 0.82 (P = .00). The best cutoff point was 4 risk factors, with sensitivity and specificity of 84% and 68%, respectively. The mean values for the CST and TUGT at the best cutoff point were 12.9 and 12.5 seconds, respectively. In the retrospective study, the values for the CST and TUGT corresponding to the best cutoff point from the prospective study were 13.2 and 11.4 seconds, respectively. This study confirms that a screening tool comprising 7 fall risk factors can be used to predict falls. The values for the CST and TUGT corresponding to the best cutoff point for the selected 7 risk factors determined in our prospective study were similar to the cutoff points for the CST and TUGT in previous studies for fall prediction. We propose that the sum of the selected risk factors of previous falls plus the other risk factors may be identified as the estimated value for physical function. These findings may contribute to earlier identification of high-risk fallers and intervention for fall prevention.

Blood test could predict risk of heart attack and subsequent death.

PubMed

2017-01-18

A high-sensitivity blood test, known as a troponin test, could predict the risk of heart attack and death and patients' response to statins, say researchers from the Universities of Edinburgh and Glasgow.

Can RSScan footscan(®) D3D™ software predict injury in a military population following plantar pressure assessment? A prospective cohort study.

PubMed

Franklyn-Miller, Andrew; Bilzon, James; Wilson, Cassie; McCrory, Paul

2014-03-01

Injury in initial military training is common with incidences from 25 to 65% of recruits sustaining musculoskeletal injury. Risk factors for injury include extrinsic factors such as rapid onset of high volume training, but intrinsic factors such as lower limb biomechanics and foot type. Prediction of injury would allow more effective training delivery, reduce manpower wastage and improve duty of care to individuals by addressing potential interventions. Plantar pressure interpretation of footfall has been shown to reflect biomechanical intrinsic abnormality although no quantifiable method of risk stratification exists. To identify if pressure plate assessment of walking gait is predictive of injury in a military population. 200 male subjects commencing Naval Officer training were assessed by plantar pressure plate recording, of foot contact pressures. A software interpretation, D3D™, stratified the interpretation to measure 4 specific areas of potential correction. Participants were graded as to high, medium and low risk of injury and subsequently followed up for injury through their basic training. Seventy two percent of all injuries were attributed to subjects in the high and medium risk of injury as defined by the risk categorization. 47% of all injuries were sustained in the high-risk group. Participants categorized in the high-risk group for injury were significantly more likely to sustain injury than in medium or low groups (p<0.001, OR 5.28 with 95% CI 2.88, 9.70). Plantar pressure assessment of risk for overuse lower limb injury can be predictive of sustaining an overuse injury in a controlled training environment. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Tissue Systems Pathology Assay for High-Risk Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Duits, Lucas C; Prichard, Jeffrey W; Davison, Jon M; Jobe, Blair A; Campbell, Bruce B; Zhang, Yi; Repa, Kathleen A; Reese, Lia M; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory; DeMarshall, Maureen; Foxwell, Tyler; Zaidi, Ali H; Lansing Taylor, D; Rustgi, Anil K; Bergman, Jacques J G H M; Falk, Gary W

2016-06-01

Better methods are needed to predict risk of progression for Barrett's esophagus. We aimed to determine whether a tissue systems pathology approach could predict progression in patients with nondysplastic Barrett's esophagus, indefinite for dysplasia, or low-grade dysplasia. We performed a nested case-control study to develop and validate a test that predicts progression of Barrett's esophagus to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC), based upon quantification of epithelial and stromal variables in baseline biopsies. Data were collected from Barrett's esophagus patients at four institutions. Patients who progressed to HGD or EAC in ≥1 year (n = 79) were matched with patients who did not progress (n = 287). Biopsies were assigned randomly to training or validation sets. Immunofluorescence analyses were performed for 14 biomarkers and quantitative biomarker and morphometric features were analyzed. Prognostic features were selected in the training set and combined into classifiers. The top-performing classifier was assessed in the validation set. A 3-tier, 15-feature classifier was selected in the training set and tested in the validation set. The classifier stratified patients into low-, intermediate-, and high-risk classes [HR, 9.42; 95% confidence interval, 4.6-19.24 (high-risk vs. low-risk); P < 0.0001]. It also provided independent prognostic information that outperformed predictions based on pathology analysis, segment length, age, sex, or p53 overexpression. We developed a tissue systems pathology test that better predicts risk of progression in Barrett's esophagus than clinicopathologic variables. The test has the potential to improve upon histologic analysis as an objective method to risk stratify Barrett's esophagus patients. Cancer Epidemiol Biomarkers Prev; 25(6); 958-68. ©2016 AACR. ©2016 American Association for Cancer Research.

Anxiety sensitivity cognitive concerns predict suicide risk.

PubMed

Oglesby, Mary Elizabeth; Capron, Daniel William; Raines, Amanda Medley; Schmidt, Norman Bradley

2015-03-30

Anxiety sensitivity (AS) cognitive concerns, which reflects fears of mental incapacitation, have been previously associated with suicidal ideation and behavior. The first study aim was to replicate and extend upon previous research by investigating whether AS cognitive concerns can discriminate between those at low risk versus high risk for suicidal behavior. Secondly, we aimed to test the incremental predictive power of AS cognitive concerns above and beyond known suicide risk factors (i.e., thwarted belongingness and insomnia). The sample consisted of 106 individuals (75% meeting current criteria for an Axis I disorder) recruited from the community. Results revealed that AS cognitive concerns were a robust predictor of elevated suicide risk after covarying for negative affect, whereas AS social and physical concerns were not. Those with high, relative to low, AS cognitive scores were 3.67 times more likely to be in the high suicide risk group. Moreover, AS cognitive concerns significantly predicted elevated suicide risk above and beyond relevant suicide risk factors. Results of this study add to a growing body of the literature demonstrating a relationship between AS cognitive concerns and increased suicidality. Incorporating AS cognitive concerns amelioration protocols into existing interventions for suicidal behavior may be beneficial. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Glycated hemoglobin measurement and prediction of cardiovascular disease.

PubMed

Di Angelantonio, Emanuele; Gao, Pei; Khan, Hassan; Butterworth, Adam S; Wormser, David; Kaptoge, Stephen; Kondapally Seshasai, Sreenivasa Rao; Thompson, Alex; Sarwar, Nadeem; Willeit, Peter; Ridker, Paul M; Barr, Elizabeth L M; Khaw, Kay-Tee; Psaty, Bruce M; Brenner, Hermann; Balkau, Beverley; Dekker, Jacqueline M; Lawlor, Debbie A; Daimon, Makoto; Willeit, Johann; Njølstad, Inger; Nissinen, Aulikki; Brunner, Eric J; Kuller, Lewis H; Price, Jackie F; Sundström, Johan; Knuiman, Matthew W; Feskens, Edith J M; Verschuren, W M M; Wald, Nicholas; Bakker, Stephan J L; Whincup, Peter H; Ford, Ian; Goldbourt, Uri; Gómez-de-la-Cámara, Agustín; Gallacher, John; Simons, Leon A; Rosengren, Annika; Sutherland, Susan E; Björkelund, Cecilia; Blazer, Dan G; Wassertheil-Smoller, Sylvia; Onat, Altan; Marín Ibañez, Alejandro; Casiglia, Edoardo; Jukema, J Wouter; Simpson, Lara M; Giampaoli, Simona; Nordestgaard, Børge G; Selmer, Randi; Wennberg, Patrik; Kauhanen, Jussi; Salonen, Jukka T; Dankner, Rachel; Barrett-Connor, Elizabeth; Kavousi, Maryam; Gudnason, Vilmundur; Evans, Denis; Wallace, Robert B; Cushman, Mary; D'Agostino, Ralph B; Umans, Jason G; Kiyohara, Yutaka; Nakagawa, Hidaeki; Sato, Shinichi; Gillum, Richard F; Folsom, Aaron R; van der Schouw, Yvonne T; Moons, Karel G; Griffin, Simon J; Sattar, Naveed; Wareham, Nicholas J; Selvin, Elizabeth; Thompson, Simon G; Danesh, John

2014-03-26

The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥ 7.5%) risk. During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.

PubMed

Rosellini, A J; Stein, M B; Benedek, D M; Bliese, P D; Chiu, W T; Hwang, I; Monahan, J; Nock, M K; Petukhova, M V; Sampson, N A; Street, A E; Zaslavsky, A M; Ursano, R J; Kessler, R C

2017-10-01

The U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service. 21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011-2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition. The best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk. Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

A Retrospective Analysis of Pressure Ulcer Incidence and Modified Braden Scale Score Risk Classifications.

PubMed

Chen, Hong-Lin; Cao, Ying-Juan; Wang, Jing; Huai, Bao-Sha

2015-09-01

The Braden Scale is the most widely used pressure ulcer risk assessment in the world, but the currently used 5 risk classification groups do not accurately discriminate among their risk categories. To optimize risk classification based on Braden Scale scores, a retrospective analysis of all consecutively admitted patients in an acute care facility who were at risk for pressure ulcer development was performed between January 2013 and December 2013. Predicted pressure ulcer incidence first was calculated by logistic regression model based on original Braden score. Risk classification then was modified based on the predicted pressure ulcer incidence and compared between different risk categories in the modified (3-group) classification and the traditional (5-group) classification using chi-square test. Two thousand, six hundred, twenty-five (2,625) patients (mean age 59.8 ± 16.5, range 1 month to 98 years, 1,601 of whom were men) were included in the study; 81 patients (3.1%) developed a pressure ulcer. The predicted pressure ulcer incidence ranged from 0.1% to 49.7%. When the predicted pressure ulcer incidence was greater than 10.0% (high risk), the corresponding Braden scores were less than 11; when the predicted incidence ranged from 1.0% to 10.0% (moderate risk), the corresponding Braden scores ranged from 12 to 16; and when the predicted incidence was less than 1.0% (mild risk), the corresponding Braden scores were greater than 17. In the modified classification, observed pressure ulcer incidence was significantly different between each of the 3 risk categories (P less than 0.05). However, in the traditional classification, the observed incidence was not significantly different between the high-risk category and moderate-risk category (P less than 0.05) and between the mild-risk category and no-risk category (P less than 0.05). If future studies confirm the validity of these findings, pressure ulcer prevention protocols of care based on Braden Scale scores can be simplified.

Validation of a Delirium Risk Assessment Using Electronic Medical Record Information.

PubMed

Rudolph, James L; Doherty, Kelly; Kelly, Brittany; Driver, Jane A; Archambault, Elizabeth

2016-03-01

Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts. Retrospective analysis followed by prospective validation. Tertiary VA Hospital in New England. A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital. The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation. Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80-0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61-0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P = .02). Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care. Published by Elsevier Inc.

Assessing Bleeding Risk in Patients Taking Anticoagulants

PubMed Central

Shoeb, Marwa; Fang, Margaret C.

2013-01-01

Anticoagulant medications are commonly used for the prevention and treatment of thromboembolism. Although highly effective, they are also associated with significant bleeding risks. Numerous individual clinical factors have been linked to an increased risk of hemorrhage, including older age, anemia, and renal disease. To help quantify hemorrhage risk for individual patients, a number of clinical risk prediction tools have been developed. These risk prediction tools differ in how they were derived and how they identify and weight individual risk factors. At present, their ability to effective predict anticoagulant-associated hemorrhage remains modest. Use of risk prediction tools to estimate bleeding in clinical practice is most influential when applied to patients at the lower spectrum of thromboembolic risk, when the risk of hemorrhage will more strongly affect clinical decisions about anticoagulation. Using risk tools may also help counsel and inform patients about their potential risk for hemorrhage while on anticoagulants, and can identify patients who might benefit from more careful management of anticoagulation. PMID:23479259

Gamma Interferon Release Assays for Detection of Mycobacterium tuberculosis Infection

PubMed Central

Denkinger, Claudia M.; Kik, Sandra V.; Rangaka, Molebogeng X.; Zwerling, Alice; Oxlade, Olivia; Metcalfe, John Z.; Cattamanchi, Adithya; Dowdy, David W.; Dheda, Keertan; Banaei, Niaz

2014-01-01

SUMMARY Identification and treatment of latent tuberculosis infection (LTBI) can substantially reduce the risk of developing active disease. However, there is no diagnostic gold standard for LTBI. Two tests are available for identification of LTBI: the tuberculin skin test (TST) and the gamma interferon (IFN-γ) release assay (IGRA). Evidence suggests that both TST and IGRA are acceptable but imperfect tests. They represent indirect markers of Mycobacterium tuberculosis exposure and indicate a cellular immune response to M. tuberculosis. Neither test can accurately differentiate between LTBI and active TB, distinguish reactivation from reinfection, or resolve the various stages within the spectrum of M. tuberculosis infection. Both TST and IGRA have reduced sensitivity in immunocompromised patients and have low predictive value for progression to active TB. To maximize the positive predictive value of existing tests, LTBI screening should be reserved for those who are at sufficiently high risk of progressing to disease. Such high-risk individuals may be identifiable by using multivariable risk prediction models that incorporate test results with risk factors and using serial testing to resolve underlying phenotypes. In the longer term, basic research is necessary to identify highly predictive biomarkers. PMID:24396134

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents

PubMed Central

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-01-01

CONTEXT Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. METHODS Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13–17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. RESULTS For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. CONCLUSION Results highlight the important influence of peer risk involvement on youths’ sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior. PMID:26308261

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents.

PubMed

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-06-01

Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13-17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. Results highlight the important influence of peer risk involvement on youths' sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior.

The Reliability and Predictive Validity of the Stalking Risk Profile.

PubMed

McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

2018-03-01

This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

Checking the predictive accuracy of basic symptoms against ultra high-risk criteria and testing of a multivariable prediction model: Evidence from a prospective three-year observational study of persons at clinical high-risk for psychosis.

PubMed

Hengartner, M P; Heekeren, K; Dvorsky, D; Walitza, S; Rössler, W; Theodoridou, A

2017-09-01

The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P<.05), whereas BS did not predict psychosis beyond mere chance (AUC=0.52, P=.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antipsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

PubMed Central

Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

2017-01-01

Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

Predictive risk mapping of West Nile virus (WNV) infection in Saskatchewan horses.

PubMed

Epp, Tasha Y; Waldner, Cheryl; Berke, Olaf

2011-07-01

The objective of this study was to develop a model using equine data from geographically limited surveillance locations to predict risk categories for West Nile virus (WNV) infection in horses in all geographic locations across the province of Saskatchewan. The province was divided geographically into low-, medium-, or high-risk categories for WNV, based on available serology information from 923 horses obtained through 4 studies of WNV infection in horse populations in Saskatchewan. Discriminant analysis was used to build models using the observed risk of WNV in horses and geographic division-specific environmental data as well as to predict the risk category for all areas, including those beyond the surveillance zones. High-risk areas were indicated by relatively lower rainfall, higher temperatures, and a lower percentage of area covered in trees, water, and wetland. These conditions were most often identified in the southwest corner of the province. Environmental conditions can be used to identify those areas that are at highest risk for WNV. Public health managers could use prediction maps, which are based on animal or human information and developed from annual early season meteorological information, to guide ongoing decisions about when and where to focus intervention strategies for WNV.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

PubMed

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

Performance characteristics of prostate-specific antigen density and biopsy core details to predict oncological outcome in patients with intermediate to high-risk prostate cancer underwent robot-assisted radical prostatectomy.

PubMed

Yashi, Masahiro; Nukui, Akinori; Tokura, Yuumi; Takei, Kohei; Suzuki, Issei; Sakamoto, Kazumasa; Yuki, Hideo; Kambara, Tsunehito; Betsunoh, Hironori; Abe, Hideyuki; Fukabori, Yoshitatsu; Nakazato, Yoshimasa; Kaji, Yasushi; Kamai, Takao

2017-06-23

Many urologic surgeons refer to biopsy core details for decision making in cases of localized prostate cancer (PCa) to determine whether an extended resection and/or lymph node dissection should be performed. Furthermore, recent reports emphasize the predictive value of prostate-specific antigen density (PSAD) for further risk stratification, not only for low-risk PCa, but also for intermediate- and high-risk PCa. This study focused on these parameters and compared respective predictive impact on oncologic outcomes in Japanese PCa patients. Two-hundred and fifty patients with intermediate- and high-risk PCa according to the National Comprehensive Cancer Network (NCCN) classification, that underwent robot-assisted radical prostatectomy at a single institution, and with observation periods of longer than 6 months were enrolled. None of the patients received hormonal treatments including antiandrogens, luteinizing hormone-releasing hormone analogues, or 5-alpha reductase inhibitors preoperatively. PSAD and biopsy core details, including the percentage of positive cores and the maximum percentage of cancer extent in each positive core, were analyzed in association with unfavorable pathologic results of prostatectomy specimens, and further with biochemical recurrence. The cut-off values of potential predictive factors were set through receiver-operating characteristic curve analyses. In the entire cohort, a higher PSAD, the percentage of positive cores, and maximum percentage of cancer extent in each positive core were independently associated with advanced tumor stage ≥ pT3 and an increased index tumor volume > 0.718 ml. NCCN classification showed an association with a tumor stage ≥ pT3 and a Gleason score ≥8, and the attribution of biochemical recurrence was also sustained. In each NCCN risk group, these preoperative factors showed various associations with unfavorable pathological results. In the intermediate-risk group, the percentage of positive cores showed an independent predictive value for biochemical recurrence. In the high-risk group, PSAD showed an independent predictive value. PSAD and biopsy core details have different performance characteristics for the prediction of oncologic outcomes in each NCCN risk group. Despite the need for further confirmation of the results with a larger cohort and longer observation, these factors are important as preoperative predictors in addition to the NCCN classification for a urologic surgeon to choose a surgical strategy.

Repeated Blood Pressure Measurements in Childhood in Prediction of Hypertension in Adulthood.

PubMed

Oikonen, Mervi; Nuotio, Joel; Magnussen, Costan G; Viikari, Jorma S A; Taittonen, Leena; Laitinen, Tomi; Hutri-Kähönen, Nina; Jokinen, Eero; Jula, Antti; Cheung, Michael; Sabin, Matthew A; Daniels, Stephen R; Raitakari, Olli T; Juonala, Markus

2016-01-01

Hypertension may be predicted from childhood risk factors. Repeated observations of abnormal blood pressure in childhood may enhance prediction of hypertension and subclinical atherosclerosis in adulthood compared with a single observation. Participants (1927, 54% women) from the Cardiovascular Risk in Young Finns Study had systolic and diastolic blood pressure measurements performed when aged 3 to 24 years. Childhood/youth abnormal blood pressure was defined as above 90th or 95th percentile. After a 21- to 31-year follow-up, at the age of 30 to 45 years, hypertension (>140/90 mm Hg or antihypertensive medication) prevalence was found to be 19%. Carotid intima-media thickness was examined, and high-risk intima-media was defined as intima-media thickness >90th percentile or carotid plaques. Prediction of adulthood hypertension and high-risk intima-media was compared between one observation of abnormal blood pressure in childhood/youth and multiple observations by improved Pearson correlation coefficients and area under the receiver operating curve. When compared with a single measurement, 2 childhood/youth observations improved the correlation for adult systolic (r=0.44 versus 0.35, P<0.001) and diastolic (r=0.35 versus 0.17, P<0.001) blood pressure. In addition, 2 abnormal childhood/youth blood pressure observations increased the prediction of hypertension in adulthood (0.63 for 2 versus 0.60 for 1 observation, P=0.003). When compared with 2 measurements, third observation did not provide any significant improvement for correlation or prediction (P always >0.05). A higher number of childhood/youth observations of abnormal blood pressure did not enhance prediction of adult high-risk intima-media thickness. Compared with a single measurement, the prediction of adult hypertension was enhanced by 2 observations of abnormal blood pressure in childhood/youth. © 2015 American Heart Association, Inc.

Predictive Validity of a Student Self-Report Screener of Behavioral and Emotional Risk in an Urban High School

ERIC Educational Resources Information Center

Dowdy, Erin; Harrell-Williams, Leigh; Dever, Bridget V.; Furlong, Michael J.; Moore, Stephanie; Raines, Tara; Kamphaus, Randy W.

2016-01-01

Increasingly, schools are implementing school-based screening for risk of behavioral and emotional problems; hence, foundational evidence supporting the predictive validity of screening instruments is important to assess. This study examined the predictive validity of the Behavior Assessment System for Children-2 Behavioral and Emotional Screening…

Geo-environmental model for the prediction of potential transmission risk of Dirofilaria in an area with dry climate and extensive irrigated crops. The case of Spain.

PubMed

Simón, Luis; Afonin, Alexandr; López-Díez, Lucía Isabel; González-Miguel, Javier; Morchón, Rodrigo; Carretón, Elena; Montoya-Alonso, José Alberto; Kartashev, Vladimir; Simón, Fernando

2014-03-01

Zoonotic filarioses caused by Dirofilaria immitis and Dirofilaria repens are transmitted by culicid mosquitoes. Therefore Dirofilaria transmission depends on climatic factors like temperature and humidity. In spite of the dry climate of most of the Spanish territory, there are extensive irrigated crops areas providing moist habitats favourable for mosquito breeding. A GIS model to predict the risk of Dirofilaria transmission in Spain, based on temperatures and rainfall data as well as in the distribution of irrigated crops areas, is constructed. The model predicts that potential risk of Dirofilaria transmission exists in all the Spanish territory. Highest transmission risk exists in several areas of Andalucía, Extremadura, Castilla-La Mancha, Murcia, Valencia, Aragón and Cataluña, where moderate/high temperatures coincide with extensive irrigated crops. High risk in Balearic Islands and in some points of Canary Islands, is also predicted. The lowest risk is predicted in Northern cold and scarcely or non-irrigated dry Southeastern areas. The existence of irrigations locally increases transmission risk in low rainfall areas of the Spanish territory. The model can contribute to implement rational preventive therapy guidelines in accordance with the transmission characteristics of each local area. Moreover, the use of humidity-related factors could be of interest in future predictions to be performed in countries with similar environmental characteristics. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting the Unpredictable? Identifying High-Risk versus Low-Risk Parents with Intellectual Disabilities

ERIC Educational Resources Information Center

McGaw, Sue; Scully, Tamara; Pritchard, Colin

2010-01-01

Objectives: This study set out to identify risk factors affecting parents with intellectual disabilities (IDs) by determining: (i) whether perception of family support differs between parents with IDs, referring professionals, and a specialist parenting service; (ii) whether multivariate familial and demographic factors differentiates "high-risk"…

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C.

PubMed

Kurosaki, Masayuki; Hiramatsu, Naoki; Sakamoto, Minoru; Suzuki, Yoshiyuki; Iwasaki, Manabu; Tamori, Akihiro; Matsuura, Kentaro; Kakinuma, Sei; Sugauchi, Fuminaka; Sakamoto, Naoya; Nakagawa, Mina; Izumi, Namiki

2012-03-01

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC. Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy). On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040). The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC. Copyright © 2011 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.

PubMed

Hu, Chen; Steingrimsson, Jon Arni

2018-01-01

A crucial component of making individualized treatment decisions is to accurately predict each patient's disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censoring. Risk prediction models based on recursive partitioning methods are becoming increasingly popular largely due to their ability to handle nonlinear relationships, higher-order interactions, and/or high-dimensional covariates. The most popular recursive partitioning methods are versions of the Classification and Regression Tree (CART) algorithm, which builds a simple interpretable tree structured model. With the aim of increasing prediction accuracy, the random forest algorithm averages multiple CART trees, creating a flexible risk prediction model. Risk prediction models used in clinical oncology commonly use both traditional demographic and tumor pathological factors as well as high-dimensional genetic markers and treatment parameters from multimodality treatments. In this article, we describe the most commonly used extensions of the CART and random forest algorithms to right-censored outcomes. We focus on how they differ from the methods for noncensored outcomes, and how the different splitting rules and methods for cost-complexity pruning impact these algorithms. We demonstrate these algorithms by analyzing a randomized Phase III clinical trial of breast cancer. We also conduct Monte Carlo simulations to compare the prediction accuracy of survival forests with more commonly used regression models under various scenarios. These simulation studies aim to evaluate how sensitive the prediction accuracy is to the underlying model specifications, the choice of tuning parameters, and the degrees of missing covariates.

A risk score including microdeletions improves relapse prediction for standard and medium risk precursor B-cell acute lymphoblastic leukaemia in children.

PubMed

Sutton, Rosemary; Venn, Nicola C; Law, Tamara; Boer, Judith M; Trahair, Toby N; Ng, Anthea; Den Boer, Monique L; Dissanayake, Anuruddhika; Giles, Jodie E; Dalzell, Pauline; Mayoh, Chelsea; Barbaric, Draga; Revesz, Tamas; Alvaro, Frank; Pieters, Rob; Haber, Michelle; Norris, Murray D; Schrappe, Martin; Dalla Pozza, Luciano; Marshall, Glenn M

2018-02-01

To prevent relapse, high risk paediatric acute lymphoblastic leukaemia (ALL) is treated very intensively. However, most patients who eventually relapse have standard or medium risk ALL with low minimal residual disease (MRD) levels. We analysed recurrent microdeletions and other clinical prognostic factors in a cohort of 475 uniformly treated non-high risk precursor B-cell ALL patients with the aim of better predicting relapse and refining risk stratification. Lower relapse-free survival at 7 years (RFS) was associated with IKZF1 intragenic deletions (P < 0·0001); P2RY8-CRLF2 gene fusion (P < 0·0004); Day 33 MRD>5 × 10 -5 (P < 0·0001) and High National Cancer Institute (NCI) risk (P < 0·0001). We created a predictive model based on a risk score (RS) for deletions, MRD and NCI risk, extending from an RS of 0 (RS0) for patients with no unfavourable factors to RS2 +  for patients with 2 or 3 high risk factors. RS0, RS1, and RS2 +  groups had RFS of 93%, 78% and 49%, respectively, and overall survival (OS) of 99%, 91% and 71%. The RS provided greater discrimination than MRD-based risk stratification into standard (89% RFS, 96% OS) and medium risk groups (79% RFS, 91% OS). We conclude that this RS may enable better early therapeutic stratification and thus improve cure rates for childhood ALL. © 2017 John Wiley & Sons Ltd.

Reduced mortality in high-risk coronary patients operated off pump with preoperative intraaortic balloon counterpulsation.

PubMed

Etienne, Pierre Yves; Papadatos, Spiridon; Glineur, David; Mairy, Yves; El Khoury, Elie; Noirhomme, Philippe; El Khoury, Gebrine

2007-08-01

Preoperative intraaortic balloon pump (IABP) counterpulsation has better outcomes compared with perioperative or postoperative insertion in critical patients, and off-pump surgical procedures have been advocated to reduce mortality in high-risk patients. However, some surgeons are reluctant to perform beating heart operations in specific patient subgroups, including those with unstable angina or patients with low ejection fraction, because of their possible perioperative hemodynamic instability. We evaluated combined beating heart procedures and preoperative IABP in selected high-risk patients and compared our results with the predictive European System for Cardiac Operative Risk Evaluation (EuroSCORE) model. Fifty-five high-risk patients with a mean logistic EuroSCORE of 24 were prospectively enrolled and then divided into emergency (group 1, n = 25) and nonemergency (group 2, n = 30) groups. IABP was inserted immediately before operation in group 1 and the day before the procedure in group 2. Compared with the EuroSCORE predictive model, a dramatic decrease in mortality occurred in both groups. Group I predicted mortality was 36.8%, and observed was 20%; and group 2 predicted mortality was 15.2% and observed was 0%. No specific complications from the use of IABP were encountered. During mid-term (2 years) follow-up, no patient died from a cardiac cause or required percutaneous coronary intervention or subsequent reoperation due to incomplete revascularization. The combined use of preoperative intraaortic counterpulsation and beating heart intervention allows complete revascularization in high-risk patients with a important reduction in operative mortality and excellent mid-term results.

Predictive model for serious bacterial infections among infants younger than 3 months of age.

PubMed

Bachur, R G; Harper, M B

2001-08-01

To develop a data-derived model for predicting serious bacterial infection (SBI) among febrile infants <3 months old. All infants /=38.0 degrees C seen in an urban emergency department (ED) were retrospectively identified. SBI was defined as a positive culture of urine, blood, or cerebrospinal fluid. Tree-structured analysis via recursive partitioning was used to develop the model. SBI or No-SBI was the dichotomous outcome variable, and age, temperature, urinalysis (UA), white blood cell (WBC) count, absolute neutrophil count, and cerebrospinal fluid WBC were entered as potential predictors. The model was tested by V-fold cross-validation. Of 5279 febrile infants studied, SBI was diagnosed in 373 patients (7%): 316 urinary tract infections (UTIs), 17 meningitis, and 59 bacteremia (8 with meningitis, 11 with UTIs). The model sequentially used 4 clinical parameters to define high-risk patients: positive UA, WBC count >/=20 000/mm(3) or /=39.6 degrees C, and age <13 days. The sensitivity of the model for SBI is 82% (95% confidence interval [CI]: 78%-86%) and the negative predictive value is 98.3% (95% CI: 97.8%-98.7%). The negative predictive value for bacteremia or meningitis is 99.6% (95% CI: 99.4%-99.8%). The relative risk between high- and low-risk groups is 12.1 (95% CI: 9.3-15.6). Sixty-six SBI patients (18%) were misclassified into the lower risk group: 51 UTIs, 14 with bacteremia, and 1 with meningitis. Decision-tree analysis using common clinical variables can reasonably predict febrile infants at high-risk for SBI. Sequential use of UA, WBC count, temperature, and age can identify infants who are at high risk of SBI with a relative risk of 12.1 compared with lower-risk infants.

The evaluation of acute physiology and chronic health evaluation II score, poisoning severity score, sequential organ failure assessment score combine with lactate to assess the prognosis of the patients with acute organophosphate pesticide poisoning.

PubMed

Yuan, Shaoxin; Gao, Yusong; Ji, Wenqing; Song, Junshuai; Mei, Xue

2018-05-01

The aim of this study was to assess the ability of acute physiology and chronic health evaluation II (APACHE II) score, poisoning severity score (PSS) as well as sequential organ failure assessment (SOFA) score combining with lactate (Lac) to predict mortality in the Emergency Department (ED) patients who were poisoned with organophosphate.A retrospective review of 59 stands-compliant patients was carried out. Receiver operating characteristic (ROC) curves were constructed based on the APACHE II score, PSS, SOFA score with or without Lac, respectively, and the areas under the ROC curve (AUCs) were determined to assess predictive value. According to SOFA-Lac (a combination of SOFA and Lac) classification standard, acute organophosphate pesticide poisoning (AOPP) patients were divided into low-risk and high-risk groups. Then mortality rates were compared between risk levels.Between survivors and non-survivors, there were significant differences in the APACHE II score, PSS, SOFA score, and Lac (all P < .05). The AUCs of the APACHE II score, PSS, and SOFA score were 0.876, 0.811, and 0.837, respectively. However, after combining with Lac, the AUCs were 0.922, 0.878, and 0.956, respectively. According to SOFA-Lac, the mortality of high-risk group was significantly higher than low-risk group (P < .05) and the patients of the non-survival group were all at high risk.These data suggest the APACHE II score, PSS, SOFA score can all predict the prognosis of AOPP patients. For its simplicity and objectivity, the SOFA score is a superior predictor. Lac significantly improved the predictive abilities of the 3 scoring systems, especially for the SOFA score. The SOFA-Lac system effectively distinguished the high-risk group from the low-risk group. Therefore, the SOFA-Lac system is significantly better at predicting mortality in AOPP patients.

Does adolescent risk taking imply weak executive function? A prospective study of relations between working memory performance, impulsivity, and risk taking in early adolescence.

PubMed

Romer, Daniel; Betancourt, Laura M; Brodsky, Nancy L; Giannetta, Joan M; Yang, Wei; Hurt, Hallam

2011-09-01

Studies of brain development suggest that the increase in risk taking observed during adolescence may be due to insufficient prefrontal executive function compared to a more rapidly developing subcortical motivation system. We examined executive function as assessed by working memory ability in a community sample of youth (n = 387, ages 10 to 12 at baseline) in three annual assessments to determine its relation to two forms of impulsivity (sensation seeking and acting without thinking) and a wide range of risk and externalizing behavior. Using structural equation modeling, we tested a model in which differential activation of the dorsal and ventral striatum produces imbalance in the function of these brain regions. For youth high in sensation seeking, both regions were predicted to develop with age. However, for youth high in the tendency to act without thinking, the ventral striatum was expected to dominate. The model predicted that working memory ability would exhibit (1) early weakness in youth high in acting without thinking but (2) growing strength in those high in sensation seeking. In addition, it predicted that (3) acting without thinking would be more strongly related to risk and externalizing behavior than sensation seeking. Finally, it predicted that (4) controlling for acting without thinking, sensation seeking would predict later increases in risky and externalizing behavior. All four of these predictions were confirmed. The results indicate that the rise in sensation seeking that occurs during adolescence is not accompanied by a deficit in executive function and therefore requires different intervention strategies from those for youth whose impulsivity is characterized by early signs of acting without thinking. © 2011 Blackwell Publishing Ltd.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

PubMed

Zhao, Lue Ping; Bolouri, Hamid

2016-04-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015). Copyright © 2016 Elsevier Inc. All rights reserved.

Object-Oriented Regression for Building Predictive Models with High Dimensional Omics Data from Translational Studies

PubMed Central

Zhao, Lue Ping; Bolouri, Hamid

2016-01-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and to make the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (p=0.015). PMID:26972839

Validation of Biomarkers for Prostate Cancer Prognosis

DTIC Science & Technology

2015-11-01

and actually have occult high-risk features or are destined to progress to high-risk disease. Therefore a critical need in localized prostate...cancer is the development of biomarkers that predict occult or incipient aggressive disease in the low-risk population. 15. SUBJECT TERMS- Nothing...causing death. However, it is well known that a significant fraction of low risk cases are misclassified and actually have occult high-risk features or

A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

PubMed

Pedersen, Mads Uffe; Hesse, Morten

2009-01-01

Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

Atypia and DNA methylation in nipple duct lavage in relation to predicted breast cancer risk.

PubMed

Euhus, David M; Bu, Dawei; Ashfaq, Raheela; Xie, Xian-Jin; Bian, Aihua; Leitch, A Marilyn; Lewis, Cheryl M

2007-09-01

Tumor suppressor gene (TSG) methylation is identified more frequently in random periareolar fine needle aspiration samples from women at high risk for breast cancer than women at lower risk. It is not known whether TSG methylation or atypia in nipple duct lavage (NDL) samples is related to predicted breast cancer risk. 514 NDL samples obtained from 150 women selected to represent a wide range of breast cancer risk were evaluated cytologically and by quantitative multiplex methylation-specific PCR for methylation of cyclin D2, APC, HIN1, RASSF1A, and RAR-beta2. Based on methylation patterns and cytology, NDL retrieved cancer cells from only 9% of breasts ipsilateral to a breast cancer. Methylation of >/=2 genes correlated with marked atypia by univariate analysis, but not multivariate analysis, that adjusted for sample cellularity and risk group classification. Both marked atypia and TSG methylation independently predicted abundant cellularity in multivariate analyses. Discrimination between Gail lower-risk ducts and Gail high-risk ducts was similar for marked atypia [odds ratio (OR), 3.48; P = 0.06] and measures of TSG methylation (OR, 3.51; P = 0.03). However, marked atypia provided better discrimination between Gail lower-risk ducts and ducts contralateral to a breast cancer (OR, 6.91; P = 0.003, compared with methylation OR, 4.21; P = 0.02). TSG methylation in NDL samples does not predict marked atypia after correcting for sample cellularity and risk group classification. Rather, both methylation and marked atypia are independently associated with highly cellular samples, Gail model risk classifications, and a personal history of breast cancer. This suggests the existence of related, but independent, pathogenic pathways in breast epithelium.

A Serological Biopsy Using Five Stomach-Specific Circulating Biomarkers for Gastric Cancer Risk Assessment: A Multi-Phase Study.

PubMed

Tu, Huakang; Sun, Liping; Dong, Xiao; Gong, Yuehua; Xu, Qian; Jing, Jingjing; Bostick, Roberd M; Wu, Xifeng; Yuan, Yuan

2017-05-01

We aimed to assess a serological biopsy using five stomach-specific circulating biomarkers-pepsinogen I (PGI), PGII, PGI/II ratio, anti-Helicobacter pylori (H. pylori) antibody, and gastrin-17 (G-17)-for identifying high-risk individuals and predicting risk of developing gastric cancer (GC). Among 12,112 participants with prospective follow-up from an ongoing population-based screening program using both serology and gastroscopy in China, we conducted a multi-phase study involving a cross-sectional analysis, a follow-up analysis, and an integrative risk prediction modeling analysis. In the cross-sectional analysis, the five biomarkers (especially PGII, the PGI/II ratio, and H. pylori sero-positivity) were associated with the presence of precancerous gastric lesions or GC at enrollment. In the follow-up analysis, low PGI levels and PGI/II ratios were associated with higher risk of developing GC, and both low (<0.5 pmol/l) and high (>4.7 pmol/l) G-17 levels were associated with higher risk of developing GC, suggesting a J-shaped association. In the risk prediction modeling analysis, the five biomarkers combined yielded a C statistic of 0.803 (95% confidence interval (CI)=0.789-0.816) and improved prediction beyond traditional risk factors (C statistic from 0.580 to 0.811, P<0.001) for identifying precancerous lesions at enrollment, and higher serological biopsy scores based on the five biomarkers at enrollment were associated with higher risk of developing GC during follow-up (P for trend <0.001). A serological biopsy composed of the five stomach-specific circulating biomarkers could be used to identify high-risk individuals for further diagnostic gastroscopy, and to stratify individuals' risk of developing GC and thus to guide targeted screening and precision prevention.

Inflammatory and Other Biomarkers: Role in Pathophysiology and Prediction of Gestational Diabetes Mellitus

PubMed Central

Abell, Sally K.; De Courten, Barbora; Boyle, Jacqueline A.; Teede, Helena J.

2015-01-01

Understanding pathophysiology and identifying mothers at risk of major pregnancy complications is vital to effective prevention and optimal management. However, in current antenatal care, understanding of pathophysiology of complications is limited. In gestational diabetes mellitus (GDM), risk prediction is mostly based on maternal history and clinical risk factors and may not optimally identify high risk pregnancies. Hence, universal screening is widely recommended. Here, we will explore the literature on GDM and biomarkers including inflammatory markers, adipokines, endothelial function and lipids to advance understanding of pathophysiology and explore risk prediction, with a goal to guide prevention and treatment of GDM. PMID:26110385

Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam.

PubMed

Kabaria, Caroline W; Molteni, Fabrizio; Mandike, Renata; Chacky, Frank; Noor, Abdisalan M; Snow, Robert W; Linard, Catherine

2016-07-30

With more than half of Africa's population expected to live in urban settlements by 2030, the burden of malaria among urban populations in Africa continues to rise with an increasing number of people at risk of infection. However, malaria intervention across Africa remains focused on rural, highly endemic communities with far fewer strategic policy directions for the control of malaria in rapidly growing African urban settlements. The complex and heterogeneous nature of urban malaria requires a better understanding of the spatial and temporal patterns of urban malaria risk in order to design effective urban malaria control programs. In this study, we use remotely sensed variables and other environmental covariates to examine the predictability of intra-urban variations of malaria infection risk across the rapidly growing city of Dar es Salaam, Tanzania between 2006 and 2014. High resolution SPOT satellite imagery was used to identify urban environmental factors associated malaria prevalence in Dar es Salaam. Supervised classification with a random forest classifier was used to develop high resolution land cover classes that were combined with malaria parasite prevalence data to identify environmental factors that influence localized heterogeneity of malaria transmission and develop a high resolution predictive malaria risk map of Dar es Salaam. Results indicate that the risk of malaria infection varied across the city. The risk of infection increased away from the city centre with lower parasite prevalence predicted in administrative units in the city centre compared to administrative units in the peri-urban suburbs. The variation in malaria risk within Dar es Salaam was shown to be influenced by varying environmental factors. Higher malaria risks were associated with proximity to dense vegetation, inland water and wet/swampy areas while lower risk of infection was predicted in densely built-up areas. The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.

The Violence Risk Scale: Predictive Validity and Linking Changes in Risk with Violent Recidivism in a Sample of High-Risk Offenders with Psychopathic Traits

ERIC Educational Resources Information Center

Lewis, Kathy; Olver, Mark E.; Wong, Stephen C. P.

2013-01-01

The Violence Risk Scale (VRS) uses ratings of static and dynamic risk predictors to assess violence risk, identify targets for treatment, and assess changes in risk following treatment. The VRS was rated pre- and posttreatment on a sample of 150 males, mostly high-risk violent offenders many with psychopathic personality traits. These individuals…

Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis

ERIC Educational Resources Information Center

Bussu, G.; Jones, E. J. H.; Charman, T.; Johnson, M. H.; Buitelaar, J. K.; Baron-Cohen, S.; Bedford, R.; Bolton, P.; Blasi, A.; Chandler, S.; Cheung, C.; Davies, K.; Elsabbagh, M.; Fernandes, J.; Gammer, I.; Garwood, H.; Gliga, T.; Guiraud, J.; Hudry, K.; Liew, M.; Lloyd-Fox, S.; Maris, H.; O'Hara, L.; Pasco, G.; Pickles, A.; Ribeiro, H.; Salomone, E.; Tucker, L.; Volein, A.

2018-01-01

We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n…

Testing a Model of Environmental Risk and Protective Factors to Predict Middle and High School Students' Academic Success

ERIC Educational Resources Information Center

Peters, S. Colby; Woolley, Michael E.

2015-01-01

Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…

Is there still a role for computed tomography and bone scintigraphy in prostate cancer staging? An analysis from the EUREKA-1 database.

PubMed

Gabriele, D; Collura, D; Oderda, M; Stura, I; Fiorito, C; Porpiglia, F; Terrone, C; Zacchero, M; Guiot, C; Gabriele, P

2016-04-01

According to the current guidelines, computed tomography (CT) and bone scintigraphy (BS) are optional in intermediate-risk and recommended in high-risk prostate cancer (PCa). We wonder whether it is time for these examinations to be dismissed, evaluating their staging accuracy in a large cohort of radical prostatectomy (RP) patients. To evaluate the ability of CT to predict lymph node involvement (LNI), we included 1091 patients treated with RP and pelvic lymph node dissection, previously staged with abdomino-pelvic CT. As for bone metastases, we included 1145 PCa patients deemed fit for surgery, previously staged with Tc-99m methylene diphosphonate planar BS. CT scan showed a sensitivity and specificity in predicting LNI of 8.8 and 98 %; subgroup analysis disclosed a significant association only for the high-risk subgroup of 334 patients (P 0.009) with a sensitivity of 11.8 % and positive predictive value (PPV) of 44.4 %. However, logistic multivariate regression analysis including preoperative risk factors excluded any additional predictive ability of CT even in the high-risk group (P 0.40). These data are confirmed by ROC curve analysis, showing a low AUC of 54 % for CT, compared with 69 % for Partin tables and 80 % for Briganti nomogram. BS showed some positivity in 74 cases, only four of whom progressed, while 49 patients with negative BS progressed during their follow-up, six of them immediately after surgery. According to our opinion, the role of CT and BS should be restricted to selected high-risk patients, while clinical predictive nomograms should be adopted for the surgical planning.

High-risk driving attitudes and everyday driving violations of car and racing enthusiasts in Ontario, Canada.

PubMed

Yildirim-Yenier, Zümrüt; Vingilis, Evelyn; Wiesenthal, David L; Mann, Robert E; Seeley, Jane

2015-01-01

Attitudes and individual difference variables of car and racing enthusiasts regarding high-risk behaviors of street racing and stunt driving have recently been investigated. Positive attitudes toward high-risk driving, personality variables such as driver thrill seeking, and other self-reported risky driving acts were associated with these behaviors. However, probable relationships among high-risk driving tendencies, everyday driving behaviors, and negative road safety outcomes have remained largely unexamined. This study aimed to investigate the associations among car and racing enthusiasts' high-risk driving attitudes, self-reported everyday driving violations (i.e., ordinary and aggressive violations), and self-reported negative outcomes (i.e., collisions and driving offense citations). A web-based survey was conducted with members and visitors of car club and racing websites in Ontario, Canada. Data were obtained from 366 participants. The questionnaire included 4 attitude measures-(1) attitudes toward new penalties for Ontario's Street Racers, Stunt and Aggressive Drivers Legislation; (2) attitudes toward new offenses of stunt driving under the same legislation; (3) general attitudes toward street racing and stunt driving; (4) comparison of street racing with other risky driving behaviors-self-reported driving violations (i.e., ordinary and aggressive violations); self-reported collisions and offense citations; and background and driving questions (e.g., age, driving frequency). Results revealed that attitudes toward stunt driving offenses negatively and general attitudes toward street racing and stunt driving positively predicted ordinary violations, which, in turn, predicted offense citations. Moreover, general attitudes toward street racing and stunt driving positively predicted aggressive violations, which, in turn, predicted offense citations. The findings indicate that positive high-risk driving attitudes may be transferring to driving violations in everyday traffic, which mediates driving offense citations.

Predicting high risk births with contraceptive prevalence and contraceptive method-mix in an ecologic analysis.

PubMed

Perin, Jamie; Amouzou, Agbessi; Walker, Neff

2017-11-07

Increased contraceptive use has been associated with a decrease in high parity births, births that occur close together in time, and births to very young or to older women. These types of births are also associated with high risk of under-five mortality. Previous studies have looked at the change in the level of contraception use and the average change in these types of high-risk births. We aim to predict the distribution of births in a specific country when there is a change in the level and method of modern contraception. We used data from full birth histories and modern contraceptive use from 207 nationally representative Demographic and Health Surveys covering 71 countries to describe the distribution of births in each survey based on birth order, preceding birth space, and mother's age at birth. We estimated the ecologic associations between the prevalence and method-mix of modern contraceptives and the proportion of births in each category. Hierarchical modelling was applied to these aggregated cross sectional proportions, so that random effects were estimated for countries with multiple surveys. We use these results to predict the change in type of births associated with scaling up modern contraception in three different scenarios. We observed marked differences between regions, in the absolute rates of contraception, the types of contraceptives in use, and in the distribution of type of birth. Contraceptive method-mix was a significant determinant of proportion of high-risk births, especially for birth spacing, but also for mother's age and parity. Increased use of modern contraceptives is especially predictive of reduced parity and more births with longer preceding space. However, increased contraception alone is not associated with fewer births to women younger than 18 years or a decrease in short-spaced births. Both the level and the type of contraception are important factors in determining the effects of family planning on changes in distribution of high-risk births. The best predictions for how birth risk changes with increased modern contraception and for different contraception methods allow for more nuanced predictions specific to each country and can aid better planning for the scaling up of modern contraception.

Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

PubMed

Van Neste, Leander; Partin, Alan W; Stewart, Grant D; Epstein, Jonathan I; Harrison, David J; Van Criekinge, Wim

2016-09-01

Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer. Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy. Low DNA-methylation levels in PCa-negative biopsies led to a NPV of 96% for high-grade cancer. The risk score, comprising DNA-methylation intensity and traditional clinical risk factors, improved the identification of men with high-grade cancer, with a maximum avoidance of unnecessary repeat biopsies. This risk score resulted in better patient risk stratification and significantly outperformed current risk prediction models such as PCPTRC and PSA. The risk score could help to identify patients with histopathologically negative biopsies harboring high-grade PCa. Prostate 76:1078-1087, 2016. © 2016 The Authors. The Prostate Published by Wiley Periodicals, Inc. © 2016 The Authors. The Prostate Published by Wiley Periodicals, Inc.

Mortality predictors in a 60-year follow-up of adolescent males: exploring delinquency, socioeconomic status, IQ, high-school drop-out status, and personality.

PubMed

Trumbetta, Susan L; Seltzer, Benjamin K; Gottesman, Irving I; McIntyre, Kathleen M

2010-01-01

To examine whether socioeconomic status (SES), high school (HS) completion, IQ, and personality traits that predict delinquency in adolescence also could explain men's delinquency-related (Dq-r) mortality risk across the life span. Through a 60-year Social Security Death Index (SSDI) follow-up of 1812 men from Hathaway's adolescent normative Minnesota Multiphasic Personality Inventory (MMPI) sample, we examined mortality risk at various ages and at various levels of prior delinquency severity. We examined SES (using family rent level), HS completion, IQ, and MMPI indicators simultaneously as mortality predictors and tested for SES (rent level) interactions with IQ and personality. We ascertained 418 decedents. Dq-r mortality peaked between ages 45 years to 64 years and continued through age 75 years, with high delinquency severity showing earlier and higher mortality risk. IQ and rent level failed to explain Dq-r mortality. HS completion robustly conferred mortality protection through ages 55 years and 75 years, explained IQ and rent level-related risk, but did not fully explain Dq-r risk. Dq-r MMPI scales, Psychopathic Deviate, and Social Introversion, respectively, predicted risk for and protection from mortality by age 75 years, explaining mortality risk otherwise attributable to delinquency. Wiggins' scales also explained Dq-r mortality risk, as Authority Conflict conferred risk for and Social Maladjustment and Hypomania conferred protection from mortality by age 75 years. HS completion robustly predicts mortality by ages 55 years and 75 years. Dq-r personality traits predict mortality by age 75 years, accounting, in part, for Dq-r mortality.

Does affective organizational commitment and experience of meaning at work predict risk of disability pensioning? An analysis of register-based outcomes using pooled data on 40,554 observations in four occupational groups.

PubMed

Clausen, Thomas; Burr, Hermann; Borg, Vilhelm

2014-06-01

The aim of this study is to investigate whether experience of meaning at work (MAW) and affective organizational commitment (AOC) predict risk of disability pensioning in four occupational groups. Survey data from 40,554 individuals were fitted to a national register (DREAM) containing information on payments of disability pension. Using multi-adjusted Cox-regression, observations were followed in the DREAM-register to assess risk of disability pensioning. Low levels of MAW significantly increased risk of disability pensioning during follow-up referencing high levels of MAW. Respondents with medium levels of AOC had a significantly reduced risk of disability pensioning, when compared to respondents with high levels of AOC. Furthermore, results indicate an interaction effect between AOC and MAW in predicting risk of disability pension. AOC and MAW are significantly associated with risk of disability pensioning. Promoting MAW and managing AOC in contemporary workplaces may contribute towards reducing risk of disability pensioning. © 2014 Wiley Periodicals, Inc.

Predicting high-risk preterm birth using artificial neural networks.

PubMed

Catley, Christina; Frize, Monique; Walker, C Robin; Petriu, Dorina C

2006-07-01

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

PAI-1 4G/5G and MTHFR C677T polymorphisms increased the accuracy of two prediction scores for the risk of acute lower extremity deep vein thrombosis.

PubMed

Pop, Tudor Radu; Vesa, Ştefan Cristian; Trifa, Adrian Pavel; Crişan, Sorin; Buzoianu, Anca Dana

2014-01-01

This study investigates the accuracy of two scores in predicting the risk of acute lower extremity deep vein thrombosis. The study included 170 patients [85 (50%) women and 85 (50%) men] who were diagnosed with acute lower extremity deep vein thrombosis (DVT) with duplex ultrasonography. Median age was 62 (52.75; 72) years. The control group consisted of 166 subjects [96 (57.8%) women and 70 (42.2%) men], without DVT, matched for age (± one year) to those in the group with DVT. The patients and controls were selected from those admitted to the internal medicine, cardiology and geriatrics wards within the Municipal Hospital of Cluj-Napoca, Romania, between October 2009 and June 2011. Clinical, demographic and lab data were recorded for each patient. For each patient we calculated the prior risk of DVT using two prediction scores: Caprini and Padua. According to the Padua score only 93 (54.7%) patients with DVT had been at high risk of developing DVT, while 48 (28.9%) of controls were at high risk of developing DVT. When Padua score included PAI-1 4G/5G and MTHFR C677T polymorphisms, the sensitivity increased at 71.7%. Using the Caprini score, we determined that 147 (86.4%) patients with DVT had been at high risk of developing DVT, while 103 (62%) controls were at high risk of developing DVT. A Caprini score higher than 5 was the strongest predictor of acute lower extremity DVT risk. The Caprini prediction score was more sensitive than the Padua score in assessing the high risk of DVT in medical patients. PAI-1 4G/5G and MTHFR C677T polymorphisms increased the sensitivity of Padua score.

Premorbid social adjustment and association with attenuated psychotic symptoms in clinical high-risk and help-seeking youth.

PubMed

Tarbox-Berry, S I; Perkins, D O; Woods, S W; Addington, J

2018-04-01

Attenuated positive symptom syndrome (APSS), characterized by 'putatively prodromal' attenuated psychotic-like pathology, indicates increased risk for psychosis. Poor premorbid social adjustment predicts severity of APSS symptoms and predicts subsequent psychosis in APSS-diagnosed individuals, suggesting application for improving detection of 'true' prodromal youth who will transition to psychosis. However, these predictive associations have not been tested in controls and therefore may be independent of the APSS diagnosis, negating utility for improving prediction in APSS-diagnosed individuals. Association between premorbid social maladjustment and severity of positive, negative, disorganized, and general APSS symptoms was tested in 156 individuals diagnosed with APSS and 76 help-seeking (non-APSS) controls enrolled in the Enhancing the Prospective Prediction of Psychosis (PREDICT) study using prediction analysis. Premorbid social maladjustment was associated with social anhedonia, reduced expression of emotion, restricted ideational richness, and deficits in occupational functioning, independent of the APSS diagnosis. Associations between social maladjustment and suspiciousness, unusual thought content, avolition, dysphoric mood, and impaired tolerance to normal stress were uniquely present in participants meeting APSS criteria. Social maladjustment was associated with odd behavior/appearance and diminished experience of emotions and self only in participants who did not meet APSS criteria. Predictive associations between poor premorbid social adjustment and attenuated psychotic-like pathology were identified, a subset of which were indicative of high risk for psychosis. This study offers a method for improving risk identification while ruling out low-risk individuals.

Predictive role of stress echocardiography before carotid endarterectomy in patients with coronary artery disease.

PubMed

Galyfos, George; Tsioufis, Constantinos; Theodorou, Dimitris; Katsaragakis, Stilianos; Zografos, Georgios; Filis, Konstantinos

2015-07-01

Our aim was to examine the predictive value of preoperative stress echocardiography regarding early myocardial ischemia and late cardiac events after carotid endarterectomy (CEA). Patients with coronary artery disease undergoing CEA were prospectively included in this study. All patients (n = 162) were classified into low, medium, and high cardiac risk group, according to preoperative stress echocardiography. Classification was based on the criteria of the American Society of Echocardiography. For all patients, cTnI was measured before surgery and on postoperative days 1, 3, and 7. Postoperative cTnI values ranging from 0.05 to 0.5 ng/mL were classified as myocardial ischemia; values >0.5 ng/mL were classified as myocardial infarction. Cardiac damage was defined as either myocardial ischemia or infarction. No deaths, strokes, or symptomatic coronary events were observed during the early postoperative period. There were 112 low cardiac risk patients, 42 medium-risk patients, and 8 high-risk patients, according to stress echocardiography findings. Overall, there were 22 patients (14%) that increased their cTnI values postoperatively (12 of low cardiac risk and 10 of medium cardiac risk), and all of them were asymptomatic. None of the high-risk patients showed any troponin increase. Late cardiac events were associated with cTnI increase, although no high-risk patients showed any late event. Preoperative stress echocardiography does not seem to independently recognize patients in high risk for asymptomatic cardiac damage after CEA. Postoperative troponin elevation seems to be more predictive for late adverse cardiac events than preoperative stress echocardiography. © 2014, Wiley Periodicals, Inc.

The child as proband for future parental cardiometabolic disease: the 26-year prospective Princeton Lipid Research Clinics Follow-up Study.

PubMed

Morrison, John A; Glueck, Charles J; Wang, Ping

2012-04-01

To evaluate children's cardiovascular disease (CVD) risk factors as predictors of parents' subsequent CVD, type 2 diabetes mellitus (T2DM), and high blood pressure (HBP). We conducted a 26-year prospective follow-up of 852 5- to 19-year-old black and white schoolchildren (mean age, 12 years; Lipid Research Clinics, 1973-8), and parents (mean age, 40 years) from 519 families in Princeton Schools, Cincinnati, Ohio. Schoolchildren were reassessed in the Princeton Follow-up study 1999-2003 at mean age 39 years; CVD, T2DM, and HBP history of their 1038 parents were reassessed by mean age 66 years. We assessed relationships of childhood risk factors with parental CVD, T2DM, and HBP. Child-probands identified with triglyceride (TG) levels, blood pressure, low-density lipoprotein cholesterol levels, body mass index (BMI), and glucose level greater than and high-density lipoprotein cholesterol levels less than established cutoff points. Pediatric HBP (P=.006) and low high-density lipoprotein cholesterol (P=.018) were predictive of parental CVD at age ≤50 years. Pediatric HBP (P=.02) and high TG (P=.03) were predictive of parental CVD at age ≤60 years. Pediatric high TG (P=.009) and high low-density lipoprotein cholesterol (P=.04) were predictive of parental CVD by age 66 years. Pediatric high BMI (P=.0006) were predictive of parental T2DM. Pediatric high BMI (P=.003) and black race (P=.004) were predictive of parental HBP. Pediatric risk factors identify families with parents at increased risk for CVD, T2DM, and HBP, emphasizing the usefulness of the child as proband. Copyright © 2012 Mosby, Inc. All rights reserved.

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.

PubMed

Brenton, Ashley; Richeimer, Steven; Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Blanchard, John; Meshkin, Brian

2017-01-01

Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes.

The cardiovascular event reduction tool (CERT)--a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS).

PubMed

L'Italien, G; Ford, I; Norrie, J; LaPuerta, P; Ehreth, J; Jackson, J; Shepherd, J

2000-03-15

The clinical decision to treat hypercholesterolemia is premised on an awareness of patient risk, and cardiac risk prediction models offer a practical means of determining such risk. However, these models are based on observational cohorts where estimates of the treatment benefit are largely inferred. The West of Scotland Coronary Prevention Study (WOSCOPS) provides an opportunity to develop a risk-benefit prediction model from the actual observed primary event reduction seen in the trial. Five-year Cox model risk estimates were derived from all WOSCOPS subjects (n = 6,595 men, aged 45 to 64 years old at baseline) using factors previously shown to be predictive of definite fatal coronary heart disease or nonfatal myocardial infarction. Model risk factors included age, diastolic blood pressure, total cholesterol/ high-density lipoprotein ratio (TC/HDL), current smoking, diabetes, family history of fatal coronary heart disease, nitrate use or angina, and treatment (placebo/ 40-mg pravastatin). All risk factors were expressed as categorical variables to facilitate risk assessment. Risk estimates were incorporated into a simple, hand-held slide rule or risk tool. Risk estimates were identified for 5-year age bands (45 to 65 years), 4 categories of TC/HDL ratio (<5.5, 5.5 to <6.5, 6.5 to <7.5, > or = 7.5), 2 levels of diastolic blood pressure (<90, > or = 90 mm Hg), from 0 to 3 additional risk factors (current smoking, diabetes, family history of premature fatal coronary heart disease, nitrate use or angina), and pravastatin treatment. Five-year risk estimates ranged from 2% in very low-risk subjects to 61% in the very high-risk subjects. Risk reduction due to pravastatin treatment averaged 31%. Thus, the Cardiovascular Event Reduction Tool (CERT) is a risk prediction model derived from the WOSCOPS trial. Its use will help physicians identify patients who will benefit from cholesterol reduction.

Gender Differences in Predicting High-Risk Drinking among Undergraduate Students

ERIC Educational Resources Information Center

Wilke, Dina J.; Siebert, Darcy Clay; Delva, Jorge; Smith, Michael P.; Howell, Richard L.

2005-01-01

The purpose of this study was to examine gender differences in college students' high-risk drinking as measured by an estimated blood alcohol concentration (eBAC) based on gender, height, weight, self-reported number of drinks, and hours spent drinking. Using a developmental/contextual framework, high-risk drinking is conceptualized as a function…

Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.

PubMed

Maciejewski, Matthew L; Liu, Chuan-Fen; Fihn, Stephan D

2009-01-01

To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.

The ability of the 2013 ACC/AHA cardiovascular risk score to identify rheumatoid arthritis patients with high coronary artery calcification scores

PubMed Central

Kawai, Vivian K.; Chung, Cecilia P.; Solus, Joseph F.; Oeser, Annette; Raggi, Paolo; Stein, C. Michael

2014-01-01

Objective Patients with rheumatoid arthritis (RA) have increased risk of atherosclerotic cardiovascular disease (ASCVD) that is underestimated by the Framingham risk score (FRS). We hypothesized that the 2013 ACC/AHA 10-year risk score would perform better than the FRS and the Reynolds risk score (RRS) in identifying RA patients known to have elevated cardiovascular risk based on high coronary artery calcification (CAC) scores. Methods Among 98 RA patients eligible for risk stratification using the ACC/AHA score we identified 34 patients with high CAC (≥ 300 Agatston units or ≥75th percentile) and compared the ability of the 10-year FRS, RRS and the ACC/AHA risk scores to correctly assign these patients to an elevated risk category. Results All three risk scores were higher in patients with high CAC (P values <0.05). The percentage of patients with high CAC correctly assigned to the elevated risk category was similar among the three scores (FRS 32%, RRS 32%, ACC/AHA 41%) (P=0.233). The c-statistics for the FRS, RRS and ACC/AHA risk scores predicting the presence of high CAC were 0.65, 0.66, and 0.65, respectively. Conclusions The ACC/AHA 10-year risk score does not offer any advantage compared to the traditional FRS and RRS in the identification of RA patients with elevated risk as determined by high CAC. The ACC/AHA risk score assigned almost 60% of patients with high CAC into a low risk category. Risk scores and standard risk prediction models used in the general population do not adequately identify many RA patients with elevated cardiovascular risk. PMID:25371313

High Center Volume Does Not Mitigate Risk Associated with Using High Donor Risk Organs in Liver Transplantation.

PubMed

Beal, Eliza W; Black, Sylvester M; Mumtaz, Khalid; Hayes, Don; El-Hinnawi, Ashraf; Washburn, Kenneth; Tumin, Dmitry

2017-09-01

High-risk donor allografts increase access to liver transplant, but potentially reduce patient and graft survival. It is unclear whether the risk associated with using marginal donor livers is mitigated by increasing center experience. The United Network for Organ Sharing registry was queried for adult first-time liver transplant recipients between 2/2002 and 12/2015. High donor risk was defined as donor risk index >1.9, and 1-year patient and graft survival were compared according to donor risk index in small and large centers. Multivariable Cox regression estimated the hazard ratio (HR) associated with using high-risk donor organs, according to a continuous measure of annual center volume. The analysis included 51,770 patients. In 67 small and 67 large centers, high donor risk index predicted increased mortality (p = 0.001). In multivariable analysis, high-donor risk index allografts predicted greater mortality hazard at centers performing 20 liver transplants per year (HR 1.35; 95% CI 1.22, 1.49; p < 0.001) and, similarly, at centers performing 70 per year (HR 1.35; 95% CI 1.26, 1.43; p < 0.001). The interaction between high donor risk index and center volume was not statistically significant (p = 0.747), confirming that the risk associated with using marginal donor livers was comparable between smaller and larger centers. Results were consistent when examining graft loss. At both small and large centers, high-risk donor allografts were associated with reduced patient and graft survival after liver transplant. Specific strategies to mitigate the risk of liver transplant involving high-risk donors are needed, in addition to accumulation of center expertise.

Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

PubMed Central

Angelantonio, Emanuele Di; Gao, Pei; Khan, Hassan; Butterworth, Adam S.; Wormser, David; Kaptoge, Stephen; Kondapally Seshasai, Sreenivasa Rao; Thompson, Alex; Sarwar, Nadeem; Willeit, Peter; Ridker, Paul M; Barr, Elizabeth L.M.; Khaw, Kay-Tee; Psaty, Bruce M.; Brenner, Hermann; Balkau, Beverley; Dekker, Jacqueline M.; Lawlor, Debbie A.; Daimon, Makoto; Willeit, Johann; Njølstad, Inger; Nissinen, Aulikki; Brunner, Eric J.; Kuller, Lewis H.; Price, Jackie F.; Sundström, Johan; Knuiman, Matthew W.; Feskens, Edith J. M.; Verschuren, W. M. M.; Wald, Nicholas; Bakker, Stephan J. L.; Whincup, Peter H.; Ford, Ian; Goldbourt, Uri; Gómez-de-la-Cámara, Agustín; Gallacher, John; Simons, Leon A.; Rosengren, Annika; Sutherland, Susan E.; Björkelund, Cecilia; Blazer, Dan G.; Wassertheil-Smoller, Sylvia; Onat, Altan; Marín Ibañez, Alejandro; Casiglia, Edoardo; Jukema, J. Wouter; Simpson, Lara M.; Giampaoli, Simona; Nordestgaard, Børge G.; Selmer, Randi; Wennberg, Patrik; Kauhanen, Jussi; Salonen, Jukka T.; Dankner, Rachel; Barrett-Connor, Elizabeth; Kavousi, Maryam; Gudnason, Vilmundur; Evans, Denis; Wallace, Robert B.; Cushman, Mary; D’Agostino, Ralph B.; Umans, Jason G.; Kiyohara, Yutaka; Nakagawa, Hidaeki; Sato, Shinichi; Gillum, Richard F.; Folsom, Aaron R.; van der Schouw, Yvonne T.; Moons, Karel G.; Griffin, Simon J.; Sattar, Naveed; Wareham, Nicholas J.; Selvin, Elizabeth; Thompson, Simon G.; Danesh, John

2015-01-01

IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS Analysis of individual-participant data available from 73 prospective studies involving 294 998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5%to <7.5%), and high (≥7.5%) risk. RESULTS During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20 840 incident fatal and nonfatal CVD outcomes (13 237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (−0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk. PMID:24668104

The Acquired Preparedness Model of Risk for Bulimic Symptom Development

PubMed Central

Combs, Jessica L.; Smith, Gregory T.; Flory, Kate; Simmons, Jean R.; Hill, Kelly K.

2010-01-01

The authors applied person-environment transaction theory to test the acquired preparedness model of eating disorder risk. The model holds that (a) middle school girls high in the trait of ineffectiveness are differentially prepared to acquire high risk expectancies for reinforcement from dieting/thinness; (b) those expectancies predict subsequent binge eating and purging; and (c) the influence of the disposition of ineffectiveness on binge eating and purging is mediated by dieting/thinness expectancies. In a three-wave longitudinal study of 394 middle school girls, they found support for the model. Seventh grade girls’ scores on ineffectiveness predicted their subsequent endorsement of high risk dieting/thinness expectancies, which in turn predicted subsequent increases in binge eating and purging. Statistical tests of mediation supported the hypothesis that the prospective relation between ineffectiveness and binge eating was mediated by dieting/thinness expectancies, as was the prospective relation between ineffectiveness and purging. This application of a basic science theory to eating disorder risk appears fruitful, and the findings suggest the importance of early interventions that address both disposition and learning. PMID:20853933

Based on BP Neural Network Stock Prediction

ERIC Educational Resources Information Center

Liu, Xiangwei; Ma, Xin

2012-01-01

The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

The Ecology of Early Childhood Risk: A Canonical Correlation Analysis of Children’s Adjustment, Family, and Community Context in a High-Risk Sample

PubMed Central

Aiyer, Sophie M.; Wilson, Melvin N.; Shaw, Daniel S.; Dishion, Thomas J.

2013-01-01

The ecology of the emergence of psycho-pathology in early childhood is often approached by the analysis of a limited number of contextual risk factors. In the present study, we provide a comprehensive analysis of ecological risk by conducting a canonical correlation analysis of 13 risk factors at child age 2 and seven narrow-band scales of internalizing and externalizing problem behaviors at child age 4, using a sample of 364 geographically and ethnically diverse, disadvantaged primary caregivers, alternative caregivers, and preschool-age children. Participants were recruited from Special Supplemental Nutrition Program for Women, Infants, and Children sites and were screened for family risk. Canonical correlation analysis revealed that (1) a first latent combination of family and individual risks of caregivers predicted combinations of child emotional and behavioral problems, and that (2) a second latent combination of contextual and structural risks predicted child somatic complaints. Specifically, (1) the combination of chaotic home, conflict with child, parental depression, and parenting hassles predicted a co-occurrence of internalizing and externalizing behaviors, and (2) the combination of father absence, perceived discrimination, neighborhood danger, and fewer children living in the home predicted child somatic complaints. The research findings are discussed in terms of the development of psychopathology, as well as the potential prevention needs of families in high-risk contexts. PMID:23700232

Social interaction anxiety and personality traits predicting engagement in health risk sexual behaviors.

PubMed

Rahm-Knigge, Ryan L; Prince, Mark A; Conner, Bradley T

2018-06-01

Individuals with social interaction anxiety, a facet of social anxiety disorder, withdraw from or avoid social encounters and generally avoid risks. However, a subset engages in health risk sexual behavior (HRSB). Because sensation seeking, emotion dysregulation, and impulsivity predict engagement in HRSB among adolescents and young adults, the present study hypothesized that latent classes of social interaction anxiety and these personality traits would differentially predict likelihood of engagement in HRSB. Finite mixture modeling was used to discern four classes: two low social interaction anxiety classes distinguished by facets of emotion dysregulation, positive urgency, and negative urgency (Low SIAS High Urgency and Low SIAS Low Urgency) and two high social interaction anxiety classes distinguished by positive urgency, negative urgency, risk seeking, and facets of emotion dysregulation (High SIAS High Urgency and High SIAS Low Urgency). HRSB were entered into the model as auxiliary distal outcomes. Of importance to this study were findings that the High SIAS High Urgency class was more likely to engage in most identified HRSB than the High SIAS Low Urgency class. This study extends previous findings on the heterogeneity of social interaction anxiety by identifying the effects of social interaction anxiety and personality on engagement in HRSB. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of adolescent and adult adiposity outcomes from early life anthropometrics.

PubMed

Graversen, Lise; Sørensen, Thorkild I A; Gerds, Thomas A; Petersen, Liselotte; Sovio, Ulla; Kaakinen, Marika; Sandbaek, Annelli; Laitinen, Jaana; Taanila, Anja; Pouta, Anneli; Järvelin, Marjo-Riitta; Obel, Carsten

2015-01-01

Maternal body mass index (BMI), birth weight, and preschool BMI may help identify children at high risk of overweight as they are (1) similarly linked to adolescent overweight at different stages of the obesity epidemic, (2) linked to adult obesity and metabolic alterations, and (3) easily obtainable in health examinations in young children. The aim was to develop early childhood prediction models of adolescent overweight, adult overweight, and adult obesity. Prediction models at various ages in the Northern Finland Birth Cohort born in 1966 (NFBC1966) were developed. Internal validation was tested using a bootstrap design, and external validation was tested for the model predicting adolescent overweight using the Northern Finland Birth Cohort born in 1986 (NFBC1986). A prediction model developed in the NFBC1966 to predict adolescent overweight, applied to the NFBC1986, and aimed at labelling 10% as "at risk" on the basis of anthropometric information collected until 5 years of age showed that half of those at risk in fact did become overweight. This group constituted one-third of all who became overweight. Our prediction model identified a subgroup of children at very high risk of becoming overweight, which may be valuable in public health settings dealing with obesity prevention. © 2014 The Obesity Society.

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports. The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization. Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services. Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

The impact of HIV infection and antiretroviral therapy on the predicted risk of Down syndrome.

PubMed

Charlton, Thomas G; Franklin, Jamie M; Douglas, Melanie; Short, Charlotte E; Mills, Ian; Smith, Rachel; Clarke, Amanda; Smith, John; Tookey, Pat A; Cortina-Borja, Mario; Taylor, Graham P

2014-02-01

The aim of this study was to assess predicted Down syndrome risk, based on three serum analytes (triple test), with HIV infection status and antiretroviral therapy regimen. Screening results in 72 HIV-positive women were compared with results from age-matched and race-matched HIV-negative controls. Mean concentrations of each analyte were compared by serostatus and antiretroviral therapy. Observed Down syndrome incidence in the offspring of HIV-positive women was calculated from national HIV surveillance data. Overall, women with HIV had a significantly higher probability of receiving a 'high-risk' result than uninfected controls (p = 0.002). Compared with matched uninfected controls, women with HIV infection had significantly higher human chorionic gonadotrophin, lower unconjugated estriol, and higher overall predicted risk of their infant having Down syndrome (1/6250 vs. 1/50 000 p = < 0.001). National surveillance data show no evidence of higher than expected incidence of Down syndrome in the offspring of HIV-positive women. HIV infection impacts the serum analytes used to assay for Down syndrome risk resulting in a high rate of 'high risk' results. However, there is no population-based association between maternal HIV infection and Down syndrome. Care should be taken when interpreting high-risk serum screening results in HIV-positive women to avoid unnecessary invasive diagnostic procedures. © 2013 John Wiley & Sons, Ltd.

Does the Sex Risk Quiz Predict Mycoplasma genitalium Infection in Urban Adolescents and Young Adult Women?

PubMed

Ronda, Jocelyn; Gaydos, Charlotte A; Perin, Jamie; Tabacco, Lisa; Coleman, Jenell; Trent, Maria

2018-06-04

Mycoplasma genitalium (MG) is a common sexually transmitted infection (STI) but there are limited strategies to identify individuals at risk of MG. Previously a sex risk quiz was used to predict STIs including Chlamydia trachomatis (CT), Neisseria gonorrhoeae (GC), and/or Trichomonas vaginalis (TV). The original quiz categorized individuals ≤25 years old as at risk of STIs, but the Centers for Disease Control identifies females <25 years old as at risk of STIs. In this study, the quiz was changed to categorize females <25 years old as high risk. The objective was to determine if the age-modified risk quiz predicted MG infection. A cross-sectional analysis of a prospective longitudinal study was performed including female adolescents and young adults (AYA) evaluated in multiple outpatient clinics. Participants completed an age-modified risk quiz about sexual practices. Scores ranged from 0 to 10 and were categorized as low-risk (0-3), medium-risk (4-7), and high-risk (8-10) based upon the STI prevalence for each score. Vaginal and/or endocervical specimens were tested for MG, TV, CT, and GC using the Aptima Gen-Probe nucleic amplification test. There were 693 participants. Most participants reported having 0-1 sexual partners in the last 90 days (91%) and inconsistent condom use (84%). Multivariable logistic regression analysis controlling for race, education, and symptom status demonstrated that a medium-risk score predicted MG infection among AYA <25 years old (adjusted OR 2.56 [95% CI 1.06-6.18]). A risk quiz may be useful during clinical encounters to identify AYA at risk of MG.

ASXL1 and BIM germ line variants predict response and identify CML patients with the greatest risk of imatinib failure

PubMed Central

Marum, Justine E.; Yeung, David T.; Purins, Leanne; Reynolds, John; Parker, Wendy T.; Stangl, Doris; Wang, Paul P. S.; Price, David J.; Tuke, Jonathan; Schreiber, Andreas W.; Scott, Hamish S.; Hughes, Timothy P.

2017-01-01

Scoring systems used at diagnosis of chronic myeloid leukemia (CML), such as Sokal risk, provide important response prediction for patients treated with imatinib. However, the sensitivity and specificity of scoring systems could be enhanced for improved identification of patients with the highest risk. We aimed to identify genomic predictive biomarkers of imatinib response at diagnosis to aid selection of first-line therapy. Targeted amplicon sequencing was performed to determine the germ line variant profile in 517 and 79 patients treated with first-line imatinib and nilotinib, respectively. The Sokal score and ASXL1 rs4911231 and BIM rs686952 variants were independent predictors of early molecular response (MR), major MR, deep MRs (MR4 and MR4.5), and failure-free survival (FFS) with imatinib treatment. In contrast, the ASXL1 and BIM variants did not consistently predict MR or FFS with nilotinib treatment. In the imatinib-treated cohort, neither Sokal or the ASXL1 and BIM variants predicted overall survival (OS) or progression to accelerated phase or blast crisis (AP/BC). The Sokal risk score was combined with the ASXL1 and BIM variants in a classification tree model to predict imatinib response. The model distinguished an ultra-high-risk group, representing 10% of patients, that predicted inferior OS (88% vs 97%; P = .041), progression to AP/BC (12% vs 1%; P = .034), FFS (P < .001), and MRs (P < .001). The ultra-high-risk patients may be candidates for more potent or combination first-line therapy. These data suggest that germ line genetic variation contributes to the heterogeneity of response to imatinib and may contribute to a prognostic risk score that allows early optimization of therapy. PMID:29296778

Utility of different cardiovascular disease prediction models in rheumatoid arthritis.

PubMed

Purcarea, A; Sovaila, S; Udrea, G; Rezus, E; Gheorghe, A; Tiu, C; Stoica, V

2014-01-01

Rheumatoid arthritis comes with a 30% higher probability for cardiovascular disease than the general population. Current guidelines advocate for early and aggressive primary prevention and treatment of risk factors in high-risk populations but this excess risk is under-addressed in RA in real life. This is mainly due to difficulties met in the correct risk evaluation. This study aims to underline the differences in results of the main cardiovascular risk screening models in the real life rheumatoid arthritis population. In a cross-sectional study, patients addressed to a tertiary care center in Romania for an biannual follow-up of rheumatoid arthritis and the ones who were considered free of any cardiovascular disease were assessed for subclinical atherosclerosis. Clinical, biological and carotidal ultrasound evaluations were performed. A number of cardiovascular disease prediction scores were performed and differences between tests were noted in regard to subclinical atherosclerosis as defined by the existence of carotid intima media thickness over 0,9 mm or carotid plaque. In a population of 29 Romanian rheumatoid arthritis patients free of cardiovascular disease, the performance of Framingham Risk Score, HeartSCORE, ARIC cardiovascular disease prediction score, Reynolds Risk Score, PROCAM risk score and Qrisk2 score were compared. All the scores under-diagnosed subclinical atherosclerosis. With an AUROC of 0,792, the SCORE model was the only one that could partially stratify patients in low, intermediate and high-risk categories. The use of the EULAR recommended modifier did not help to reclassify patients. The only score that showed a statistically significant prediction capacity for subclinical atherosclerosis in a Romanian rheumatoid arthritis population was SCORE. The additional calibration or the use of imaging techniques in CVD risk prediction for the intermediate risk category might be warranted.

Utility of different cardiovascular disease prediction models in rheumatoid arthritis

PubMed Central

Purcarea, A; Sovaila, S; Udrea, G; Rezus, E; Gheorghe, A; Tiu, C; Stoica, V

2014-01-01

Background. Rheumatoid arthritis comes with a 30% higher probability for cardiovascular disease than the general population. Current guidelines advocate for early and aggressive primary prevention and treatment of risk factors in high-risk populations but this excess risk is under-addressed in RA in real life. This is mainly due to difficulties met in the correct risk evaluation. This study aims to underline the differences in results of the main cardiovascular risk screening models in the real life rheumatoid arthritis population. Methods. In a cross-sectional study, patients addressed to a tertiary care center in Romania for an biannual follow-up of rheumatoid arthritis and the ones who were considered free of any cardiovascular disease were assessed for subclinical atherosclerosis. Clinical, biological and carotidal ultrasound evaluations were performed. A number of cardiovascular disease prediction scores were performed and differences between tests were noted in regard to subclinical atherosclerosis as defined by the existence of carotid intima media thickness over 0,9 mm or carotid plaque. Results. In a population of 29 Romanian rheumatoid arthritis patients free of cardiovascular disease, the performance of Framingham Risk Score, HeartSCORE, ARIC cardiovascular disease prediction score, Reynolds Risk Score, PROCAM risk score and Qrisk2 score were compared. All the scores under-diagnosed subclinical atherosclerosis. With an AUROC of 0,792, the SCORE model was the only one that could partially stratify patients in low, intermediate and high-risk categories. The use of the EULAR recommended modifier did not help to reclassify patients. Conclusion. The only score that showed a statistically significant prediction capacity for subclinical atherosclerosis in a Romanian rheumatoid arthritis population was SCORE. The additional calibration or the use of imaging techniques in CVD risk prediction for the intermediate risk category might be warranted. PMID:25713628

Functional development in clinical high risk youth: Prediction of schizophrenia versus other psychotic disorders

PubMed Central

Tarbox, Sarah I.; Addington, Jean; Cadenhead, Kristin S.; Cannon, Tyrone D.; Cornblatt, Barbara A.; Perkins, Diana O.; Seidman, Larry J.; Tsuang, Ming T.; Walker, Elaine F.; Heinssen, Robert; McGlashan, Thomas H.; Woods, Scott W.

2013-01-01

This study evaluates premorbid social and academic functioning in clinical high-risk individuals as predictors of transition to schizophrenia versus another psychotic disorder. Participants were 54 individuals enrolled in phase one of the North American Prodrome Longitudinal Study who over two and a half years of follow-up met criteria for schizophrenia/schizophreniform disorder (n = 28) or another psychotic disorder (n = 26). Social and academic functioning in childhood, early adolescence, and late adolescence was assessed at baseline using the Cannon-Spoor Premorbid Adjustment Scale. Social maladjustment in late adolescence predicted significantly higher odds of transition to schizophrenia versus another psychotic disorder independent of childhood and early adolescent adjustment (OR = 4.02) and conveyed unique risk over academic maladjustment (OR = 5.64). Premorbid academic maladjustment was not associated with psychotic disorder diagnosis. Results support diagnostic specificity of premorbid social dysfunction to schizophrenia in clinical high-risk youth and underscore an important role for social maladjustment in the developmental pathology of schizophrenia and its prediction. PMID:24200216

Derivation of genetic biomarkers for cancer risk stratification in Barrett's oesophagus: a prospective cohort study

PubMed Central

Timmer, Margriet R.; Martinez, Pierre; Lau, Chiu T.; Westra, Wytske M.; Calpe, Silvia; Rygiel, Agnieszka M.; Rosmolen, Wilda D.; Meijer, Sybren L.; ten Kate, Fiebo J.W.; Dijkgraaf, Marcel G.W.; Mallant-Hent, Rosalie C.; Naber, Anton H.J.; van Oijen, Arnoud H.A.M.; Baak, Lubbertus C.; Scholten, Pieter; Böhmer, Clarisse J.M.; Fockens, Paul; Maley, Carlo C.; Graham, Trevor A.; Bergman, Jacques J.G.H.M.; Krishnadath, Kausilia K.

2016-01-01

Objective The risk of developing adenocarcinoma in non-dysplastic Barrett's oesophagus is low and difficult to predict. Accurate tools for risk stratification are needed to increase the efficiency of surveillance. We aimed to develop a prediction model for progression using clinical variables and genetic markers. Methods In a prospective cohort of patients with non-dysplastic Barrett's oesophagus, we evaluated six molecular markers: p16, p53, Her-2/neu, 20q, MYC, and aneusomy by DNA fluorescence in situ hybridisation on brush cytology specimens. Primary study outcomes were the development of high-grade dysplasia or oesophageal adenocarcinoma. The most predictive clinical variables and markers were determined using Cox proportional-hazards models, receiver-operating-characteristic curves and a leave-one-out analysis. Results A total of 428 patients participated (345 men; median age 60 years) with a cumulative follow-up of 2019 patient-years (median 45 months per patient). Of these patients, 22 progressed; nine developed high-grade dysplasia and 13 oesophageal adenocarcinoma. The clinical variables, age and circumferential Barrett's length, and the markers, p16 loss, MYC gain, and aneusomy, were significantly associated with progression on univariate analysis. We defined an ‘Abnormal Marker Count’ that counted abnormalities in p16, MYC and aneusomy, which significantly improved risk prediction beyond using just age and Barrett's length. In multivariate analysis, these three factors identified a high-risk group with an 8.7-fold (95% CI, 2.6 to 29.8) increased hazard ratio compared with the low-risk group, with an area under the curve of 0.76 (95% CI, 0.66 to 0.86). Conclusion A prediction model based on age, Barrett's length, and the markers p16, MYC, and aneusomy determines progression risk in non-dysplastic Barrett's oesophagus. PMID:26104750

VTE Risk assessment - a prognostic Model: BATER Cohort Study of young women.

PubMed

Heinemann, Lothar Aj; Dominh, Thai; Assmann, Anita; Schramm, Wolfgang; Schürmann, Rolf; Hilpert, Jan; Spannagl, Michael

2005-04-18

BACKGROUND: Community-based cohort studies are not available that evaluated the predictive power of both clinical and genetic risk factors for venous thromboembolism (VTE). There is, however, clinical need to forecast the likelihood of future occurrence of VTE, at least qualitatively, to support decisions about intensity of diagnostic or preventive measures. MATERIALS AND METHODS: A 10-year observation period of the Bavarian Thromboembolic Risk (BATER) study, a cohort study of 4337 women (18-55 years), was used to develop a predictive model of VTE based on clinical and genetic variables at baseline (1993). The objective was to prepare a probabilistic scheme that discriminates women with virtually no VTE risk from those at higher levels of absolute VTE risk in the foreseeable future. A multivariate analysis determined which variables at baseline were the best predictors of a future VTE event, provided a ranking according to the predictive power, and permitted to design a simple graphic scheme to assess the individual VTE risk using five predictor variables. RESULTS: Thirty-four new confirmed VTEs occurred during the observation period of over 32,000 women-years (WYs). A model was developed mainly based on clinical information (personal history of previous VTE and family history of VTE, age, BMI) and one composite genetic risk markers (combining Factor V Leiden and Prothrombin G20210A Mutation). Four levels of increasing VTE risk were arbitrarily defined to map the prevalence in the study population: No/low risk of VTE (61.3%), moderate risk (21.1%), high risk (6.0%), very high risk of future VTE (0.9%). In 10.6% of the population the risk assessment was not possible due to lacking VTE cases. The average incidence rates for VTE in these four levels were: 4.1, 12.3, 47.2, and 170.5 per 104 WYs for no, moderate, high, and very high risk, respectively. CONCLUSION: Our prognostic tool - containing clinical information (and if available also genetic data) - seems to be worthwhile testing in medical practice in order to confirm or refute the positive findings of this study. Our cohort study will be continued to include more VTE cases and to increase predictive value of the model.

Predicting suicide with the SAD PERSONS scale.

PubMed

Katz, Cara; Randall, Jason R; Sareen, Jitender; Chateau, Dan; Walld, Randy; Leslie, William D; Wang, JianLi; Bolton, James M

2017-09-01

Suicide is a major public health issue, and a priority requirement is accurately identifying high-risk individuals. The SAD PERSONS suicide risk assessment scale is widely implemented in clinical settings despite limited supporting evidence. This article aims to determine the ability of the SAD PERSONS scale (SPS) to predict future suicide in the emergency department. Five thousand four hundred sixty-two consecutive adults were seen by psychiatry consultation teams in two tertiary emergency departments with linkage to population-based administrative data to determine suicide deaths within 6 months, 1, and 5 years. Seventy-seven (1.4%) individuals died by suicide during the study period. When predicting suicide at 12 months, medium- and high-risk scores on SPS had a sensitivity of 49% and a specificity of 60%; the positive and negative predictive values were 0.9 and 99%, respectively. Half of the suicides at both 6- and 12-month intervals were classified as low risk by SPS at index visit. The area under the curve at 12 months for the Modified SPS was 0.59 (95% confidence interval [CI] range 0.51-0.67). High-risk scores (compared to low risk) were significantly associated with death by suicide over the 5-year study period using the SPS (hazard ratio 2.49; 95% CI 1.34-4.61) and modified version (hazard ratio 2.29; 95% CI 1.24-2.29). Although widely used in educational and clinical settings, these findings do not support the use of the SPS and Modified SPS to predict suicide in adults seen by psychiatric services in the emergency department. © 2017 Wiley Periodicals, Inc.

Presence of High-Risk HPV mRNA in Relation to Future High-Grade Lesions among High-Risk HPV DNA Positive Women with Minor Cytological Abnormalities

PubMed Central

Johansson, Hanna; Bjelkenkrantz, Kaj; Darlin, Lotten; Dilllner, Joakim; Forslund, Ola

2015-01-01

Objective Continuous expression of E6- and E7-oncogenes of high-risk human papillomavirus (HPV) types is necessary for the development and maintenance of the dysplastic phenotype. The aim of the study was to determine the sensitivity and specificity of the APTIMA HPV mRNA assay (Hologic) in predicting future development of high-grade cervical intraepithelial neoplasia (CIN) among high-risk HPV-DNA-positive women with atypical squamous cells of undetermined significance (ASCUS) or low-grade squamous epithelial lesion (LSIL) cytology. Methods Archived SurePath cervical samples of women ≥ 35 years of age with high-risk HPV DNA-positive ASCUS (n = 211) or LSIL, (n = 131) were tested for the presence of high-risk HPV E6/E7 mRNA using the APTIMA HPV assay, and the women were monitored for development of histopathologically verified CIN2+. Results Twenty-nine percent (61/211) of the women in the ASCUS group, and 34.3% (45/131) in the LSIL group developed CIN2+ within 4.5 years of follow-up. The prevalence of HPV mRNA was 90.0% (95% CI 85.9-94.0) among women with ASCUS and 95.4% (95% CI 91.8-99.0) among women with LSIL. The presence of HPV E6/E7 mRNA was associated with future development of CIN2+ among women with ASCUS and LSIL (p=0.02). The mRNA assay demonstrated high sensitivity in predicting future CIN2+ and CIN3 for index ASCUS (96.7%; 95% CI 87.6-99.4 and 100%; 95% CI 82.2-100, respectively) and LSIL (97.8%, 95% CI 86.8-99.9 and 100%, 95% CI 79.9-100, respectively). The corresponding specificity was low, 12.7% (95% CI 7.9-19.3) and 5.8% (95% CI 2.2-13.6), for future CIN2+, respectively. The negative predictive value of the HPV mRNA assay for detecting future CIN3 was 100%, since no mRNA-negative woman developed CIN3 (0/27) as compared to 13.6% (43/315) of the mRNA-positive women (p = 0.03). Conclusion The APTIMA mRNA assay demonstrated high sensitivity but low specificity in predicting future CIN2+ among women with minor cytological abnormalities. The assay had high negative predictive value for future CIN3, indicating that HPV-mRNA-negative women are at low risk of progression to high grade CIN. PMID:25893988

Human Population Density and Extinction Risk in the World's Carnivores

PubMed Central

Purvis, Andy; Sechrest, Wes; Gittleman, John L; Bielby, Jon; Mace, Georgina M

2004-01-01

Understanding why some species are at high risk of extinction, while others remain relatively safe, is central to the development of a predictive conservation science. Recent studies have shown that a species' extinction risk may be determined by two types of factors: intrinsic biological traits and exposure to external anthropogenic threats. However, little is known about the relative and interacting effects of intrinsic and external variables on extinction risk. Using phylogenetic comparative methods, we show that extinction risk in the mammal order Carnivora is predicted more strongly by biology than exposure to high-density human populations. However, biology interacts with human population density to determine extinction risk: biological traits explain 80% of variation in risk for carnivore species with high levels of exposure to human populations, compared to 45% for carnivores generally. The results suggest that biology will become a more critical determinant of risk as human populations expand. We demonstrate how a model predicting extinction risk from biology can be combined with projected human population density to identify species likely to move most rapidly towards extinction by the year 2030. African viverrid species are particularly likely to become threatened, even though most are currently considered relatively safe. We suggest that a preemptive approach to species conservation is needed to identify and protect species that may not be threatened at present but may become so in the near future. PMID:15252445

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation.

PubMed

Meertens, Linda J E; van Montfort, Pim; Scheepers, Hubertina C J; van Kuijk, Sander M J; Aardenburg, Robert; Langenveld, Josje; van Dooren, Ivo M A; Zwaan, Iris M; Spaanderman, Marc E A; Smits, Luc J M

2018-04-17

Prediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models. Prediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds. Four studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models. This review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth. © 2018 The Authors Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

PubMed Central

Yang, Lili; Yu, Menggang; Gao, Sujuan

2016-01-01

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort. PMID:26439685

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

Accuracy of risk scales for predicting repeat self-harm and suicide: a multicentre, population-level cohort study using routine clinical data.

PubMed

Steeg, Sarah; Quinlivan, Leah; Nowland, Rebecca; Carroll, Robert; Casey, Deborah; Clements, Caroline; Cooper, Jayne; Davies, Linda; Knipe, Duleeka; Ness, Jennifer; O'Connor, Rory C; Hawton, Keith; Gunnell, David; Kapur, Nav

2018-04-25

Risk scales are used widely in the management of patients presenting to hospital following self-harm. However, there is evidence that their diagnostic accuracy in predicting repeat self-harm is limited. Their predictive accuracy in population settings, and in identifying those at highest risk of suicide is not known. We compared the predictive accuracy of the Manchester Self-Harm Rule (MSHR), ReACT Self-Harm Rule (ReACT), SAD PERSONS Scale (SPS) and Modified SAD PERSONS Scale (MSPS) in an unselected sample of patients attending hospital following self-harm. Data on 4000 episodes of self-harm presenting to Emergency Departments (ED) between 2010 and 2012 were obtained from four established monitoring systems in England. Episodes were assigned a risk category for each scale and followed up for 6 months. The episode-based repeat rate was 28% (1133/4000) and the incidence of suicide was 0.5% (18/3962). The MSHR and ReACT performed with high sensitivity (98% and 94% respectively) and low specificity (15% and 23%). The SPS and the MSPS performed with relatively low sensitivity (24-29% and 9-12% respectively) and high specificity (76-77% and 90%). The area under the curve was 71% for both MSHR and ReACT, 51% for SPS and 49% for MSPS. Differences in predictive accuracy by subgroup were small. The scales were less accurate at predicting suicide than repeat self-harm. The scales failed to accurately predict repeat self-harm and suicide. The findings support existing clinical guidance not to use risk classification scales alone to determine treatment or predict future risk.

Aquatic predicted no-effect concentrations of 16 polycyclic aromatic hydrocarbons and their ecological risks in surface seawater of Liaodong Bay, China.

PubMed

Wang, Ying; Wang, Juying; Mu, Jingli; Wang, Zhen; Cong, Yi; Yao, Ziwei; Lin, Zhongsheng

2016-06-01

Polycyclic aromatic hydrocarbons (PAHs), a class of ubiquitous pollutants in marine environments, exhibit moderate to high adverse effects on aquatic organisms and humans. However, the lack of PAH toxicity data for aquatic organism has limited evaluation of their ecological risks. In the present study, aquatic predicted no-effect concentrations (PNECs) of 16 priority PAHs were derived based on species sensitivity distribution models, and their probabilistic ecological risks in seawater of Liaodong Bay, Bohai Sea, China, were assessed. A quantitative structure-activity relationship method was adopted to achieve the predicted chronic toxicity data for the PNEC derivation. Good agreement for aquatic PNECs of 8 PAHs based on predicted and experimental chronic toxicity data was observed (R(2)  = 0.746), and the calculated PNECs ranged from 0.011 µg/L to 205.3 µg/L. A significant log-linear relationship also existed between the octanol-water partition coefficient and PNECs derived from experimental toxicity data (R(2)  = 0.757). A similar order of ecological risks for the 16 PAH species in seawater of Liaodong Bay was found by probabilistic risk quotient and joint probability curve methods. The individual high ecological risk of benzo[a]pyrene, benzo[b]fluoranthene, and benz[a]anthracene needs to be determined. The combined ecological risk of PAHs in seawater of Liaodong Bay calculated by the joint probability curve method was 13.9%, indicating a high risk as a result of co-exposure to PAHs. Environ Toxicol Chem 2016;35:1587-1593. © 2015 SETAC. © 2015 SETAC.

How safe is safe enough? Radiation risk for a human mission to Mars.

PubMed

Cucinotta, Francis A; Kim, Myung-Hee Y; Chappell, Lori J; Huff, Janice L

2013-01-01

Astronauts on a mission to Mars would be exposed for up to 3 years to galactic cosmic rays (GCR)--made up of high-energy protons and high charge (Z) and energy (E) (HZE) nuclei. GCR exposure rate increases about three times as spacecraft venture out of Earth orbit into deep space where protection of the Earth's magnetosphere and solid body are lost. NASA's radiation standard limits astronaut exposures to a 3% risk of exposure induced death (REID) at the upper 95% confidence interval (CI) of the risk estimate. Fatal cancer risk has been considered the dominant risk for GCR, however recent epidemiological analysis of radiation risks for circulatory diseases allow for predictions of REID for circulatory diseases to be included with cancer risk predictions for space missions. Using NASA's models of risks and uncertainties, we predicted that central estimates for radiation induced mortality and morbidity could exceed 5% and 10% with upper 95% CI near 10% and 20%, respectively for a Mars mission. Additional risks to the central nervous system (CNS) and qualitative differences in the biological effects of GCR compared to terrestrial radiation may significantly increase these estimates, and will require new knowledge to evaluate.

How Safe Is Safe Enough? Radiation Risk for a Human Mission to Mars

PubMed Central

Cucinotta, Francis A.; Kim, Myung-Hee Y.; Chappell, Lori J.; Huff, Janice L.

2013-01-01

Astronauts on a mission to Mars would be exposed for up to 3 years to galactic cosmic rays (GCR) — made up of high-energy protons and high charge (Z) and energy (E) (HZE) nuclei. GCR exposure rate increases about three times as spacecraft venture out of Earth orbit into deep space where protection of the Earth's magnetosphere and solid body are lost. NASA's radiation standard limits astronaut exposures to a 3% risk of exposure induced death (REID) at the upper 95% confidence interval (CI) of the risk estimate. Fatal cancer risk has been considered the dominant risk for GCR, however recent epidemiological analysis of radiation risks for circulatory diseases allow for predictions of REID for circulatory diseases to be included with cancer risk predictions for space missions. Using NASA's models of risks and uncertainties, we predicted that central estimates for radiation induced mortality and morbidity could exceed 5% and 10% with upper 95% CI near 10% and 20%, respectively for a Mars mission. Additional risks to the central nervous system (CNS) and qualitative differences in the biological effects of GCR compared to terrestrial radiation may significantly increase these estimates, and will require new knowledge to evaluate. PMID:24146746

Strategies to predict rheumatoid arthritis development in at-risk populations

PubMed Central

van der Helm-van Mil, Annette H.

2016-01-01

The development of RA is conceived as a multiple hit process and the more hits that are acquired, the greater the risk of developing clinically apparent RA. Several at-risk phases have been described, including the presence of genetic and environmental factors, RA-related autoantibodies and biomarkers and symptoms. Intervention in these preclinical phases may be more effective compared with intervention in the clinical phase. One prerequisite for preventive strategies is the ability to estimate an individual’s risk adequately. This review evaluates the ability to predict the risk of RA in the various preclinical stages. Present data suggest that a combination of genetic and environmental factors is helpful to identify persons at high risk of RA among first-degree relatives. Furthermore, a combination of symptoms, antibody characteristics and environmental factors has been shown to be relevant for risk prediction in seropositive arthralgia patients. Large prospective studies are needed to validate and improve risk prediction in preclinical disease stages. PMID:25096602

Is Being Popular a Risky Proposition?

ERIC Educational Resources Information Center

Mayeux, Lara; Sandstrom, Marlene J.; Cillessen, Antonius H. N.

2008-01-01

Longitudinal associations between social preference, perceived popularity, and risk behaviors (smoking, alcohol use, and sexual activity) were examined in a sample of high school students. Social preference did not predict any of the risk behaviors assessed, although the interaction between gender and social preference was predictive of sexual…

Predicting Factors for High-Grade Cervical Dysplasia in Women With Low-Grade Cervical Cytology and Nonvisible Squamocolumnar Junction.

PubMed

Bogani, Giorgio; Taverna, Francesca; Lombardo, Claudia; Ditto, Antonino; Martinelli, Fabio; Signorelli, Mauro; Chiappa, Valentina; Leone Roberti Maggiore, U; Mosca, Lavinia; Sabatucci, Ilaria; Scaffa, Cono; Lorusso, Domenica; Raspagliesi, Francesco

2018-01-01

To assess the risk of developing high-grade cervical dysplasia among women with low-grade cervical cytology and nonvisible squamocolumnar junction (SCJ) at colposcopic examination. Data of consecutive women with low-grade intraepithelial lesion(≤LSIL) undergoing colposcopic examination, which was unsatisfactory (due to the lack of the visualization of the entire SCJ), were retrospectively reviewed. The risk of developing high-grade cervical intraepithelial neoplasia (CIN2+) was assessed using Kaplan-Meier and Cox models. Data of 86 women were retrieved. Mean (standard deviation [SD]) age was 36.3 (13.4) years. A total of 71 (82.5%) patients had high-risk human papillomavirus (HR-HPV) at the time of diagnosis. Among the 63 patients undergoing repetition of HPV testing, 15 (24%) and 48 (76%) women had positive and negative tests for HR-HPV at 12 months, respectively. We observed that 5 (33%) of 15 patients with HPV persistence developed CIN2+, while only 1 (2%) patient of 48 patients without HPV persistence developed CIN2+ (odds ratio [OR]: 23.5; 95% confidence interval [CI]: 2.46-223.7; P < .001). The length of HR-HPV persistence correlated with an increased risk of developing CIN2+ ( P < .001; P for trend). High-risk HPV persistence is the only factor predicting for CIN2+ (hazard ratio: 3.19; 95% CI: 1.55-6.57; P = .002). High-risk HPV persistence predicts the risk of developing CIN2+ in patients with unsatisfactory colposcopic examination. Further studies are warranted in order to implement the use of HPV testing in patients with unsatisfactory colposcopy.

Protective factors can mitigate behavior problems after prenatal cocaine and other drug exposures.

PubMed

Bada, Henrietta S; Bann, Carla M; Whitaker, Toni M; Bauer, Charles R; Shankaran, Seetha; Lagasse, Linda; Lester, Barry M; Hammond, Jane; Higgins, Rosemary

2012-12-01

We determined the role of risk and protective factors on the trajectories of behavior problems associated with high prenatal cocaine exposure (PCE)/polydrug exposure. The Maternal Lifestyle Study enrolled 1388 children with or without PCE, assessed through age 15 years. Because most women using cocaine during pregnancy also used other substances, we analyzed for the effects of 4 categories of prenatal drug exposure: high PCE/other drugs (OD), some PCE/OD, OD/no PCE, and no PCE/no OD. Risks and protective factors at individual, family, and community levels that may be associated with behavior outcomes were entered stepwise into latent growth curve models, then replaced by cumulative risk and protective indexes, and finally by a combination of levels of risk and protective indexes. Main outcome measures were the trajectories of externalizing, internalizing, total behavior, and attention problems scores from the Child Behavior Checklist (parent). A total of 1022 (73.6%) children had known outcomes. High PCE/OD significantly predicted externalizing, total, and attention problems when considering the balance between risk and protective indexes. Some PCE/OD predicted externalizing and attention problems. OD/no PCE also predicted behavior outcomes except for internalizing behavior. High level of protective factors was associated with declining trajectories of problem behavior scores over time, independent of drug exposure and risk index scores. High PCE/OD is a significant risk for behavior problems in adolescence; protective factors may attenuate its detrimental effects. Clinical practice and public health policies should consider enhancing protective factors while minimizing risks to improve outcomes of drug-exposed children.

Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort.

PubMed

Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo; Overgaard, Jens

2014-10-15

To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. ©2014 American Association for Cancer Research.

Prediction of Febrile Neutropenia after Chemotherapy Based on Pretreatment Risk Factors among Cancer Patients

PubMed Central

Aagaard, Theis; Roen, Ashley; Daugaard, Gedske; Brown, Peter; Sengeløv, Henrik; Mocroft, Amanda; Lundgren, Jens; Helleberg, Marie

2017-01-01

Abstract Background Febrile neutropenia (FN) is a common complication to chemotherapy associated with a high burden of morbidity and mortality. Reliable prediction of individual risk based on pretreatment risk factors allows for stratification of preventive interventions. We aimed to develop such a risk stratification model to predict FN in the 30 days after initiation of chemotherapy. Methods We included consecutive treatment-naïve patients with solid cancers and diffuse large B-cell lymphomas at Copenhagen University Hospital, 2010–2015. Data were obtained from the PERSIMUNE repository of electronic health records. FN was defined as neutrophils ≤0.5 × 10E9/L ​at the time of either a blood culture sample or death. Time from initiation of chemotherapy to FN was analyzed using Fine-Gray models with death as a competing event. Risk factors investigated were: age, sex, body surface area, haemoglobin, albumin, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI) and chemotherapy drugs. Parameter estimates were scaled and summed to create the risk score. The scores were grouped into four: low, intermediate, high and very high risk. Results Among 8,585 patients, 467 experienced FN, incidence rate/30 person-days 0.05 (95% CI, 0.05–0.06). Age (1 point if > 65 years), albumin (1 point if < 39 g/L), CCI (1 point if > 2) and chemotherapy (range -5 to 6 points/drug) predicted FN. Median score at inclusion was 2 points (range –5 to 9). The cumulative incidence and the incidence rates and hazard ratios of FN are shown in Figure 1 and Table 1, respectively. Conclusion We developed a risk score to predict FN the first month after initiation of chemotherapy. The score is easy to use and provides good differentiation of risk groups; the score needs independent validation before routine use. Disclosures All authors: No reported disclosures.

Prediction of cardiovascular disease risk among low-income urban dwellers in metropolitan Kuala Lumpur, Malaysia.

PubMed

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Bulgiba, Awang; Majid, Hazreen Abdul

2015-01-01

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia

PubMed Central

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Majid, Hazreen Abdul

2015-01-01

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers. PMID:25821810

Prediction of Lateral Ankle Sprains in Football Players Based on Clinical Tests and Body Mass Index.

PubMed

Gribble, Phillip A; Terada, Masafumi; Beard, Megan Q; Kosik, Kyle B; Lepley, Adam S; McCann, Ryan S; Pietrosimone, Brian G; Thomas, Abbey C

2016-02-01

The lateral ankle sprain (LAS) is the most common injury suffered in sports, especially in football. While suggested in some studies, a predictive role of clinical tests for LAS has not been established. To determine which clinical tests, focused on potentially modifiable factors of movement patterns and body mass index (BMI), could best demonstrate risk of LAS among high school and collegiate football players. Case-control study; Level of evidence, 3. A total of 539 high school and collegiate football players were evaluated during the preseason with the Star Excursion Balance Test (SEBT) and Functional Movement Screen as well as BMI. Results were compared between players who did and did not suffer an LAS during the season. Logistic regression analyses and calculated odds ratios were used to determine which measures predicted risk of LAS. The LAS group performed worse on the SEBT-anterior reaching direction (SEBT-ANT) and had higher BMI as compared with the noninjured group (P < .001). The strongest prediction models corresponded with the SEBT-ANT. Low performance on the SEBT-ANT predicted a risk of LAS in football players. BMI was also significantly higher in football players who sustained an LAS. Identifying clinical tools for successful LAS injury risk prediction will be a critical step toward the creation of effective prevention programs to reduce risk of sustaining an LAS during participation in football. © 2015 The Author(s).

External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

PubMed

Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2018-01-01

The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample. The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles. Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0.639). Across the 5 risk quintiles, the VSGNE model predicted observed mortality significantly with great accuracy. This simple VSGNE AAA risk predictive model showed very high discriminative ability in predicting mortality after elective AAA repair among a large external independent sample of AAA cases performed by a diverse array of physicians nationwide. The risk score based on this simple VSGNE model can reliably stratify patients according to their risk of mortality after elective AAA repair better than other established models. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Predictors of outcome and methodological issues in children with acute lymphoblastic leukaemia in El Salvador.

PubMed

Bonilla, Miguel; Gupta, Sumit; Vasquez, Roberto; Fuentes, Soad L; deReyes, Gladis; Ribeiro, Raul; Sung, Lillian

2010-12-01

Most children with cancer live in low-income countries (LICs) where risk factors in paediatric acute lymphoblastic leukaemia (ALL) developed in high-income countries may not apply. We describe predictors of survival for children in El Salvador with ALL. We included patients <16 years diagnosed with ALL between January 2001 and July 2007 treated with the El Salvador-Guatemala-Honduras II protocol. Demographic, disease-related, socioeconomic and nutritional variables were examined as potential predictors of event-free survival (EFS) and overall survival (OS). 260/443 patients (58.7%) were classified as standard risk. Standard- and high-risk 5-year EFS were 56.3 ± 4.5% and 48.6 ± 5.5%; 5-year OS were 77.7 ± 3.8% and 61.9 ± 5.8%, respectively. Among standard-risk children, socioeconomic variables such as higher monthly income (hazard ratio [HR] per $100 = 0.84 [95% confidence interval (CI) 0.70-0.99; P=0.04]) and parental secondary education (HR = 0.49, 95% CI 0.29-0.84; P = 0.01) were associated with better EFS. Among high-risk children, higher initial white blood cell (HR per 10×10(9)/L = 1.03, 95% CI 1.02-1.05; P<0.001) predicted worse EFS; socioeconomic variables were not predictive. The difference in EFS and OS appeared related to overestimating OS secondary to poor follow-up after abandonment/relapse. Socioeconomic variables predicted worse EFS in standard-risk children while disease-related variables were predictive in high-risk patients. Further studies should delineate pathways through which socioeconomic status affects EFS in order to design effective interventions. EFS should be the primary outcome in LIC studies. Copyright © 2010 Elsevier Ltd. All rights reserved.

Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

2016-06-01

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

Clinical potentials of methylator phenotype in stage 4 high-risk neuroblastoma: an open challenge.

PubMed

Banelli, Barbara; Merlo, Domenico Franco; Allemanni, Giorgio; Forlani, Alessandra; Romani, Massimo

2013-01-01

Approximately 20% of stage 4 high-risk neuroblastoma patients are alive and disease-free 5 years after disease onset while the remaining experience rapid and fatal progression. Numerous findings underline the prognostic role of methylation of defined target genes in neuroblastoma without taking into account the clinical and biological heterogeneity of this disease. In this report we have investigated the methylation of the PCDHB cluster, the most informative member of the "Methylator Phenotype" in neuroblastoma, hypothesizing that if this epigenetic mark can predict overall and progression free survival in high-risk stage 4 neuroblastoma, it could be utilized to improve the risk stratification of the patients, alone or in conjunction with the previously identified methylation of the SFN gene (14.3.3sigma) that can accurately predict outcome in these patients. We have utilized univariate and multivariate models to compare the prognostic power of PCDHB methylation in terms of overall and progression free survival, quantitatively determined by pyrosequencing, with that of other markers utilized for the patients' stratification utilizing methylation thresholds calculated on neuroblastoma at stage 1-4 and only on stage 4, high-risk patients. Our results indicate that PCDHB accurately distinguishes between high- and intermediate/low risk stage 4 neuroblastoma in agreement with the established risk stratification criteria. However PCDHB cannot predict outcome in the subgroup of stage 4 patients at high-risk whereas methylation levels of SFN are suggestive of a "methylation gradient" associated with tumor aggressiveness as suggested by the finding of a higher threshold that defines a subset of patients with an extremely severe disease (OS <24 months). Because of the heterogeneity of neuroblastoma we believe that clinically relevant methylation markers should be selected and tested on homogeneous groups of patients rather than on patients at all stages.

Performance of stroke risk scores in older people with atrial fibrillation not taking warfarin: comparative cohort study from BAFTA trial.

PubMed

Hobbs, F D R; Roalfe, A K; Lip, G Y H; Fletcher, K; Fitzmaurice, D A; Mant, J

2011-06-23

To compare the predictive power of the main existing and recently proposed schemes for stratification of risk of stroke in older patients with atrial fibrillation. Comparative cohort study of eight risk stratification scores. Trial of thromboprophylaxis in stroke, the Birmingham Atrial Fibrillation in the Aged (BAFTA) trial. 665 patients aged 75 or over with atrial fibrillation based in the community who were randomised to the BAFTA trial and were not taking warfarin throughout or for part of the study period. Events rates of stroke and thromboembolism. 54 (8%) patients had an ischaemic stroke, four (0.6%) had a systemic embolism, and 13 (2%) had a transient ischaemic attack. The distribution of patients classified into the three risk categories (low, moderate, high) was similar across three of the risk stratification scores (revised CHADS(2), NICE, ACC/AHA/ESC), with most patients categorised as high risk (65-69%, n = 460-457) and the remaining classified as moderate risk. The original CHADS(2) (Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes, previous Stroke) score identified the lowest number as high risk (27%, n = 180). The incremental risk scores of CHADS(2), Rietbrock modified CHADS(2), and CHA(2)DS(2)-VASc (CHA(2)DS(2)-Vascular disease, Age 65-74 years, Sex) failed to show an increase in risk at the upper range of scores. The predictive accuracy was similar across the tested schemes with C statistic ranging from 0.55 (original CHADS(2)) to 0.62 (Rietbrock modified CHADS(2)), with all except the original CHADS(2) predicting better than chance. Bootstrapped paired comparisons provided no evidence of significant differences between the discriminatory ability of the schemes. Based on this single trial population, current risk stratification schemes in older people with atrial fibrillation have only limited ability to predict the risk of stroke. Given the systematic undertreatment of older people with anticoagulation, and the relative safety of warfarin versus aspirin in those aged over 70, there could be a pragmatic rationale for classifying all patients over 75 as "high risk" until better tools are available.

Coronary Risk Factor Scoring as a Guide for Counseling

NASA Technical Reports Server (NTRS)

Fleck, R. L.

1971-01-01

A risk factor scoring system for early detection, possible prediction, and counseling to coronary heart disease patients is discussed. Scoring data include dynamic EKG, cholesterol levels, triglycerine content, total lipid level, total phospolipid levels, and electrophoretic patterns. Results indicate such a system is effective in identifying high risk subjects, but that the ability to predict exceeds the ability to prevent heart disease or its complications.

Personality Traits, Perceived Risk, and Risk-Reduction Behaviors: A Further Study of Smoking and Radon

PubMed Central

Hampson, Sarah E.; Andrews, Judy A.; Barckley, Maureen; Lichtenstein, Edward; Lee, Michael E.

2008-01-01

Personality traits and risk perceptions were examined as predictors of changes in smoking behavior. Participants (N = 697) were part of a randomized controlled trial of interventions to reduce exposure to the combined hazard of radon and cigarette smoke. Participants with higher perceived risk at baseline for the combination of smoking and radon were more likely to have a more restrictive household smoking ban in place at 12 months follow-up (p <. 05). Risk perceptions also predicted reductions in the total number of cigarettes smoked in the home for participants in the video intervention who had high or moderate levels of Extraversion (p <.001). Greater perceived risk predicted quitting for highly or moderately conscientious women (p <.05). The moderating effects of personality traits should be considered when evaluating risk-reduction interventions. PMID:16846328

Sexual Behaviors of US Women at Risk of HIV Acquisition: A Longitudinal Analysis of Findings from HPTN 064.

PubMed

Justman, J; Befus, M; Hughes, J; Wang, J; Golin, C E; Adimora, A A; Kuo, I; Haley, D F; Del Rio, C; El-Sadr, W M; Rompalo, A; Mannheimer, S; Soto-Torres, L; Hodder, S

2015-07-01

We describe the sexual behaviors of women at elevated risk of HIV acquisition who reside in areas of high HIV prevalence and poverty in the US. Participants in HPTN 064, a prospective HIV incidence study, provided information about individual sexual behaviors and male sexual partners in the past 6 months at baseline, 6- and 12-months. Independent predictors of consistent or increased temporal patterns for three high-risk sexual behaviors were assessed separately: exchange sex, unprotected anal intercourse (UAI) and concurrent partnerships. The baseline prevalence of each behavior was >30 % among the 2,099 participants, 88 % reported partner(s) with >1 HIV risk characteristic and both individual and partner risk characteristics decreased over time. Less than high school education and food insecurity predicted consistent/increased engagement in exchange sex and UAI, and partner's concurrency predicted participant concurrency. Our results demonstrate how interpersonal and social factors may influence sustained high-risk behavior by individuals and suggest that further study of the economic issues related to HIV risk could inform future prevention interventions.

Failure prediction using machine learning and time series in optical network.

PubMed

Wang, Zhilong; Zhang, Min; Wang, Danshi; Song, Chuang; Liu, Min; Li, Jin; Lou, Liqi; Liu, Zhuo

2017-08-07

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

Development of a Middle-Age and Geriatric Trauma Mortality Risk Score A Tool to Guide Palliative Care Consultations.

PubMed

Konda, Sanjit R; Seymour, Rachel; Manoli, Arthur; Gales, Jordan; Karunakar, Madhav A

2016-11-01

This study aimed to develop a tool to quantify risk of inpatient mortality among geriatric and middleaged trauma patients. This study sought to demonstrate the ability of the novel risk score in the early identification of high risk trauma patients for resource-sparing interventions, including referral to palliative medicine. This retrospective cohort study utilized data from a single level 1 trauma center. Regression analysis was used to create a novel risk of inpatient mortality score. A total of 2,387 low energy and 1,201 high-energy middle-aged (range: 55 to 64 years of age) and geriatric (65 years of age or odler) trauma patients comprised the study cohort. Model validation was performed using 37,474 lowenergy and 97,034 high-energy patients from the National Trauma Databank (NTDB). Potential hospital cost reduction was calculated for early referral of high risk trauma patients to palliative medicine services in comparison to no palliative medicine referral. Factors predictive of inpatient mortality among the study and validation patient cohorts included; age, Glasgow Coma Scale, and Abbreviated Injury Scale for the head and neck and chest. Within the validation cohort, the novel mortality risk score demonstrated greater predictive capacity than existing trauma scores [STTGMALE-AUROC: 0.83 vs. TRISS 0.80, (p < 0.01), STTGMAHE-AUROC: 0.86 vs. TRISS 0.85, (p < 0.01)]. Our model demonstrated early palliative medicine evaluation could produce $1,083,082 in net hospital savings per year. This novel risk score for older trauma patients has shown fidelity in prediction of inpatient mortality; in the study and validation cohorts. This tool may be used for early intervention in the care of patients at high risk of mortality and resource expenditure.

Prognostic importance of DNA ploidy in non-endometrioid, high-risk endometrial carcinomas.

PubMed

Sorbe, Bengt

2016-03-01

The present study investigated the predictive and prognostic impact of DNA ploidy together with other well-known prognostic factors in a series of non-endometrioid, high-risk endometrial carcinomas. From a complete consecutive series of 4,543 endometrial carcinomas of International Federation of Gynecology and Obstetrics (FIGO) stages I-IV, 94 serous carcinomas, 48 clear cell carcinomas and 231 carcinosarcomas were selected as a non-endometrioid, high-risk group for further studies regarding prognosis. The impact of DNA ploidy, as assessed by flow cytometry, was of particular focus. The age of the patients, FIGO stage, depth of myometrial infiltration and tumor expression of p53 were also included in the analyses (univariate and multivariate). In the complete series of cases, the recurrence rate was 37%, and the 5-year overall survival rate was 39% with no difference between the three histological subtypes. The primary cure rate (78%) was also similar for all tumor types studied. DNA ploidy was a significant predictive factor (on univariate analysis) for primary tumor cure rate, and a prognostic factor for survival rate (on univariate and multivariate analyses). The predictive and prognostic impact of DNA ploidy was higher in carcinosarcomas than in serous and clear cell carcinomas. In the majority of multivariate analyses, FIGO stage and depth of myometrial infiltration were the most important predictive (tumor recurrence) and prognostic (survival rate) factors. DNA ploidy status is a less important predictive and prognostic factor in non-endometrioid, high-risk endometrial carcinomas than in the common endometrioid carcinomas, in which FIGO and nuclear grade also are highly significant and important factors.

[Predicting individual risk of high healthcare cost to identify complex chronic patients].

PubMed

Coderch, Jordi; Sánchez-Pérez, Inma; Ibern, Pere; Carreras, Marc; Pérez-Berruezo, Xavier; Inoriza, José M

2014-01-01

To develop a predictive model for the risk of high consumption of healthcare resources, and assess the ability of the model to identify complex chronic patients. A cross-sectional study was performed within a healthcare management organization by using individual data from 2 consecutive years (88,795 people). The dependent variable consisted of healthcare costs above the 95th percentile (P95), including all services provided by the organization and pharmaceutical consumption outside of the institution. The predictive variables were age, sex, morbidity-based on clinical risk groups (CRG)-and selected data from previous utilization (use of hospitalization, use of high-cost drugs in ambulatory care, pharmaceutical expenditure). A univariate descriptive analysis was performed. We constructed a logistic regression model with a 95% confidence level and analyzed sensitivity, specificity, positive predictive values (PPV), and the area under the ROC curve (AUC). Individuals incurring costs >P95 accumulated 44% of total healthcare costs and were concentrated in ACRG3 (aggregated CRG level 3) categories related to multiple chronic diseases. All variables were statistically significant except for sex. The model had a sensitivity of 48.4% (CI: 46.9%-49.8%), specificity of 97.2% (CI: 97.0%-97.3%), PPV of 46.5% (CI: 45.0%-47.9%), and an AUC of 0.897 (CI: 0.892 to 0.902). High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

The Gastric/Pancreatic Amylase Ratio Predicts Postoperative Pancreatic Fistula With High Sensitivity and Specificity

PubMed Central

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

Abstract This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD). POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed. In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase. Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity. This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers. PMID:25621676

The gastric/pancreatic amylase ratio predicts postoperative pancreatic fistula with high sensitivity and specificity.

PubMed

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD).POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed.In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase.Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity.This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers.

Identifying cardiovascular disease risk and outcome: use of the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio versus metabolic syndrome criteria.

PubMed

Salazar, M R; Carbajal, H A; Espeche, W G; Aizpurúa, M; Leiva Sisnieguez, C E; March, C E; Balbín, E; Stavile, R N; Reaven, G M

2013-06-01

Metabolic syndrome (MetS) has been shown to predict both risk and CVD events. We have identified sex-specific values for the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio associated with an unfavourable cardio-metabolic risk profile, but it is not known whether it also predicts CVD outcome. To quantify risk for CVD outcomes associated with a high TG/HDL-C ratio and to compare this risk with that predicted using MetS, a population longitudinal prospective observational study was performed in Rauch City, Buenos Aires, Argentina. In 2003 surveys were performed on a population random sample of 926 inhabitants. In 2012, 527 women and 269 men were surveyed again in search of new CVD events. The first CVD event was the primary endpoint. Relative risks for CVD events between individuals above and below the TG/HDL-C cut-points, and with or without MetS, were estimated using Cox proportional hazard. The first CVD event was the primary endpoint. Relative risks for CVD events between individuals above and below the TG/HDL-C cut-points, and with or without MetS, were estimated using Cox proportional hazard. The number of subjects deemed at 'high' CVD risk on the basis of an elevated TG/HDL-C ratio (30%) or having the MetS (35%) was relatively comparable. The unadjusted hazard risk was significantly increased when comparing 'high' versus 'low' risk groups no matter which criteria was used, although it was somewhat higher in those with the MetS (HR = 3.17, 95% CI:1.79-5.60 vs. 2.16, 95% CI:1.24-3.75). However, this difference essentially disappeared when adjusted for sex and age (HR = 2.09, 95% CI:1.18-3.72 vs. 2.01, 95% CI:1.14-3.50 for MetS and TG/HDL-C respectively). An elevated TG/HDL-C ratio appears to be just as effective as the MetS diagnosis in predicting the development of CVD. © 2013 The Association for the Publication of the Journal of Internal Medicine.

A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.

PubMed

Hughes, Maria F; Saarela, Olli; Blankenberg, Stefan; Zeller, Tanja; Havulinna, Aki S; Kuulasmaa, Kari; Yarnell, John; Schnabel, Renate B; Tiret, Laurence; Salomaa, Veikko; Evans, Alun; Kee, Frank

2012-08-01

We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences. We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event. The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.

Assessment of risks for high-speed rail grade crossings on the Empire Corridor : next generation high-speed rail program

DOT National Transportation Integrated Search

2000-08-01

The report describes a risk-based approach for assessing the implications of higher train speeds on highway-railroad grade crossing safety, and allocating limited resources to best reduce this risk. To predict accident frequency, an existing DOT mode...

A clinical score to predict the need for intraaortic balloon pump in patients undergoing coronary artery bypass grafting.

PubMed

Miceli, Antonio; Duggan, Simon M J; Capoun, Radek; Romeo, Francesco; Caputo, Massimo; Angelini, Gianni D

2010-08-01

There is no accepted consensus on the definition of high-risk patients who may benefit from the use of intraaortic balloon pump (IABP) in coronary artery bypass grafting (CABG). The aim of this study was to develop a risk model to identify high-risk patients and predict the need for IABP insertion during CABG. From April 1996 to December 2006, 8,872 consecutive patients underwent isolated CABG; of these 182 patients (2.1%) received intraoperative or postoperative IABP. The scoring risk model was developed in 4,575 patients (derivation dataset) and validated on the remaining patients (validation dataset). Predictive accuracy was evaluated by the area under the receiver operating characteristic curve. Mortality was 1% in the entire cohort and 18.7% (22 patients) in the group which received IABP. Multivariable analysis showed that age greater than 70 years, moderate and poor left ventricular dysfunction, previous cardiac surgery, emergency operation, left main disease, Canadian Cardiovascular Society 3-4 class, and recent myocardial infarction were independent risk factors for the need of IABP insertion. Three risk groups were identified. The observed probability of receiving IABP and mortality in the validation dataset was 36.4% and 10% in the high-risk group (score >14), 10.9% and 2.8% in the medium-risk group (score 7 to 13), and 1.7% and 0.7% in the low-risk group (score 0 to 6). This simple clinical risk model based on preoperative clinical data can be used to identify high-risk patients who may benefit from elective insertion of IABP during CABG. Copyright 2010 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

The Autism Parent Screen for Infants: Predicting risk of autism spectrum disorder based on parent-reported behavior observed at 6-24 months of age.

PubMed

Sacrey, Lori-Ann R; Bryson, Susan; Zwaigenbaum, Lonnie; Brian, Jessica; Smith, Isabel M; Roberts, Wendy; Szatmari, Peter; Vaillancourt, Tracy; Roncadin, Caroline; Garon, Nancy

2018-04-01

This study examined whether a novel parent-report questionnaire, the Autism Parent Screen for Infants, could differentiate infants subsequently diagnosed with autism spectrum disorder from a high-risk cohort (siblings of children diagnosed with autism spectrum disorder (n = 66)) from high-risk and low-risk comparison infants (no family history of autism spectrum disorder) who did not develop autism spectrum disorder (n = 138 and 79, respectively). Participants were assessed prospectively at 6, 9, 12, 15, 18, and 24 months of age. At 36 months, a blind independent diagnostic assessment for autism spectrum disorder was completed. Parent report on the Autism Parent Screen for Infants was examined in relation to diagnostic outcome and risk status (i.e. high-risk sibling with autism spectrum disorder, high-risk sibling without autism spectrum disorder, and low-risk control). The results indicated that from 6 months of age, total score on the Autism Parent Screen for Infants differentiated between the siblings with autism spectrum disorder and the other two groups. The sensitivity, specificity, and positive and negative predictive validity of the Autism Parent Screen for Infants highlight its potential for the early screening of autism spectrum disorder in high-risk cohorts.

Child and environmental risk factors predicting readiness for learning in children at high risk of dyslexia.

PubMed

Dilnot, Julia; Hamilton, Lorna; Maughan, Barbara; Snowling, Margaret J

2017-02-01

We investigate the role of distal, proximal, and child risk factors as predictors of reading readiness and attention and behavior in children at risk of dyslexia. The parents of a longitudinal sample of 251 preschool children, including children at family risk of dyslexia and children with preschool language difficulties, provided measures of socioeconomic status, home literacy environment, family stresses, and child health via interviews and questionnaires. Assessments of children's reading-related skills, behavior, and attention were used to define their readiness for learning at school entry. Children at family risk of dyslexia and children with preschool language difficulties experienced more environmental adversities and health risks than controls. The risks associated with family risk of dyslexia and with language status were additive. Both home literacy environment and child health predicted reading readiness while home literacy environment and family stresses predicted attention and behavior. Family risk of dyslexia did not predict readiness to learn once other risks were controlled and so seems likely to be best conceptualized as representing gene-environment correlations. Pooling across risks defined a cumulative risk index, which was a significant predictor of reading readiness and, together with nonverbal ability, accounted for 31% of the variance between children.

Disentangling the risk assessment and intimate partner violence relation: Estimating mediating and moderating effects.

PubMed

Williams, Kirk R; Stansfield, Richard

2017-08-01

To manage intimate partner violence (IPV), the criminal justice system has turned to risk assessment instruments to predict if a perpetrator will reoffend. Empirically determining whether offenders assessed as high risk are those who recidivate is critical for establishing the predictive validity of IPV risk assessment instruments and for guiding the supervision of perpetrators. But by focusing solely on the relation between calculated risk scores and subsequent IPV recidivism, previous studies of the predictive validity of risk assessment instruments omitted mediating factors intended to mitigate the risk of this behavioral recidivism. The purpose of this study was to examine the mediating effects of such factors and the moderating effects of risk assessment on the relation between assessed risk (using the Domestic Violence Screening Instrument-Revised [DVSI-R]) and recidivistic IPV. Using a sample of 2,520 perpetrators of IPV, results revealed that time sentenced to jail and time sentenced to probation each significantly mediated the relation between DVSI-R risk level and frequency of reoffending. The results also revealed that assessed risk moderated the relation between these mediating factors and IPV recidivism, with reduced recidivism (negative estimated effects) for high-risk perpetrators but increased recidivism (positive estimate effects) for low-risk perpetrators. The implication is to assign interventions to the level of risk so that no harm is done. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Daddy's little girl goes to college: an investigation of females' perceived closeness with fathers and later risky behaviors.

PubMed

Rostad, Whitney L; Silverman, Paul; McDonald, Molly K

2014-01-01

The present study investigated the extent to which father-daughter relationships predicted risk-taking in a sample of female college students. Specifically, this study examined whether female adolescents' models of father psychological presence predicted substance use and sexual risk-taking, over and above impulsivity, depression, and other risky behaviors. A sample of 203 female college students were administered several scales assessing father psychological presence, sexual risk-taking, substance use, impulsivity, and depression. Father psychological presence did predict sexual risk-taking and illicit drug use (but not alcohol use) after controlling for impulsivity, other risky behavior, and mood. Further, when grouped into low and high levels of psychological presence, those in the low group were more likely to engage in a variety of risky behaviors than those who perceived high psychological presence. Consistent with evolutionary perspectives, paternal psychological presence may function as protection against risky behavior.

Predictive risk models for proximal aortic surgery

PubMed Central

Díaz, Rocío; Pascual, Isaac; Álvarez, Rubén; Alperi, Alberto; Rozado, Jose; Morales, Carlos; Silva, Jacobo; Morís, César

2017-01-01

Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery. PMID:28616348

Sexual risk behavior among youth: modeling the influence of prosocial activities and socioeconomic factors.

PubMed

Ramirez-Valles, J; Zimmerman, M A; Newcomb, M D

1998-09-01

Sexual activity among high-school-aged youths has steadily increased since the 1970s, emerging as a significant public health concern. Yet, patterns of youth sexual risk behavior are shaped by social class, race, and gender. Based on sociological theories of financial deprivation and collective socialization, we develop and test a model of the relationships among neighborhood poverty; family structure and social class position; parental involvement; prosocial activities; race; and gender as they predict youth sexual risk behavior. We employ structural equation modeling to test this model on a cross-sectional sample of 370 sexually active high-school students from a midwestern city; 57 percent (n = 209) are males and 86 percent are African American. We find that family structure indirectly predicts sexual risk behavior through neighborhood poverty, parental involvement, and prosocial activities. In addition, family class position indirectly predicts sexual risk behavior through neighborhood poverty and prosocial activities. We address implications for theory and health promotion.

Predicting risk for medical malpractice claims using quality-of-care characteristics.

PubMed Central

Charles, S C; Gibbons, R D; Frisch, P R; Pyskoty, C E; Hedeker, D; Singha, N K

1992-01-01

The current fault-based tort system assumes that claims made against physicians are inversely related to the quality of care they provide. In this study we identified physician characteristics associated with elements of medical care that make physicians vulnerable to malpractice claims. A sample of physicians (n = 248) thought to be at high or low risk for claims was surveyed on various personal and professional characteristics. Statistical analysis showed that 9 characteristics predicted risk group. High risk was associated with increased age, surgical specialty, emergency department coverage, increased days away from practice, and the feeling that the litigation climate was "unfair." Low risk was associated with scheduling enough time to talk with patients, answering patients' telephone calls directly, feeling "satisfied" with practice arrangements, and acknowledging greater emotional distress. Prediction was more accurate for physicians in practice 15 years or less. We conclude that a relationship exists between a history of malpractice claims and selected physician characteristics. PMID:1462538

Separating sensitivity from exposure in assessing extinction risk from climate change.

PubMed

Dickinson, Maria G; Orme, C David L; Suttle, K Blake; Mace, Georgina M

2014-11-04

Predictive frameworks of climate change extinction risk generally focus on the magnitude of climate change a species is expected to experience and the potential for that species to track suitable climate. A species' risk of extinction from climate change will depend, in part, on the magnitude of climate change the species experiences, its exposure. However, exposure is only one component of risk. A species' risk of extinction will also depend on its intrinsic ability to tolerate changing climate, its sensitivity. We examine exposure and sensitivity individually for two example taxa, terrestrial amphibians and mammals. We examine how these factors are related among species and across regions and how explicit consideration of each component of risk may affect predictions of climate change impacts. We find that species' sensitivities to climate change are not congruent with their exposures. Many highly sensitive species face low exposure to climate change and many highly exposed species are relatively insensitive. Separating sensitivity from exposure reveals patterns in the causes and drivers of species' extinction risk that may not be evident solely from predictions of climate change. Our findings emphasise the importance of explicitly including sensitivity and exposure to climate change in assessments of species' extinction risk.

Separating sensitivity from exposure in assessing extinction risk from climate change

PubMed Central

Dickinson, Maria G.; Orme, C. David L.; Suttle, K. Blake; Mace, Georgina M.

2014-01-01

Predictive frameworks of climate change extinction risk generally focus on the magnitude of climate change a species is expected to experience and the potential for that species to track suitable climate. A species' risk of extinction from climate change will depend, in part, on the magnitude of climate change the species experiences, its exposure. However, exposure is only one component of risk. A species' risk of extinction will also depend on its intrinsic ability to tolerate changing climate, its sensitivity. We examine exposure and sensitivity individually for two example taxa, terrestrial amphibians and mammals. We examine how these factors are related among species and across regions and how explicit consideration of each component of risk may affect predictions of climate change impacts. We find that species' sensitivities to climate change are not congruent with their exposures. Many highly sensitive species face low exposure to climate change and many highly exposed species are relatively insensitive. Separating sensitivity from exposure reveals patterns in the causes and drivers of species' extinction risk that may not be evident solely from predictions of climate change. Our findings emphasise the importance of explicitly including sensitivity and exposure to climate change in assessments of species' extinction risk. PMID:25367429

Excess coronary artery disease risk in South Asian immigrants: Can dysfunctional high-density lipoprotein explain increased risk?

PubMed Central

Dodani, Sunita

2008-01-01

Background: Coronary artery disease (CAD) is the leading cause of mortality and morbidity in the United States (US), and South Asian immigrants (SAIs) have a higher risk of CAD compared to Caucasians. Traditional risk factors may not completely explain high risk, and some of the unknown risk factors need to be explored. This short review is mainly focused on the possible role of dysfunctional high-density lipoprotein (HDL) in causing CAD and presents an overview of available literature on dysfunctional HDL. Discussion: The conventional risk factors, insulin resistance parameters, and metabolic syndrome, although important in predicting CAD risk, may not sufficiently predict risk in SAIs. HDL has antioxidant, antiinflammatory, and antithrombotic properties that contribute to its function as an antiatherogenic agent. Recent Caucasian studies have shown HDL is not only ineffective as an antioxidant but, paradoxically, appears to be prooxidant, and has been found to be associated with CAD. Several causes have been hypothesized for HDL to become dysfunctional, including Apo lipoprotein A-I (Apo A-I) polymorphisms. New risk factors and markers like dysfunctional HDL and genetic polymorphisms may be associated with CAD. Conclusions: More research is required in SAIs to explore associations with CAD and to enhance early detection and prevention of CAD in this high risk group. PMID:19183743

Memory Resilience to Alzheimer's Genetic Risk: Sex Effects in Predictor Profiles.

PubMed

McDermott, Kirstie L; McFall, G Peggy; Andrews, Shea J; Anstey, Kaarin J; Dixon, Roger A

2017-10-01

Apolipoprotein E (APOE) ɛ4 and Clusterin (CLU) C alleles are risk factors for Alzheimer's disease (AD) and episodic memory (EM) decline. Memory resilience occurs when genetically at-risk adults perform at high and sustained levels. We investigated whether (a) memory resilience to AD genetic risk is predicted by biological and other risk markers and (b) the prediction profiles vary by sex and AD risk variant. Using a longitudinal sample of nondemented adults (n = 642, aged 53-95) we focused on memory resilience (over 9 years) to 2 AD risk variants (APOE, CLU). Growth mixture models classified resilience. Random forest analysis, stratified by sex, tested the predictive importance of 22 nongenetic risk factors from 5 domains (n = 24-112). For both sexes, younger age, higher education, stronger grip, and everyday novel cognitive activity predicted memory resilience. For women, 9 factors from functional, health, mobility, and lifestyle domains were also predictive. For men, only fewer depressive symptoms was an additional important predictor. The prediction profiles were similar for APOE and CLU. Although several factors predicted resilience in both sexes, a greater number applied only to women. Sex-specific mechanisms and intervention targets are implied. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario

PubMed Central

Dueñas-Espín, Ivan; Vela, Emili; Pauws, Steffen; Bescos, Cristina; Cano, Isaac; Cleries, Montserrat; Contel, Joan Carles; de Manuel Keenoy, Esteban; Garcia-Aymerich, Judith; Gomez-Cabrero, David; Kaye, Rachelle; Lahr, Maarten M H; Lluch-Ariet, Magí; Moharra, Montserrat; Monterde, David; Mora, Joana; Nalin, Marco; Pavlickova, Andrea; Piera, Jordi; Ponce, Sara; Santaeugenia, Sebastià; Schonenberg, Helen; Störk, Stefan; Tegner, Jesper; Velickovski, Filip; Westerteicher, Christoph; Roca, Josep

2016-01-01

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation. PMID:27084274

Comparison of the Walz Nomogram and Presence of Secondary Circulating Prostate Cells for Predicting Early Biochemical Failure after Radical Prostatectomy for Prostate Cancer in Chilean Men.

PubMed

Murray, Nigel P; Reyes, Eduardo; Orellana, Nelson; Fuentealba, Cynthia; Jacob, Omar

2015-01-01

To determine the utility of secondary circulating prostate cells for predicting early biochemical failure after radical prostatectomy for prostate cancer and compare the results with the Walz nomagram. A single centre, prospective study of men with prostate cancer treated with radical prostatectomy between 2004 and 2014 was conducted, with registration of clinical-pathological details, total serum PSA pre-surgery, Gleason score, extracapsular extension, positive surgical margins, infiltration of lymph nodes, seminal vesicles and pathological stage. Secondary circulating prostate cells were obtained using differential gel centrifugation and assessed using standard immunocytochemistry with anti-PSA. Biochemical failure was defined as a PSA >0.2ng/ml, predictive values werecalculated using the Walz nomagram and CPC detection. A total of 326 men participated, with a median follow up of 5 years; 64 had biochemical failure within two years. Extracapsular extension, positive surgical margins, pathological stage, Gleason score ≥ 8, infiltration of seminal vesicles and lymph nodes were all associated with higher risk of biochemical failure. The discriminative value for the nomogram and circulating prostate cells was high (AUC >0.80), predictive values were higher for circulating prostate cell detection, with a negative predictive value of 99%, sensitivity of 96% and specificity of 75%. The nomagram had good predictive power to identify men with a high risk of biochemical failure within two years. The presence of circulating prostate cells had the same predictive power, with a higher sensitivity and negative predictive value. The presence of secondary circulating prostate cells identifies a group of men with a high risk of early biochemical failure. Those negative for secondary CPCs have a very low risk of early biochemical failure.

Performance of Comorbidity, Risk Adjustment, and Functional Status Measures in Expenditure Prediction for Patients With Diabetes

PubMed Central

Maciejewski, Matthew L.; Liu, Chuan-Fen; Fihn, Stephan D.

2009-01-01

OBJECTIVE—To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. RESEARCH DESIGN AND METHODS—This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R2 statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. RESULTS—Administrative data–based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. CONCLUSIONS—Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed. PMID:18945927

Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

PubMed

Wen, Yalu; Shen, Xiaoxi; Lu, Qing

2018-05-31

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

The Lack of Utility of Circulating Biomarkers of Inflammation and Endothelial Dysfunction for Type 2 Diabetes Risk Prediction Among Postmenopausal Women

PubMed Central

Chao, Chun; Song, Yiqing; Cook, Nancy; Tseng, Chi-Hong; Manson, JoAnn E.; Eaton, Charles; Margolis, Karen L.; Rodriguez, Beatriz; Phillips, Lawrence S.; Tinker, Lesley F.; Liu, Simin

2011-01-01

Background Recent studies have linked plasma markers of inflammation and endothelial dysfunction to type 2 diabetes mellitus (DM) development. However, the utility of these novel biomarkers for type 2 DM risk prediction remains uncertain. Methods The Women’s Health Initiative Observational Study (WHIOS), a prospective cohort, and a nested case-control study within the WHIOS of 1584 incident type 2 DM cases and 2198 matched controls were used to evaluate the utility of plasma markers of inflammation and endothelial dysfunction for type 2 DM risk prediction. Between September 1994 and December 1998, 93 676 women aged 50 to 79 years were enrolled in the WHIOS. Fasting plasma levels of glucose, insulin, white blood cells, tumor necrosis factor receptor 2, interleukin 6, high-sensitivity C-reactive protein, E-selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 were measured using blood samples collected at baseline. A series of prediction models including traditional risk factors and novel plasma markers were evaluated on the basis of global model fit, model discrimination, net reclassification improvement, and positive and negative predictive values. Results Although white blood cell count and levels of interleukin 6, high-sensitivity C-reactive protein, and soluble intercellular adhesion molecule 1 significantly enhanced model fit, none of the inflammatory and endothelial dysfunction markers improved the ability of model discrimination (area under the receiver operating characteristic curve, 0.93 vs 0.93), net reclassification, or predictive values (positive, 0.22 vs 0.24; negative, 0.99 vs 0.99 [using 15% 6-year type 2 DM risk as the cutoff]) compared with traditional risk factors. Similar results were obtained in ethnic-specific analyses. Conclusion Beyond traditional risk factors, measurement of plasma markers of systemic inflammation and endothelial dysfunction contribute relatively little additional value in clinical type 2 DM risk prediction in a multiethnic cohort of postmenopausal women. PMID:20876407

The impact of youth, family, peer and neighborhood risk factors on developmental trajectories of risk involvement from early through middle adolescence.

PubMed

Wang, Bo; Deveaux, Lynette; Li, Xiaoming; Marshall, Sharon; Chen, Xinguang; Stanton, Bonita

2014-04-01

Few studies have analyzed the development course beginning in pre-/early adolescence of overall engagement in health-risk behaviors and associated social risk factors that place individuals in different health-risk trajectories through mid-adolescence. The current longitudinal study identified 1276 adolescents in grade six and followed them for three years to investigate their developmental trajectories of risk behaviors and to examine the association of personal and social risk factors with each trajectory. Group-based trajectory modeling was applied to identify distinctive trajectory patterns of risk behaviors. Multivariate multinomial logistic regression analyses were performed to examine the effects of the personal and social risk factors on adolescents' trajectories. Three gender-specific behavioral trajectories were identified for males (55.3% low-risk, 37.6% moderate-risk, increasing, and 7.1% high-risk, increasing) and females (41.4% no-risk, 53.4% low-risk, increasing and 5.2% moderate to high-risk, increasing). Sensation-seeking, family, peer, and neighborhood factors at baseline predicted following the moderate-risk, increasing trajectory and the high-risk, increasing trajectory in males; these risk factors predicted following the moderate to high-risk, increasing trajectory in females. The presence of all three social risk factors (high-risk neighborhood, high-risk peers and low parental monitoring) had a dramatic impact on increased probability of being in a high-risk trajectory group. These findings highlight the developmental significance of early personal and social risk factors on subsequent risk behaviors in early to middle adolescence. Future adolescent health behavior promotion interventions might consider offering additional prevention resources to pre- and early adolescent youth who are exposed to multiple contextual risk factors (even in the absence of risk behaviors) or youth who are early-starters of delinquency and substance use behaviors in early adolescence. Copyright © 2014. Published by Elsevier Ltd.

The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission.

PubMed

Baillie, Charles A; VanZandbergen, Christine; Tait, Gordon; Hanish, Asaf; Leas, Brian; French, Benjamin; Hanson, C William; Behta, Maryam; Umscheid, Craig A

2013-12-01

Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions. To develop and implement an automated prediction model integrated into our health system's EHR that identifies on admission patients at high risk for readmission within 30 days of discharge. Retrospective and prospective cohort. Healthcare system consisting of 3 hospitals. All adult patients admitted from August 2009 to September 2012. An automated readmission risk flag integrated into the EHR. Thirty-day all-cause and 7-day unplanned healthcare system readmissions. Using retrospective data, a single risk factor, ≥ 2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a C statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%), and C statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation. An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge. © 2013 Society of Hospital Medicine.

Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

PubMed Central

Skinner, James E; Meyer, Michael; Nester, Brian A; Geary, Una; Taggart, Pamela; Mangione, Antoinette; Ramalanjaona, George; Terregino, Carol; Dalsey, William C

2009-01-01

Objective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD). Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data. Methods: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria. Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001). Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test. PMID:19707283

The Number of Sexual Partners and Health-Risking Sexual Behavior: Prediction from High School Entry to High School Exit

PubMed Central

Van Ryzin, Mark J.; Johnson, Amber B.; Leve, Leslie D.; Hyoun, Kim K.

2013-01-01

Precursors to adolescent health-risking sexual behavior (HRSB) were examined in a normative sample of 373 adolescents (48.0% female, n = 178). Using a variable-oriented approach, we regressed the number of sexual partners at high school exit (age 17) on parental monitoring, association with delinquent peers, romantic relationship status, problem behavior, physical maturity, and tobacco and alcohol use at high school entry (age 14); all emerged as significant predictors except alcohol use and physical maturity (we found sex differences in physical maturity and romantic relationship status, with females being more advanced in both areas). Sexual experimentation at high school entry served to partially or fully mediate the impact of these factors. A person-oriented approach, using a broader measure of HRSB, found three subgroups of adolescents: abstainers, low-risk-takers, and high-risk-takers. Results predicting membership in these groups generally followed those from the variable-oriented analysis. Implications for the prevention of HRSB and future research directions are discussed. PMID:20703789

The number of sexual partners and health-risking sexual behavior: prediction from high school entry to high school exit.

PubMed

Van Ryzin, Mark J; Johnson, Amber B; Leve, Leslie D; Kim, Hyoun K

2011-10-01

Precursors to adolescent health-risking sexual behavior (HRSB) were examined in a normative sample of 373 adolescents (48.0% female, n = 178). Using a variable-oriented approach, we regressed the number of sexual partners at high school exit (age 17) on parental monitoring, association with delinquent peers, romantic relationship status, problem behavior, physical maturity, and tobacco and alcohol use at high school entry (age 14); all emerged as significant predictors except alcohol use and physical maturity (we found sex differences in physical maturity and romantic relationship status, with females being more advanced in both areas). Sexual experimentation at high school entry served to partially or fully mediate the impact of these factors. A person-oriented approach, using a broader measure of HRSB, found three subgroups of adolescents: abstainers, low-risk-takers, and high-risk-takers. Results predicting membership in these groups generally followed those from the variable-oriented analysis. Implications for the prevention of HRSB and future research directions are discussed.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

PubMed Central

2014-01-01

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed. PMID:24885128

Increased pulmonary alveolar-capillary permeability in patients at risk for adult respiratory distress syndrome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tennenberg, S.D.; Jacobs, M.P.; Solomkin, J.S.

1987-04-01

Two methods for predicting adult respiratory distress syndrome (ARDS) were evaluated prospectively in a group of 81 multitrauma and sepsis patients considered at clinical high risk. A popular ARDS risk-scoring method, employing discriminant analysis equations (weighted risk criteria and oxygenation characteristics), yielded a predictive accuracy of 59% and a false-negative rate of 22%. Pulmonary alveolar-capillary permeability (PACP) was determined with a radioaerosol lung-scan technique in 23 of these 81 patients, representing a statistically similar subgroup. Lung scanning achieved a predictive accuracy of 71% (after excluding patients with unilateral pulmonary contusion) and gave no false-negatives. We propose a combination of clinicalmore » risk identification and functional determination of PACP to assess a patient's risk of developing ARDS.« less

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

PubMed Central

Brenton, Ashley; Richeimer, Steven; Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Blanchard, John; Meshkin, Brian

2017-01-01

Background Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). Patients and methods The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes. PMID:28572737

Childhood physical, environmental, and genetic predictors of adult hypertension: the cardiovascular risk in young Finns study.

PubMed

Juhola, Jonna; Oikonen, Mervi; Magnussen, Costan G; Mikkilä, Vera; Siitonen, Niina; Jokinen, Eero; Laitinen, Tomi; Würtz, Peter; Gidding, Samuel S; Taittonen, Leena; Seppälä, Ilkka; Jula, Antti; Kähönen, Mika; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma S A; Juonala, Markus; Raitakari, Olli T

2012-07-24

Hypertension is a major modifiable cardiovascular risk factor. The present longitudinal study aimed to examine the best combination of childhood physical and environmental factors to predict adult hypertension and furthermore whether newly identified genetic variants for blood pressure increase the prediction of adult hypertension. The study cohort included 2625 individuals from the Cardiovascular Risk in Young Finns Study who were followed up for 21 to 27 years since baseline (1980; age, 3-18 years). In addition to dietary factors and biomarkers related to blood pressure, we examined whether a genetic risk score based on 29 newly identified single-nucleotide polymorphisms enhances the prediction of adult hypertension. Hypertension in adulthood was defined as systolic blood pressure ≥ 130 mm Hg and/or diastolic blood pressure ≥ 85 mm Hg or medication for the condition. Independent childhood risk factors for adult hypertension included the individual's own blood pressure (P<0.0001), parental hypertension (P<0.0001), childhood overweight/obesity (P=0.005), low parental occupational status (P=0.003), and high genetic risk score (P<0.0001). Risk assessment based on childhood overweight/obesity status, parental hypertension, and parental occupational status was superior in predicting hypertension compared with the approach using only data on childhood blood pressure levels (C statistics, 0.718 versus 0.733; P=0.0007). Inclusion of both parental hypertension history and data on novel genetic variants for hypertension further improved the C statistics (0.742; P=0.015). Prediction of adult hypertension was enhanced by taking into account known physical and environmental childhood risk factors, family history of hypertension, and novel genetic variants. A multifactorial approach may be useful in identifying children at high risk for adult hypertension.

Leptospirosis in American Samoa – Estimating and Mapping Risk Using Environmental Data

PubMed Central

Lau, Colleen L.; Clements, Archie C. A.; Skelly, Chris; Dobson, Annette J.; Smythe, Lee D.; Weinstein, Philip

2012-01-01

Background The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk. Methodology and Principal Findings Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases. Conclusions and Significance Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions. PMID:22666516

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

PubMed

Evans, R Scott; Benuzillo, Jose; Horne, Benjamin D; Lloyd, James F; Bradshaw, Alejandra; Budge, Deborah; Rasmusson, Kismet D; Roberts, Colleen; Buckway, Jason; Geer, Norma; Garrett, Teresa; Lappé, Donald L

2016-09-01

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Neural predictors of substance use disorders in Young adulthood.

PubMed

O'Brien, Jessica W; Hill, Shirley Y

2017-10-30

Offspring from multiplex, alcohol-dependent families are at heightened risk for substance use disorders (SUDs) in adolescence and young adulthood. These high-risk offspring have also been shown to have atypical structure and function of brain regions implicated in emotion regulation, social cognition, and reward processing. This study assessed the relationship between amygdala and orbitofrontal cortex (OFC) volumes obtained in adolescence and SUD outcomes in young adulthood among high-risk offspring and low-risk controls. A total of 78 participants (40 high-risk; 38 low-risk) from a longitudinal family study, ages 8-19, underwent magnetic resonance imaging; volumes of the amygdala and OFC were obtained with manual tracing. SUD outcomes were assessed at approximately yearly intervals. Cox regression survival analyses were used to assess the effect of regional brain volumes on SUD outcomes. The ratio of OFC to amygdala volume significantly predicted SUD survival time across the sample; reduction in survival time was seen in those with smaller ratios for both high-risk and low-risk groups. Morphology of prefrontal relative to limbic regions in adolescence prospectively predicts age of onset for substance use disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Depression and blood pressure in high-risk children and adolescents: an investigation using two longitudinal cohorts

PubMed Central

Hammerton, Gemma; Harold, Gordon; Thapar, Anita; Thapar, Ajay

2013-01-01

Objective To examine the relationship between blood pressure and depressive disorder in children and adolescents at high risk for depression. Design Multisample longitudinal design including a prospective longitudinal three-wave high-risk study of offspring of parents with recurrent depression and an on-going birth cohort for replication. Setting Community-based studies. Participants High-risk sample includes 281 families where children were aged 9–17 years at baseline and 10–19 years at the final data point. Replication cohort includes 4830 families where children were aged 11–14 years at baseline and 14–17 years at follow-up and a high-risk subsample of 612 offspring with mothers that had reported recurrent depression. Main outcome measures The new-onset of Diagnostic and Statistical Manual of Mental Disorder, fourth edition defined depressive disorder in the offspring using established research diagnostic assessments—the Child and Adolescent Psychiatric Assessment in the high-risk sample and the Development and Wellbeing Assessment in the replication sample. Results Blood pressure was standardised for age and gender to create SD scores and child's weight was statistically controlled in all analyses. In the high-risk sample, lower systolic blood pressure at wave 1 significantly predicted new-onset depressive disorder in children (OR=0.65, 95% CI 0.44 to 0.96; p=0.029) but diastolic blood pressure did not. Depressive disorder at wave 1 did not predict systolic blood pressure at wave 3. A significant association between lower systolic blood pressure and future depression was also found in the replication cohort in the second subset of high-risk children whose mothers had experienced recurrent depression in the past. Conclusions Lower systolic blood pressure predicts new-onset depressive disorder in the offspring of parents with depression. Further studies are needed to investigate how this association arises. PMID:24071459

Predictive value of CHADS2 and CHA2DS2-VASc scores for acute myocardial infarction in patients with atrial fibrillation.

PubMed

Pang, Hui; Han, Bing; Fu, Qiang; Zong, Zhenkun

2017-07-05

The presence of acute myocardial infarction (AMI) confers a poor prognosis in atrial fibrillation (AF), associated with increased mortality dramatically. This study aimed to evaluate the predictive value of CHADS 2 and CHA 2 DS 2 -VASc scores for AMI in patients with AF. This retrospective study enrolled 5140 consecutive nonvalvular AF patients, 300 patients with AMI and 4840 patients without AMI. We identified the optimal cut-off values of the CHADS 2 and CHA 2 DS 2 -VASc scores each based on receiver operating characteristic curves to predict the risk of AMI. Both CHADS 2 score and CHA 2 DS 2 -VASc score were associated with an increased odds ratio of the prevalence of AMI in patients with AF, after adjustment for hyperlipidaemia, hyperuricemia, hyperthyroidism, hypothyroidism and obstructive sleep apnea. The present results showed that the area under the curve (AUC) for CHADS 2 score was 0.787 with a similar accuracy of the CHA 2 DS 2 -VASc score (AUC 0.750) in predicting "high-risk" AF patients who developed AMI. However, the predictive accuracy of the two clinical-based risk scores was fair. The CHA 2 DS 2 -VASc score has fair predictive value for identifying high-risk patients with AF and is not significantly superior to CHADS 2 in predicting patients who develop AMI.

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

PubMed

Jamei, Mehdi; Nisnevich, Aleksandr; Wetchler, Everett; Sudat, Sylvia; Liu, Eric

2017-01-01

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks

PubMed Central

2017-01-01

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health’s EHR system, we built and tested an artificial neural network (NN) model based on Google’s TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions. PMID:28708848

Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China.

PubMed

Zhang, Han; Si, Yali; Wang, Xiaofeng; Gong, Peng

2017-07-14

Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk.

Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China

PubMed Central

Si, Yali; Gong, Peng

2017-01-01

Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk. PMID:28708077

Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

PubMed

Cherkaoui, Imad; Sabouni, Radia; Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E

2014-01-01

Public tuberculosis (TB) clinics in urban Morocco. Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

Do psychosocial work conditions predict risk of disability pensioning? An analysis of register-based outcomes using pooled data on 40,554 observations.

PubMed

Clausen, Thomas; Burr, Hermann; Borg, Vilhelm

2014-06-01

To investigate whether high psychosocial job demands (quantitative demands and work pace) and low psychosocial job resources (influence at work and quality of leadership) predicted risk of disability pensioning among employees in four occupational groups--employees working with customers, employees working with clients, office workers and manual workers--in line with the propositions of the Job Demands-Resources (JD-R) model. Survey data from 40,554 individuals were fitted to the DREAM register containing information on payments of disability pension. Using multi-adjusted Cox regression, observations were followed in the DREAM-register to assess risk of disability pensioning. Average follow-up time was 5.9 years (SD=3.0). Low levels of influence at work predicted an increased risk of disability pensioning and medium levels of quantitative demands predicted a decreased risk of disability pensioning in the study population. We found significant interaction effects between job demands and job resources as combinations low quality of leadership and high job demands predicted the highest rate of disability pensioning. Further analyses showed some, but no statistically significant, differences between the four occupational groups in the associations between job demands, job resources and risk of disability pensioning. The study showed that psychosocial job demands and job resources predicted risk of disability pensioning. The direction of some of the observed associations countered the expectations of the JD-R model and the findings of the present study therefore imply that associations between job demands, job resources and adverse labour market outcomes are more complex than conceptualised in the JD-R model. © 2014 the Nordic Societies of Public Health.

HSV-2 serology can be predictive of HIV epidemic potential and hidden sexual-risk behavior in the Middle East and North Africa

PubMed Central

Abu-Raddad, Laith J.; Schiffer, Joshua T.; Ashley, Rhoda; Mumtaz, Ghina; Alsallaq, Ramzi A.; Akala, Francisca Ayodeji; Semini, Iris; Riedner, Gabriele; Wilson, David

2013-01-01

Background HIV prevalence is low in the Middle East and North Africa (MENA) region, though the risk or potential for further spread in the future is not well understood. Behavioral surveys are limited in this region and when available have serious limitations in assessing the risk of HIV acquisition. We demonstrate the potential use of herpes simplex virus-2 (HSV-2) seroprevalence as a marker for HIV risk within MENA. Methods We designed a mathematical model to assess whether HSV-2 prevalence can be predictive of future HIV spread. We also conducted a systematic literature review of HSV-2 seroprevalence studies within MENA. Results We found that HSV-2 prevalence data are rather limited in this region. Prevalence is typically low among the general population but high in established core groups prone to sexually transmitted infections such as men who have sex with men and female sex workers. Our model predicts that if HSV-2 prevalence is low and stable, then the risk of future HIV epidemics is low. However, expanding or high HSV-2 prevalence (greater than about 20%), implies a risk for a considerable HIV epidemic. Based on available HSV-2 prevalence data, it is not likely that the general population in MENA is experiencing or will experience such a considerable HIV epidemic. Nevertheless, the risk for concentrated HIV epidemics among several high-risk core groups is high. Conclusions HSV-2 prevalence surveys provide a useful mechanism for identifying and corroborating populations at risk for HIV within MENA. HSV-2 serology offers an effective tool for probing hidden risk behaviors in a region where quality behavioral data are limited. PMID:21352788

[Open narcissism, covered narcissism and personality disorders as predictive factors of treatment response in an out-patient Drug Addiction Unit].

PubMed

Salazar-Fraile, José; Ripoll-Alandes, Carmen; Bobes, Julio

2010-01-01

Although a high prevalence of personality disorders has been reported in substance users, the literature on their value for predicting treatment response is controversial. On the other hand, while the predictive validity of personality traits as predictors of response to drug abuse or dependence has been studied, research on the validity of narcissistic personality traits is scarce. To study the predictive value of personality disorders, narcissistic personality traits and self-esteem for predicting treatment response. We assessed 78 patients attended at an addiction treatment unit using personality disorder diagnoses and measures of self-esteem, narcissism and covert (hypersensitive) narcissism. These variables were used in a Cox survival model as predictive variables of time to relapse into drug use. Hypersensitive (covert) narcissism and borderline and passive-aggressive personality disorders were risk factors for relapse into drug use, while open narcissism was a protective factor. Self-esteem did not show predictive validity. Personality disorders characterized by impulsivity-instability and passivity-resentfulness show higher risk of relapse into drug abuse. Personality traits characterized by high sensitivity to humiliation increase the risk of relapse, whereas pride and self-confidence are protective factors.

Immune status does not predict high-risk HPV in anal condyloma.

PubMed

Lee, Janet T; Goldberg, Stanley M; Madoff, Robert D; Tawadros, Patrick S

2016-03-01

More than 90% of anal condyloma is attributed to nonhigh risk strains of human papillomavirus (HPV), thus patients with anal condyloma do not necessarily undergo HPV serotyping unless they are immunocompromised (IC). We hypothesized that IC patients with anal condyloma have a higher risk of high-risk HPV and dysplasia than nonimmunocompromised (NIC) patients. We performed a retrospective chart review of patients who underwent surgical treatment by a single surgeon for anal condyloma from 1/2000 to 1/2012. HPV serotyping was performed on all patient samples. We compared incidence of high-risk HPV and dysplasia in condyloma specimens from IC and NIC patients. High-risk HPV was identified in 14 specimens with serotypes 16, 18, 31, 33, 51, 52, and 67. Twenty-two cases (18.3%) had dysplasia. Invasive carcinoma was identified in one IC patient. The prevalence of dysplasia or high-risk HPV was not significantly different between IC and NIC groups. High-risk HPV was a significant independent predictor of dysplasia (odds ratio [OR] = 5.2; 95% CI = 1.24-21.62). Immune status, however, was not a significant predictor of high-risk HPV (OR = 1.11; 95% CI = 0.16-5.12) nor dysplasia (OR = 0.27; 95% CI = 0.037-1.17). IC patients did not have a significantly higher prevalence or risk of high-risk HPV or dysplasia in our study. HPV typing of all condylomata, regardless of immune status, should be considered as it may help predict risk of neoplastic transformation or identify NIC patients with an increased risk of developing anal intraepithelial neoplasia. Copyright © 2016 Elsevier Inc. All rights reserved.

Pharmacogenomic prediction of anthracycline-induced cardiotoxicity in children.

PubMed

Visscher, Henk; Ross, Colin J D; Rassekh, S Rod; Barhdadi, Amina; Dubé, Marie-Pierre; Al-Saloos, Hesham; Sandor, George S; Caron, Huib N; van Dalen, Elvira C; Kremer, Leontien C; van der Pal, Helena J; Brown, Andrew M K; Rogers, Paul C; Phillips, Michael S; Rieder, Michael J; Carleton, Bruce C; Hayden, Michael R

2012-05-01

Anthracycline-induced cardiotoxicity (ACT) is a serious adverse drug reaction limiting anthracycline use and causing substantial morbidity and mortality. Our aim was to identify genetic variants associated with ACT in patients treated for childhood cancer. We carried out a study of 2,977 single-nucleotide polymorphisms (SNPs) in 220 key drug biotransformation genes in a discovery cohort of 156 anthracycline-treated children from British Columbia, with replication in a second cohort of 188 children from across Canada and further replication of the top SNP in a third cohort of 96 patients from Amsterdam, the Netherlands. We identified a highly significant association of a synonymous coding variant rs7853758 (L461L) within the SLC28A3 gene with ACT (odds ratio, 0.35; P = 1.8 × 10(-5) for all cohorts combined). Additional associations (P < .01) with risk and protective variants in other genes including SLC28A1 and several adenosine triphosphate-binding cassette transporters (ABCB1, ABCB4, and ABCC1) were present. We further explored combining multiple variants into a single-prediction model together with clinical risk factors and classification of patients into three risk groups. In the high-risk group, 75% of patients were accurately predicted to develop ACT, with 36% developing this within the first year alone, whereas in the low-risk group, 96% of patients were accurately predicted not to develop ACT. We have identified multiple genetic variants in SLC28A3 and other genes associated with ACT. Combined with clinical risk factors, genetic risk profiling might be used to identify high-risk patients who can then be provided with safer treatment options.

Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients.

PubMed

Antic, Darko; Milic, Natasa; Nikolovski, Srdjan; Todorovic, Milena; Bila, Jelena; Djurdjevic, Predrag; Andjelic, Bosko; Djurasinovic, Vladislava; Sretenovic, Aleksandra; Vukovic, Vojin; Jelicic, Jelena; Hayman, Suzanne; Mihaljevic, Biljana

2016-10-01

Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score, the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study.

PubMed

Kendell, Michelle; Beales, Darren; O'Sullivan, Peter; Rabey, Martin; Hill, Jonathan; Smith, Anne

2018-04-01

In people with chronic non-specific low back pain (LBP), what is the predictive and discriminative validity of the STarT Back Tool (SBT) for pain intensity, self-reported LBP-related disability, and global self-perceived change at 1-year follow-up? What is the profile of the SBT risk subgroups with respect to demographic variables, pain intensity, self-reported LBP-related disability, and psychological measures? Prospective cohort study. A total of 290 adults with dominant axial LBP of≥3months' duration recruited from the general community, and private physiotherapy, psychology, and pain-management clinics in Western Australia. The 1-year follow-up measures were pain intensity, LBP-related disability, and global self-perceived change. Outcomes were collected on 264 participants. The SBT categorised 82 participants (28%) as low risk, 116 (40%) as medium risk, and 92 (32%) as high risk. The risk subgroups differed significantly (p<0.05) on baseline pain, disability, and psychological scores. The SBT's predictive ability was strongest for disability: RR was 2.30 (95% CI 1.28 to 4.10) in the medium-risk group and 2.86 (95% CI 1.60 to 5.11) in the high-risk group. The SBT's predictive ability was weaker for pain: RR was 1.25 (95% CI 1.04 to 1.51) in the medium-risk group and 1.26 (95% CI 1.03 to 1.52) in the high-risk group. For the SBT total score, the AUC was 0.71 (95% CI 0.64 to 0.77) for disability and 0.63 (95% CI 0.55 to 0.71) for pain. This was the first large study to investigate the SBT in a population exclusively with chronic LBP. The SBT provided an acceptable indication of 1-year disability, had poor predictive and discriminative ability for future pain, and was unable to predict or discriminate global perceived change. In this cohort with chronic non-specific LBP, the SBT's predictive and discriminative abilities were restricted to disability at 1year. [Kendell M, Beales D, O'Sullivan P, Rabey M, Hill J, Smith A (2018) The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study. Journal of Physiotherapy 64: 107-113]. Copyright © 2018 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ku, Ja Hyeon; Kim, Myong; Jeong, Chang Wook

2014-08-01

Purpose: To evaluate the predictive accuracy and general applicability of the locoregional failure model in a different cohort of patients treated with radical cystectomy. Methods and Materials: A total of 398 patients were included in the analysis. Death and isolated distant metastasis were considered competing events, and patients without any events were censored at the time of last follow-up. The model included the 3 variables pT classification, the number of lymph nodes identified, and margin status, as follows: low risk (≤pT2), intermediate risk (≥pT3 with ≥10 nodes removed and negative margins), and high risk (≥pT3 with <10 nodes removed ormore » positive margins). Results: The bootstrap-corrected concordance index of the model 5 years after radical cystectomy was 66.2%. When the risk stratification was applied to the validation cohort, the 5-year locoregional failure estimates were 8.3%, 21.2%, and 46.3% for the low-risk, intermediate-risk, and high-risk groups, respectively. The risk of locoregional failure differed significantly between the low-risk and intermediate-risk groups (subhazard ratio [SHR], 2.63; 95% confidence interval [CI], 1.35-5.11; P<.001) and between the low-risk and high-risk groups (SHR, 4.28; 95% CI, 2.17-8.45; P<.001). Although decision curves were appropriately affected by the incidence of the competing risk, decisions about the value of the models are not likely to be affected because the model remains of value over a wide range of threshold probabilities. Conclusions: The model is not completely accurate, but it demonstrates a modest level of discrimination, adequate calibration, and meaningful net benefit gain for prediction of locoregional failure after radical cystectomy.« less

Validation of the Retinal Detachment after Open Globe Injury (RD-OGI) Score as an Effective Tool for Predicting Retinal Detachment.

PubMed

Brodowska, Katarzyna; Stryjewski, Tomasz P; Papavasileiou, Evangelia; Chee, Yewlin E; Eliott, Dean

2017-05-01

The Retinal Detachment after Open Globe Injury (RD-OGI) Score is a clinical prediction model that was developed at the Massachusetts Eye and Ear Infirmary to predict the risk of retinal detachment (RD) after open globe injury (OGI). This study sought to validate the RD-OGI Score in an independent cohort of patients. Retrospective cohort study. The predictive value of the RD-OGI Score was evaluated by comparing the original RD-OGI Scores of 893 eyes with OGI that presented between 1999 and 2011 (the derivation cohort) with 184 eyes with OGI that presented from January 1, 2012, to January 31, 2014 (the validation cohort). Three risk classes (low, moderate, and high) were created and logistic regression was undertaken to evaluate the optimal predictive value of the RD-OGI Score. A Kaplan-Meier survival analysis evaluated survival experience between the risk classes. Time to RD. At 1 year after OGI, 255 eyes (29%) in the derivation cohort and 66 eyes (36%) in the validation cohort were diagnosed with an RD. At 1 year, the low risk class (RD-OGI Scores 0-2) had a 3% detachment rate in the derivation cohort and a 0% detachment rate in the validation cohort, the moderate risk class (RD-OGI Scores 2.5-4.5) had a 29% detachment rate in the derivation cohort and a 35% detachment rate in the validation cohort, and the high risk class (RD-OGI scores 5-7.5) had a 73% detachment rate in the derivation cohort and an 86% detachment rate in the validation cohort. Regression modeling revealed the RD-OGI to be highly discriminative, especially 30 days after injury, with an area under the receiver operating characteristic curve of 0.939 in the validation cohort. Survival experience was significantly different depending upon the risk class (P < 0.0001, log-rank chi-square). The RD-OGI Score can reliably predict the future risk of developing an RD based on clinical variables that are present at the time of the initial evaluation after OGI. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Tools for assessing fall risk in the elderly: a systematic review and meta-analysis.

PubMed

Park, Seong-Hi

2018-01-01

The prevention of falls among the elderly is arguably one of the most important public health issues in today's aging society. The aim of this study was to assess which tools best predict the risk of falls in the elderly. Electronic searches were performed using Medline, EMBASE, the Cochrane Library, CINAHL, etc., using the following keywords: "fall risk assessment", "elderly fall screening", and "elderly mobility scale". The QUADAS-2 was applied to assess the internal validity of the diagnostic studies. Selected studies were meta-analyzed with MetaDisc 1.4. A total of 33 studies were eligible out of the 2,321 studies retrieved from selected databases. Twenty-six assessment tools for fall risk were used in the selected articles, and they tended to vary based on the setting. The fall risk assessment tools currently used for the elderly did not show sufficiently high predictive validity for differentiating high and low fall risks. The Berg Balance scale and Mobility Interaction Fall chart showed stable and high specificity, while the Downton Fall Risk Index, Hendrich II Fall Risk Model, St. Thomas's Risk Assessment Tool in Falling elderly inpatients, Timed Up and Go test, and Tinetti Balance scale showed the opposite results. We concluded that rather than a single measure, two assessment tools used together would better evaluate the characteristics of falls by the elderly that can occur due to a multitude of factors and maximize the advantages of each for predicting the occurrence of falls.

Risk factors for persistent cervical intraepithelial neoplasia grades 1 and 2: managed by watchful waiting.

PubMed

Ho, Gloria Y F; Einstein, Mark H; Romney, Seymour L; Kadish, Anna S; Abadi, Maria; Mikhail, Magdy; Basu, Jayasri; Thysen, Benjamin; Reimers, Laura; Palan, Prabhudas R; Trim, Shelly; Soroudi, Nafisseh; Burk, Robert D

2011-10-01

: This study examines risk factors for persistent cervical intraepithelial neoplasia (CIN) and examines whether human papillomavirus (HPV) testing predicts persistent lesions. : Women with histologically diagnosed CIN 1 or CIN 2 (n = 206) were followed up every 3 months without treatment. Human papillomavirus genotyping, plasma levels of ascorbic acid, and red blood cell folate levels were obtained. Cervical biopsy at 12 months determined the presence of CIN. Relative risk (RR) was estimated by log-linked binomial regression models. : At 12 months, 70% of CIN 1 versus 54% of CIN 2 lesions spontaneously regressed (p < .001). Levels of folate or ascorbic acid were not associated with persistent CIN at 12 months. Compared with HPV-negative women, those with multiple HPV types (RRs ranged from 1.68 to 2.17 at each follow-up visit) or high-risk types (RRs range = 1.74-2.09) were at increased risk for persistent CIN; women with HPV-16/18 had the highest risk (RRs range = 1.91-2.21). Persistent infection with a high-risk type was also associated with persistent CIN (RRs range = 1.50-2.35). Typing for high-risk HPVs at 6 months only had a sensitivity of 46% in predicting persistence of any lesions at 12 months. : Spontaneous regression of CIN 1 and 2 occurs frequently within 12 months. Human papillomavirus infection is the major risk factor for persistent CIN. However, HPV testing cannot reliably predict persistence of any lesion.

Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

PubMed

Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

2007-01-01

Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

Disrupted latent inhibition in individuals at ultra high-risk for developing psychosis.

PubMed

Kraus, Michael; Rapisarda, Attilio; Lam, Max; Thong, Jamie Y J; Lee, Jimmy; Subramaniam, Mythily; Collinson, Simon L; Chong, Siow Ann; Keefe, Richard S E

2016-12-01

The addition of off-the-shelf cognitive measures to established prodromal criteria has resulted in limited improvement in the prediction of conversion to psychosis. Tests that assess cognitive processes central to schizophrenia might better identify those at highest risk. The latent inhibition paradigm assesses a subject's tendency to ignore irrelevant stimuli, a process integral to healthy perceptual and cognitive function that has been hypothesized to be a key deficit underlying the development of schizophrenia. In this study, 142 young people at ultra high-risk for developing psychosis and 105 controls were tested on a within-subject latent inhibition paradigm. Additionally, we later inquired about the strategy that each subject employed to complete the test, and further investigated the relationship between reported strategy and the extent of latent inhibition exhibited. Unlike controls, ultra high-risk subjects did not demonstrate a significant latent inhibition effect. This difference between groups became greater when controlling for strategy. The lack of latent inhibition effect in our ultra high-risk sample suggests that individuals at ultra high-risk for psychosis are impaired in their allocation of attentional resources based on past predictive value of repeated stimuli. This fundamental deficit in the allocation of attention may contribute to the broader array of cognitive impairments and clinical symptoms displayed by individuals at ultra high-risk for psychosis.

High-sensitive factor I and C-reactive protein based biomarkers for coronary artery disease.

PubMed

Zhao, Qing; Du, Jian-Shi; Han, Dong-Mei; Ma, Ying

2014-01-01

An analysis of high-sensitive factor I and C-reactive proteins as biomarkers for coronary artery disease has been performed from 19 anticipated cohort studies that included 21,567 participants having no information about coronary artery disease. Besides, the clinical implications of statin therapy initiated due to assessment of factor I and C-reactive proteins have also been modeled during studies. The measure of risk discrimination (C-index) was increased (by 0.0101) as per the prognostic model for coronary artery disease with respect to sex, smoking status, age, blood pressure, total cholesterol level along with diabetic history characteristic parameters. The C-index was further raised by 0.0045 and 0.0053 when factor I and C-reactive proteins based information were added, respectively which finally predicted 10-year risk categories as: high (> 20%), medium (10% to < 20%), and low (< 10%) risks. We found 2,254 persons (among 15,000 adults (age ≥ 45 years)) would initially be classified as being at medium risk for coronary artery disease when only conventional risk factors were used as calculated risk. Besides, persons with a predicted risk of more than 20% as well as for persons suffering from other risk factors (i.e. diabetes), statin therapy was initiated (irrespective of their decade old predicted risk). We conclude that under current treatment guidelines assessment of factor I and C-reactive proteins levels (as biomarker) in people at medium risk for coronary artery disease could prevent one additional coronary artery disease risk over a period a decade for every 390-500 people screened.

Serotonin transporter genotype, salivary cortisol, neuroticism and life events: impact on subsequent psychopathology in healthy twins at high and low risk for affective disorder.

PubMed

Vinberg, Maj; Miskowiak, Kamilla; Kessing, Lars Vedel

2014-01-03

To investigate if cortisol alone or in interaction with other risk factors (familial risk, the serotonin transporter genotype, neuroticism and life events (LEs)) predicts onset of psychiatric disorder in healthy individuals at heritable risk. In a high-risk study, 234 healthy monozygotic and dizygotic twins with or without a co-twin history of affective disorder (high and low risk twins) were baseline assessed. Participants were followed up for seven years and then reassessed with a personal interview revealing whether they had developed psychiatric illness. 36 participants (15.4%) developed psychiatric disorder. Using Cox proportional hazards ratio (HR) estimates neither morning nor evening salivary cortisol at baseline did predict illness onset. In multivariate Cox models, the two-way interaction between morning cortisol and LEs lifetime before baseline was significantly associated with onset. Further, the HR of onset was higher concerning individuals carrying the short allele of the 5-HTTPLR and having experienced more LEs lifetime. Familial risk for affective disorder predicted illness and the risk of onset was further increased in individuals at familial risk carrying the short allele of the 5-HTTPLR. Cortisol levels alone do not increase the risk of onset of psychiatric illness but the interaction of a lower cortisol level and the experience of more LEs do. The 5-HTTLPR genotype seems to interact and contribute to increased stress vulnerability in combination with other stress indicators of illness thereby adding to the risk of subsequent psychopathology. © 2013.

Long-term cortisol measures predict Alzheimer disease risk.

PubMed

Ennis, Gilda E; An, Yang; Resnick, Susan M; Ferrucci, Luigi; O'Brien, Richard J; Moffat, Scott D

2017-01-24

To examine whether long-term measures of cortisol predict Alzheimer disease (AD) risk. We used a prospective longitudinal design to examine whether cortisol dysregulation was related to AD risk. Participants were from the Baltimore Longitudinal Study of Aging (BLSA) and submitted multiple 24-hour urine samples over an average interval of 10.56 years. Urinary free cortisol (UFC) and creatinine (Cr) were measured, and a UFC/Cr ratio was calculated to standardize UFC. To measure cortisol regulation, we used within-person UFC/Cr level (i.e., within-person mean), change in UFC/Cr over time (i.e., within-person slope), and UFC/Cr variability (i.e., within-person coefficient of variation). Cox regression was used to assess whether UFC/Cr measures predicted AD risk. UFC/Cr level and UFC/Cr variability, but not UFC/Cr slope, were significant predictors of AD risk an average of 2.9 years before AD onset. Elevated UFC/Cr level and elevated UFC/Cr variability were related to a 1.31- and 1.38-times increase in AD risk, respectively. In a sensitivity analysis, increased UFC/Cr level and increased UFC/Cr variability predicted increased AD risk an average of 6 years before AD onset. Cortisol dysregulation as manifested by high UFC/Cr level and high UFC/Cr variability may modulate the downstream clinical expression of AD pathology or be a preclinical marker of AD. © 2016 American Academy of Neurology.

High serum total cholesterol is a long-term cause of osteoporotic fracture.

PubMed

Trimpou, P; Odén, A; Simonsson, T; Wilhelmsen, L; Landin-Wilhelmsen, K

2011-05-01

Risk factors for osteoporotic fractures were evaluated in 1,396 men and women for a period of 20 years. Serum total cholesterol was found to be an independent osteoporotic fracture risk factor whose predictive power improves with time. The purpose of this study was to evaluate long-term risk factors for osteoporotic fracture. A population random sample of men and women aged 25-64 years (the Gothenburg WHO MONICA project, N = 1,396, 53% women) was studied prospectively. The 1985 baseline examination recorded physical activity at work and during leisure time, psychological stress, smoking habits, coffee consumption, BMI, waist/hip ratio, blood pressure, total, HDL and LDL cholesterol, triglycerides, and fibrinogen. Osteoporotic fractures over a period of 20 years were retrieved from the Gothenburg hospital registers. Poisson regression was used to analyze the predictive power for osteoporotic fracture of each risk factor. A total number of 258 osteoporotic fractures occurred in 143 participants (10.2%). As expected, we found that previous fracture, smoking, coffee consumption, and lower BMI each increase the risk for osteoporotic fracture independently of age and sex. More unexpectedly, we found that the gradient of risk of serum total cholesterol to predict osteoporotic fracture significantly increases over time (p = 0.0377). Serum total cholesterol is an independent osteoporotic fracture risk factor whose predictive power improves with time. High serum total cholesterol is a long-term cause of osteoporotic fracture.

Multidimensional Assessment of Criminal Recidivism: Problems, Pitfalls, and Proposed Solutions

ERIC Educational Resources Information Center

Vrieze, Scott I.; Grove, William M.

2010-01-01

All states have statutes in place to civilly commit individuals at high risk for violence. The authors address difficulties in assessing such risk but use as an example the task of predicting sexual violence recidivism; the principles espoused here generalize to predicting all violence. As part of the commitment process, mental health…

Assessing the reliability, predictive and construct validity of historical, clinical and risk management-20 (HCR-20) in Mexican psychiatric inpatients.

PubMed

Sada, Andrea; Robles-García, Rebeca; Martínez-López, Nicolás; Hernández-Ramírez, Rafael; Tovilla-Zarate, Carlos-Alfonso; López-Munguía, Fernando; Suárez-Alvarez, Enrique; Ayala, Xochitl; Fresán, Ana

2016-08-01

Assessing dangerousness to gauge the likelihood of future violent behaviour has become an integral part of clinical mental health practice in forensic and non-forensic psychiatric settings, one of the most effective instruments for this being the Historical, Clinical and Risk Management-20 (HCR-20). To examine the HCR-20 factor structure in Mexican psychiatric inpatients and to obtain its predictive validity and reliability for use in this population. In total, 225 patients diagnosed with psychotic, affective or personality disorders were included. The HCR-20 was applied at hospital admission and violent behaviours were assessed during psychiatric hospitalization using the Overt Aggression Scale (OAS). Construct validity, predictive validity and internal consistency were determined. Violent behaviour remains more severe in patients classified in the high-risk group during hospitalization. Fifteen items displayed adequate communalities in the original designated domains of the HCR-20 and internal consistency of the instruments was high. The HCR-20 is a suitable instrument for predicting violence risk in Mexican psychiatric inpatients.

Recent Advances in Understanding the Personality Underpinnings of Impulsive Behavior and their Role in Risk for Addictive Behaviors

PubMed Central

Birkley, Erica L.; Smith, Gregory T.

2013-01-01

Impulsivity has been a widely explored construct, particularly as a personality-based risk factor for addictive behaviors. The authors review evidence that (a) there is no single impulsivity trait; rather, there are at least five different personality traits that dispose individuals to rash or impulsive action; (b) the five traits predict different behaviors longitudinally; for example, the emotion-based urgency traits predict problematic involvement in several risky behaviors and sensation seeking instead predicts the frequency of engaging in such behaviors; (c) the traits can be measured in pre-adolescent children; (d) individual differences in the traits among preadolescent children predict the subsequent onset of, and increases in, risky behaviors including alcohol use; (e) the traits may operate by biasing the learning process, such that high-risk traits make high-risk learning more likely, thus leading to maladaptive behavior; (f) the emotion-based urgency traits may contribute to compulsive engagement in addictive behaviors; and (g) there is evidence that different interventions are appropriate for the different trait structures. PMID:22126707

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers

PubMed Central

Street, Amy E.; Rosellini, Anthony J.; Ursano, Robert J.; Heeringa, Steven G.; Hill, Eric D.; Monahan, John; Naifeh, James A.; Petukhova, Maria V.; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul D.; Stein, Murray B.; Zaslavsky, Alan M.; Kessler, Ronald C.

2016-01-01

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83−.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks. PMID:28154788

High gain signal averaged electrocardiogram combined with 24 hour monitoring in patients early after myocardial infarction for bedside prediction of arrhythmic events.

PubMed Central

Cripps, T; Bennett, E D; Camm, A J; Ward, D E

1988-01-01

The value of the high gain, signal averaged electrocardiogram combined with 24 hour electrocardiographic monitoring in the prediction of arrhythmic events was assessed in 159 patients in the first week after myocardial infarction. Eleven patients (7%) suffered arrhythmic events during a mean (SD) of 12 (6) months of follow up (range 2-22, median 13 months). The combination of high gain, signal averaged electrocardiography and 24 hour electrocardiographic monitoring was more accurate than either technique alone or than clinical information collected during admission in predicting these events. The combination identified a high risk group of 13 (8%) patients, with an arrhythmic event rate of 62% and a low risk group with an event rate of 2%. The combination of high gain, signal averaged electrocardiography and 24 hour electrocardiographic monitoring in the first week after myocardial infarction provides a rapid, cheap, and non-invasive bedside method for the prediction of arrhythmias. PMID:3179133

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Identification of men with low-risk biopsy-confirmed prostate cancer as candidates for active surveillance.

PubMed

Lin, Daniel W; Crawford, E David; Keane, Thomas; Evans, Brent; Reid, Julia; Rajamani, Saradha; Brown, Krystal; Gutin, Alexander; Tward, Jonathan; Scardino, Peter; Brawer, Michael; Stone, Steven; Cuzick, Jack

2018-06-01

A combined clinical cell-cycle risk (CCR) score that incorporates prognostic molecular and clinical information has been recently developed and validated to improve prostate cancer mortality (PCM) risk stratification over clinical features alone. As clinical features are currently used to select men for active surveillance (AS), we developed and validated a CCR score threshold to improve the identification of men with low-risk disease who are appropriate for AS. The score threshold was selected based on the 90th percentile of CCR scores among men who might typically be considered for AS based on NCCN low/favorable-intermediate risk criteria (CCR = 0.8). The threshold was validated using 10-year PCM in an unselected, conservatively managed cohort and in the subset of the same cohort after excluding men with high-risk features. The clinical effect was evaluated in a contemporary clinical cohort. In the unselected validation cohort, men with CCR scores below the threshold had a predicted mean 10-year PCM of 2.7%, and the threshold significantly dichotomized low- and high-risk disease (P = 1.2 × 10 -5 ). After excluding high-risk men from the validation cohort, men with CCR scores below the threshold had a predicted mean 10-year PCM of 2.3%, and the threshold significantly dichotomized low- and high-risk disease (P = 0.020). There were no prostate cancer-specific deaths in men with CCR scores below the threshold in either analysis. The proportion of men in the clinical testing cohort identified as candidates for AS was substantially higher using the threshold (68.8%) compared to clinicopathologic features alone (42.6%), while mean 10-year predicted PCM risks remained essentially identical (1.9% vs. 2.0%, respectively). The CCR score threshold appropriately dichotomized patients into low- and high-risk groups for 10-year PCM, and may enable more appropriate selection of patients for AS. Copyright © 2018 Elsevier Inc. All rights reserved.

Validity of the Framingham point scores in the elderly: results from the Rotterdam study.

PubMed

Koller, Michael T; Steyerberg, Ewout W; Wolbers, Marcel; Stijnen, Theo; Bucher, Heiner C; Hunink, M G Myriam; Witteman, Jacqueline C M

2007-07-01

The National Cholesterol Education Program recommends assessing 10-year risk of coronary heart disease (CHD) in individuals free of established CHD with the Framingham Point Scores (FPS). Individuals with a risk >20% are classified as high risk and are candidates for preventive intervention. We aimed to validate the FPS in a European population of elderly subjects. Subjects free of established CHD at baseline were selected from the Rotterdam study, a population-based cohort of subjects 55 years or older in The Netherlands. We studied calibration, discrimination (c-index), and the accuracy of high-risk classifications. Events consisted of fatal CHD and nonfatal myocardial infarction. Among 6795 subjects, 463 died because of CHD and 336 had nonfatal myocardial infarction. Predicted 10-year risk of CHD was on average well calibrated for women (9.9% observed vs 10.1% predicted) but showed substantial overestimation in men (14.3% observed vs 19.8% predicted), particularly with increasing age. This resulted in substantial number of false-positive classifications (specificity 70%) in men. In women, discrimination of the FPS was better than that in men (c-index 0.73 vs 0.63, respectively). However, because of the low baseline risk of CHD and limited discriminatory power, only 33% of all CHD events occurred in women classified as high risk. The FPS need recalibration for elderly men with better incorporation of the effect of age. In elderly women, FPS perform reasonably well. However, maintaining the rational of the high-risk threshold requires better performing models for a population with low incidence of CHD.

Assessing the Clinical Role of Genetic Markers of Early-Onset Prostate Cancer Among High-Risk Men Enrolled in Prostate Cancer Early Detection

PubMed Central

Hughes, Lucinda; Zhu, Fang; Ross, Eric; Gross, Laura; Uzzo, Robert G.; Chen, David Y. T.; Viterbo, Rosalia; Rebbeck, Timothy R.; Giri, Veda N.

2011-01-01

Background Men with familial prostate cancer (PCA) and African American men are at risk for developing PCA at younger ages. Genetic markers predicting early-onset PCA may provide clinically useful information to guide screening strategies for high-risk men. We evaluated clinical information from six polymorphisms associated with early-onset PCA in a longitudinal cohort of high-risk men enrolled in PCA early detection with significant African American participation. Methods Eligibility criteria include ages 35–69 with a family history of PCA or African American race. Participants undergo screening and biopsy per study criteria. Six markers associated with early-onset PCA (rs2171492 (7q32), rs6983561 (8q24), rs10993994 (10q11), rs4430796 (17q12), rs1799950 (17q21), and rs266849 (19q13)) were genotyped. Cox models were used to evaluate time to PCA diagnosis and PSA prediction for PCA by genotype. Harrell’s concordance index was used to evaluate predictive accuracy for PCA by PSA and genetic markers. Results 460 participants with complete data and ≥1 follow-up visit were included. 56% were African American. Among African American men, rs6983561 genotype was significantly associated with earlier time to PCA diagnosis (p=0.005) and influenced prediction for PCA by the PSA (p<0.001). When combined with PSA, rs6983561 improved predictive accuracy for PCA compared to PSA alone among African American men (PSA= 0.57 vs. PSA+rs6983561=0.75, p=0.03). Conclusions Early-onset marker rs6983561 adds potentially useful clinical information for African American men undergoing PCA risk assessment. Further study is warranted to validate these findings. Impact Genetic markers of early-onset PCA have potential to refine and personalize PCA early detection for high-risk men. PMID:22144497

Clinical Predictive Models for Chemotherapy-Induced Febrile Neutropenia in Breast Cancer Patients: A Validation Study

PubMed Central

Zhu, Liling; Su, Fengxi; Jia, Weijuan; Deng, Xiaogeng

2014-01-01

Background Predictive models for febrile neutropenia (FN) would be informative for physicians in clinical decision making. This study aims to validate a predictive model (Jenkin’s model) that comprises pretreatment hematological parameters in early-stage breast cancer patients. Patients and Methods A total of 428 breast cancer patients who received neoadjuvant/adjuvant chemotherapy without any prophylactic use of colony-stimulating factor were included. Pretreatment absolute neutrophil counts (ANC) and absolute lymphocyte counts (ALC) were used by the Jenkin’s model to assess the risk of FN. In addition, we modified the threshold of Jenkin’s model and generated Model-A and B. We also developed Model-C by incorporating the absolute monocyte count (AMC) as a predictor into Model-A. The rates of FN in the 1st chemotherapy cycle were calculated. A valid model should be able to significantly identify high-risk subgroup of patients with FN rate >20%. Results Jenkin’s model (Predicted as high-risk when ANC≦3.1*10∧9/L;ALC≦1.5*10∧9/L) did not identify any subgroups with significantly high risk (>20%) of FN in our population, even if we used different thresholds in Model-A(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L) or B(ANC≦3.8*10∧9/L;ALC≦1.8*10∧9/L). However, with AMC added as an additional predictor, Model-C(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L; AMC≦0.28*10∧9/L) identified a subgroup of patients with a significantly high risk of FN (23.1%). Conclusions In our population, Jenkin’s model, cannot accurately identify patients with a significant risk of FN. The threshold should be changed and the AMC should be incorporated as a predictor, to have excellent predictive ability. PMID:24945817

A Prospective Examination of the Interpersonal-Psychological Theory of Suicidal Behavior Among Psychiatric Adolescent Inpatients

PubMed Central

Czyz, Ewa K.; Berona, Johnny; King, Cheryl A.

2016-01-01

The challenge of identifying suicide risk in adolescents, and particularly among high-risk subgroups such as adolescent inpatients, calls for further study of models of suicidal behavior that could meaningfully aid in the prediction of risk. This study examined how well the Interpersonal-Psychological Theory of Suicidal Behavior (IPTS)—with its constructs of thwarted belongingness (TB), perceived burdensomeness (PB), and an acquired capability (AC) for lethal self-injury—predicts suicide attempts among adolescents (N = 376) 3 and 12 months after hospitalization. The three-way interaction between PB, TB, and AC, defined as a history of multiple suicide attempts, was not significant. However, there were significant 2-way interaction effects, which varied by sex: girls with low AC and increasing TB, and boys with high AC and increasing PB, were more likely to attempt suicide at 3 months. Only high AC predicted 12-month attempts. Results suggest gender-specific associations between theory components and attempts. The time-limited effects of these associations point to TB and PB being dynamic and modifiable in high-risk populations, whereas the effects of AC are more lasting. The study also fills an important gap in existing research by examining IPTS prospectively. PMID:25263410

A Prospective Examination of the Interpersonal-Psychological Theory of Suicidal Behavior Among Psychiatric Adolescent Inpatients

PubMed Central

Czyz, Ewa K.; Berona, Johnny; King, Cheryl A.

2016-01-01

The challenge of identifying suicide risk in adolescents, and particularly among high-risk subgroups such as adolescent inpatients, calls for further study of models of suicidal behavior that could meaningfully aid in the prediction of risk. This study examined how well the Interpersonal-Psychological Theory of Suicidal Behavior (IPTS)—with its constructs of thwarted belongingness (TB), perceived burdensomeness (PB), and an acquired capability (AC) for lethal self-injury—predicts suicide attempts among adolescents (N = 376) 3 and 12 months after hospitalization. The three-way interaction between PB, TB, and AC, defined as a history of multiple suicide attempts, was not significant. However, there were significant 2-way interaction effects, which varied by sex: girls with low AC and increasing TB, and boys with high AC and increasing PB, were more likely to attempt suicide at 3 months. Only high AC predicted 12-month attempts. Results suggest gender-specific associations between theory components and attempts. The time-limited effects of these associations point to TB and PB being dynamic and modifiable in high-risk populations, whereas the effects of AC are more lasting. The study also fills an important gap in existing research by examining IPTS prospectively. PMID:26872965

Risk of Acute Liver Failure in Patients With Drug-Induced Liver Injury: Evaluation of Hy's Law and a New Prognostic Model.

PubMed

Lo Re, Vincent; Haynes, Kevin; Forde, Kimberly A; Goldberg, David S; Lewis, James D; Carbonari, Dena M; Leidl, Kimberly B F; Reddy, K Rajender; Nezamzadeh, Melissa S; Roy, Jason; Sha, Daohang; Marks, Amy R; De Boer, Jolanda; Schneider, Jennifer L; Strom, Brian L; Corley, Douglas A

2015-12-01

Few studies have evaluated the ability of laboratory tests to predict risk of acute liver failure (ALF) among patients with drug-induced liver injury (DILI). We aimed to develop a highly sensitive model to identify DILI patients at increased risk of ALF. We compared its performance with that of Hy's Law, which predicts severity of DILI based on levels of alanine aminotransferase or aspartate aminotransferase and total bilirubin, and validated the model in a separate sample. We conducted a retrospective cohort study of 15,353 Kaiser Permanente Northern California members diagnosed with DILI from 2004 through 2010, liver aminotransferase levels above the upper limit of normal, and no pre-existing liver disease. Thirty ALF events were confirmed by medical record review. Logistic regression was used to develop prognostic models for ALF based on laboratory results measured at DILI diagnosis. External validation was performed in a sample of 76 patients with DILI at the University of Pennsylvania. Hy's Law identified patients that developed ALF with a high level of specificity (0.92) and negative predictive value (0.99), but low level of sensitivity (0.68) and positive predictive value (0.02). The model we developed, comprising data on platelet count and total bilirubin level, identified patients with ALF with a C statistic of 0.87 (95% confidence interval [CI], 0.76-0.96) and enabled calculation of a risk score (Drug-Induced Liver Toxicity ALF Score). We found a cut-off score that identified patients at high risk patients for ALF with a sensitivity value of 0.91 (95% CI, 0.71-0.99) and a specificity value of 0.76 (95% CI, 0.75-0.77). This cut-off score identified patients at high risk for ALF with a high level of sensitivity (0.89; 95% CI, 0.52-1.00) in the validation analysis. Hy's Law identifies patients with DILI at high risk for ALF with low sensitivity but high specificity. We developed a model (the Drug-Induced Liver Toxicity ALF Score) based on platelet count and total bilirubin level that identifies patients at increased risk for ALF with high sensitivity. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network

PubMed Central

AMINI, Payam; AHMADINIA, Hasan; POOROLAJAL, Jalal; MOQADDASI AMIRI, Mohammad

2016-01-01

Background: We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM). Methods: We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated. Results: Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. PMID:27957463

Predictive factors for poor prognosis febrile neutropenia.

PubMed

Ahn, Shin; Lee, Yoon-Seon

2012-07-01

Most patients with chemotherapy-induced febrile neutropenia recover rapidly without serious complications. However, it still remains a life-threatening treatment-related toxicity, and is associated with dose reductions and delays of chemotherapeutic agents that may compromise treatment outcomes. Recent developments of risk stratification enabled early discharge with oral antibiotics for low-risk patients. However, even in low-risk patients, medical complications including bacteremia could happen. The authors reviewed recent literature to provide an update on research regarding predictive factors for poor prognosis in patients with febrile neutropenia. Various prognostic factors have been suggested with controversies. Hematological parameters, prophylactic measurements and patient-specific risk factors showed inconsistent results. MASCC risk-index score, which was originally developed to identify low-risk patients, in turn showed that the lower the MASCC score, the poorer the prognosis of febrile neutropenia, with very low levels (<15), the rate of complications was high. Patients with severe sepsis and septic shock commonly had procalcitonin concentration above 2.0 ng/ml, and this level should be considered at high risk of poor prognosis. Lower MASCC score and higher procalcitonin concentration can predict poor outcomes in febrile neutropenia. More research is required with regard to the other factors showing controversies.

Correlates of tuberculosis risk: predictive biomarkers for progression to active tuberculosis

PubMed Central

Petruccioli, Elisa; Scriba, Thomas J.; Petrone, Linda; Hatherill, Mark; Cirillo, Daniela M.; Joosten, Simone A.; Ottenhoff, Tom H.; Denkinger, Claudia M.; Goletti, Delia

2016-01-01

New approaches to control the spread of tuberculosis (TB) are needed, including tools to predict development of active TB from latent TB infection (LTBI). Recent studies have described potential correlates of risk, in order to inform the development of prognostic tests for TB disease progression. These efforts have included unbiased approaches employing “omics” technologies, as well as more directed, hypothesis-driven approaches assessing a small set or even individual selected markers as candidate correlates of TB risk. Unbiased high-throughput screening of blood RNAseq profiles identified signatures of active TB risk in individuals with LTBI, ≥1 year before diagnosis. A recent infant vaccination study identified enhanced expression of T-cell activation markers as a correlate of risk prior to developing TB; conversely, high levels of Ag85A antibodies and high frequencies of interferon (IFN)-γ specific T-cells were associated with reduced risk of disease. Others have described CD27−IFN-γ+CD4+ T-cells as possibly predictive markers of TB disease. T-cell responses to TB latency antigens, including heparin-binding haemagglutinin and DosR-regulon-encoded antigens have also been correlated with protection. Further studies are needed to determine whether correlates of risk can be used to prevent active TB through targeted prophylactic treatment, or to allow targeted enrolment into efficacy trials of new TB vaccines and therapeutic drugs. PMID:27836953

Combined use of endometrial sample and magnetic resonance imaging in the preoperative risk-stratification of endometrial carcinomas.

PubMed

Luomaranta, Anna; Bützow, Ralf; Pauna, Arja-Riitta; Leminen, Arto; Loukovaara, Mikko

2015-01-01

To compare two treatment strategies in women undergoing surgery for endometrial carcinoma. Retrospective cohort study. Tertiary care center. 1166 patients. Uterine biopsy/curettage was obtained in 1140 women, of whom 229 also had pelvic magnetic resonance imaging (MRI). We compared two strategies: (i) routine pelvic lymphadenectomy and (ii) selective pelvic lymphadenectomy for women with high-risk carcinomas as determined from preoperative histology and MRI. High-risk carcinomas included grade 1-2 endometrioid carcinomas with ≥50% myometrial invasion, grade 3 endometrioid carcinomas, and nonendometrioid carcinomas. Others were considered low-risk carcinomas. Diagnostic indices, treatment algorithms. Of the women who underwent lymphadenectomy, positive pelvic nodes were found in 2.3% of low-risk carcinomas and 18.3% of high-risk carcinomas. The combination of preoperative histology and MRI detected high-risk carcinomas with a sensitivity of 85.7%, a specificity of 75.0%, a positive predictive value of 74.4%, and a negative predictive value of 86.1%. Area under curve was 0.804. In the routine lymphadenectomy algorithm, 54.1% of lymphadenectomies were performed for low-risk carcinomas. In the selective lymphadenectomy algorithm, 14.3% of women with high-risk carcinomas did not receive lymphadenectomy. Missed positive pelvic nodes were estimated to occur in 2.1% of patients in the selective strategy. Similarly, the estimated risk for isolated para-aortic metastasis was 2.1%, regardless of treatment strategy. The combination of preoperative histology and MRI is moderately sensitive and specific in detecting high-risk endometrial carcinomas. The clinical utility of the method is hampered by the relatively high proportion of high-risk cases that remain unrecognized preoperatively. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Assessing the Performance of 3 Human Immunodeficiency Virus Incidence Risk Scores in a Cohort of Black and White Men Who Have Sex With Men in the South.

PubMed

Jones, Jeb; Hoenigl, Martin; Siegler, Aaron J; Sullivan, Patrick S; Little, Susan; Rosenberg, Eli

2017-05-01

Risk scores have been developed to identify men at high risk of human immunodeficiency virus (HIV) seroconversion. These scores can be used to more efficiently allocate public health prevention resources, such as pre-exposure prophylaxis. However, the published scores were developed with data sets that comprise predominantly white men who have sex with men (MSM) collected several years prior and recruited from a limited geographic area. Thus, it is unclear how well these scores perform in men of different races or ethnicities or men in different geographic regions. We assessed the predictive ability of 3 published scores to predict HIV seroconversion in a cohort of black and white MSM in Atlanta, GA. Questionnaire data from the baseline study visit were used to derive individual scores for each participant. We assessed the discriminatory ability of each risk score to predict HIV seroconversion over 2 years of follow-up. The predictive ability of each score was low among all MSM and lower among black men compared to white men. Each score had lower sensitivity to predict seroconversion among black MSM compared to white MSM and low area under the curve values for the receiver operating characteristic curve indicating poor discriminatory ability. Reliance on the currently available risk scores will result in misclassification of high proportions of MSM, especially black MSM, in terms of HIV risk, leading to missed opportunities for HIV prevention services.

In-hospital fall-risk screening in 4,735 geriatric patients from the LUCAS project.

PubMed

Neumann, L; Hoffmann, V S; Golgert, S; Hasford, J; Von Renteln-Kruse, W

2013-03-01

In-hospital falls in older patients are frequent, but the identification of patients at risk of falling is challenging. Aim of this study was to improve the identification of high-risk patients. Therefore, a simplified screening-tool was developed, validated, and compared to the STRATIFY predictive accuracy. Retrospective analysis of 4,735 patients; evaluation of predictive accuracy of STRATIFY and its single risk factors, as well as age, gender and psychotropic medication; splitting the dataset into a learning and a validation sample for modelling fall-risk screening and independent, temporal validation. Geriatric clinic at an academic teaching hospital in Hamburg, Germany. 4,735 hospitalised patients ≥65 years. Sensitivity, specificity, positive and negative predictive value, Odds Ratios, Youden-Index and the rates of falls and fallers were calculated. There were 10.7% fallers, and the fall rate was 7.9/1,000 hospital days. In the learning sample, mental alteration (OR 2.9), fall history (OR 2.1), and insecure mobility (Barthel-Index items 'transfer' + 'walking' score = 5, 10 or 15) (OR 2.3) had the most strongest association to falls. The LUCAS Fall-Risk Screening uses these risk factors, and patients with ≥2 risk factors contributed to the high-risk group (30.9%). In the validation sample, STRATIFY SENS was 56.8, SPEC 59.6, PPV 13.5 and NPV 92.6 vs. LUCAS Fall-Risk Screening was SENS 46.0, SPEC 71.1, PPV 14.9 and NPV 92.3. Both the STRATIFY and the LUCAS Fall-Risk Screening showed comparable results in defining a high-risk group. Impaired mobility and cognitive status were closely associated to falls. The results do underscore the importance of functional status as essential fall-risk factor in older hospitalised patients.

Oncotype DX breast cancer recurrence score can be predicted with a novel nomogram using clinicopathologic data.

PubMed

Orucevic, Amila; Bell, John L; McNabb, Alison P; Heidel, Robert E

2017-05-01

Oncotype DX (ODX) recurrence score (RS) breast cancer (BC) assay is costly, and performed in only ~1/3 of estrogen receptor (ER)-positive BC patients in the USA. We have now developed a user-friendly nomogram surrogate prediction model for ODX based on a large dataset from the National Cancer Data Base (NCDB) to assist in selecting patients for which further ODX testing may not be necessary and as a surrogate for patients for which ODX testing is not affordable or available. Six clinicopathologic variables of 27,719 ODX-tested ER+/HER2-/lymph node-negative patients with 6-50 mm tumor size captured by the NCDB from 2010 to 2012 were assessed with logistic regression to predict high-risk or low-risk ODXRS test results with TAILORx-trial and commercial cut-off values; 12,763 ODX-tested patients in 2013 were used for external validation. The predictive accuracy of the regression model was yielded using a Receiver Operator Characteristic analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. A user-friendly calculator version of nomograms is available online at the University of Tennessee Medical Center website (Knoxville, TN). Grade and progesterone receptor status were the highest predictors of both low-risk and high-risk ODXRS, followed by age, tumor size, histologic tumor type and lymph-vascular invasion (C-indexes-.0.85 vs. 0.88 for TAILORx-trial vs. commercial cut-off values, respectively). This is the first study of this scale showing confidently that clinicopathologic variables can be used for prediction of low-risk or high-risk ODXRS using our nomogram models. These novel nomograms will be useful tools to help physicians and patients decide whether further ODX testing is necessary and are excellent surrogates for patients for which ODX testing is not affordable or available.

Validation of MASCC Score for Risk Stratification in Patients of Hematological Disorders with Febrile Neutropenia.

PubMed

Taj, M; Nadeem, M; Maqsood, S; Shah, T; Farzana, T; Shamsi, T S

2017-09-01

The purpose of this study is to evaluate the association of MASCC score (Multinational Association for Supportive Care in Cancer Score) in patients with febrile neutropenia (as resultant treatment of hematological disorders) for risk assessment of morbidity and mortality. Patients presenting with Febrile Neutropenia from November 2011 till December 2013 were enrolled in the study. Initially all patients were hospitalized and their MASCC score was calculated, however those with high risk stayed in hospital till full ANC recovery while low risk group was discharged earlier and keenly followed as out-patient while being on prophylactic oral antibiotics. The MASCC risk-index score was calculated and patients with risk score >21 were regarded as low-risk while <21 were labeled as high-risk. On the basis of 226 febrile neutropenia patient 132(58.4 %) were categorized as low risk while 94(41.5 %) as high risk patients according to MASCC risk index score. In low risk group 123(93 %) had uncomplicated infection while 9(7 %) had complicated infections. There was no mortality documented in low risk group while eight patients died in high risk group. In this study we correctly predicted outcome of 123(93 %) low risk group patients. The study had positive predictive value of 93 % with both sensitivity and specificity of 65 and 75 % respectively. The MASCC risk score is a valuable tool in determining the outcome in patients with febrile neutropenia.

Use of the HPV MLPA assay in cervical cytology for the prediction of high grade lesions.

PubMed

Litjens, Rogier J N T M; Theelen, Wendy; van de Pas, Yvonne; Ossel, Jessica; Reijans, Martin; Simons, Guus; Speel, Ernst-Jan M; Slangen, Brigitte F M; Ramaekers, Frans C S; Kruitwagen, Roy F P M; Hopman, Anton H N

2013-08-01

Current screening methods for uterine cervical cancer such as Papanicolaou smears and/or high risk human Papillomavirus (HR-HPV) detection have a high negative predictive value but a low positive predictive value for the presence of high grade cervical lesions. Therefore, new parameters are needed to reduce the rate of unnecessary referrals for colposcopy. The predictive value of the HPV multiplex ligation-dependent probe amplification (MLPA) assay, which can assess simultaneously HPV16/18 viral load and viral integration, was evaluated. The assay was applied to 170 cervical cytological samples, and the results were correlated with the matching histological follow-up. The GP5+/6+ assay and qPCR were used as a control for HR-HPV typing. The MLPA assay classified a higher percentage of cases as high-risk (high-viral load and/or viral integration) with higher grades of dysplasia. There was a high correlation between the HPV MLPA assay and qPCR for viral load and HPV genotyping, and between the MLPA assay and the GP5+/6+ assay for HPV genotyping. The sensitivity and specificity of the HPV MLPA assay for the detection of high-grade lesions were 44% and 93%, respectively. This study demonstrates that the HPV MLPA assay can reliably detect HPV 16/18, viral load, and viral integration in cytological samples. Also, high-risk classification correlated well with the presence of high-grade dysplasia. However, for the implementation of the MLPA assay into clinical practice, additional HR-HPV types need to be included to increase the sensitivity of the assay, and thereby increase its negative predictive value. Copyright © 2013 Wiley Periodicals, Inc.

Early Adolescent Relationship Predictors of Emerging Adult Outcomes: Youth with and without Type 1 Diabetes

PubMed Central

Helgeson, Vicki S.; Palladino, Dianne K.; Reynolds, Kerry A.; Becker, Dorothy; Escobar, Oscar; Siminerio, Linda

2013-01-01

Background Emerging adulthood is a high-risk period for mental health problems and risk behaviors for youth generally and for physical health problems among those with type 1 diabetes. Purpose To examine whether adolescents’ relationships with parents and friends predict health and risk behaviors during emerging adulthood. Method Youth with and without diabetes were enrolled at average age 12 and followed for 7 years. Parent and friend relationship variables, measured during adolescence, were used to predict emerging adulthood outcomes: depression, risk behavior, and, for those with diabetes, diabetes outcomes. Results Parent relationship quality predicted decreased depressive symptoms and, for those with diabetes, decreased alcohol use. Parent control predicted increased smoking, reduced college attendance, and, for control participants, increased depressive symptoms. For those with diabetes, parent control predicted decreased depressive symptoms and better self-care. Friend relationship variables predicted few outcomes. Conclusions Adolescent parent relationships remain an important influence on emerging adults’ lives. PMID:24178509

Proband Mental Health Difficulties and Parental Stress Predict Mental Health in Toddlers at High-Risk for Autism Spectrum Disorders

ERIC Educational Resources Information Center

Crea, Katherine; Dissanayake, Cheryl; Hudry, Kristelle

2016-01-01

Family-related predictors of mental health problems were investigated among 30 toddlers at familial high-risk for autism spectrum disorders (ASD) and 28 controls followed from age 2- to 3-years. Parents completed the self-report Depression Anxiety Stress Scales and the parent-report Behavior Assessment System for Children. High-risk toddlers were…

Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid.

PubMed

Sneyd, Mary Jane; Cameron, Claire; Cox, Brian

2014-05-22

New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma. A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years. For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71. We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.

Substance Abuse among High-Risk Sexual Offenders: Do Measures of Lifetime History of Substance Abuse Add to the Prediction of Recidivism over Actuarial Risk Assessment Instruments?

ERIC Educational Resources Information Center

Looman, Jan; Abracen, Jeffrey

2011-01-01

There has been relatively little research on the degree to which measures of lifetime history of substance abuse add to the prediction of risk based on actuarial measures alone among sexual offenders. This issue is of relevance in that a history of substance abuse is related to relapse to substance using behavior. Furthermore, substance use has…

The predictive value of magnetic resonance imaging of retinoblastoma for the likelihood of high-risk pathologic features.

PubMed

Hiasat, Jamila G; Saleh, Alaa; Al-Hussaini, Maysa; Al Nawaiseh, Ibrahim; Mehyar, Mustafa; Qandeel, Monther; Mohammad, Mona; Deebajah, Rasha; Sultan, Iyad; Jaradat, Imad; Mansour, Asem; Yousef, Yacoub A

2018-06-01

To evaluate the predictive value of magnetic resonance imaging in retinoblastoma for the likelihood of high-risk pathologic features. A retrospective study of 64 eyes enucleated from 60 retinoblastoma patients. Contrast-enhanced magnetic resonance imaging was performed before enucleation. Main outcome measures included demographics, laterality, accuracy, sensitivity, and specificity of magnetic resonance imaging in detecting high-risk pathologic features. Optic nerve invasion and choroidal invasion were seen microscopically in 34 (53%) and 28 (44%) eyes, respectively, while they were detected in magnetic resonance imaging in 22 (34%) and 15 (23%) eyes, respectively. The accuracy of magnetic resonance imaging in detecting prelaminar invasion was 77% (sensitivity 89%, specificity 98%), 56% for laminar invasion (sensitivity 27%, specificity 94%), 84% for postlaminar invasion (sensitivity 42%, specificity 98%), and 100% for optic cut edge invasion (sensitivity100%, specificity 100%). The accuracy of magnetic resonance imaging in detecting focal choroidal invasion was 48% (sensitivity 33%, specificity 97%), and 84% for massive choroidal invasion (sensitivity 53%, specificity 98%), and the accuracy in detecting extrascleral extension was 96% (sensitivity 67%, specificity 98%). Magnetic resonance imaging should not be the only method to stratify patients at high risk from those who are not, eventhough it can predict with high accuracy extensive postlaminar optic nerve invasion, massive choroidal invasion, and extrascleral tumor extension.

The Autism Parent Screen for Infants: Predicting Risk of Autism Spectrum Disorder Based on Parent-Reported Behavior Observed at 6-24 Months of Age

ERIC Educational Resources Information Center

Sacrey, Lori-Ann R.; Bryson, Susan; Zwaigenbaum, Lonnie; Brian, Jessica; Smith, Isabel M.; Roberts, Wendy; Szatmari, Peter; Vaillancourt, Tracy; Roncadin, Caroline; Garon, Nancy

2018-01-01

This study examined whether a novel parent-report questionnaire, the Autism Parent Screen for Infants, could differentiate infants subsequently diagnosed with autism spectrum disorder from a high-risk cohort (siblings of children diagnosed with autism spectrum disorder (n = 66)) from high-risk and low-risk comparison infants (no family history of…

Derivation and Validation of a Clostridium difficile Infection Recurrence Prediction Rule in a National Cohort of Veterans.

PubMed

Reveles, Kelly R; Mortensen, Eric M; Koeller, Jim M; Lawson, Kenneth A; Pugh, Mary Jo V; Rumbellow, Sarah A; Argamany, Jacqueline R; Frei, Christopher R

2018-03-01

Prior studies have identified risk factors for recurrent Clostridium difficile infection (CDI), but few studies have integrated these factors into a clinical prediction rule that can aid clinical decision-making. The objectives of this study were to derive and validate a CDI recurrence prediction rule to identify patients at risk for first recurrence in a national cohort of veterans. Retrospective cohort study. Veterans Affairs Informatics and Computing Infrastructure. A total of 22,615 adult Veterans Health Administration beneficiaries with first-episode CDI between October 1, 2002, and September 30, 2014; of these patients, 7538 were assigned to the derivation cohort and 15,077 to the validation cohort. A 60-day CDI recurrence prediction rule was created in a derivation cohort using backward logistic regression. Those variables significant at p<0.01 were assigned an integer score proportional to the regression coefficient. The model was then validated in the derivation cohort and a separate validation cohort. Patients were then split into three risk categories, and rates of recurrence were described for each category. The CDI recurrence prediction rule included the following predictor variables with their respective point values: prior third- and fourth-generation cephalosporins (1 point), prior proton pump inhibitors (1 point), prior antidiarrheals (1 point), nonsevere CDI (2 points), and community-onset CDI (3 points). In the derivation cohort, the 60-day CDI recurrence risk for each score ranged from 7.5% (0 points) to 57.9% (8 points). The risk score was strongly correlated with recurrence (R 2  = 0.94). Patients were split into low-risk (0-2 points), medium-risk (3-5 points), and high-risk (6-8 points) classes and had the following recurrence rates: 8.9%, 20.2%, and 35.0%, respectively. Findings were similar in the validation cohort. Several CDI and patient-specific factors were independently associated with 60-day CDI recurrence risk. When integrated into a clinical prediction rule, higher risk scores and risk classes were strongly correlated with CDI recurrence. This clinical prediction rule can be used by providers to identify patients at high risk for CDI recurrence and help guide preventive strategy decisions, while accounting for clinical judgment. © 2018 Pharmacotherapy Publications, Inc.

Prediction and perception of hazards in professional drivers: Does hazard perception skill differ between safe and less-safe fire-appliance drivers?

PubMed

Crundall, David; Kroll, Victoria

2018-05-18

Can hazard perception testing be useful for the emergency services? Previous research has found emergency response drivers' (ERDs) to perform better than controls, however these studies used clips of normal driving. In contrast, the current study filmed footage from a fire-appliance on blue-light training runs through Nottinghamshire, and endeavoured to discriminate between different groups of EDRs based on experience and collision risk. Thirty clips were selected to create two variants of the hazard perception test: a traditional push-button test requiring speeded-responses to hazards, and a prediction test that occludes at hazard onset and provides four possible outcomes for participants to choose between. Three groups of fire-appliance drivers (novices, low-risk experienced and high-risk experienced), and age-matched controls undertook both tests. The hazard perception test only discriminated between controls and all FA drivers, whereas the hazard prediction test was more sensitive, discriminating between high and low-risk experienced fire appliance drivers. Eye movement analyses suggest that the low-risk drivers were better at prioritising the hazardous precursors, leading to better predictive accuracy. These results pave the way for future assessment and training tools to supplement emergency response driver training, while supporting the growing literature that identifies hazard prediction as a more robust measure of driver safety than traditional hazard perception tests. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of breast cancer risk with volatile biomarkers in breath.

PubMed

Phillips, Michael; Cataneo, Renee N; Cruz-Ramos, Jose Alfonso; Huston, Jan; Ornelas, Omar; Pappas, Nadine; Pathak, Sonali

2018-03-23

Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

A Tissue Systems Pathology Test Detects Abnormalities Associated with Prevalent High-Grade Dysplasia and Esophageal Cancer in Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Davison, Jon M; Prichard, Jeffrey W; Reese, Lia M; Zhang, Yi; Repa, Kathleen; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory G; DeMarshall, Maureen; Foxwell, Tyler; Jobe, Blair A; Zaidi, Ali H; Duits, Lucas C; Bergman, Jacques J G H M; Rustgi, Anil; Falk, Gary W

2017-02-01

There is a need for improved tools to detect high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in patients with Barrett's esophagus. In previous work, we demonstrated that a 3-tier classifier predicted risk of incident progression in Barrett's esophagus. Our aim was to determine whether this risk classifier could detect a field effect in nondysplastic (ND), indefinite for dysplasia (IND), or low-grade dysplasia (LGD) biopsies from Barrett's esophagus patients with prevalent HGD/EAC. We performed a multi-institutional case-control study to evaluate a previously developed risk classifier that is based upon quantitative image features derived from 9 biomarkers and morphology, and predicts risk for HGD/EAC in Barrett's esophagus patients. The risk classifier was evaluated in ND, IND, and LGD biopsies from Barrett's esophagus patients diagnosed with HGD/EAC on repeat endoscopy (prevalent cases, n = 30, median time to HGD/EAC diagnosis 140.5 days) and nonprogressors (controls, n = 145, median HGD/EAC-free surveillance time 2,015 days). The risk classifier stratified prevalent cases and non-progressor patients into low-, intermediate-, and high-risk classes [OR, 46.0; 95% confidence interval, 14.86-169 (high-risk vs. low-risk); P < 0.0001]. The classifier also provided independent prognostic information that outperformed the subspecialist and generalist diagnosis. A tissue systems pathology test better predicts prevalent HGD/EAC in Barrett's esophagus patients than pathologic variables. The results indicate that molecular and cellular changes associated with malignant transformation in Barrett's esophagus may be detectable as a field effect using the test. A tissue systems pathology test may provide an objective method to facilitate earlier identification of Barrett's esophagus patients requiring therapeutic intervention. Cancer Epidemiol Biomarkers Prev; 26(2); 240-8. ©2016 AACR. ©2016 American Association for Cancer Research.

Breast cancer risks and risk prediction models.

PubMed

Engel, Christoph; Fischer, Christine

2015-02-01

BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts.

PubMed

Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

2016-01-01

Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.

External validation of the Probability of repeated admission (Pra) risk prediction tool in older community-dwelling people attending general practice: a prospective cohort study.

PubMed

Wallace, Emma; McDowell, Ronald; Bennett, Kathleen; Fahey, Tom; Smith, Susan M

2016-11-14

Emergency admission is associated with the potential for adverse events in older people and risk prediction models are available to identify those at highest risk of admission. The aim of this study was to externally validate and compare the performance of the Probability of repeated admission (Pra) risk model and a modified version (incorporating a multimorbidity measure) in predicting emergency admission in older community-dwelling people. 15 general practices (GPs) in the Republic of Ireland. n=862, ≥70 years, community-dwelling people prospectively followed up for 2 years (2010-2012). Pra risk model (original and modified) calculated for baseline year where ≥0.5 denoted high risk (patient questionnaire, GP medical record review) of future emergency admission. Emergency admission over 1 year (GP medical record review). descriptive statistics, model discrimination (c-statistic) and calibration (Hosmer-Lemeshow statistic). Of 862 patients, a total of 154 (18%) had ≥1 emergency admission(s) in the follow-up year. 63 patients (7%) were classified as high risk by the original Pra and of these 26 (41%) were admitted. The modified Pra classified 391 (45%) patients as high risk and 103 (26%) were subsequently admitted. Both models demonstrated only poor discrimination (original Pra: c-statistic 0.65 (95% CI 0.61 to 0.70); modified Pra: c-statistic 0.67 (95% CI 0.62 to 0.72)). When categorised according to risk-category model, specificity was highest for the original Pra at cut-point of ≥0.5 denoting high risk (95%), and for the modified Pra at cut-point of ≥0.7 (95%). Both models overestimated the number of admissions across all risk strata. While the original Pra model demonstrated poor discrimination, model specificity was high and a small number of patients identified as high risk. Future validation studies should examine higher cut-points denoting high risk for the modified Pra, which has practical advantages in terms of application in GP. The original Pra tool may have a role in identifying higher-risk community-dwelling older people for inclusion in future trials aiming to reduce emergency admissions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Theory-Based Cartographic Risk Model Development and Application for Home Fire Safety.

PubMed

Furmanek, Stephen; Lehna, Carlee; Hanchette, Carol

There is a gap in the use of predictive risk models to identify areas at risk for home fires and burn injury. The purpose of this study was to describe the creation, validation, and application of such a model using a sample from an intervention study with parents of newborns in Jefferson County, KY, as an example. Performed was a literature search to identify risk factors for home fires and burn injury in the target population. Obtained from the American Community Survey at the census tract level and synthesized to create a predictive cartographic risk model was risk factor data. Model validation was performed through correlation, regression, and Moran's I with fire incidence data from open records. Independent samples t-tests were used to examine the model in relation to geocoded participant addresses. Participant risk level for fire rate was determined and proximity to fire station service areas and hospitals. The model showed high and severe risk clustering in the northwest section of the county. Strongly correlated with fire rate was modeled risk; the best predictive model for fire risk contained home value (low), race (black), and non high school graduates. Applying the model to the intervention sample, the majority of participants were at lower risk and mostly within service areas closest to a fire department and hospital. Cartographic risk models were useful in identifying areas at risk and analyzing participant risk level. The methods outlined in this study are generalizable to other public health issues.

Predicting suicide attempts with the SAD PERSONS scale: a longitudinal analysis.

PubMed

Bolton, James M; Spiwak, Rae; Sareen, Jitender

2012-06-01

The SAD PERSONS scale is a widely used risk assessment tool for suicidal behavior despite a paucity of supporting data. The objective of this study was to examine the ability of the scale in predicting suicide attempts. Participants consisted of consecutive referrals (N=4,019) over 2 years (January 1, 2009 to December 31, 2010) to psychiatric services in the emergency departments of the 2 largest tertiary care hospitals in the province of Manitoba, Canada. SAD PERSONS and Modified SAD PERSONS (MSPS) scale scores were recorded for individuals at their index and all subsequent presentations. The 2 main outcome measures in the study included current suicide attempts (at index presentation) and future suicide attempts (within the next 6 months). The ability of the scales to predict suicide attempts was evaluated with logistic regression, sensitivity and specificity analyses, and receiver operating characteristic curves. 566 people presented with suicide attempts (14.1% of the sample). Both SAD PERSONS and MSPS showed poor predictive ability for future suicide attempts. Compared to low risk scores, high risk baseline scores had low sensitivity (19.6% and 40.0%, respectively) and low positive predictive value (5.3% and 7.4%, respectively). SAD PERSONS did not predict suicide attempts better than chance (area under the curve =0.572; 95% confidence interval [CI], 0.51-0.64; P value nonsignificant). Stepwise regression identified 5 original scale items that accounted for the greatest proportion of future suicide attempt variance. High risk scores using this model had high sensitivity (93.5%) and were associated with a 5-fold higher likelihood of future suicide attempt presentation (odds ratio =5.58; 95% CI, 2.24-13.86; P<.001). In their current form, SAD PERSONS and MSPS do not accurately predict future suicide attempts. © Copyright 2012 Physicians Postgraduate Press, Inc.

Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.

PubMed

Esbenshade, Adam J; Zhao, Zhiguo; Aftandilian, Catherine; Saab, Raya; Wattier, Rachel L; Beauchemin, Melissa; Miller, Tamara P; Wilkes, Jennifer J; Kelly, Michael J; Fernbach, Alison; Jeng, Michael; Schwartz, Cindy L; Dvorak, Christopher C; Shyr, Yu; Moons, Karl G M; Sulis, Maria-Luisa; Friedman, Debra L

2017-10-01

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness. A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables. From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI. The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. © 2017 American Cancer Society.

The role of high-risk HPV-DNA testing in the male sexual partners of women with HPV-induced lesions.

PubMed

Giraldo, Paulo C; Eleutério, Jose; Cavalcante, Diane Isabelle M; Gonçalves, Ana Katherine S; Romão, Juliana A A; Eleutério, Renata M N

2008-03-01

The objectives were to assess the prevalence of high-risk HPV in the male sexual partners of women with HPV-induced lesions, and correlate it with biopsies guided by peniscopy. Fifty-four asymptomatic male sexual partners of women with low-grade squamous intra-epithelial lesions (LSIL) associated with high-risk HPV were examined between April 2003 and June 2005. The DNA-HPV was tested using a second-generation hybrid capture technique in scraped penile samples. Peniscopy identified acetowhite lesions leading to biopsy. High-risk HPV was present in 25.9% (14 out of 54) of the cases. Peniscopy led to 13 biopsies (24.07%), which resulted in two cases of condyloma, two cases of intra-epithelial neoplasia (PIN) I, one case of PIN II, and eight cases of normal tissue. The high-risk HPV test demonstrated 80% sensitivity, 100% specificity, 100% positive predictive value, and 88.9% negative predictive value for the identification of penile lesions. There was a greater chance of finding HPV lesions in the biopsy in the positive cases of high-risk HPV with abnormal peniscopy (p=0.007); OR=51 (CI 1.7-1527.1). Among asymptomatic male sexual partners of women with low-grade intra-epithelial squamous lesions, those infected by high-risk HPV have a higher chance of having abnormal penile tissue compared with male partners without that infection.

Multi-Risk Infants: Predicting Attachment Security from Sociodemographic, Psychosocial, and Health Risk among African-American Preterm Infants

ERIC Educational Resources Information Center

Candelaria, Margo; Teti, Douglas M.; Black, Maureen M.

2011-01-01

Background: Ecological and transactional theories link child outcomes to accumulated risk. This study hypothesized that cumulative risk was negatively related to attachment, and that maternal sensitivity mediated linkages between risk and attachment. Methods: One hundred and twelve high-risk African-American premature infant-mother dyads…

Cautious to a Fault: Self-Protection and the Trajectory of Marital Satisfaction

PubMed Central

Murray, Sandra L.; Holmes, John G.; Derrick, Jaye L.; Harris, Brianna; Griffin, Dale W.; Pinkus, Rebecca T.

2012-01-01

A contextual model of self-protection is proposed to explain when adhering to cautious “if-then” rules in daily interaction erodes marital satisfaction. People can self-protect against partner non-responsiveness by distancing when a partner seems rejecting, promoting a partner’s dependence when feeling unworthy, or by devaluing a partner in the face of costs. The model implies that being less trusting elicits self-protection, and that mismatches between self-protective practices and encountered risk accelerate declines in satisfaction. A longitudinal study of newlyweds revealed that the fit between self-protection practices and risk predicted declines in satisfaction over three years. When people self-protected more initially, satisfaction declined more in low-risk (i.e., low conflict, resilient partner) than high-risk relationships (i.e., high conflict, vulnerable partner). However, when people self-protected less initially, satisfaction declined more in high-risk than low-risk relationships. Process evidence was consistent with moderated mediation: In low-risk relationships only, being less trusting predicted higher levels of self-protective caution that forecast later declines in satisfaction. PMID:25013236

Prediction of Preeclampsia Using the Soluble fms-Like Tyrosine Kinase 1 to Placental Growth Factor Ratio

PubMed Central

Gaccioli, Francesca; Cook, Emma; Hund, Martin; Charnock-Jones, D. Stephen; Smith, Gordon C.S.

2017-01-01

We sought to assess the ratio of sFlt-1 (soluble fms-like tyrosine kinase 1) to PlGF (placental growth factor) in maternal serum as a screening test for preeclampsia in unselected nulliparous women with a singleton pregnancy. We studied 4099 women recruited to the POP study (Pregnancy Outcome Prediction) (Cambridge, United Kingdom). The sFlt-1:PlGF ratio was measured using the Roche Cobas e411 platform at ≈20, ≈28, and ≈36 weeks of gestational age (wkGA). Screen positive was defined as an sFlt-1:PlGF ratio >38, but higher thresholds were also studied. At 28 wkGA, an sFlt-1:PlGF ratio >38 had a positive predictive value (PPV) of 32% for preeclampsia and preterm birth, and the PPV was similar comparing women with low and high prior risk of disease. At 36 wkGA, an sFlt-1:PlGF ratio >38 had a PPV for severe preeclampsia of 20% in high-risk women and 6.4% in low-risk women. At 36 wkGA, an sFlt-1:PlGF ratio >110 had a PPV of 30% for severe preeclampsia, and the PPV was similar comparing low- and high-risk women. Overall, at 36 wkGA, 195 (5.2%) women either had an sFlt-1:PlGF ratio of >110 or an sFlt-1:PlGF ratio >38 plus maternal risk factors: 43% of these women developed preeclampsia, about half with severe features. Among low-risk women at 36 wkGA, an sFlt-1:PlGF ratio ≤38 had a negative predictive value for severe preeclampsia of 99.2%. The sFlt-1:PlGF ratio provided clinically useful prediction of the risk of the most important manifestations of preeclampsia in a cohort of unselected nulliparous women. PMID:28167687

Derivation of genetic biomarkers for cancer risk stratification in Barrett's oesophagus: a prospective cohort study.

PubMed

Timmer, Margriet R; Martinez, Pierre; Lau, Chiu T; Westra, Wytske M; Calpe, Silvia; Rygiel, Agnieszka M; Rosmolen, Wilda D; Meijer, Sybren L; Ten Kate, Fiebo J W; Dijkgraaf, Marcel G W; Mallant-Hent, Rosalie C; Naber, Anton H J; van Oijen, Arnoud H A M; Baak, Lubbertus C; Scholten, Pieter; Böhmer, Clarisse J M; Fockens, Paul; Maley, Carlo C; Graham, Trevor A; Bergman, Jacques J G H M; Krishnadath, Kausilia K

2016-10-01

The risk of developing adenocarcinoma in non-dysplastic Barrett's oesophagus is low and difficult to predict. Accurate tools for risk stratification are needed to increase the efficiency of surveillance. We aimed to develop a prediction model for progression using clinical variables and genetic markers. In a prospective cohort of patients with non-dysplastic Barrett's oesophagus, we evaluated six molecular markers: p16, p53, Her-2/neu, 20q, MYC and aneusomy by DNA fluorescence in situ hybridisation on brush cytology specimens. Primary study outcomes were the development of high-grade dysplasia or oesophageal adenocarcinoma. The most predictive clinical variables and markers were determined using Cox proportional-hazards models, receiver operating characteristic curves and a leave-one-out analysis. A total of 428 patients participated (345 men; median age 60 years) with a cumulative follow-up of 2019 patient-years (median 45 months per patient). Of these patients, 22 progressed; nine developed high-grade dysplasia and 13 oesophageal adenocarcinoma. The clinical variables, age and circumferential Barrett's length, and the markers, p16 loss, MYC gain and aneusomy, were significantly associated with progression on univariate analysis. We defined an 'Abnormal Marker Count' that counted abnormalities in p16, MYC and aneusomy, which significantly improved risk prediction beyond using just age and Barrett's length. In multivariate analysis, these three factors identified a high-risk group with an 8.7-fold (95% CI 2.6 to 29.8) increased HR when compared with the low-risk group, with an area under the curve of 0.76 (95% CI 0.66 to 0.86). A prediction model based on age, Barrett's length and the markers p16, MYC and aneusomy determines progression risk in non-dysplastic Barrett's oesophagus. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Developmental dyslexia: predicting individual risk

PubMed Central

Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

2015-01-01

Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as ‘dyslexic’ or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Results Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Conclusions Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. PMID:25832320

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

PubMed Central

Yoon, Sungroh; Park, Man Sik; Choi, Hoon; Bae, Jae Hyun; Moon, Du Geon; Hong, Sung Kyu; Lee, Sang Eun; Park, Chanwang

2017-01-01

Purpose We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort. Materials and Methods Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots. Results PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed. Conclusions KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings. PMID:28046017

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort.

PubMed

Park, Jae Young; Yoon, Sungroh; Park, Man Sik; Choi, Hoon; Bae, Jae Hyun; Moon, Du Geon; Hong, Sung Kyu; Lee, Sang Eun; Park, Chanwang; Byun, Seok-Soo

2017-01-01

We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort. Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots. PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed. KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.

Field-expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury.

PubMed

Lehr, M E; Plisky, P J; Butler, R J; Fink, M L; Kiesel, K B; Underwood, F B

2013-08-01

In athletics, efficient screening tools are sought to curb the rising number of noncontact injuries and associated health care costs. The authors hypothesized that an injury prediction algorithm that incorporates movement screening performance, demographic information, and injury history can accurately categorize risk of noncontact lower extremity (LE) injury. One hundred eighty-three collegiate athletes were screened during the preseason. The test scores and demographic information were entered into an injury prediction algorithm that weighted the evidence-based risk factors. Athletes were then prospectively followed for noncontact LE injury. Subsequent analysis collapsed the groupings into two risk categories: Low (normal and slight) and High (moderate and substantial). Using these groups and noncontact LE injuries, relative risk (RR), sensitivity, specificity, and likelihood ratios were calculated. Forty-two subjects sustained a noncontact LE injury over the course of the study. Athletes identified as High Risk (n = 63) were at a greater risk of noncontact LE injury (27/63) during the season [RR: 3.4 95% confidence interval 2.0 to 6.0]. These results suggest that an injury prediction algorithm composed of performance on efficient, low-cost, field-ready tests can help identify individuals at elevated risk of noncontact LE injury. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

PubMed

Cox, Louis Anthony Tony

2015-10-01

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

The Readmission Risk Flag: Using the Electronic Health Record to Automatically Identify Patients at Risk for 30-day Readmission

PubMed Central

Baillie, Charles A.; VanZandbergen, Christine; Tait, Gordon; Hanish, Asaf; Leas, Brian; French, Benjamin; Hanson, C. William; Behta, Maryam; Umscheid, Craig A.

2015-01-01

Background Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions. Objective To develop and implement an automated prediction model integrated into our health system’s EHR that identifies on admission patients at high risk for readmission within 30 days of discharge. Design Retrospective and prospective cohort. Setting Healthcare system consisting of three hospitals. Patients All adult patients admitted from August 2009 to September 2012. Interventions An automated readmission risk flag integrated into the EHR. Measures Thirty-day all-cause and 7-day unplanned healthcare system readmissions. Results Using retrospective data, a single risk factor, ≥2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a c-statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%) and c-statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation. Conclusions An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge. PMID:24227707

External Validation of the HERNIAscore: An Observational Study.

PubMed

Cherla, Deepa V; Moses, Maya L; Mueck, Krislynn M; Hannon, Craig; Ko, Tien C; Kao, Lillian S; Liang, Mike K

2017-09-01

The HERNIAscore is a ventral incisional hernia (VIH) risk assessment tool that uses only preoperative variables and predictable intraoperative variables. The aim of this study was to validate and modify, if needed, the HERNIAscore in an external dataset. This was a retrospective observational study of all patients undergoing resection for gastrointestinal malignancy from 2011 through 2015 at a safety-net hospital. The primary end point was clinical postoperative VIH. Patients were stratified into low-risk, medium-risk, and high-risk groups based on HERNIAscore. A revised HERNIAscore was calculated with the addition of earlier abdominal operation as a categorical variable. Cox regression of incisional hernia with stratification by risk class was performed. Incidence rates of clinical VIH formation within each risk class were also calculated. Two hundred and forty-seven patents were enrolled. On Cox regression, in addition to the 3 variables of the HERNIAscore (BMI, COPD, and incision length), earlier abdominal operation was also predictive of VIH. The revised HERNIAscore demonstrated improved predictive accuracy for clinical VIH. Although the original HERNIAscore effectively stratified the risk of an incisional radiographic VIH developing, the revised HERNIAscore provided a statistically significant stratification for both clinical and radiographic VIHs in this patient cohort. We have externally validated and improved the HERNIAscore. The revised HERNIAscore uses BMI, incision length, COPD, and earlier abdominal operation to predict risk of postoperative incisional hernia. Future research should assess methods to prevent incisional hernias in moderate-to-high risk patients. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

The joint impact of cognitive performance in adolescence and familial cognitive aptitude on risk for major psychiatric disorders: a delineation of four potential pathways to illness.

PubMed

Kendler, K S; Ohlsson, H; Keefe, R S E; Sundquist, K; Sundquist, J

2018-04-01

How do joint measures of premorbid cognitive ability and familial cognitive aptitude (FCA) reflect risk for a diversity of psychiatric and substance use disorders? To address this question, we examined, using Cox models, the predictive effects of school achievement (SA) measured at age 16 and FCA-assessed from SA in siblings and cousins, and educational attainment in parents-on risk for 12 major psychiatric syndromes in 1 140 608 Swedes born 1972-1990. Four developmental patterns emerged. In the first, risk was predicted jointly by low levels of SA and high levels of FCA-that is a level of SA lower than would be predicted from the FCA. This pattern was strongest in autism spectrum disorders and schizophrenia, and weakest in bipolar illness. In these disorders, a pathologic process seems to have caused cognitive functioning to fall substantially short of familial potential. In the second pattern, seen in the internalizing conditions of major depression and anxiety disorders, risk was associated with low SA but was unrelated to FCA. Externalizing disorders-drug abuse and alcohol use disorders-demonstrated the third pattern, in which risk was predicted jointly by low SA and low FCA. The fourth pattern, seen in eating disorders, was directly opposite of that observed in externalizing disorders with risk associated with high SA and high FCA. When measured together, adolescent cognitive ability and FCA identified four developmental patterns leading to diverse psychiatric disorders. The value of cognitive assessments in psychiatric research can be substantially increased by also evaluating familial cognitive potential.

The Relationship Between Functional Movement, Balance Deficits, and Previous Injury History in Deploying Marine Warfighters.

PubMed

de la Motte, Sarah J; Lisman, Peter; Sabatino, Marc; Beutler, Anthony I; OʼConnor, Francis G; Deuster, Patricia A

2016-06-01

Screening for primary musculoskeletal injury (MSK-I) is costly and time-consuming. Both the Functional Movement Screen (FMS) and the Y-Balance Test (YBT) have been shown to predict future MSK-I. With a goal of optimizing the efficiency of primary MSK-I screening, we studied associations between performance on the FMS and YBT and whether history of MSK-I influenced FMS and YBT scores. In total, 365 deploying Marines performed the FMS and YBT as prescribed. Composite and individual scores were each categorized as high risk or low risk using published injury thresholds: High-risk FMS included composite scores ≤14 and right-to-left (R/L) asymmetry for Shoulder Mobility, In-Line Lunge, Straight Leg Raise, Hurdle Step, or Rotary Stability. High-risk YBT consisted of anterior, posteromedial, and/or posterolateral R/L differences >4 cm and/or composite differences ≥12 cm. Pearson's χ tests evaluated associations between: (a) all FMS and YBT risk groups and (b) previous MSK-I and all FMS and YBT risk groups. Marines with high-risk FMS were twice as likely to have high-risk YBT posteromedial scores (χ = 10.2, p = 0.001; odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.3-3.2). History of any MSK-I was not associated with high-risk FMS or high-risk YBT. However, previous lower extremity MSK-I was associated with In-Line Lunge asymmetries (χ = 9.8, p = 0.002, OR = 2.2, 95% CI = 1.3-3.6). Overall, we found limited overlap in FMS and YBT risk. Because both methods seem to assess different risk factors for injury, we recommend FMS and YBT continue to be used together in combination with a thorough injury history until their predictive capacities are further established.

Who pays the price for high neuroticism? Moderators of longitudinal risks for depression and anxiety.

PubMed

Vittengl, J R

2017-07-01

High neuroticism is a well-established risk for present and future depression and anxiety, as well as an emerging target for treatment and prevention. The current analyses tested the hypothesis that physical, social and socio-economic disadvantages each amplify risks from high neuroticism for longitudinal increases in depression and anxiety symptoms. A national sample of adults (n = 7108) provided structured interview and questionnaire data in the Midlife Development in the United States Survey. Subsamples were reassessed roughly 9 and 18 years later. Time-lagged multilevel models predicted changes in depression and anxiety symptom intensity across survey waves. High neuroticism predicted increases in a depression/anxiety symptom composite across retest intervals. Three disadvantage dimensions - physical limitations (e.g. chronic illness, impaired functioning), social problems (e.g. less social support, more social strain) and low socio-economic status (e.g. less education, lower income) - each moderated risks from high neuroticism for increases in depression and anxiety symptoms. Collectively, high scores on the three disadvantage dimensions amplified symptom increases attributable to high neuroticism by 0.67 standard deviations. In contrast, neuroticism was not a significant risk for increases in symptoms among participants with few physical limitations, few social problems or high socio-economic status. Risks from high neuroticism are not shared equally among adults in the USA. Interventions preventing or treating depression or anxiety via neuroticism could be targeted toward vulnerable subpopulations with physical, social or socio-economic disadvantages. Moreover, decreasing these disadvantages may reduce mental health risks from neuroticism.

Approaches for predicting long-term sickness absence. Re: Schouten et al. "Screening manual and office workers for risk of long-term sickness absence: cut-off points for the Work Ability Index".

PubMed

van Amelsvoort, Ludovic Gpm; Jansen, Nicole W H; Kant, I Jmert

2015-05-01

We read with much interest the article of Schouten et al (1) on identifying workers with a high risk for future long-term sickness absence using the Work Ability Index (WAI). The ability to identify high-risk workers might facilitate targeted interventions for such workers and, consequently, can reduce sickness absence levels and improve workers' health. Earlier studies by both Tamela et al (2), Kant et al (3), and Lexis et al (4) have demonstrated that such an approach, based on the identification of high-risk workers and a subsequent intervention, can be effectively applied in practice to reduce sickness absence significantly. The reason for our letter on Schouten et al's article is twofold. First, by including workers already on sick leave in a study predicting long-term sick leave will result in an overestimation of the predictive properties of the instrument and biased predictors, especially when also the outcome of interest is included as a factor in the prediction model. Second, we object to the use of the term "screening" when subjects with the condition screened for are included in the study. Reinforced by the inclusion of sickness absence in the prediction model, including workers already on sick leave will shift the focus of the study findings towards the prediction of (re)current sickness absence and workers with a below-average return-to-work rate, rather than the identification of workers at high risk for the onset of future long-term sickness absence. The possibilities for prevention will shift from pure secondary prevention to a mix of secondary and tertiary prevention. As a consequence, the predictors of the model presented in the Schouten et al article can be used as a basis for tailoring neither preventive measures nor interventions. Moreover, including the outcome (sickness absence) as a predictor in the model, especially in a mixed population including workers with and without the condition (on sick leave), will result in biased predictors and an overestimation of the predictive value. A methodological approach of related issues is provided in the works of Glymour et al (5) and Hamilton et al (6). This phenomenon is even more clearly illustrated by the predictive properties of the workability index, as described by Alavinia et al (7, page 328), which reported that "when adjusted for individual characteristics, lifestyle factors, and work characteristics, two dimensions of the WAI were significant predictors for both moderate and long durations of sickness absence: (i) the presence of sickness absence in the past 12 months prior to the medical examination and (ii) experienced limitations due to health problems." So, when applied to the study by Schouten et al (1), this means that most of the predictive value would be related to the factors "sickness absence in the past 12 months". In addition, we object to the use of the term "screening" in the Schouten et al study as it includes workers with the intended outcome (long-term sickness absence). One can identify three separate aims to study the longitudinal association between risk factors and subsequent long-term sickness absence: (i) to establish causal risk factors for long-term sickness absence, often to find clues for primary preventive strategies (beyond the scope here); (ii) to identify high-risk workers who are still at work and might benefit from an intervention before sickness absence occurs (secondary prevention); and (iii) to identify workers on sick leave who might suffer a below-average return-to-work rate or have a high risk for the recurrence of (long-term) sickness absence and might benefit from intensification or optimization of the return-to-work process (tertiary prevention). In this light, one needs to separate screening instruments from predictive instruments and reserve the term "screening" for the situation as defined by Wilson and Junger (8, page 7): "The object of screening for disease is to discover those among the apparently well who are in fact suffering from disease" (ie, situations of secondary prevention). This means that, when applying this definition on long-term sickness absence under the precondition that the individuals are still at work, screening enables the identification of high-risk individuals in the early "stages" of a "disease" that can progress into long-term sickness absence. In the case of the Schouten et al study, the population at risk, as derived from their predictive instrument, consists of workers with and without sickness absence, and as such excludes the use of the term "screening" in this case. To conclude, we have substantiated that, in addition to correct usage of the term "screening", careful selection of the study population, predictors and most importantly the aim of the predictive model are essential in the process of developing predictive instruments aimed at identifying workers at high risk of long-term sickness absence. Two fundamentally different approaches are possible. One approach aims at identifying workers on sick leave with either a below-average chance to return to work an/or a high risk for a successive episode of long-term sickness absence. From a methodological and practical point of view, such an instrument should be developed and validated among workers already on sick leave. A second approach aims at identifying workers who are still at work but at high risk for future long-term sickness absence. To develop and validate such an instrument, a study sample where workers already on sick leave are excluded is a prerequisite. Such instruments fit in a pro-active approach of preventing future sickness absence, where an early intervention can be offered to those workers with an increased risk for future sickness absence.

Are the major risk/need factors predictive of both female and male reoffending?: a test with the eight domains of the level of service/case management inventory.

PubMed

Andrews, Donald A; Guzzo, Lina; Raynor, Peter; Rowe, Robert C; Rettinger, L Jill; Brews, Albert; Wormith, J Stephen

2012-02-01

The Level of Service/Case Management Inventory (LS/CMI) and the Youth version (YLS/CMI) generate an assessment of risk/need across eight domains that are considered to be relevant for girls and boys and for women and men. Aggregated across five data sets, the predictive validity of each of the eight domains was gender-neutral. The composite total score (LS/CMI total risk/need) was strongly associated with the recidivism of males (mean r = .39, mean AUC = .746) and very strongly associated with the recidivism of females (mean r = .53, mean AUC = .827). The enhanced validity of LS total risk/need with females was traced to the exceptional validity of Substance Abuse with females. The intra-data set conclusions survived the introduction of two very large samples composed of female offenders exclusively. Finally, the mean incremental contributions of gender and the gender-by-risk level interactions in the prediction of criminal recidivism were minimal compared to the relatively strong validity of the LS/CMI risk level. Although the variance explained by gender was minimal and although high-risk cases were high-risk cases regardless of gender, the recidivism rates of lower risk females were lower than the recidivism rates of lower risk males, suggesting possible implications for test interpretation and policy.

At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction

PubMed Central

Fusar-Poli, Paolo; Cappucciati, Marco; Rutigliano, Grazia; Schultze-Lutter, Frauke; Bonoldi, Ilaria; Borgwardt, Stefan; Riecher-Rössler, Anita; Addington, Jean; Perkins, Diana; Woods, Scott W; McGlashan, Thomas H; Lee, Jimmy; Klosterkötter, Joachim; Yung, Alison R; McGuire, Philip

2015-01-01

An accurate detection of individuals at clinical high risk (CHR) for psychosis is a prerequisite for effective preventive interventions. Several psychometric interviews are available, but their prognostic accuracy is unknown. We conducted a prognostic accuracy meta-analysis of psychometric interviews used to examine referrals to high risk services. The index test was an established CHR psychometric instrument used to identify subjects with and without CHR (CHR+ and CHR−). The reference index was psychosis onset over time in both CHR+ and CHR− subjects. Data were analyzed with MIDAS (STATA13). Area under the curve (AUC), summary receiver operating characteristic curves, quality assessment, likelihood ratios, Fagan’s nomogram and probability modified plots were computed. Eleven independent studies were included, with a total of 2,519 help-seeking, predominately adult subjects (CHR+: N=1,359; CHR−: N=1,160) referred to high risk services. The mean follow-up duration was 38 months. The AUC was excellent (0.90; 95% CI: 0.87-0.93), and comparable to other tests in preventive medicine, suggesting clinical utility in subjects referred to high risk services. Meta-regression analyses revealed an effect for exposure to antipsychotics and no effects for type of instrument, age, gender, follow-up time, sample size, quality assessment, proportion of CHR+ subjects in the total sample. Fagan’s nomogram indicated a low positive predictive value (5.74%) in the general non-help-seeking population. Albeit the clear need to further improve prediction of psychosis, these findings support the use of psychometric prognostic interviews for CHR as clinical tools for an indicated prevention in subjects seeking help at high risk services worldwide. PMID:26407788

Predictions in the face of clinical reality: HistoCheck versus high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease.

PubMed

Askar, Medhat; Sobecks, Ronald; Morishima, Yasuo; Kawase, Takakazu; Nowacki, Amy; Makishima, Hideki; Maciejewski, Jaroslaw

2011-09-01

HLA polymorphism remains a major hurdle for hematopoietic stem cell transplantation (HSCT). In 2004, Elsner et al. proposed the HistoCheck Web-based tool to estimate the allogeneic potential between HLA-mismatched stem cell donor/recipient pairs expressed as a sequence similarity matching (SSM). SSM is based on the structure of HLA molecules and the functional similarity of amino acids. According to this algorithm, a high SSM score represents high dissimilarity between MHC molecules, resulting in a potentially more deleterious impact on stem cell transplant outcomes. We investigated the potential of SSM to predict high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease (aGVHD grades III and IV) published by Kawase et al., by comparing SSM in low- and high-risk combinations. SSM was calculated for allele mismatch combinations using the HistoCheck tool available on the Web (www.histocheck.org). We compared ranges and means of SSM among high-risk (15 combinations observed in 722 donor/recipient pairs) versus low-risk allele combinations (94 combinations in 3490 pairs). Simulation scenarios were created where the recipient's HLA allele was involved in multiple allele mismatch combinations with at least 1 high-risk and 1 low-risk mismatch combination. SSM values were then compared. The mean SSM for high- versus low-risk combinations were 2.39 and 2.90 at A, 1.06 and 2.53 at B, 16.60 and 14.99 at C, 4.02 and 3.81 at DRB1, and 7.47 and 6.94 at DPB1 loci, respectively. In simulation scenarios, no predictable SSM association with high- or low-risk combinations could be distinguished. No DQB1 combinations met the statistical criteria for our study. In conclusion, our analysis demonstrates that mean SSM scores were not significantly different, and SSM distributions were overlapping among high- and low-risk allele combinations within loci HLA-A, B, C, DRB1, and DPB1. This analysis does not support selecting donors for HSCT recipients based on low HistoCheck SSM scores. Copyright © 2011 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record.

PubMed

Olenick, Evelyn M; Zimbro, Kathie S; DʼLima, Gabrielle M; Ver Schneider, Patricia; Jones, Danielle

The Sepsis "Sniffer" Algorithm (SSA) has merit as a digital sepsis alert but should be considered an adjunct to versus an alternative for the Nurse Screening Tool (NST), given lower specificity and positive predictive value. The SSA reduced the risk of incorrectly categorizing patients at low risk for sepsis, detected sepsis high risk in half the time, and reduced redundant NST screens by 70% and manual screening hours by 64% to 72%. Preserving nurse hours expended on manual sepsis alerts may translate into time directed toward other patient priorities.

Application of a predictive Bayesian model to environmental accounting.

PubMed

Anex, R P; Englehardt, J D

2001-03-30

Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

The Cost-Effectiveness of High-Risk Lung Cancer Screening and Drivers of Program Efficiency.

PubMed

Cressman, Sonya; Peacock, Stuart J; Tammemägi, Martin C; Evans, William K; Leighl, Natasha B; Goffin, John R; Tremblay, Alain; Liu, Geoffrey; Manos, Daria; MacEachern, Paul; Bhatia, Rick; Puksa, Serge; Nicholas, Garth; McWilliams, Annette; Mayo, John R; Yee, John; English, John C; Pataky, Reka; McPherson, Emily; Atkar-Khattra, Sukhinder; Johnston, Michael R; Schmidt, Heidi; Shepherd, Frances A; Soghrati, Kam; Amjadi, Kayvan; Burrowes, Paul; Couture, Christian; Sekhon, Harmanjatinder S; Yasufuku, Kazuhiro; Goss, Glenwood; Ionescu, Diana N; Hwang, David M; Martel, Simon; Sin, Don D; Tan, Wan C; Urbanski, Stefan; Xu, Zhaolin; Tsao, Ming-Sound; Lam, Stephen

2017-08-01

Lung cancer risk prediction models have the potential to make programs more affordable; however, the economic evidence is limited. Participants in the National Lung Cancer Screening Trial (NLST) were retrospectively identified with the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The high-risk subgroup was assessed for lung cancer incidence and demographic characteristics compared with those in the low-risk subgroup and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), which is an observational study that was high-risk-selected in Canada. A comparison of high-risk screening versus standard care was made with a decision-analytic model using data from the NLST with Canadian cost data from screening and treatment in the PanCan study. Probabilistic and deterministic sensitivity analyses were undertaken to assess uncertainty and identify drivers of program efficiency. Use of the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial with a threshold set at 2% over 6 years would have reduced the number of individuals who needed to be screened in the NLST by 81%. High-risk screening participants in the NLST had more adverse demographic characteristics than their counterparts in the PanCan study. High-risk screening would cost $20,724 (in 2015 Canadian dollars) per quality-adjusted life-year gained and would be considered cost-effective at a willingness-to-pay threshold of $100,000 in Canadian dollars per quality-adjusted life-year gained with a probability of 0.62. Cost-effectiveness was driven primarily by non-lung cancer outcomes. Higher noncurative drug costs or current costs for immunotherapy and targeted therapies in the United States would render lung cancer screening a cost-saving intervention. Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and screening may even offer cost savings if noncurative treatment costs continue to rise. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

PubMed

Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

2016-01-01

We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Can parents' concerns predict autism spectrum disorder? A prospective study of high-risk siblings from 6 to 36 months of age.

PubMed

Sacrey, Lori-Ann R; Zwaigenbaum, Lonnie; Bryson, Susan; Brian, Jessica; Smith, Isabel M; Roberts, Wendy; Szatmari, Peter; Roncadin, Caroline; Garon, Nancy; Novak, Christopher; Vaillancourt, Tracy; McCormick, Theresa; MacKinnon, Bonnie; Jilderda, Sanne; Armstrong, Vickie

2015-06-01

This prospective study characterized parents' concerns about infants at high risk for developing autism spectrum disorder (ASD; each with an older sibling with ASD) at multiple time points in the first 2 years, and assessed their relation to diagnostic outcome at 3 years. Parents of low-risk controls (LR) and high-risk infant siblings (HR) reported any concerns that they had regarding their children's development between 6 and 24 months of age regarding sleep, diet, sensory behavior, gross/fine motor skills, repetitive movements, communication, communication regression, social skills, play, and behavioral problems, using a parent concern form designed for this study. At 3 years of age, an independent, gold-standard diagnostic assessment for ASD was conducted for all participants. As predicted, parents of HR children who received an ASD diagnosis reported more concerns than parents of LR and HR children who did not have ASD. The total number of concerns predicted a subsequent diagnosis of ASD as early as 12 months within the HR group. Concerns regarding sensory behavior and motor development predicted a subsequent diagnosis of ASD as early as 6 months, whereas concerns about social communication and repetitive behaviors did not predict diagnosis of ASD until after 12 months. Parent-reported concerns can improve earlier recognition of ASD in HR children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Online gaming and risks predict cyberbullying perpetration and victimization in adolescents.

PubMed

Chang, Fong-Ching; Chiu, Chiung-Hui; Miao, Nae-Fang; Chen, Ping-Hung; Lee, Ching-Mei; Huang, Tzu-Fu; Pan, Yun-Chieh

2015-02-01

The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan. A total of 2,315 students from 26 high schools were assessed in the 10th grade, with follow-up performed in the 11th grade. Self-administered questionnaires were collected in 2010 and 2011. Multiple logistic regression was conducted to examine the factors. Multivariate analysis results indicated that higher levels of risk factors (online game use, exposure to violence in media, internet risk behaviors, cyber/school bullying experiences) in the 10th grade coupled with an increase in risk factors from grades 10 to 11 could be used to predict the emergence of cyberbullying perpetration/victimization. In contrast, lower levels of risk factors in the 10th grade and higher levels of protective factors coupled with a decrease in risk factors predicted the cessation of cyberbullying perpetration/victimization. Online game use, exposure to violence in media, Internet risk behaviors, and cyber/school bullying experiences can be used to predict the emergence and cessation of youth cyberbullying perpetration and victimization.

20180312 - Uncertainty and Variability in High-Throughput Toxicokinetics for Risk Prioritization (SOT)

EPA Science Inventory

Streamlined approaches that use in vitro experimental data to predict chemical toxicokinetics (TK) are increasingly being used to perform risk-based prioritization based upon dosimetric adjustment of high-throughput screening (HTS) data across thousands of chemicals. However, ass...

An examination of the predictors of blood donors' intentions to donate during two phases of an avian influenza outbreak.

PubMed

Masser, Barbara M; White, Katherine M; Hamilton, Kyra; McKimmie, Blake M

2011-03-01

Data from prior health scares suggest that an avian influenza outbreak will impact on people's intention to donate blood; however, research exploring this is scarce. Using an augmented theory of planned behavior (TPB), incorporating threat perceptions alongside the rational decision-making components of the TPB, the current study sought to identify predictors of blood donors' intentions to donate during two phases of an avian influenza outbreak. Blood donors (n = 172) completed an on-line survey assessing the standard TPB predictors as well as measures of threat perceptions from the health belief model (i.e., perceived susceptibility and severity). Path analyses examined the utility of the augmented TPB to predict donors' intentions to donate during a low- and high-risk phase of an avian influenza outbreak. In both phases, the model provided a good fit to the data explaining 69% (low risk) and 72% (high risk) of the variance in intentions. Attitude, subjective norm, and perceived susceptibility significantly predicted donor intentions in both phases. Within the low-risk phase, sex was an additional significant predictor of intention, while in the high-risk phase, perceived behavioral control was significantly related to intentions. An augmented TPB model can be used to predict donors' intentions to donate blood in a low-risk and a high-risk phase of an outbreak of avian influenza. As such, the results provide important insights into donors' decision-making that can be used by blood agencies to maintain the blood supply in the context of an avian influenza outbreak. © 2010 American Association of Blood Banks.

Nutritional Status and Tuberculosis Risk in Adult and Pediatric Household Contacts.

PubMed

Aibana, Omowunmi; Acharya, Xeno; Huang, Chuan-Chin; Becerra, Mercedes C; Galea, Jerome T; Chiang, Silvia S; Contreras, Carmen; Calderon, Roger; Yataco, Rosa; Velásquez, Gustavo E; Tintaya, Karen; Jimenez, Judith; Lecca, Leonid; Murray, Megan B

2016-01-01

Studies show obesity decreases risk of tuberculosis (TB) disease. There is limited evidence on whether high body mass index also protects against TB infection; how very high body mass indices influence TB risk; or whether nutritional status predicts this risk in children. We assessed the impact of body mass index on incident TB infection and disease among adults and children. We conducted a prospective cohort study among household contacts of pulmonary TB cases in Lima, Peru. We determined body mass index at baseline and followed participants for one year for TB infection and disease. We used Cox proportional regression analyses to estimate hazard ratios for incident TB infection and disease. We enrolled 14,044 household contacts, and among 6853 negative for TB infection and disease at baseline, 1787 (26.1%) became infected. A total of 406 contacts developed secondary TB disease during follow-up. Body mass index did not predict risk of TB infection but overweight household contacts had significantly decreased risk of TB disease (HR 0.48; 95% CI 0.37-0.64; p <0.001) compared to those with normal weight. Among adults, body mass index ≥ 35 kg/m2 continued to predict a lower risk of TB disease (HR 0.30; 95% CI 0.12-0.74; p 0.009). We found no association between high body mass index and TB infection or disease among children under 12 years of age. High body mass index protects adults against TB disease even at levels ≥ 35 kg/m2. This protective effect does not extend to TB infection and is not seen in children.

Gene-environment correlation in the development of adolescent substance abuse: selection effects of child personality and mediation via contextual risk factors.

PubMed

Hicks, Brian M; Johnson, Wendy; Durbin, C Emily; Blonigen, Daniel M; Iacono, William G; McGue, Matt

2013-02-01

We used a longitudinal twin design to examine selection effects of personality traits at age 11 on high-risk environmental contexts at age 14 and the extent to which these contexts mediated risk for substance abuse at age 17. Socialization at age 11 (willingness to follow rules and endorse conventional values) predicted exposure to contextual risk at age 14. Contextual risk partially mediated the effect of socialization on substance abuse, though socialization also had a direct effect. In contrast, boldness at age 11 (social engagement and assurance, thrill seeking, and stress resilience) also predicted substance abuse directly but was unrelated to contextual risk. There was substantial overlap in the genetic and shared environmental influences on socialization and contextual risk, and genetic risk in socialization contributed to substance abuse indirectly via increased exposure to contextual risk. This suggests that active gene-environment correlations related to individual differences in socialization contributed to an early, high-risk developmental trajectory for adolescent substance abuse. In contrast, boldness appeared to index an independent and direct genetic risk factor for adolescent substance abuse.

The mathematical limits of genetic prediction for complex chronic disease.

PubMed

Keyes, Katherine M; Smith, George Davey; Koenen, Karestan C; Galea, Sandro

2015-06-01

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases. Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease. The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses. Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Methods and Techniques for Risk Prediction of Space Shuttle Upgrades

NASA Technical Reports Server (NTRS)

Hoffman, Chad R.; Pugh, Rich; Safie, Fayssal

1998-01-01

Since the Space Shuttle Accident in 1986, NASA has been trying to incorporate probabilistic risk assessment (PRA) in decisions concerning the Space Shuttle and other NASA projects. One major study NASA is currently conducting is in the PRA area in establishing an overall risk model for the Space Shuttle System. The model is intended to provide a tool to predict the Shuttle risk and to perform sensitivity analyses and trade studies including evaluation of upgrades. Marshall Space Flight Center (MSFC) and its prime contractors including Pratt and Whitney (P&W) are part of the NASA team conducting the PRA study. MSFC responsibility involves modeling the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). A major challenge that faced the PRA team is modeling the shuttle upgrades. This mainly includes the P&W High Pressure Fuel Turbopump (HPFTP) and the High Pressure Oxidizer Turbopump (HPOTP). The purpose of this paper is to discuss the various methods and techniques used for predicting the risk of the P&W redesigned HPFTP and HPOTP.

Not all that glitters is RMT in the forecasting of risk of portfolios in the Brazilian stock market

NASA Astrophysics Data System (ADS)

Sandoval, Leonidas; Bortoluzzo, Adriana Bruscato; Venezuela, Maria Kelly

2014-09-01

Using stocks of the Brazilian stock exchange (BM&F-Bovespa), we build portfolios of stocks based on Markowitz's theory and test the predicted and realized risks. This is done using the correlation matrices between stocks, and also using Random Matrix Theory in order to clean such correlation matrices from noise. We also calculate correlation matrices using a regression model in order to remove the effect of common market movements and their cleaned versions using Random Matrix Theory. This is done for years of both low and high volatility of the Brazilian stock market, from 2004 to 2012. The results show that the use of regression to subtract the market effect on returns greatly increases the accuracy of the prediction of risk, and that, although the cleaning of the correlation matrix often leads to portfolios that better predict risks, in periods of high volatility of the market this procedure may fail to do so. The results may be used in the assessment of the true risks when one builds a portfolio of stocks during periods of crisis.

A longitudinal study of child sleep in high and low risk families: relationship to early maternal settling strategies and child psychological functioning.

PubMed

Sheridan, Andrew; Murray, Lynne; Cooper, Peter J; Evangeli, Michael; Byram, Victoria; Halligan, Sarah L

2013-03-01

To investigate whether sleep disturbances previously found to characterise high risk infants: (a) persist into childhood; (b) are influenced by early maternal settling strategies and (c) predict cognitive and emotional/behavioural functioning. Mothers experiencing high and low levels of psychosocial adversity (risk) were recruited antenatally and longitudinally assessed with their children. Mothers completed measures of settling strategies and infant sleep postnatally, and at 12 and 18 months, infant age. At five years, child sleep characteristics were measured via an actigraphy and maternal report; IQ and child adjustment were also assessed. Sleep disturbances observed in high-risk infants persisted at five years. Maternal involvement in infant settling was greater in high risk mothers, and predicted less optimal sleep at five years. Poorer five year sleep was associated with concurrent child anxiety/depression and aggression, but there was limited evidence for an influence of early sleep problems. Associations between infant/child sleep characteristics and IQ were also limited. Early maternal over-involvement in infant settling is associated with less optimal sleep in children, which in turn, is related to child adjustment. The findings highlight the importance of supporting parents in the early development of good settling practices, particularly in high-risk populations. Copyright © 2012 Elsevier B.V. All rights reserved.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Factors predicting clinical nurses' willingness to care for Ebola virus disease-infected patients: A cross-sectional, descriptive survey.

PubMed

Kim, Ji Soo; Choi, Jeong Sil

2016-09-01

The purpose of this study was to identify factors predicting clinical nurses' willingness to care for Ebola virus disease (EVD)-infected patients. Data were collected from 179 nurses employed at 10 hospitals in Korea using self-reporting questionnaires. Only 26.8% of the participants were willing to care for EVD-infected patients. Factors predicting their willingness to provide care were their belief in public service, risk perception, and age. Nurses' willingness to provide care was high when their belief in public service was high, low when their risk perception was high, and low as their age increased. In order to strengthen nurses' willingness to care for EVD-infected patients, education that targets the enhancement of belief in public service should be included in nurse training. Efforts should be directed toward lowering EVD risk perception and developing systematic responses through government-led organized support. © 2015 Wiley Publishing Asia Pty Ltd.

High-Throughput Models for Exposure-Based Chemical ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlie

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

PubMed

Fusar-Poli, P; Schultze-Lutter, F

2016-02-01

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Application of a prediction model for work-related sensitisation in bakery workers.

PubMed

Meijer, E; Suarthana, E; Rooijackers, J; Grobbee, D E; Jacobs, J H; Meijster, T; de Monchy, J G R; van Otterloo, E; van Rooy, F G B G J; Spithoven, J J G; Zaat, V A C; Heederik, D J J

2010-10-01

Identification of work-related allergy, particularly work-related asthma, in a (nationwide) medical surveillance programme among bakery workers requires an effective and efficient strategy. Bakers at high risk of having work-related allergy were indentified by use of a questionnaire-based prediction model for work-related sensitisation. The questionnaire was applied among 5,325 participating bakers. Sequential diagnostic investigations were performed only in those with an elevated risk. Performance of the model was evaluated in 674 randomly selected bakers who participated in the medical surveillance programme and the validation study. Clinical investigations were evaluated in the first 73 bakers referred at high risk. Overall 90% of bakers at risk of having asthma could be identified. Individuals at low risk showed 0.3-3.8% work-related respiratory symptoms, medication use or absenteeism. Predicting flour sensitisation by a simple questionnaire and score chart seems more effective at detecting work-related allergy than serology testing followed by clinical investigation in all immunoglobulin E class II-positive individuals. This prediction based stratification procedure appeared effective in detecting work-related allergy among bakers and can accurately be used for periodic examination, especially in small enterprises where delivery of adequate care is difficult. This approach may contribute to cost reduction.

Predictive Validity of the Suicide Trigger Scale (STS-3) for Post-Discharge Suicide Attempt in High-Risk Psychiatric Inpatients

PubMed Central

Yaseen, Zimri S.; Kopeykina, Irina; Gutkovich, Zinoviy; Bassirnia, Anahita; Cohen, Lisa J.; Galynker, Igor I.

2014-01-01

Background The greatly increased risk of suicide after psychiatric hospitalization is a critical problem, yet we are unable to identify individuals who would attempt suicide upon discharge. The Suicide Trigger Scale v.3 (STS-3), was designed to measure the construct of an affective ‘suicide trigger state’ hypothesized to precede a suicide attempt (SA). This study aims to test the predictive validity of the STS-3 for post-discharge SA on a high-risk psychiatric-inpatient sample. Methods The STS-3, and a psychological test battery measuring suicidality, mood, impulsivity, trauma history, and attachment style were administered to 161 adult psychiatric patients hospitalized following suicidal ideation (SI) or SA. Receiver Operator Characteristic and logistic regression analyses were used to assess prediction of SA in the 6-month period following discharge from hospitalization. Results STS-3 scores for the patients who made post-discharge SA followed a bimodal distribution skewed to high and low scores, thus a distance from median transform was applied to the scores. The transformed score was a significant predictor of post-discharge SA (AUC 0.731), and a subset of six STS-3 scale items was identified that produced improved prediction of post-discharge SA (AUC 0.814). Scores on C-SSRS and BSS were not predictive. Patients with ultra-high (90th percentile) STS-3 scores differed significantly from ultra-low (10th percentile) scorers on measures of affective intensity, depression, impulsiveness, abuse history, and attachment security. Conclusion STS-3 transformed scores at admission to the psychiatric hospital predict suicide attempts following discharge among the high-risk group of suicidal inpatients. Patients with high transformed scores appear to comprise two clinically distinct groups; an impulsive, affectively intense, fearfully attached group with high raw STS-3 scores and a low-impulsivity, low affect and low trauma-reporting group with low raw STS-3 scores. These groups may correspond to low-plan and planned suicide attempts, respectively, but this remains to be established by future research. PMID:24466229

A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

PubMed

Paik, Soonmyung; Shak, Steven; Tang, Gong; Kim, Chungyeul; Baker, Joffre; Cronin, Maureen; Baehner, Frederick L; Walker, Michael G; Watson, Drew; Park, Taesung; Hiller, William; Fisher, Edwin R; Wickerham, D Lawrence; Bryant, John; Wolmark, Norman

2004-12-30

The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor-positive tumors is poorly defined by clinical and histopathological measures. We tested whether the results of a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancer-related genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan-Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer. Copyright 2004 Massachusetts Medical Society.

Combining lymphovascular invasion with reactive stromal grade predicts prostate cancer mortality.

PubMed

Saeter, Thorstein; Vlatkovic, Ljiljana; Waaler, Gudmund; Servoll, Einar; Nesland, Jahn M; Axcrona, Karol; Axcrona, Ulrika

2016-09-01

Previous studies suggest that lymphovascular invasion (LVI) has a weak and variable effect on prognosis. It is uncertain whether LVI, determined by diagnostic prostate biopsy, predicts prostate cancer death. Data from experimental studies have indicated that carcinoma-associated fibroblasts in the reactive stroma could promote LVI and progression to metastasis. Thus, combining LVI with reactive stromal grade may identify prostate cancer patients at high risk of an unfavorable outcome. The purpose of the present study was to examine if LVI, determined by diagnostic biopsy, alone and in combination with reactive stromal grade could predict prostate cancer death. This population-based study included 283 patients with prostate cancer diagnosed by needle biopsy in Aust-Agder County (Norway) from 1991 to 1999. Clinical data were obtained by medical charts review. Two uropathologists evaluated LVI and reactive stromal grade. The endpoint was prostate cancer death. Patients with LVI had marginally higher risk of prostate cancer death compared to patients without LVI (hazard ratio: 1.8, P-value = 0.04). LVI had a stronger effect on prostate cancer death risk when a high reactive stromal grade was present (hazard ratio: 16.0, P-value <0.001). Therefore, patients with concomitant LVI and high reactive stromal grade were at particularly high risk for prostate cancer death. Evaluating LVI together with reactive stromal grade on diagnostic biopsies could be used to identify patients at high risk of death from prostate cancer. Prostate 76:1088-1094, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario.

PubMed

Dueñas-Espín, Ivan; Vela, Emili; Pauws, Steffen; Bescos, Cristina; Cano, Isaac; Cleries, Montserrat; Contel, Joan Carles; de Manuel Keenoy, Esteban; Garcia-Aymerich, Judith; Gomez-Cabrero, David; Kaye, Rachelle; Lahr, Maarten M H; Lluch-Ariet, Magí; Moharra, Montserrat; Monterde, David; Mora, Joana; Nalin, Marco; Pavlickova, Andrea; Piera, Jordi; Ponce, Sara; Santaeugenia, Sebastià; Schonenberg, Helen; Störk, Stefan; Tegner, Jesper; Velickovski, Filip; Westerteicher, Christoph; Roca, Josep

2016-04-15

Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Responsible teams for regional data management in the five ACT regions. We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Carotid Plaque Score and Risk of Cardiovascular Mortality in the Oldest Old: Results from the TOOTH Study.

PubMed

Hirata, Takumi; Arai, Yasumichi; Takayama, Michiyo; Abe, Yukiko; Ohkuma, Kiyoshi; Takebayashi, Toru

2018-01-01

Accumulating evidence suggests that predictability of traditional cardiovascular risk factors declines with advancing age. We investigated whether carotid plaque scores (CPSs) were associated with cardiovascular disease (CVD) death in the oldest old, and whether asymmetrical dimethylarginine (ADMA), a marker of endothelial dysfunction, moderated the association between the CPS and CVD death. We conducted a prospective cohort study of Japanese subjects aged ≥85 years without CVD at baseline. We followed this cohort for 6 years to investigate the association of CPS with CVD death via multivariable Cox proportional hazard analysis. We divided participants into three groups according to CPS (no, 0 points; low, 1.2-4.9 points; high, ≥5.0 points). The predictive value of CPS for estimating CVD death risk over CVD risk factors, including ADMA, was examined using C-statistics. We analyzed 347 participants (151 men, 196 women; mean age, 87.6 years), of which 135 (38.9%) had no carotid plaque at baseline, and 48 (13.8%) had high CPS. Of the total, 29 (8.4%) participants experienced CVD-related death during the study period. Multivariable analysis revealed a significant association of high CPS with CVD-related mortality relative to no CPS (hazard ratio, 3.90; 95% confidence interval: 1.47-10.39). ADMA was not associated with CVD death, but the significant association between CPS and CVD death was observed only in lower ADMA level. The addition of CPS to other risk factors improved the predictability of CVD death (p=0.032). High CPS correlated significantly with a higher CVD death risk in the oldest old with low cardiovascular risk. Ultrasound carotid plaque evaluation might facilitate risk evaluations of CVD death in the very old.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

On the association between sexual attraction and adolescent risk behavior involvement: Examining mediation and moderation.

PubMed

Busseri, Michael A; Willoughby, Teena; Chalmers, Heather; Bogaert, Anthony F

2008-01-01

On the basis of a large-scale survey of high-school youth, the authors compared adolescents reporting exclusively heterosexual, mostly heterosexual, bisexual, and predominately same-sex attraction based on high-risk involvement across a range of risk behaviors. Bisexual and same-sex attracted groups were characterized by heightened high-risk involvement relative to the other two groups. Mediation analysis was used to determine whether these group disparities were explained by a set of normative predictive factors spanning multiple life domains. Differences among a combined exclusively/mostly heterosexual attraction group and both the bisexual and same-sex attraction groups were attenuated (66% and 50%, respectively) after incorporating the hypothesized intervening predictive factors, providing evidence of partial mediation. Primary mediators included intrapersonal (attitudes toward risk-taking; academic orientation), interpersonal (peer victimization; parental relationships; unstructured activities), and environmental (substance availability) factors. Mediation results were consistent across participant age and sex. Implications, limitations, and directions for future research are discussed. Copyright (c) 2008 APA.

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Matsui, Shigeyuki; Michimae, Hirofumi; Rondeau, Virginie

2017-01-01

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

Lipid-related markers and cardiovascular disease prediction.

PubMed

Di Angelantonio, Emanuele; Gao, Pei; Pennells, Lisa; Kaptoge, Stephen; Caslake, Muriel; Thompson, Alexander; Butterworth, Adam S; Sarwar, Nadeem; Wormser, David; Saleheen, Danish; Ballantyne, Christie M; Psaty, Bruce M; Sundström, Johan; Ridker, Paul M; Nagel, Dorothea; Gillum, Richard F; Ford, Ian; Ducimetiere, Pierre; Kiechl, Stefan; Koenig, Wolfgang; Dullaart, Robin P F; Assmann, Gerd; D'Agostino, Ralph B; Dagenais, Gilles R; Cooper, Jackie A; Kromhout, Daan; Onat, Altan; Tipping, Robert W; Gómez-de-la-Cámara, Agustín; Rosengren, Annika; Sutherland, Susan E; Gallacher, John; Fowkes, F Gerry R; Casiglia, Edoardo; Hofman, Albert; Salomaa, Veikko; Barrett-Connor, Elizabeth; Clarke, Robert; Brunner, Eric; Jukema, J Wouter; Simons, Leon A; Sandhu, Manjinder; Wareham, Nicholas J; Khaw, Kay-Tee; Kauhanen, Jussi; Salonen, Jukka T; Howard, William J; Nordestgaard, Børge G; Wood, Angela M; Thompson, Simon G; Boekholdt, S Matthijs; Sattar, Naveed; Packard, Chris; Gudnason, Vilmundur; Danesh, John

2012-06-20

The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated. To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction. Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years). Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (≥20%) risk. The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines. In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.

Differing predictive relationships between baseline LDL-C, systolic blood pressure, and cardiovascular outcomes.

PubMed

Deedwania, Prakash C; Pedersen, Terje R; DeMicco, David A; Breazna, Andrei; Betteridge, D John; Hitman, Graham A; Durrington, Paul; Neil, Andrew

2016-11-01

Traditional cardiovascular risk factors, such as hypertension and dyslipidemia, predispose individuals to cardiovascular disease, particularly patients with diabetes. We investigated the predictive value of baseline systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) on the risk of vascular outcomes in a large population of patients at high risk of future cardiovascular events. Data were pooled from the TNT (Treating to New Targets), CARDS (Collaborative Atorvastatin Diabetes Study), and IDEAL (Incremental Decrease in End-Points Through Aggressive Lipid Lowering) trials and included a total of 21,727 patients (TNT: 10,001; CARDS: 2838; IDEAL: 8888). The effect of baseline SBP and LDL-C on cardiovascular events, coronary events, and stroke was evaluated using a multivariate Cox proportional-hazards model. Overall, risk of cardiovascular events was significantly higher for patients with higher baseline SBP or LDL-C. Higher baseline SBP was significantly predictive of stroke but not coronary events. Conversely, higher baseline LDL-C was significantly predictive of coronary events but not stroke. Results from the subgroup with diabetes (5408 patients; TNT: 1501; CARDS: 2838; IDEAL: 1069) were broadly consistent with those of the total cohort: baseline SBP and LDL-C were significantly predictive of cardiovascular events overall, with the association to LDL-C predominantly related to an effect on coronary events. However, baseline SBP was not predictive of either coronary or stroke events in the pooled diabetic population. In this cohort of high-risk patients, baseline SBP and LDL-C were significantly predictive of cardiovascular outcomes, but this effect may differ between the cerebrovascular and coronary systems. NCT00327691 (TNT); NCT00327418 (CARDS); NCT00159835 (IDEAL). Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Usefulness of an Online Risk Estimator for Bronchopulmonary Dysplasia in Predicting Corticosteroid Treatment in Infants Born Preterm.

PubMed

Cuna, Alain; Liu, Cynthia; Govindarajan, Shree; Queen, Margaret; Dai, Hongying; Truog, William E

2018-06-01

To assess the usefulness of a bronchopulmonary dysplasia (BPD) outcome estimator developed by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) in identifying high-risk preterm infants treated with steroids. This was a single-center retrospective study of infants born ≤30 weeks of gestational age. The NICHD BPD outcome estimator was used to retrospectively calculate BPD risk at various postnatal ages. The best combination of risk estimates for identifying steroid treatment was identified using stepwise model selection. A cut-off value with the best combination of sensitivity and specificity was identified using receiver operating characteristic analysis. A total of 165 infants born preterm (mean gestational age 26 ± 1.6 weeks, mean birth weight 837 ± 171 g) were included. Of these, 61 were treated with steroids for BPD and 104 were not. Risk estimates for BPD or death were significantly greater in infants treated with steroids compared with controls. Both combined risk for severe BPD or death and single risk of no BPD were identified as factors with the best predictive power for identifying treatment with steroids, with accurate prediction possible as early as the second week of life. A greater than 37% risk for severe BPD or death or a less than 3% risk of no BPD on day of life 14 had 84%-92% sensitivity and 77%-80% specificity for predicting steroid treatment. The NICHD BPD outcome estimator can be a useful objective tool for identifying infants at high risk for BPD who may benefit from postnatal steroids. Copyright © 2018 Elsevier Inc. All rights reserved.

Personalized Prediction of Psychosis: External validation of the NAPLS2 Psychosis Risk Calculator with the EDIPPP project

PubMed Central

Carrión, Ricardo E.; Cornblatt, Barbara A.; Burton, Cynthia Z.; Tso, Ivy F; Auther, Andrea; Adelsheim, Steven; Calkins, Roderick; Carter, Cameron S.; Niendam, Tara; Taylor, Stephan F.; McFarlane, William R.

2016-01-01

Objective In the current issue, Cannon and colleagues, as part of the second phase of the North American Prodrome Longitudinal Study (NAPLS2), report on a risk calculator for the individualized prediction of developing a psychotic disorder in a 2-year period. The present study represents an external validation of the NAPLS2 psychosis risk calculator using an independent sample of subjects at clinical high risk for psychosis collected as part of the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP). Methods 176 subjects with follow-up (from the total EDIPPP sample of 210) rated as clinical high-risk (CHR) based on the Structured Interview for Prodromal Syndromes were used to construct a new prediction model with the 6 significant predictor variables in the NAPLS2 psychosis risk calculator (unusual thoughts, suspiciousness, Symbol Coding, verbal learning, social functioning decline, baseline age, and family history). Discrimination performance was assessed with the area under the receiver operating curve (AUC). The NAPLS2 risk calculator was then used to generate a psychosis risk estimate for each case in the external validation sample. Results The external validation model showed good discrimination, with an AUC of 79% (95% CI 0.644–0.937). In addition, the personalized risk generated by the NAPLS calculator provided a solid estimation of the actual conversion outcome in the validation sample. Conclusions In the companion papers in this issue, two independent samples of CHR subjects converge to validate the NAPLS2 psychosis risk calculator. This prediction calculator represents a meaningful step towards early intervention and personalized treatment of psychotic disorders. PMID:27363511

Validation of a new mortality risk prediction model for people 65 years and older in northwest Russia: The Crystal risk score.

PubMed

Turusheva, Anna; Frolova, Elena; Bert, Vaes; Hegendoerfer, Eralda; Degryse, Jean-Marie

2017-07-01

Prediction models help to make decisions about further management in clinical practice. This study aims to develop a mortality risk score based on previously identified risk predictors and to perform internal and external validations. In a population-based prospective cohort study of 611 community-dwelling individuals aged 65+ in St. Petersburg (Russia), all-cause mortality risks over 2.5 years follow-up were determined based on the results obtained from anthropometry, medical history, physical performance tests, spirometry and laboratory tests. C-statistic, risk reclassification analysis, integrated discrimination improvement analysis, decision curves analysis, internal validation and external validation were performed. Older adults were at higher risk for mortality [HR (95%CI)=4.54 (3.73-5.52)] when two or more of the following components were present: poor physical performance, low muscle mass, poor lung function, and anemia. If anemia was combined with high C-reactive protein (CRP) and high B-type natriuretic peptide (BNP) was added the HR (95%CI) was slightly higher (5.81 (4.73-7.14)) even after adjusting for age, sex and comorbidities. Our models were validated in an external population of adults 80+. The extended model had a better predictive capacity for cardiovascular mortality [HR (95%CI)=5.05 (2.23-11.44)] compared to the baseline model [HR (95%CI)=2.17 (1.18-4.00)] in the external population. We developed and validated a new risk prediction score that may be used to identify older adults at higher risk for mortality in Russia. Additional studies need to determine which targeted interventions improve the outcomes of these at-risk individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparative Evaluation of Arabin Pessary and Cervical Cerclage for the Prevention of Preterm Labor in Asymptomatic Women with High Risk Factors

PubMed Central

Tsikouras, Panagiotis; Anastasopoulos, George; Maroulis, Vasileios; Bothou, Anastasia; Chalkidou, Anna; Deuteraiou, Dorelia; Anthoulaki, Xanthoula; Bourazan, Arzou Halil; Iatrakis, George; Zervoudis, Stefanos; Galazios, Georgios; Inagamova, Lola-Katerina; Csorba, Roland; Teichmann, Alexander-Tobias

2018-01-01

Objective: Preterm labor is one of the most significant obstetric problems associated with high rate of actual and long-term perinatal complications. Despite the creation of scoring systems, uterine activity monitoring, cervical ultrasound and several biochemical markers, the prediction and prevention of preterm labor is still a matter of concern. The aim of this study was to examine cervical findings for the prediction and the comparative use of Arabin pessary or cerclage for the prevention of preterm birth in asymptomatic women with high risk factors for preterm labor. Material and methods: The study group was composed of singleton pregnancies (spontaneously conceived) with high risk factors for preterm labor. Cervical length, dilatation of the internal cervical os and funneling, were estimated with transvaginal ultrasound during the first and the second trimesters of pregnancy. Results: Cervical funneling, during the second trimester of pregnancy, was the most significant factor for the prediction of preterm labor. The use of Arabin cervical pessary was found to be more effective than cerclage in the prolongation of pregnancy. Conclusion: In women at risk for preterm labor, the detection of cervical funneling in the second trimester of pregnancy may help to predict preterm labor and to apply the appropriate treatment for its prevention. Although the use of cervical pessary was found to be more effective than cerclage, more studies are needed to classify the effectiveness of different methods for such prevention. PMID:29670041

Developing and testing a decision model for predicting influenza vaccination compliance.

PubMed Central

Carter, W B; Beach, L R; Inui, T S; Kirscht, J P; Prodzinski, J C

1986-01-01

Influenza vaccination has long been recommended for elderly high-risk patients, yet national surveys indicate that vaccination compliance rates are remarkably low (20 percent). We conducted a study to model prospectively the flu shot decisions and subsequent behavior of an elderly and/or chronically diseased (at high risk for complications of influenza) ambulatory care population at the Seattle VA Medical Center. Prior to the 1980-81 flu shot season, a random (stratified by disease) sample of 63 patients, drawn from the total population of high-risk patients in the general medicine clinic, was interviewed to identify patient-defined concerns regarding flu shots. Six potential consequences of influenza and nine of vaccination were emphasized by patients and provided the content for a weighted hierarchical utility model questionnaire. The utility model provides an operational framework for (1) obtaining subjective value and relative importance judgments from patients; (2) combining these judgments to obtain a prediction of behavioral intention and behavior for each patient; and, if the model is valid (predictive of behavior), (3) identifying those factors which are most salient to patient's decisions and subsequent behavior. Prior to the 1981-82 flu season, the decision model questionnaire was administered to 350 other high-risk patients from the same general medicine clinic population. The decision model correctly predicted behavioral intention for 87 percent and vaccination behavior for 82 percent of this population and, more importantly, differentiated shot "takers" and "nontakers" along several attitudinal dimensions that suggest specific content areas for clinical compliance intervention strategies. PMID:3949541

Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: forecasting psychosis risk with mismatch negativity.

PubMed

Perez, Veronica B; Woods, Scott W; Roach, Brian J; Ford, Judith M; McGlashan, Thomas H; Srihari, Vinod H; Mathalon, Daniel H

2014-03-15

Only about one third of patients at high risk for psychosis based on current clinical criteria convert to a psychotic disorder within a 2.5-year follow-up period. Targeting clinical high-risk (CHR) individuals for preventive interventions could expose many to unnecessary treatments, underscoring the need to enhance predictive accuracy with nonclinical measures. Candidate measures include event-related potential components with established sensitivity to schizophrenia. Here, we examined the mismatch negativity (MMN) component of the event-related potential elicited automatically by auditory deviance in CHR and early illness schizophrenia (ESZ) patients. We also examined whether MMN predicted subsequent conversion to psychosis in CHR patients. Mismatch negativity to auditory deviants (duration, frequency, and duration + frequency double deviant) was assessed in 44 healthy control subjects, 19 ESZ, and 38 CHR patients. Within CHR patients, 15 converters to psychosis were compared with 16 nonconverters with at least 12 months of clinical follow-up. Hierarchical Cox regression examined the ability of MMN to predict time to psychosis onset in CHR patients. Irrespective of deviant type, MMN was significantly reduced in ESZ and CHR patients relative to healthy control subjects and in CHR converters relative to nonconverters. Mismatch negativity did not significantly differentiate ESZ and CHR patients. The duration + frequency double deviant MMN, but not the single deviant MMNs, significantly predicted the time to psychosis onset in CHR patients. Neurophysiological mechanisms underlying automatic processing of auditory deviance, as reflected by the duration + frequency double deviant MMN, are compromised before psychosis onset and can enhance the prediction of psychosis risk among CHR patients. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

PubMed

Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

2015-09-20

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system. Copyright © 2015 John Wiley & Sons, Ltd.

Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

PubMed

Arnold, Suzanne V; Spertus, John A; Jones, Philip G; McGuire, Darren K; Lipska, Kasia J; Xu, Yaping; Stolker, Joshua M; Goyal, Abhinav; Kosiborod, Mikhail

2016-07-01

Although patients with diabetes mellitus experience high rates of adverse events after acute myocardial infarction (AMI), including death and recurrent ischemia, some diabetic patients are likely at low risk, whereas others are at high risk. We sought to develop prediction models to stratify risk after AMI in patients with diabetes mellitus. We developed prediction models for long-term mortality and angina among 1613 patients with diabetes mellitus discharged alive after AMI from 24 US hospitals and then validated the models in a separate, multicenter registry of 786 patients with diabetes mellitus. Event rates in the derivation cohort were 27% for 5-year mortality and 27% for 1-year angina. Parsimonious prediction models demonstrated good discrimination (c-indices=0.78 and 0.69, respectively) and excellent calibration. Within the context of the predictors we estimated, the strongest predictors for mortality were higher creatinine, not working at the time of the AMI, older age, lower hemoglobin, left ventricular dysfunction, and chronic heart failure. The strongest predictors for angina were angina burden in the 4 weeks before the AMI, younger age, history of prior coronary bypass graft surgery, and non-white race. The lowest and highest deciles of predicted risk ranged from 4% to 80% for mortality and 12% to 59% for angina. The models also performed well in external validation (c-indices=0.78 and 0.73, respectively). We found a wide range of risk for adverse outcomes after AMI in diabetic patients. Predictive models can identify patients with diabetes mellitus for whom closer follow-up and aggressive secondary prevention strategies should be considered. © 2016 American Heart Association, Inc.

Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

PubMed

Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

2016-10-01

The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

PubMed Central

Kessler, Ronald C.; Stein, Murray B.; Petukhova, Maria V.; Bliese, Paul; Bossarte, Robert M.; Bromet, Evelyn J.; Fullerton, Carol S.; Gilman, Stephen E.; Ivany, Christopher; Lewandowski-Romps, Lisa; Bell, Amy Millikan; Naifeh, James A.; Nock, Matthew K.; Reis, Benjamin Y.; Rosellini, Anthony J.; Sampson, Nancy A.; Zaslavsky, Alan M.; Ursano, Robert J.

2016-01-01

The 2013 U.S. Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are known not to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male non-deployed Regular U.S. Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naïve Bayes, random forests, support vector regression, elastic net penalized regression) were explored. 41.5% of Army suicides in 2004-2009 occurred among the 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100,000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded. PMID:27431294

Predicting Coronary Artery Aneurysms in Kawasaki Disease at a North American Center: An Assessment of Baseline z Scores.

PubMed

Son, Mary Beth F; Gauvreau, Kimberlee; Kim, Susan; Tang, Alexander; Dedeoglu, Fatma; Fulton, David R; Lo, Mindy S; Baker, Annette L; Sundel, Robert P; Newburger, Jane W

2017-05-31

Accurate risk prediction of coronary artery aneurysms (CAAs) in North American children with Kawasaki disease remains a clinical challenge. We sought to determine the predictive utility of baseline coronary dimensions adjusted for body surface area ( z scores) for future CAAs in Kawasaki disease and explored the extent to which addition of established Japanese risk scores to baseline coronary artery z scores improved discrimination for CAA development. We explored the relationships of CAA with baseline z scores; with Kobayashi, Sano, Egami, and Harada risk scores; and with the combination of baseline z scores and risk scores. We defined CAA as a maximum z score (zMax) ≥2.5 of the left anterior descending or right coronary artery at 4 to 8 weeks of illness. Of 261 patients, 77 patients (29%) had a baseline zMax ≥2.0. CAAs occurred in 15 patients (6%). CAAs were strongly associated with baseline zMax ≥2.0 versus <2.0 (12 [16%] versus 3 [2%], respectively, P <0.001). Baseline zMax ≥2.0 had a C statistic of 0.77, good sensitivity (80%), and excellent negative predictive value (98%). None of the risk scores alone had adequate discrimination. When high-risk status per the Japanese risk scores was added to models containing baseline zMax ≥2.0, none were significantly better than baseline zMax ≥2.0 alone. In a North American center, baseline zMax ≥2.0 in children with Kawasaki disease demonstrated high predictive utility for later development of CAA. Future studies should validate the utility of our findings. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Correlates of AUDIT risk status for male and female college students.

PubMed

Demartini, Kelly S; Carey, Kate B

2009-01-01

The current study identified gender-specific correlates of hazardous drinker status as defined by the AUDIT. A total of 462 college student volunteers completed the study in 2006. The sample was predominantly Caucasian (75%) and female (55%). Participants completed a survey assessing demographics, alcohol use patterns, and health indices. Scores of 8 or more on the AUDIT defined the at-risk subsample. Logistic regression models determined which variables predicted AUDIT risk status for men and women. The at-risk participants reported higher alcohol use and related problems, elevated sleep problems and lower health ratings. High typical blood alcohol concentration (BAC), lifetime drug use, and psychosocial problems predicted risk status for males. Binge frequency and psychosocial problems predicted risk status for females. Different behavioral profiles emerged for men and women identified as hazardous drinkers on the AUDIT. The efficacy of brief alcohol interventions could be enhanced by addressing these behavioral correlates.

A risk prediction model for xerostomia: a retrospective cohort study.

PubMed

Villa, Alessandro; Nordio, Francesco; Gohel, Anita

2016-12-01

We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

Recent development of risk-prediction models for incident hypertension: An updated systematic review

PubMed Central

Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

2017-01-01

Background Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment. PMID:29084293

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study.

PubMed

Pollock, Benjamin D; Hu, Tian; Chen, Wei; Harville, Emily W; Li, Shengxu; Webber, Larry S; Fonseca, Vivian; Bazzano, Lydia A

2017-01-01

To evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population. Surveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic. All risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11-2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%-88%), but poor sensitivity (event NRIs: -23% to -3%). Five diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed. Copyright © 2017 Elsevier Inc. All rights reserved.

A prospective examination of the interpersonal-psychological theory of suicidal behavior among psychiatric adolescent inpatients.

PubMed

Czyz, Ewa K; Berona, Johnny; King, Cheryl A

2015-04-01

The challenge of identifying suicide risk in adolescents, and particularly among high-risk subgroups such as adolescent inpatients, calls for further study of models of suicidal behavior that could meaningfully aid in the prediction of risk. This study examined how well the Interpersonal-Psychological Theory of Suicidal Behavior (IPTS)--with its constructs of thwarted belongingness (TB), perceived burdensomeness (PB), and an acquired capability (AC) for lethal self-injury--predicts suicide attempts among adolescents (N = 376) 3 and 12 months after hospitalization. The three-way interaction between PB, TB, and AC, defined as a history of multiple suicide attempts, was not significant. However, there were significant 2-way interaction effects, which varied by sex: girls with low AC and increasing TB, and boys with high AC and increasing PB, were more likely to attempt suicide at 3 months. Only high AC predicted 12-month attempts. Results suggest gender-specific associations between theory components and attempts. The time-limited effects of these associations point to TB and PB being dynamic and modifiable in high-risk populations, whereas the effects of AC are more lasting. The study also fills an important gap in existing research by examining IPTS prospectively. © 2014 The American Association of Suicidology.

Neuroblastoma mRNAs predict outcome in children with stage 4 neuroblastoma: a European HR-NBL1/SIOPEN study.

PubMed

Viprey, Virginie F; Gregory, Walter M; Corrias, Maria V; Tchirkov, Andrei; Swerts, Katrien; Vicha, Ales; Dallorso, Sandro; Brock, Penelope; Luksch, Roberto; Valteau-Couanet, Dominique; Papadakis, Vassilios; Laureys, Genevieve; Pearson, Andrew D; Ladenstein, Ruth; Burchill, Susan A

2014-04-01

To evaluate the hypothesis that detection of neuroblastoma mRNAs by reverse transcriptase quantitative polymerase chain reaction (RTqPCR) in peripheral blood (PB) and bone marrow aspirates (BM) from children with stage 4 neuroblastoma are clinically useful biomarkers of risk. RTqPCR for paired-like homeobox 2b (PHOX2B), tyrosine hydroxylase (TH), and doublecortin (DCX) mRNA in PB and BM of children enrolled onto the High-Risk Neuroblastoma Trial-1 of the European Society of Pediatric Oncology Neuroblastoma Group (HR-NBL1/SIOPEN) was performed at diagnosis and after induction therapy. High levels of TH, PHOX2B, or DCX mRNA in PB or BM at diagnosis strongly predicted for worse event-free survival (EFS) and overall survival (OS) in a cohort of 290 children. After induction therapy, high levels of these mRNAs predicted worse EFS and OS in BM but not in PB. Combinations of mRNAs in BM did not add to the predictive power of any single mRNA. However, in the original (n = 182) and validation (n = 137) PB cohorts, high TH (log10TH > 0.8) or high PHOX2B (log10PHOX2B > 0.28) identify 19% of children as ultrahigh risk, with 5-year EFS and OS rates of 0%; OS rate was 25% (95% CI, 16% to 36%) and EFS rate was 38% (95% CI, 28% to 49%) in the remaining children. The magnitude of reduction in mRNA level between diagnosis and postinduction therapy in BM or PB was not of additional predictive value. High levels of TH and PHOX2B mRNA in PB at diagnosis objectively identify children with ultrahigh-risk disease who may benefit from novel treatment approaches. The level of TH, PHOX2B, and DCX mRNA in BM and/or PB at diagnosis might contribute to an algorithm to improve stratification of children for treatment.

High EDSS can predict risk for upper urinary tract damage in patients with multiple sclerosis.

PubMed

Ineichen, Benjamin V; Schneider, Marc P; Hlavica, Martin; Hagenbuch, Niels; Linnebank, Michael; Kessler, Thomas M

2018-04-01

Neurogenic lower urinary tract dysfunction (NLUTD) is very common in patients with multiple sclerosis (MS), and it might jeopardize renal function and thereby increase mortality. Although there are well-known urodynamic risk factors for upper urinary tract damage, no clinical prediction parameters are available. We aimed to assess clinical parameters potentially predicting urodynamic risk factors for upper urinary tract damage. A consecutive series of 141 patients with MS referred from neurologists for primary neuro-urological work-up including urodynamics were prospectively evaluated. Clinical parameters taken into account were age, sex, duration, and clinical course of MS and Expanded Disability Status Scale (EDSS). Multivariate modeling revealed EDSS as a clinical parameter significantly associated with urodynamic risk factors for upper urinary tract damage (odds ratio = 1.34, 95% confidence interval (CI) = 1.06-1.71, p = 0.02). Using receiver operator characteristic (ROC) curves, an EDSS of 5.0 as cutoff showed a sensitivity of 86%-87% and a specificity of 52% for at least one urodynamic risk factor for upper urinary tract damage. High EDSS is significantly associated with urodynamic risk factors for upper urinary tract damage and allows a risk-dependent stratification in daily neurological clinical practice to identify MS patients requiring further neuro-urological assessment and treatment.

Selective Methicillin-Resistant Staphylococcus Aureus (MRSA) screening of a high risk population does not adequately detect MRSA carriers within a country with low MRSA prevalence.

PubMed

de Wouters, Solange; Daxhelet, Jérémy; Kaminski, Ludovic; Thienpont, Emmanuel; Cornu, Olivier; Yombi, Jean Cyr

2015-12-01

Methicillin-Resistant Staphylococcus Aureus (MRSA) has been widely recognized as a serious problem in hospital settings. The purpose of this study is to evaluate the predictive value of MRSA colonization factors in the detection of MRSA carriers in an orthopedic ward. A systematic MRSA detection strategy was set up to assess the predictive value of MRSA colonization factors among 554 patients undergoing elective knee arthroplasty. In total 116 patients were found positive for Staphylococcus Aureus; among those 110/116 patients were found positive for Methicillin-Sensitive Staphylococcus Aureus (MSSA) and 6/116 for MRSA. Only one patient out of six presented two risk factors according to MRSA risk factors. In this study, no correlation was found between the remaining conventional risk factors, according to Belgian guidelines, defined to target high-risk populations and to identify MRSA carriers. Established criteria for selective MRSA screening do not allow detecting MRSA carriers. The objective of detecting MRSA carriers is not correctly met by the actual applied criteria (Belgian consensus) for a selective screening policy. Future studies should aim at identifying the right risk factors, depending of the country's prevalence of MRSA, to improve the ability to predict the risk of MRSA carriage at hospital admission.

Utility functions predict variance and skewness risk preferences in monkeys

PubMed Central

Genest, Wilfried; Stauffer, William R.; Schultz, Wolfram

2016-01-01

Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals’ preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals’ preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys’ choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences. PMID:27402743

Utility functions predict variance and skewness risk preferences in monkeys.

PubMed

Genest, Wilfried; Stauffer, William R; Schultz, Wolfram

2016-07-26

Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals' preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals' preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys' choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences.

High-definition endoscopy with digital chromoendoscopy for histologic prediction of distal colorectal polyps.

PubMed

Rath, Timo; Tontini, Gian E; Nägel, Andreas; Vieth, Michael; Zopf, Steffen; Günther, Claudia; Hoffman, Arthur; Neurath, Markus F; Neumann, Helmut

2015-10-22

Distal diminutive colorectal polyps are common and accurate endoscopic prediction of hyperplastic or adenomatous polyp histology could reduce procedural time, costs and potential risks associated with the resection. Within this study we assessed whether digital chromoendoscopy can accurately predict the histology of distal diminutive colorectal polyps according to the ASGE PIVI statement. In this prospective cohort study, 224 consecutive patients undergoing screening or surveillance colonoscopy were included. Real time histology of 121 diminutive distal colorectal polyps was evaluated using high-definition endoscopy with digital chromoendoscopy and the accuracy of predicting histology with digital chromoendoscopy was assessed. The overall accuracy of digital chromoendoscopy for prediction of adenomatous polyp histology was 90.1 %. Sensitivity, specificity, positive and negative predictive values were 93.3, 88.7, 88.7, and 93.2 %, respectively. In high-confidence predictions, the accuracy increased to 96.3 % while sensitivity, specificity, positive and negative predictive values were calculated as 98.1, 94.4, 94.5, and 98.1 %, respectively. Surveillance intervals with digital chromoendoscopy were correctly predicted with >90 % accuracy. High-definition endoscopy in combination with digital chromoendoscopy allowed real-time in vivo prediction of distal colorectal polyp histology and is accurate enough to leave distal colorectal polyps in place without resection or to resect and discard them without pathologic assessment. This approach has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps. ClinicalTrials NCT02217449.

The Priority Heuristic: Making Choices Without Trade-Offs

PubMed Central

Brandstätter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2010-01-01

Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, we generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (i) Allais' paradox, (ii) risk aversion for gains if probabilities are high, (iii) risk seeking for gains if probabilities are low (lottery tickets), (iv) risk aversion for losses if probabilities are low (buying insurance), (v) risk seeking for losses if probabilities are high, (vi) certainty effect, (vii) possibility effect, and (viii) intransitivities. We test how accurately the heuristic predicts people's choices, compared to previously proposed heuristics and three modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory. PMID:16637767

Clustering of lifestyle risk behaviours among residents of forty deprived neighbourhoods in London: lessons for targeting public health interventions.

PubMed

Watts, P; Buck, D; Netuveli, G; Renton, A

2016-06-01

Clustering of lifestyle risk behaviours is very important in predicting premature mortality. Understanding the extent to which risk behaviours are clustered in deprived communities is vital to most effectively target public health interventions. We examined co-occurrence and associations between risk behaviours (smoking, alcohol consumption, poor diet, low physical activity and high sedentary time) reported by adults living in deprived London neighbourhoods. Associations between sociodemographic characteristics and clustered risk behaviours were examined. Latent class analysis was used to identify underlying clustering of behaviours. Over 90% of respondents reported at least one risk behaviour. Reporting specific risk behaviours predicted reporting of further risk behaviours. Latent class analyses revealed four underlying classes. Membership of a maximal risk behaviour class was more likely for young, white males who were unable to work. Compared with recent national level analysis, there was a weaker relationship between education and clustering of behaviours and a very high prevalence of clustering of risk behaviours in those unable to work. Young, white men who report difficulty managing on income were at high risk of reporting multiple risk behaviours. These groups may be an important target for interventions to reduce premature mortality caused by multiple risk behaviours. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Ultrasonographic measurements of subclinical carotid atherosclerosis in prediction of ischemic stroke.

PubMed

Mathiesen, E B; Johnsen, S H

2009-01-01

Carotid intima-media thickness (IMT) and plaque measurements are widely used to quantify atherosclerosis and assess the risk of future stroke, and are used as surrogate endpoints for clinical disease. In recent years, it has become clear that carotid IMT and plaque reflect biologically and genetically different aspects of the atherosclerotic process, and are differentially related to risk factors and cardiovascular disease. Plaques are focal manifestations of atherosclerosis while increased IMT represents mainly hypertensive medial hypertrophy. Several prospective studies have showed that IMT and plaque measurements, such as total plaque area and plaque number, are predictive of future stroke. Plaque echogenicity predicts future stroke independent of plaque size. The contribution of IMT and plaque measurements in individual stroke risk prediction in the general population seems to be limited, but may be useful as a tool for individual stratification of high-risk patients.

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis.

PubMed

Fazel, Seena; Singh, Jay P; Doll, Helen; Grann, Martin

2012-07-24

To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator-the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Risk assessments were conducted on 73 samples comprising 24,847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management.

Risk Map of Cholera Infection for Vaccine Deployment: The Eastern Kolkata Case

PubMed Central

You, Young Ae; Ali, Mohammad; Kanungo, Suman; Sah, Binod; Manna, Byomkesh; Puri, Mahesh; Nair, G. Balakrish; Bhattacharya, Sujit Kumar; Convertino, Matteo; Deen, Jacqueline L.; Lopez, Anna Lena; Wierzba, Thomas F.; Clemens, John; Sur, Dipika

2013-01-01

Background Despite advancement of our knowledge, cholera remains a public health concern. During March-April 2010, a large cholera outbreak afflicted the eastern part of Kolkata, India. The quantification of importance of socio-environmental factors in the risk of cholera, and the calculation of the risk is fundamental for deploying vaccination strategies. Here we investigate socio-environmental characteristics between high and low risk areas as well as the potential impact of vaccination on the spatial occurrence of the disease. Methods and Findings The study area comprised three wards of Kolkata Municipal Corporation. A mass cholera vaccination campaign was conducted in mid-2006 as the part of a clinical trial. Cholera cases and data of the trial to identify high risk areas for cholera were analyzed. We used a generalized additive model (GAM) to detect risk areas, and to evaluate the importance of socio-environmental characteristics between high and low risk areas. During the one-year pre-vaccination and two-year post-vaccination periods, 95 and 183 cholera cases were detected in 111,882 and 121,827 study participants, respectively. The GAM model predicts that high risk areas in the west part of the study area where the outbreak largely occurred. High risk areas in both periods were characterized by poor people, use of unsafe water, and proximity to canals used as the main drainage for rain and waste water. Cholera vaccine uptake was significantly lower in the high risk areas compared to low risk areas. Conclusion The study shows that even a parsimonious model like GAM predicts high risk areas where cholera outbreaks largely occurred. This is useful for indicating where interventions would be effective in controlling the disease risk. Data showed that vaccination decreased the risk of infection. Overall, the GAM-based risk map is useful for policymakers, especially those from countries where cholera remains to be endemic with periodic outbreaks. PMID:23936491

The Johns Hopkins Fall Risk Assessment Tool: A Study of Reliability and Validity.

PubMed

Poe, Stephanie S; Dawson, Patricia B; Cvach, Maria; Burnett, Margaret; Kumble, Sowmya; Lewis, Maureen; Thompson, Carol B; Hill, Elizabeth E

Patient falls and fall-related injury remain a safety concern. The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) was developed to facilitate early detection of risk for anticipated physiologic falls in adult inpatients. Psychometric properties in acute care settings have not yet been fully established; this study sought to fill that gap. Results indicate that the JHFRAT is reliable, with high sensitivity and negative predictive validity. Specificity and positive predictive validity were lower than expected.

Predicting Epidemic Risk from Past Temporal Contact Data

PubMed Central

Valdano, Eugenio; Poletto, Chiara; Giovannini, Armando; Palma, Diana; Savini, Lara; Colizza, Vittoria

2015-01-01

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies. PMID:25763816

Predictive Validity of the Columbia-Suicide Severity Rating Scale for Short-Term Suicidal Behavior: A Danish Study of Adolescents at a High Risk of Suicide.

PubMed

Conway, Paul Maurice; Erlangsen, Annette; Teasdale, Thomas William; Jakobsen, Ida Skytte; Larsen, Kim Juul

2017-07-03

Using the Columbia-Suicide Severity Rating Scale (C-SSRS), we examined the predictive and incremental predictive validity of past-month suicidal behavior and ideation for short-term suicidal behavior among adolescents at high risk of suicide. The study was conducted in 2014 on a sample of 85 adolescents (90.6% females) who participated at follow-up (85.9%) out of the 99 (49.7%) baseline respondents. All adolescents were recruited from a specialized suicide-prevention clinic in Denmark. Through multivariate logistic regression analyses, we examined whether baseline suicidal behavior predicted subsequent suicidal behavior (actual attempts and suicidal behavior of any type, including preparatory acts, aborted, interrupted and actual attempts; mean follow-up of 80.8 days, SD = 52.4). Furthermore, we examined whether suicidal ideation severity and intensity incrementally predicted suicidal behavior at follow-up over and above suicidal behavior at baseline. Actual suicide attempts at baseline strongly predicted suicide attempts at follow-up. Baseline suicidal ideation severity and intensity did not significantly predict future actual attempts over and above baseline attempts. The suicidal ideation intensity items deterrents and duration were significant predictors of subsequent actual attempts after adjustment for baseline suicide attempts and suicidal behavior of any type, respectively. Suicidal ideation severity and intensity, and the intensity items frequency, duration and deterrents, all significantly predicted any type of suicidal behavior at follow-up, also after adjusting for baseline suicidal behavior. The present study points to an incremental predictive validity of the C-SSRS suicidal ideation scales for short-term suicidal behavior of any type among high-risk adolescents.

Metabolomic analysis of 92 pulmonary embolism patients from a nested case-control study identifies metabolites associated with adverse clinical outcomes.

PubMed

Zeleznik, O A; Poole, E M; Lindstrom, S; Kraft, P; Van Hylckama Vlieg, A; Lasky-Su, J A; Harrington, L B; Hagan, K; Kim, J; Parry, B A; Giordano, N; Kabrhel, C

2018-03-01

Essentials Risk-stratification often fails to predict clinical deterioration in pulmonary embolism (PE). First-ever high-throughput metabolomics analysis of risk-stratified PE patients. Changes in circulating metabolites reflect a compromised energy metabolism in PE. Metabolites play a key role in the pathophysiology and risk stratification of PE. Background Patients with acute pulmonary embolism (PE) exhibit wide variation in clinical presentation and outcomes. Our understanding of the pathophysiologic mechanisms differentiating low-risk and high-risk PE is limited, so current risk-stratification efforts often fail to predict clinical deterioration and are insufficient to guide management. Objectives To improve our understanding of the physiology differentiating low-risk from high-risk PE, we conducted the first-ever high-throughput metabolomics analysis (843 named metabolites) comparing PE patients across risk strata within a nested case-control study. Patients/methods We enrolled 92 patients diagnosed with acute PE and collected plasma within 24 h of PE diagnosis. We used linear regression and pathway analysis to identify metabolites and pathways associated with PE risk-strata. Results When we compared 46 low-risk with 46 intermediate/high-risk PEs, 50 metabolites were significantly different after multiple testing correction. These metabolites were enriched in the following pathways: tricarboxylic acid (TCA) cycle, fatty acid metabolism (acyl carnitine) and purine metabolism, (hypo)xanthine/inosine containing. Additionally, energy, nucleotide and amino acid pathways were downregulated in intermediate/high-risk PE patients. When we compared 28 intermediate-risk with 18 high-risk PE patients, 41 metabolites differed at a nominal P-value level. These metabolites were enriched in fatty acid metabolism (acyl cholines), and hemoglobin and porphyrin metabolism. Conclusion Our results suggest that high-throughput metabolomics can provide insight into the pathophysiology of PE. Specifically, changes in circulating metabolites reflect compromised energy metabolism in intermediate/high-risk PE patients. These findings demonstrate the important role metabolites play in the pathophysiology of PE and highlight metabolomics as a potential tool for risk stratification of PE. © 2017 International Society on Thrombosis and Haemostasis.

Race, Genetic West African Ancestry, and Prostate Cancer Prediction by PSA in Prospectively Screened High-Risk Men

PubMed Central

Giri, Veda N.; Egleston, Brian; Ruth, Karen; Uzzo, Robert G.; Chen, David Y.T.; Buyyounouski, Mark; Raysor, Susan; Hooker, Stanley; Torres, Jada Benn; Ramike, Teniel; Mastalski, Kathleen; Kim, Taylor Y.; Kittles, Rick

2008-01-01

Introduction “Race-specific” PSA needs evaluation in men at high-risk for prostate cancer (PCA) for optimizing early detection. Baseline PSA and longitudinal prediction for PCA was examined by self-reported race and genetic West African (WA) ancestry in the Prostate Cancer Risk Assessment Program, a prospective high-risk cohort. Materials and Methods Eligibility criteria are age 35–69 years, FH of PCA, African American (AA) race, or BRCA1/2 mutations. Biopsies have been performed at low PSA values (<4.0 ng/mL). WA ancestry was discerned by genotyping 100 ancestry informative markers. Cox proportional hazards models evaluated baseline PSA, self-reported race, and genetic WA ancestry. Cox models were used for 3-year predictions for PCA. Results 646 men (63% AA) were analyzed. Individual WA ancestry estimates varied widely among self-reported AA men. “Race-specific” differences in baseline PSA were not found by self-reported race or genetic WA ancestry. Among men with ≥ 1 follow-up visit (405 total, 54% AA), three-year prediction for PCA with a PSA of 1.5–4.0 ng/mL was higher in AA men with age in the model (p=0.025) compared to EA men. Hazard ratios of PSA for PCA were also higher by self-reported race (1.59 for AA vs. 1.32 for EA, p=0.04). There was a trend for increasing prediction for PCA with increasing genetic WA ancestry. Conclusions “Race-specific” PSA may need to be redefined as higher prediction for PCA at any given PSA in AA men. Large-scale studies are needed to confirm if genetic WA ancestry explains these findings to make progress in personalizing PCA early detection. PMID:19240249

Analysis of the American Society of Anesthesiologists Physical Status Classification System and Caprini Risk Assessment Model in Predicting Venous Thromboembolic Outcomes in Plastic Surgery Patients.

PubMed

Shaikh, Mohammad-Ali; Jeong, Haneol S; Mastro, Andrew; Davis, Kathryn; Lysikowski, Jerzy; Kenkel, Jeffrey M

2016-04-01

Venous thromboembolism (VTE) can be a fatal outcome of plastic surgery. Risk assessment models attempt to determine a patient's risk, yet few studies have compared different models in plastic surgery patients. The authors investigated preoperative ASA physical status and 2005 Caprini scores to determine which model was more predictive of VTE. A retrospective chart review examined 1801 patients undergoing contouring and reconstructive procedures from January 2008 to January 2012. Patients were grouped into risk tiers for ASA scores (1-2 = low, 3+ = high) with 2 cutoffs for Caprini scores (1-4 = low, 5+ high; 1-5 = low, 6+ = high), then re-stratified into 3 tiers using Caprini score cutoffs (1-4 = low, 5-8 = high, 9+ = highest; 1-5 = low, 6-8 = high, 9+ = highest). Median scores of VTE patients were compared to those without VTE. Odds ratio and chi-squared analyses were performed. Of the 1598 patients included in the study, 1.50% developed VTE. Median ASA scores differed significantly between comparison groups but Caprini scores did not vary regardless of cutoff. When examining the 2-tiered Caprini scores, using low risk = 1-5 showed a significant relationship between risk tier and DVT development (P = 0.0266). The ASA system yielded the highest odds ratio of VTE development between low and high-risk patients. The Caprini model captured more patients with VTE in its high-risk category. Combining the two models for a more heuristic approach to preoperative care may identify patients at higher risk. 4 Risk. © 2015 The American Society for Aesthetic Plastic Surgery, Inc. Reprints and permission: journals.permissions@oup.com.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

Gestational Diabetes Mellitus Risk score: A practical tool to predict Gestational Diabetes Mellitus risk in Tanzania.

PubMed

Patrick Nombo, Anna; Wendelin Mwanri, Akwilina; Brouwer-Brolsma, Elske M; Ramaiya, Kaushik L; Feskens, Edith

2018-05-28

Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania. We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives). Mid-upper arm circumference (MUAC) ≥28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals. The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain. Copyright © 2018. Published by Elsevier B.V.

Serum creatinine and bilirubin predict renal failure and mortality in patients with spontaneous bacterial peritonitis: a retrospective study.

PubMed

Terg, Rubén; Gadano, Adrian; Cartier, Mariano; Casciato, Paola; Lucero, Romina; Muñoz, Alberto; Romero, Gustavo; Levi, Diana; Terg, Gonzalo; Miguez, Carlos; Abecasis, Raquel

2009-03-01

Patients with spontaneous bacterial peritonitis (SBP) are at a high risk for renal failure and death despite successful treatment of infection. Intravenous (IV) albumin administration combined with antibiotic treatment has been shown to significantly decrease these risks. Clinical evidence is lacking on which patients are appropriate candidates for albumin treatment. To retrospectively analyse the usefulness of serum creatinine and bilirubin levels in predicting renal failure and mortality of patients hospitalized for SBP. Between March 1995 and September 1998, 127 cirrhotic patients with SBP who had not received plasma expansion were evaluated. Eighty-one patients (64%) were classified as having a high risk for renal failure and mortality (serum bilirubin >4 mg/dl or serum creatinine >1 mg/dl) and 46 (36%) as having a low risk. At admission, 36.3% of all patients presented renal failure. Mortality during their hospitalization was 23% among those with a high risk and 6.5% among those with a low risk (P=0.01). Renal failure occurred in 23% of the high-risk patients, compared with 2.6% of the low-risk patients (P=0.006). The presence of hyponatraemia was significantly associated with higher mortality and renal failure in the high-risk group. Our retrospective review of patients with SBP suggests that serum bilirubin levels >4 mg and serum creatinine levels >1 mg/dl at the time of diagnosis represent significant risk factors for the clinical outcomes of patients with SBP. Patients without these risk factors may have a very low likelihood of death or renal failure.

A time series modeling approach in risk appraisal of violent and sexual recidivism.

PubMed

Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E

2010-10-01

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

Biomarkers from distinct biological pathways improve early risk stratification in medical emergency patients: the multinational, prospective, observational TRIAGE study.

PubMed

Schuetz, Philipp; Hausfater, Pierre; Amin, Devendra; Amin, Adina; Haubitz, Sebastian; Faessler, Lukas; Kutz, Alexander; Conca, Antoinette; Reutlinger, Barbara; Canavaggio, Pauline; Sauvin, Gabrielle; Bernard, Maguy; Huber, Andreas; Mueller, Beat

2015-10-29

Early risk stratification in the emergency department (ED) is vital to reduce time to effective treatment in high-risk patients and to improve patient flow. Yet, there is a lack of investigations evaluating the incremental usefulness of multiple biomarkers measured upon admission from distinct biological pathways for predicting fatal outcome and high initial treatment urgency in unselected ED patients in a multicenter and multinational setting. We included consecutive, adult, medical patients seeking ED care into this observational, cohort study in Switzerland, France and the USA. We recorded initial clinical parameters and batch-measured prognostic biomarkers of inflammation (pro-adrenomedullin [ProADM]), stress (copeptin) and infection (procalcitonin). During a 30-day follow-up, 331 of 7132 (4.6 %) participants reached the primary endpoint of death within 30 days. In logistic regression models adjusted for conventional risk factors available at ED admission, all three biomarkers strongly predicted the risk of death (AUC 0.83, 0.78 and 0.75), ICU admission (AUC 0.67, 0.69 and 0.62) and high initial triage priority (0.67, 0.66 and 0.58). For the prediction of death, ProADM significantly improved regression models including (a) clinical information available at ED admission (AUC increase from 0.79 to 0.84), (b) full clinical information at ED discharge (AUC increase from 0.85 to 0.88), and (c) triage information (AUC increase from 0.67 to 0.83) (p <0.01 for each comparison). Similarly, ProADM also improved clinical models for prediction of ICU admission and high initial treatment urgency. Results were robust in regard to predefined patient subgroups by center, main diagnosis, presenting symptoms, age and gender. Combination of clinical information with results of blood biomarkers measured upon ED admission allows early and more adequate risk stratification in individual unselected medical ED patients. A randomized trial is needed to answer the question whether biomarker-guided initial patient triage reduces time to initial treatment of high-risk patients in the ED and thereby improves patient flow and clinical outcomes. ClinicalTrials.gov NCT01768494 . Registered January 9, 2013.

C-reactive protein, fibrinogen, and cardiovascular disease prediction.

PubMed

Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa; Wood, Angela M; White, Ian R; Gao, Pei; Walker, Matthew; Thompson, Alexander; Sarwar, Nadeem; Caslake, Muriel; Butterworth, Adam S; Amouyel, Philippe; Assmann, Gerd; Bakker, Stephan J L; Barr, Elizabeth L M; Barrett-Connor, Elizabeth; Benjamin, Emelia J; Björkelund, Cecilia; Brenner, Hermann; Brunner, Eric; Clarke, Robert; Cooper, Jackie A; Cremer, Peter; Cushman, Mary; Dagenais, Gilles R; D'Agostino, Ralph B; Dankner, Rachel; Davey-Smith, George; Deeg, Dorly; Dekker, Jacqueline M; Engström, Gunnar; Folsom, Aaron R; Fowkes, F Gerry R; Gallacher, John; Gaziano, J Michael; Giampaoli, Simona; Gillum, Richard F; Hofman, Albert; Howard, Barbara V; Ingelsson, Erik; Iso, Hiroyasu; Jørgensen, Torben; Kiechl, Stefan; Kitamura, Akihiko; Kiyohara, Yutaka; Koenig, Wolfgang; Kromhout, Daan; Kuller, Lewis H; Lawlor, Debbie A; Meade, Tom W; Nissinen, Aulikki; Nordestgaard, Børge G; Onat, Altan; Panagiotakos, Demosthenes B; Psaty, Bruce M; Rodriguez, Beatriz; Rosengren, Annika; Salomaa, Veikko; Kauhanen, Jussi; Salonen, Jukka T; Shaffer, Jonathan A; Shea, Steven; Ford, Ian; Stehouwer, Coen D A; Strandberg, Timo E; Tipping, Robert W; Tosetto, Alberto; Wassertheil-Smoller, Sylvia; Wennberg, Patrik; Westendorp, Rudi G; Whincup, Peter H; Wilhelmsen, Lars; Woodward, Mark; Lowe, Gordon D O; Wareham, Nicholas J; Khaw, Kay-Tee; Sattar, Naveed; Packard, Chris J; Gudnason, Vilmundur; Ridker, Paul M; Pepys, Mark B; Thompson, Simon G; Danesh, John

2012-10-04

There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. We analyzed data from 52 prospective studies that included 246,669 participants without a history of cardiovascular disease to investigate the value of adding CRP or fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. We calculated measures of discrimination and reclassification during follow-up and modeled the clinical implications of initiation of statin therapy after the assessment of CRP or fibrinogen. The addition of information on high-density lipoprotein cholesterol to a prognostic model for cardiovascular disease that included age, sex, smoking status, blood pressure, history of diabetes, and total cholesterol level increased the C-index, a measure of risk discrimination, by 0.0050. The further addition to this model of information on CRP or fibrinogen increased the C-index by 0.0039 and 0.0027, respectively (P<0.001), and yielded a net reclassification improvement of 1.52% and 0.83%, respectively, for the predicted 10-year risk categories of "low" (<10%), "intermediate" (10% to <20%), and "high" (≥20%) (P<0.02 for both comparisons). We estimated that among 100,000 adults 40 years of age or older, 15,025 persons would initially be classified as being at intermediate risk for a cardiovascular event if conventional risk factors alone were used to calculate risk. Assuming that statin therapy would be initiated in accordance with Adult Treatment Panel III guidelines (i.e., for persons with a predicted risk of ≥20% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), additional targeted assessment of CRP or fibrinogen levels in the 13,199 remaining participants at intermediate risk could help prevent approximately 30 additional cardiovascular events over the course of 10 years. In a study of people without known cardiovascular disease, we estimated that under current treatment guidelines, assessment of the CRP or fibrinogen level in people at intermediate risk for a cardiovascular event could help prevent one additional event over a period of 10 years for every 400 to 500 people screened. (Funded by the British Heart Foundation and others.).

Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool.

PubMed

Fazel, Seena; Chang, Zheng; Fanshawe, Thomas; Långström, Niklas; Lichtenstein, Paul; Larsson, Henrik; Mallett, Susan

2016-06-01

More than 30 million people are released from prison worldwide every year, who include a group at high risk of perpetrating interpersonal violence. Because there is considerable inconsistency and inefficiency in identifying those who would benefit from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent offending in released prisoners. We did a cohort study of a population of released prisoners in Sweden. Through linkage of population-based registers, we developed predictive models for violent reoffending for the cohort. First, we developed a derivation model to determine the strength of prespecified, routinely obtained criminal history, sociodemographic, and clinical risk factors using multivariable Cox proportional hazard regression, and then tested them in an external validation. We measured discrimination and calibration for prediction of our primary outcome of violent reoffending at 1 and 2 years using cutoffs of 10% for 1-year risk and 20% for 2-year risk. We identified a cohort of 47 326 prisoners released in Sweden between 2001 and 2009, with 11 263 incidents of violent reoffending during this period. We developed a 14-item derivation model to predict violent reoffending and tested it in an external validation (assigning 37 100 individuals to the derivation sample and 10 226 to the validation sample). The model showed good measures of discrimination (Harrell's c-index 0·74) and calibration. For risk of violent reoffending at 1 year, sensitivity was 76% (95% CI 73-79) and specificity was 61% (95% CI 60-62). Positive and negative predictive values were 21% (95% CI 19-22) and 95% (95% CI 94-96), respectively. At 2 years, sensitivity was 67% (95% CI 64-69) and specificity was 70% (95% CI 69-72). Positive and negative predictive values were 37% (95% CI 35-39) and 89% (95% CI 88-90), respectively. Of individuals with a predicted risk of violent reoffending of 50% or more, 88% had drug and alcohol use disorders. We used the model to generate a simple, web-based, risk calculator (OxRec) that is free to use. We have developed a prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent offending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment. Further assessments in other populations and countries are needed. Wellcome Trust, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare. Copyright © 2016 Fazel et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.

The function of credibility in information processing for risk perception.

PubMed

Trumbo, Craig W; McComas, Katherine A

2003-04-01

This study examines how credibility affects the way people process information and how they subsequently perceive risk. Three conceptual areas are brought together in this analysis: the psychometric model of risk perception, Eagly and Chaiken's heuristic-systematic information processing model, and Meyer's credibility index. Data come from a study of risk communication in the circumstance of state health department investigations of suspected cancer clusters (five cases, N = 696). Credibility is assessed for three information sources: state health departments, citizen groups, and industries involved in each case. Higher credibility for industry and the state directly predicts lower risk perception, whereas high credibility for citizen groups predicts greater risk perception. A path model shows that perceiving high credibility for industry and state-and perceiving low credibility for citizen groups-promotes heuristic processing, which in turn is a strong predictor of lower risk perception. Alternately, perceiving industry and the state to have low credibility also promotes greater systematic processing, which consistently leads to perception of greater risk. Between a one-fifth and one-third of the effect of credibility on risk perception is shown to be indirectly transmitted through information processing.

A test of an interactive model of binge eating among undergraduate men.

PubMed

Minnich, Allison M; Gordon, Kathryn H; Holm-Denoma, Jill M; Troop-Gordon, Wendy

2014-12-01

Past research has shown that a combination of high perfectionism, high body dissatisfaction, and low self-esteem is predictive of binge eating in college women (Bardone-Cone et al., 2006). In the current study, we examined whether this triple interaction model is applicable to men. Male undergraduate college students from a large Midwestern university (n=302) completed self-report measures online at two different time points, a minimum of eight weeks apart. Analyses revealed a significant interaction between the three risk factors, such that high perfectionism, high body dissatisfaction, and low self-esteem at Time 1 were associated with higher levels of Time 2 binge eating symptoms. The triple interaction model did not predict Time 2 anxiety or depressive symptoms, which suggests model specificity. These findings offer a greater understanding of the interactive nature of risk factors in predicting binge eating symptoms among men. Copyright © 2014 Elsevier Ltd. All rights reserved.

The impact of numeracy on reactions to different graphic risk presentation formats: An experimental analogue study.

PubMed

Wright, Alison J; Whitwell, Sophia C L; Takeichi, Chika; Hankins, Matthew; Marteau, Theresa M

2009-02-01

Numeracy, the ability to process basic mathematical concepts, may affect responses to graphical displays of health risk information. Displays of probabilistic risk information using grouped dots are easier to understand than displays using dispersed dots. However, dispersed dots may better convey the randomness with which health threats occur, so increasing perceived susceptibility. We hypothesized that low numeracy participants would better understand risks presented using grouped dot displays, while high numeracy participants would have good understanding, regardless of display type. Moreover, we predicted that dispersed dot displays, in contrast to grouped dot displays, would increase risk perceptions and worry only for highly numerate individuals. One hundred and forty smokers read vignettes asking them to imagine being at risk of Crohn's disease, in a 2(display type: dispersed/grouped dots) x 3(risk magnitude: 3%/6%/50%) x 2(numeracy: high/low) design. They completed measures of risk comprehension, perceived susceptibility and worry. More numerate participants had better objective risk comprehension, but this effect was not moderated by display type. There was marginally significant support for the predicted numeracy x display type interaction for worry about Crohn's disease, but not for perceived susceptibility to the condition. Dispersed dot displays somewhat increase worry in highly numerate individuals, but only numeracy influenced objective risk comprehension. The most effective display type for communicating risk information will depend on the numeracy of the population and the goal(s) of the communication.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

The cost of being valuable: predictors of extinction risk in marine invertebrates exploited as luxury seafood

PubMed Central

Purcell, Steven W.; Polidoro, Beth A.; Hamel, Jean-François; Gamboa, Ruth U.; Mercier, Annie

2014-01-01

Extinction risk has been linked to biological and anthropogenic variables. Prediction of extinction risk in valuable fauna may not follow mainstream drivers when species are exploited for international markets. We use results from an International Union for Conservation of Nature Red List assessment of extinction risk in all 377 known species of sea cucumber within the order Aspidochirotida, many of which are exploited worldwide as luxury seafood for Asian markets. Extinction risk was primarily driven by high market value, compounded by accessibility and familiarity (well known) in the marketplace. Extinction risk in marine animals often relates closely to body size and small geographical range but our study shows a clear exception. Conservation must not lose sight of common species, especially those of high value. Greater human population density and poorer economies in the geographical ranges of endangered species illustrate that anthropogenic variables can also predict extinction risks in marine animals. Local-level regulatory measures must prevent opportunistic exploitation of high-value species. Trade agreements, for example CITES, may aid conservation but will depend on international technical support to low-income tropical countries. The high proportion of data deficient species also stresses a need for research on the ecology and population demographics of unglamorous invertebrates. PMID:24598425

The cost of being valuable: predictors of extinction risk in marine invertebrates exploited as luxury seafood.

PubMed

Purcell, Steven W; Polidoro, Beth A; Hamel, Jean-François; Gamboa, Ruth U; Mercier, Annie

2014-04-22

Extinction risk has been linked to biological and anthropogenic variables. Prediction of extinction risk in valuable fauna may not follow mainstream drivers when species are exploited for international markets. We use results from an International Union for Conservation of Nature Red List assessment of extinction risk in all 377 known species of sea cucumber within the order Aspidochirotida, many of which are exploited worldwide as luxury seafood for Asian markets. Extinction risk was primarily driven by high market value, compounded by accessibility and familiarity (well known) in the marketplace. Extinction risk in marine animals often relates closely to body size and small geographical range but our study shows a clear exception. Conservation must not lose sight of common species, especially those of high value. Greater human population density and poorer economies in the geographical ranges of endangered species illustrate that anthropogenic variables can also predict extinction risks in marine animals. Local-level regulatory measures must prevent opportunistic exploitation of high-value species. Trade agreements, for example CITES, may aid conservation but will depend on international technical support to low-income tropical countries. The high proportion of data deficient species also stresses a need for research on the ecology and population demographics of unglamorous invertebrates.

A biomarker-based risk score to predict death in patients with atrial fibrillation: the ABC (age, biomarkers, clinical history) death risk score

PubMed Central

Hijazi, Ziad; Oldgren, Jonas; Lindbäck, Johan; Alexander, John H; Connolly, Stuart J; Eikelboom, John W; Ezekowitz, Michael D; Held, Claes; Hylek, Elaine M; Lopes, Renato D; Yusuf, Salim; Granger, Christopher B; Siegbahn, Agneta; Wallentin, Lars

2018-01-01

Abstract Aims In atrial fibrillation (AF), mortality remains high despite effective anticoagulation. A model predicting the risk of death in these patients is currently not available. We developed and validated a risk score for death in anticoagulated patients with AF including both clinical information and biomarkers. Methods and results The new risk score was developed and internally validated in 14 611 patients with AF randomized to apixaban vs. warfarin for a median of 1.9 years. External validation was performed in 8548 patients with AF randomized to dabigatran vs. warfarin for 2.0 years. Biomarker samples were obtained at study entry. Variables significantly contributing to the prediction of all-cause mortality were assessed by Cox-regression. Each variable obtained a weight proportional to the model coefficients. There were 1047 all-cause deaths in the derivation and 594 in the validation cohort. The most important predictors of death were N-terminal pro B-type natriuretic peptide, troponin-T, growth differentiation factor-15, age, and heart failure, and these were included in the ABC (Age, Biomarkers, Clinical history)-death risk score. The score was well-calibrated and yielded higher c-indices than a model based on all clinical variables in both the derivation (0.74 vs. 0.68) and validation cohorts (0.74 vs. 0.67). The reduction in mortality with apixaban was most pronounced in patients with a high ABC-death score. Conclusion A new biomarker-based score for predicting risk of death in anticoagulated AF patients was developed, internally and externally validated, and well-calibrated in two large cohorts. The ABC-death risk score performed well and may contribute to overall risk assessment in AF. ClinicalTrials.gov identifier NCT00412984 and NCT00262600 PMID:29069359

Usefulness of the novel risk estimation software, Heart Risk View, for the prediction of cardiac events in patients with normal myocardial perfusion SPECT.

PubMed

Sakatani, Tomohiko; Shimoo, Satoshi; Takamatsu, Kazuaki; Kyodo, Atsushi; Tsuji, Yumika; Mera, Kayoko; Koide, Masahiro; Isodono, Koji; Tsubakimoto, Yoshinori; Matsuo, Akiko; Inoue, Keiji; Fujita, Hiroshi

2016-12-01

Myocardial perfusion single-photon emission-computed tomography (SPECT) can predict cardiac events in patients with coronary artery disease with high accuracy; however, pseudo-negative cases sometimes occur. Heart Risk View, which is based on the prospective cohort study (J-ACCESS), is a software for evaluating cardiac event probability. We examined whether Heart Risk View was useful to evaluate the cardiac risk in patients with normal myocardial perfusion SPECT (MPS). We studied 3461 consecutive patients who underwent MPS to detect myocardial ischemia and those who had normal MPS were enrolled in this study (n = 698). We calculated cardiac event probability by Heart Risk View and followed-up for 3.8 ± 2.4 years. The cardiac events were defined as cardiac death, non-fatal myocardial infarction, and heart failure requiring hospitalization. During the follow-up period, 21 patients (3.0 %) had cardiac events. The event probability calculated by Heart Risk View was higher in the event group (5.5 ± 2.6 vs. 2.9 ± 2.6 %, p < 0.001). According to the receiver-operating characteristics curve, the cut-off point of the event probability for predicting cardiac events was 3.4 % (sensitivity 0.76, specificity 0.72, and AUC 0.85). Kaplan-Meier curves revealed that a higher event rate was observed in the high-event probability group by the log-rank test (p < 0.001). Although myocardial perfusion SPECT is useful for the prediction of cardiac events, risk estimation by Heart Risk View adds more prognostic information, especially in patients with normal MPS.

Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer.

PubMed

Chow, Eric J; Chen, Yan; Hudson, Melissa M; Feijen, Elizabeth A M; Kremer, Leontien C; Border, William L; Green, Daniel M; Meacham, Lillian R; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Ronckers, Cécile M; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; van Dijk, Irma W E M; van Leeuwen, Flora E; Weathers, Rita E; Robison, Leslie L; Armstrong, Gregory T; Yasui, Yutaka

2018-01-01

Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children's Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

Identifying Patients With Vesicovaginal Fistula at High Risk of Urinary Incontinence After Surgery

PubMed Central

Bengtson, Angela M.; Kopp, Dawn; Tang, Jennifer H.; Chipungu, Ennet; Moyo, Margaret; Wilkinson, Jeffrey

2016-01-01

Objective To develop a risk score to identify women with vesicovaginal fistula at high risk of residual urinary incontinence after surgical repair. Methods We conducted a prospective cohort study among 401 women undergoing their first vesicovaginal fistula repair at a referral fistula repair center in Lilongwe, Malawi, between September 2011 and December 2014, who returned for follow-up within 120 days of surgery. We used logistic regression to develop a risk score to identify women with high likelihood of residual urinary incontinence, defined as incontinence grade 2-5 within 120 days of vesicovaginal fistula repair, based on preoperative clinical and demographic characteristics (age, number of years with fistula, HIV status, body mass index, previous repair surgery at an outside facility, revised Goh Classification, Goh vesicovaginal fistula size, circumferential fistula, vaginal scaring, bladder size, and urethral length). The sensitivity, specificity, positive and negative predictive values of the risk score at each cut-point were assessed. Results Overall, 11 (3%) women had unsuccessful fistula closure. Of those with successful fistula closure (n=372), 85 (23%) experienced residual incontinence. A risk score cut-point of 20 had sensitivity 82% (95% CI 72%, 89%) and specificity 63% (95% CI 57%, 69%) to potentially identify women with residual incontinence. In our population, the positive predictive value for a risk score cut-point of _20 or higher was 43% (95% CI 36%, 51%) and the negative predictive value was 91% (95% CI 86%, 94%). Forty-eight percent of our study population had a risk score ≥20 and therefore, would have been identified for further intervention. Conclusions A risk score 20 or higher was associated with an increased likelihood of residual incontinence, with satisfactory sensitivity and specificity. If validated in alternative settings, the risk score could be used to refer women with high likelihood of postoperative incontinence to more experienced surgeons. PMID:27741181

Predicting bycatch hotspots for endangered leatherback turtles on longlines in the Pacific Ocean.

PubMed

Roe, John H; Morreale, Stephen J; Paladino, Frank V; Shillinger, George L; Benson, Scott R; Eckert, Scott A; Bailey, Helen; Tomillo, Pilar Santidrián; Bograd, Steven J; Eguchi, Tomoharu; Dutton, Peter H; Seminoff, Jeffrey A; Block, Barbara A; Spotila, James R

2014-02-22

Fisheries bycatch is a critical source of mortality for rapidly declining populations of leatherback turtles, Dermochelys coriacea. We integrated use-intensity distributions for 135 satellite-tracked adult turtles with longline fishing effort to estimate predicted bycatch risk over space and time in the Pacific Ocean. Areas of predicted bycatch risk did not overlap for eastern and western Pacific nesting populations, warranting their consideration as distinct management units with respect to fisheries bycatch. For western Pacific nesting populations, we identified several areas of high risk in the north and central Pacific, but greatest risk was adjacent to primary nesting beaches in tropical seas of Indo-Pacific islands, largely confined to several exclusive economic zones under the jurisdiction of national authorities. For eastern Pacific nesting populations, we identified moderate risk associated with migrations to nesting beaches, but the greatest risk was in the South Pacific Gyre, a broad pelagic zone outside national waters where management is currently lacking and may prove difficult to implement. Efforts should focus on these predicted hotspots to develop more targeted management approaches to alleviate leatherback bycatch.

Predicting bycatch hotspots for endangered leatherback turtles on longlines in the Pacific Ocean

PubMed Central

Roe, John H.; Morreale, Stephen J.; Paladino, Frank V.; Shillinger, George L.; Benson, Scott R.; Eckert, Scott A.; Bailey, Helen; Tomillo, Pilar Santidrián; Bograd, Steven J.; Eguchi, Tomoharu; Dutton, Peter H.; Seminoff, Jeffrey A.; Block, Barbara A.; Spotila, James R.

2014-01-01

Fisheries bycatch is a critical source of mortality for rapidly declining populations of leatherback turtles, Dermochelys coriacea. We integrated use-intensity distributions for 135 satellite-tracked adult turtles with longline fishing effort to estimate predicted bycatch risk over space and time in the Pacific Ocean. Areas of predicted bycatch risk did not overlap for eastern and western Pacific nesting populations, warranting their consideration as distinct management units with respect to fisheries bycatch. For western Pacific nesting populations, we identified several areas of high risk in the north and central Pacific, but greatest risk was adjacent to primary nesting beaches in tropical seas of Indo-Pacific islands, largely confined to several exclusive economic zones under the jurisdiction of national authorities. For eastern Pacific nesting populations, we identified moderate risk associated with migrations to nesting beaches, but the greatest risk was in the South Pacific Gyre, a broad pelagic zone outside national waters where management is currently lacking and may prove difficult to implement. Efforts should focus on these predicted hotspots to develop more targeted management approaches to alleviate leatherback bycatch. PMID:24403331

Maternal Characteristics for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndromes (GOS) Study.

PubMed

Boutin, Amélie; Gasse, Cédric; Demers, Suzanne; Giguère, Yves; Tétu, Amélie; Bujold, Emmanuel

2018-05-01

Low-dose aspirin started in early pregnancy significantly reduces the risk of preeclampsia (PE) in high-risk women, especially preterm PE. This study aimed to evaluate the influence of maternal characteristics on the risk of PE in nulliparous women. The Great Obstetrical Syndromes (GOS) study recruited nulliparous women with singleton pregnancies at 11 to 13 weeks. The following maternal characteristics were collected: age, BMI, ethnicity, chronic diseases, smoking, and assisted reproductive technologies. Relative weight analyses were conducted, and predictive multivariate proportional hazard models were constructed. Receiver operating characteristic curve analyses with their area under the curve (AUC) were used to evaluate the value of each factor for the prediction of PE and preterm PE. The study also evaluated the SOGC guidelines for identification of women at high risk of PE. Of 4739 participants, 232 (4.9%) developed PE, including 30 (0.6%) with preterm PE. In univariate analyses, only BMI was significantly associated with the risk of PE (AUC 0.60; 95% CI 0.55-0.65) and preterm PE (AUC 0.64; 95% CI 054-0.73). Adding other maternal characteristics to BMI had a non-significant and marginal impact on the discriminative ability to the models for PE (AUC 0.62; 95% CI 0.58-0.66) and preterm PE (AUC 0.65; 95% CI 0.56-0.74). At a false-positive rate of 10%, maternal characteristics could have predicted 23% of PE and 19% of preterm PE. The SOGC guidelines were not discriminant for PE (detecting 96% of PE and 93% of preterm PE with a 94% false-positive rate). In nulliparous women, BMI is the most discriminant maternal characteristic for the prediction of PE. Maternal characteristics should not be used alone to identify nulliparous women at high risk of PE. Copyright © 2018 Society of Obstetricians and Gynaecologists of Canada. Published by Elsevier Inc. All rights reserved.

Influence of household rat infestation on leptospira transmission in the urban slum environment.

PubMed

Costa, Federico; Ribeiro, Guilherme S; Felzemburgh, Ridalva D M; Santos, Norlan; Reis, Renato Barbosa; Santos, Andreia C; Fraga, Deborah Bittencourt Mothe; Araujo, Wildo N; Santana, Carlos; Childs, James E; Reis, Mitermayer G; Ko, Albert I

2014-12-01

The Norway rat (Rattus norvegicus) is the principal reservoir for leptospirosis in many urban settings. Few studies have identified markers for rat infestation in slum environments while none have evaluated the association between household rat infestation and Leptospira infection in humans or the use of infestation markers as a predictive model to stratify risk for leptospirosis. We enrolled a cohort of 2,003 urban slum residents from Salvador, Brazil in 2004, and followed the cohort during four annual serosurveys to identify serologic evidence for Leptospira infection. In 2007, we performed rodent infestation and environmental surveys of 80 case households, in which resided at least one individual with Leptospira infection, and 109 control households. In the case-control study, signs of rodent infestation were identified in 78% and 42% of the households, respectively. Regression modeling identified the presence of R. norvegicus feces (OR, 4.95; 95% CI, 2.13-11.47), rodent burrows (2.80; 1.06-7.36), access to water (2.79; 1.28-6.09), and un-plastered walls (2.71; 1.21-6.04) as independent risk factors associated with Leptospira infection in a household. We developed a predictive model for infection, based on assigning scores to each of the rodent infestation risk factors. Receiver operating characteristic curve analysis found that the prediction score produced a good/excellent fit based on an area under the curve of 0.78 (0.71-0.84). Our study found that a high proportion of slum households were infested with R. norvegicus and that rat infestation was significantly associated with the risk of Leptospira infection, indicating that high level transmission occurs among slum households. We developed an easily applicable prediction score based on rat infestation markers, which identified households with highest infection risk. The use of the prediction score in community-based screening may therefore be an effective risk stratification strategy for targeting control measures in slum settings of high leptospirosis transmission.

Treatment Default amongst Patients with Tuberculosis in Urban Morocco: Predicting and Explaining Default and Post-Default Sputum Smear and Drug Susceptibility Results

PubMed Central

Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C.; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E.

2014-01-01

Setting Public tuberculosis (TB) clinics in urban Morocco. Objective Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Design Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals’ perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. Results 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one’s treatment duration. Age >50 years, never smoking, and having friends who knew one’s diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. Conclusion The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings. PMID:24699682

Prediction of psychosis across protocols and risk cohorts using automated language analysis.

PubMed

Corcoran, Cheryl M; Carrillo, Facundo; Fernández-Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C; Bearden, Carrie E; Cecchi, Guillermo A

2018-02-01

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier - comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns - that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. © 2018 World Psychiatric Association.

A prospective study of mandibular trabecular bone to predict fracture incidence in women: a low-cost screening tool in the dental clinic.

PubMed

Jonasson, Grethe; Sundh, Valter; Ahlqwist, Margareta; Hakeberg, Magnus; Björkelund, Cecilia; Lissner, Lauren

2011-10-01

Bone structure is the key to the understanding of fracture risk. The hypothesis tested in this prospective study is that dense mandibular trabeculation predicts low fracture risk, whereas sparse trabeculation is predictive of high fracture risk. Out of 731 women from the Prospective Population Study of Women in Gothenburg with dental examinations at baseline 1968, 222 had their first fracture in the follow-up period until 2006. Mandibular trabeculation was defined as dense, mixed dense plus sparse, and sparse based on panoramic radiographs from 1968 and/or 1980. Time to fracture was ascertained and used as the dependent variable in three Cox proportional hazards regression analyses. The first analysis covered 12 years of follow-up with self-reported endpoints; the second covered 26 years of follow-up with hospital verified endpoints; and the third combined the two follow-up periods, totaling 38 years. Mandibular trabeculation was the main independent variable predicting incident fractures, with age, physical activity, alcohol consumption and body mass index as covariates. The Kaplan-Meier curve indicated a graded association between trabecular density and fracture risk. During the whole period covered, the hazard ratio of future fracture for sparse trabeculation compared to mixed trabeculation was 2.9 (95% CI: 2.2-3.8, p<0.0001), and for dense versus mixed trabeculation was 0.21 (95% CI: 0.1-0.4, p<0.0001). The trabecular pattern was a highly significant predictor of future fracture risk. Our findings imply that dentists, using ordinary dental radiographs, can identify women at high risk for future fractures at 38-54 years of age, often long before the first fracture occurs. Copyright © 2011 Elsevier Inc. All rights reserved.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

PubMed

Whitney, Jon; Corredor, German; Janowczyk, Andrew; Ganesan, Shridar; Doyle, Scott; Tomaszewski, John; Feldman, Michael; Gilmore, Hannah; Madabhushi, Anant

2018-05-30

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Development of a Korean Fracture Risk Score (KFRS) for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service

PubMed Central

Jang, Eun Jin; Park, ByeongJu; Kim, Tae-Young; Shin, Soon-Ae

2016-01-01

Background Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model. Methods A total of 718,306 Korean men and women aged 50–90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS). Results During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women) in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS) is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk. Conclusion We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr. PMID:27399597

Development of a Korean Fracture Risk Score (KFRS) for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

PubMed

Kim, Ha Young; Jang, Eun Jin; Park, ByeongJu; Kim, Tae-Young; Shin, Soon-Ae; Ha, Yong-Chan; Jang, Sunmee

2016-01-01

Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model. A total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS). During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women) in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS) is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk. We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

The utility of the additive EuroSCORE, RIFLE and AKIN staging scores in the prediction and diagnosis of acute kidney injury after cardiac surgery.

PubMed

Duthie, Fiona A I; McGeehan, Paul; Hill, Sharleen; Phelps, Richard; Kluth, David C; Zamvar, Vipin; Hughes, Jeremy; Ferenbach, David A

2014-01-01

Acute kidney injury (AKI) following cardiac surgery is a complication associated with high rates of morbidity and mortality. We compared staging systems for the diagnosis of AKI after cardiac surgery, and assessed pre-operative factors predictive of post-operative AKI. Clinical data, surgical risk scores, procedure and clinical outcome were obtained on all 4,651 patients undergoing cardiac surgery to the Royal Infirmary of Edinburgh between April 2006 and March 2011, of whom 4,572 had sufficient measurements of creatinine before and after surgery to permit inclusion and analysis. The presence of AKI was assessed using the AKIN and RIFLE criteria. By AKIN criteria, 12.4% of the studied population developed AKI versus 6.5% by RIFLE criteria. Any post-operation AKI was associated with increased mortality from 2.2 to 13.5% (relative risk 7.0, p < 0.001), and increased inpatient stay from a median of 7 (IQR 4) to 9 (IQR 11) days (p < 0.05). Patients identified by AKIN, but not RIFLE, had a mean peak creatinine rise of 34% from baseline and had a significantly lower mortality compared to RIFLE-'Risk' AKI (mortality 6.1 vs. 9.7%; p < 0.05). Pre-operative creatinine, diabetes, NYHA Class IV dyspnoea and EuroSCORE-1 (a surgical risk score) all predicted subsequent AKI on multivariate analysis. EuroSCORE-1 outperformed any single demographic factor in predicting post-operative AKI risk, equating to an 8% increase in relative risk for each additional point. AKI after cardiac surgery is associated with delayed discharge and high mortality rates. The AKIN and RIFLE criteria identify patients at a range of AKI severity levels suitable for trial recruitment. The utility of EuroSCORE as a risk stratification tool to identify high AKI-risk subjects for prospective intervention merits further study.

A High-yield Fall Risk and Adverse Events Screening Questions From the Stopping Elderly Accidents, Death, and Injuries (STEADI) Guideline for Older Emergency Department Fall Patients.

PubMed

Sri-On, Jiraporn; Tirrell, Gregory Philip; Kamsom, Anucha; Marill, Keith A; Shankar, Kalpana Narayan; Liu, Shan W

2018-03-25

The objectives were to examine whether responses to the Stopping Elderly Accidents, Death, and Injuries (STEADI) questions responses predicted adverse events after an older adult emergency department (ED) fall visits and to identify factors associated with such recurrent fall. We conducted a prospective study at two urban, teaching hospitals. We included patients aged ≥ 65 years who presented to the ED for an accidental fall. Data were gathered for fall-relevant comorbidities, high-risk medications for falls, and the responses to 12 questions from the STEADI guideline recommendation. Our outcomes were the number of 6-month adverse events that were defined as mortality, ED revisit, subsequent hospitalization, recurrent falls, and a composite outcome. There were 548 (86.3%) patients who completed follow-up and 243 (44.3%) patients experienced an adverse event after a fall within 6 months. In multivariate analysis, seven questions from the STEADI guideline predicted various outcomes. The question "Had previous fall" predicted recurrent falls (odds ratio [OR] = 2.45, 95% confidence interval [CI] = 1.52 to 3.97), the question "Feels unsteady when walking sometimes" (OR = 2.34, 95% CI = 1.44 to 3.81), and "Lost some feeling in their feet" predicted recurrent falls. In addition to recurrent falls risk, the supplemental questions "Use or have been advised to use a cane or walker," "Take medication that sometimes makes them feel light-headed or more tired than usual," "Take medication to help sleep or improve mood," and "Have to rush to a toilet" predicted other outcomes. A STEADI score of ≥4 did not predict adverse outcomes although seven individual questions from the STEADI guidelines were associated with increased adverse outcomes within 6 months. These may be organized into three categories (previous falls, physical activity, and high-risk medications) and may assist emergency physicians to evaluate and refer high-risk fall patients for a comprehensive falls evaluation. © 2018 by the Society for Academic Emergency Medicine.

Standard cardiovascular disease risk algorithms underestimate the risk of cardiovascular disease in schizophrenia: evidence from a national primary care database.

PubMed

McLean, Gary; Martin, Julie Langan; Martin, Daniel J; Guthrie, Bruce; Mercer, Stewart W; Smith, Daniel J

2014-10-01

Schizophrenia is associated with increased cardiovascular mortality. Although cardiovascular disease (CVD) risk prediction algorithms are widely in the general population, their utility for patients with schizophrenia is unknown. A primary care dataset was used to compare CVD risk scores (Joint British Societies (JBS) score), cardiovascular risk factors, rates of pre-existing CVD and age of first diagnosis of CVD for schizophrenia (n=1997) relative to population controls (n=215,165). Pre-existing rates of CVD and the recording of risk factors for those without CVD were higher in the schizophrenia cohort in the younger age groups, for both genders. Those with schizophrenia were more likely to have a first diagnosis of CVD at a younger age, with nearly half of men with schizophrenia plus CVD diagnosed under the age of 55 (schizophrenia men 46.1% vs. control men 34.8%, p<0.001; schizophrenia women 28.9% vs. control women 23.8%, p<0.001). However, despite high rates of CVD risk factors within the schizophrenia group, only a very small percentage (3.2% of men and 7.5% of women) of those with schizophrenia under age 55 were correctly identified as high risk for CVD according to the JBS risk algorithm. The JBS2 risk score identified only a small proportion of individuals with schizophrenia under the age of 55 as being at high risk of CVD, despite high rates of risk factors and high rates of first diagnosis of CVD within this age group. The validity of CVD risk prediction algorithms for schizophrenia needs further research. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting aggressive behaviour in acute forensic mental health units: A re-examination of the dynamic appraisal of situational aggression's predictive validity.

PubMed

Maguire, Tessa; Daffern, Michael; Bowe, Steven J; McKenna, Brian

2017-10-01

In the present study, we explored the predictive validity of the Dynamic Appraisal of Situational Aggression (DASA) assessment tool in male (n = 30) and female (n = 30) patients admitted to the acute units of a forensic mental health hospital. We also tested the psychometric properties of the original DASA bands and novel risk bands. The first 60 days of each patient's file was reviewed to identify daily DASA scores and subsequent risk-related nursing interventions and aggressive behaviour within the following 24 hours. Risk assessments, followed by documented nursing interventions, were removed to preserve the integrity of the risk-assessment analysis. Receiver-operator characteristics were used to test the predictive accuracy of the DASA, and generalized estimating equations (GEE) were used to account for repeated risk assessments, which occurs when analysing short-term risk-assessment data. The results revealed modest predictive validity for males and females. GEE analyses suggested the need to adjust the DASA risk bands to the following (with associated odds ratios (OR) for aggressive behaviour): 0 = low risk; 1, 2, 3 = moderate-risk OR, 4.70 (95% confidence interval (CI): 2.84-7.80); and 4, 5, 6, 7 = high-risk OR, 16.13 (95% CI: 9.71-26.78). The adjusted DASA risk bands could assist nurses by prompting violence-prevention interventions when the level of risk is elevated. © 2017 Australian College of Mental Health Nurses Inc.

Multigene signature for predicting prognosis of patients with 1p19q co-deletion diffuse glioma.

PubMed

Hu, Xin; Martinez-Ledesma, Emmanuel; Zheng, Siyuan; Kim, Hoon; Barthel, Floris; Jiang, Tao; Hess, Kenneth R; Verhaak, Roel G W

2017-06-01

Co-deletion of 1p and 19q marks a diffuse glioma subtype associated with relatively favorable overall survival; however, heterogeneous clinical outcomes are observed within this category. We assembled gene expression profiles and sample annotation of 374 glioma patients carrying the 1p/19q co-deletion. We predicted 1p/19q status using gene expression when annotation was missing. A first cohort was randomly split into training (n = 170) and a validation dataset (n = 163). A second validation set consisted of 41 expression profiles. An elastic-net penalized Cox proportional hazards model was applied to build a classifier model through cross-validation within the training dataset. The selected 35-gene signature was used to identify high-risk and low-risk groups in the validation set, which showed significantly different overall survival (P = .00058, log-rank test). For time-to-death events, the high-risk group predicted by the gene signature yielded a hazard ratio of 1.78 (95% confidence interval, 1.02-3.11). The signature was also significantly associated with clinical outcome in the The Cancer Genome Atlas (CGA) IDH-mutant 1p/19q wild-type and IDH-wild-type glioma cohorts. Pathway analysis suggested that high risk was associated with increased acetylation activity and inflammatory response. Tumor purity was found to be significantly decreased in high-risk IDH-mutant with 1p/19q co-deletion gliomas and IDH-wild-type glioblastomas but not in IDH-wild-type lower grade or IDH-mutant, non-co-deleted gliomas. We identified a 35-gene signature that identifies high-risk and low-risk categories of 1p/19q positive glioma patients. We have demonstrated heterogeneity amongst a relatively new glioma subtype and provided a stepping stone towards risk stratification. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Risk Assessment Stability: A Revalidation Study of the Arizona Risk/Needs Assessment Instrument

ERIC Educational Resources Information Center

Schwalbe, Craig S.

2009-01-01

The actuarial method is the gold standard for risk assessment in child welfare, juvenile justice, and criminal justice. It produces risk classifications that are highly predictive and that may be robust to sampling error. This article reports a revalidation study of the Arizona Risk/Needs Assessment instrument, an actuarial instrument for juvenile…

Intelligent Monitoring? Assessing the ability of the Care Quality Commission's statistical surveillance tool to predict quality and prioritise NHS hospital inspections.

PubMed

Griffiths, Alex; Beaussier, Anne-Laure; Demeritt, David; Rothstein, Henry

2017-02-01

The Care Quality Commission (CQC) is responsible for ensuring the quality of the health and social care delivered by more than 30 000 registered providers in England. With only limited resources for conducting on-site inspections, the CQC has used statistical surveillance tools to help it identify which providers it should prioritise for inspection. In the face of planned funding cuts, the CQC plans to put more reliance on statistical surveillance tools to assess risks to quality and prioritise inspections accordingly. To evaluate the ability of the CQC's latest surveillance tool, Intelligent Monitoring (IM), to predict the quality of care provided by National Health Service (NHS) hospital trusts so that those at greatest risk of providing poor-quality care can be identified and targeted for inspection. The predictive ability of the IM tool is evaluated through regression analyses and χ 2 testing of the relationship between the quantitative risk score generated by the IM tool and the subsequent quality rating awarded following detailed on-site inspection by large expert teams of inspectors. First, the continuous risk scores generated by the CQC's IM statistical surveillance tool cannot predict inspection-based quality ratings of NHS hospital trusts (OR 0.38 (0.14 to 1.05) for Outstanding/Good, OR 0.94 (0.80 to -1.10) for Good/Requires improvement, and OR 0.90 (0.76 to 1.07) for Requires improvement/Inadequate). Second, the risk scores cannot be used more simply to distinguish the trusts performing poorly-those subsequently rated either 'Requires improvement' or 'Inadequate'-from the trusts performing well-those subsequently rated either 'Good' or 'Outstanding' (OR 1.07 (0.91 to 1.26)). Classifying CQC's risk bandings 1-3 as high risk and 4-6 as low risk, 11 of the high risk trusts were performing well and 43 of the low risk trusts were performing poorly, resulting in an overall accuracy rate of 47.6%. Third, the risk scores cannot be used even more simply to distinguish the worst performing trusts-those subsequently rated 'Inadequate'-from the remaining, better performing trusts (OR 1.11 (0.94 to 1.32)). Classifying CQC's risk banding 1 as high risk and 2-6 as low risk, the highest overall accuracy rate of 72.8% was achieved, but still only 6 of the 13 Inadequate trusts were correctly classified as being high risk. Since the IM statistical surveillance tool cannot predict the outcome of NHS hospital trust inspections, it cannot be used for prioritisation. A new approach to inspection planning is therefore required. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

PubMed

Cameron, Adrian

2010-02-01

The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

High-Risk Carotid Plaques Identified by CT-Angiogram can Predict Acute Myocardial Infarction

PubMed Central

Mosleh, Wassim; Adib, Keenan; Natdanai, Punnanithinont; Carmona-Rubio, Andres; Karki, Roshan; Paily, Jacienta; Ahmed, Mohamed Abdel-Aal; Vakkalanka, Sujit; Madam, Narasa; Gudleski, Gregory D; Chung, Charles; Sharma, Umesh C

2016-01-01

Purpose Prior studies identified the incremental value of non-invasive imaging by CT-angiogram (CTA) to detect high-risk coronary atherosclerotic plaques. Due to their superficial locations, larger calibers and motion-free imaging, the carotid arteries provide the best anatomic access for the non-invasive characterization of atherosclerotic plaques. We aim to assess the ability of predicting obstructive coronary artery disease (CAD) or acute myocardial infarction (MI) based on high-risk carotid plaque features identified by CTA. Methods We retrospectively examined carotid CTAs of 492 patients that presented with acute stroke to characterize the atherosclerotic plaques of the carotid arteries and examined development of acute MI and obstructive CAD within 12-months. Carotid lesions were defined in terms of calcifications (large or speckled), presence of low-attenuation plaques, positive remodeling, and presence of napkin ring sign (NRS). Adjusted relative risks were calculated for each plaque features. Results Patients with speckled (<3mm) calcifications and/or larger calcifications on CTA had a higher risk of developing an MI and/or obstructive CAD within one year compared to patients without [adjusted RR of 7.51, 95%CI 1.26 to 73.42, P= 0.001]. Patients with low-attenuation plaques on CTA had a higher risk of developing an MI and/or obstructive CAD within one year than patients without [adjusted RR of 2.73, 95%CI 1.19 to 8.50, P= 0.021]. Presence of carotid calcifications and low-attenuation plaques also portended higher sensitivity (100% and 79.17%, respectively) for the development of acute MI. Conclusions Presence of carotid calcifications and low-attenuation plaques can predict the risk of developing acute MI and/or obstructive CAD within 12-months. Given their high sensitivity, their absence can reliably exclude 12-month events. PMID:27866279

High-risk carotid plaques identified by CT-angiogram can predict acute myocardial infarction.

PubMed

Mosleh, Wassim; Adib, Keenan; Natdanai, Punnanithinont; Carmona-Rubio, Andres; Karki, Roshan; Paily, Jacienta; Ahmed, Mohamed Abdel-Aal; Vakkalanka, Sujit; Madam, Narasa; Gudleski, Gregory D; Chung, Charles; Sharma, Umesh C

2017-04-01

Prior studies identified the incremental value of non-invasive imaging by CT-angiogram (CTA) to detect high-risk coronary atherosclerotic plaques. Due to their superficial locations, larger calibers and motion-free imaging, the carotid arteries provide the best anatomic access for the non-invasive characterization of atherosclerotic plaques. We aim to assess the ability of predicting obstructive coronary artery disease (CAD) or acute myocardial infarction (MI) based on high-risk carotid plaque features identified by CTA. We retrospectively examined carotid CTAs of 492 patients that presented with acute stroke to characterize the atherosclerotic plaques of the carotid arteries and examined development of acute MI and obstructive CAD within 12-months. Carotid lesions were defined in terms of calcifications (large or speckled), presence of low-attenuation plaques, positive remodeling, and presence of napkin ring sign. Adjusted relative risks were calculated for each plaque features. Patients with speckled (<3 mm) calcifications and/or larger calcifications on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year compared to patients without (adjusted RR of 7.51, 95%CI 1.26-73.42, P = 0.001). Patients with low-attenuation plaques on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year than patients without (adjusted RR of 2.73, 95%CI 1.19-8.50, P = 0.021). Presence of carotid calcifications and low-attenuation plaques also portended higher sensitivity (100 and 79.17%, respectively) for the development of acute MI. Presence of carotid calcifications and low-attenuation plaques can predict the risk of developing acute MI and/or obstructive CAD within 12-months. Given their high sensitivity, their absence can reliably exclude 12-month events.

Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30)

PubMed Central

Billings, John; Blunt, Ian; Steventon, Adam; Georghiou, Theo; Lewis, Geraint; Bardsley, Martin

2012-01-01

Objectives To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes. Design Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping. Setting HES data covering all NHS hospital admissions in England. Participants The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868). Main outcome measures Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds. Results The algorithm produces a ‘risk score’ ranging (0–1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70). Conclusions We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice. PMID:22885591

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

The Predictive Value of Indocyanine Green Clearance in Future Liver Remnant for Posthepatectomy Liver Failure Following Hepatectomy with Extrahepatic Bile Duct Resection.

PubMed

Yokoyama, Yukihiro; Ebata, Tomoki; Igami, Tsuyoshi; Sugawara, Gen; Mizuno, Takashi; Yamaguchi, Junpei; Nagino, Masato

2016-06-01

Postoperative liver failure (PHLF) is one of the most common complications following major hepatectomy. The preoperative assessment of future liver remnant (FLR) function is critical to predict the incidence of PHLF. To determine the efficacy of the plasma clearance rate of indocyanine green clearance of FLR (ICGK-F) in predicting PHLF in cases of highly invasive hepatectomy with extrahepatic bile duct resection. Five hundred and eighty-five patients who underwent major hepatectomy with extrahepatic bile duct resection, from 2002 to 2014 in a single institution, were evaluated. Among them, 192 patients (33 %) had PHLF. The predictive value of ICGK-F for PHLF was determined and compared with other risk factors for PHLF. The incidence of PHLF was inversely proportional to the level of ICGK-F. With multivariate logistic regression analysis, ICGK-F, combined pancreatoduodenectomy, the operation time, and blood loss were identified as independent risk factors of PHLF. The risk of PHLF increased according to the decrement of ICGK-F (the odds ratio of ICGK-F for each decrement of 0.01 was 1.22; 95 % confidence interval 1.12-1.33; P < 0.001). Low ICGK-F was also identified as an independent risk factor predicting the postoperative mortality. ICGK-F is useful in predicting the PHLF and mortality in patients undergoing major hepatectomy with extrahepatic bile duct resection. This criterion may be useful for highly invasive hepatectomy, such as that with extrahepatic bile duct resection.

Two-Tiered Violence Risk Estimates: a validation study of an integrated-actuarial risk assessment instrument.

PubMed

Mills, Jeremy F; Gray, Andrew L

2013-11-01

This study is an initial validation study of the Two-Tiered Violence Risk Estimates instrument (TTV), a violence risk appraisal instrument designed to support an integrated-actuarial approach to violence risk assessment. The TTV was scored retrospectively from file information on a sample of violent offenders. Construct validity was examined by comparing the TTV with instruments that have shown utility to predict violence that were prospectively scored: The Historical-Clinical-Risk Management-20 (HCR-20) and Lifestyle Criminality Screening Form (LCSF). Predictive validity was examined through a long-term follow-up of 12.4 years with a sample of 78 incarcerated offenders. Results show the TTV to be highly correlated with the HCR-20 and LCSF. The base rate for violence over the follow-up period was 47.4%, and the TTV was equally predictive of violent recidivism relative to the HCR-20 and LCSF. Discussion centers on the advantages of an integrated-actuarial approach to the assessment of violence risk.

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

PubMed

Jang, Jin-Young; Park, Taesung; Lee, Selyeong; Kim, Yongkang; Lee, Seung Yeoun; Kim, Sun-Whe; Kim, Song-Cheol; Song, Ki-Byung; Yamamoto, Masakazu; Hatori, Takashi; Hirono, Seiko; Satoi, Sohei; Fujii, Tsutomu; Hirano, Satoshi; Hashimoto, Yasushi; Shimizu, Yashuhiro; Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok; Motoi, Fuyuhiko; Matsumoto, Ippei; Lee, Woo Jung; Kang, Chang Moo; Han, Ho-Seong; Yoon, Yoo-Seok; Sho, Masayuki; Nagano, Hiroaki; Honda, Goro; Kim, Sang Geol; Yu, Hee Chul; Chung, Jun Chul; Nagakawa, Yuichi; Seo, Hyung Il; Yamaue, Hiroki

2017-12-01

This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers.

PubMed

Klimov, Sergey; Rida, Padmashree Cg; Aleskandarany, Mohammed A; Green, Andrew R; Ellis, Ian O; Janssen, Emiel Am; Rakha, Emad A; Aneja, Ritu

2017-09-05

Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC. A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses. Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status. Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism.

Advantages of new cardiovascular risk-assessment strategies in high-risk patients with hypertension.

PubMed

Ruilope, Luis M; Segura, Julian

2005-10-01

Accurate assessment of cardiovascular disease (CVD) risk in patients with hypertension is important when planning appropriate treatment of modifiable risk factors. The causes of CVD are multifactorial, and hypertension seldom exists as an isolated risk factor. Classic models of risk assessment are more accurate than a simple counting of risk factors, but they are not generalizable to all populations. In addition, the risk associated with hypertension is graded, continuous, and independent of other risk factors, and this is not reflected in classic models of risk assessment. This article is intended to review both classic and newer models of CVD risk assessment. MEDLINE was searched for articles published between 1990 and 2005 that contained the terms cardiovascular disease, hypertension, or risk assessment. Articles describing major clinical trials, new data about cardiovascular risk, or global risk stratification were selected for review. Some patients at high long-term risk for CVD events (eg, patients aged <50 years with multiple risk factors) may go untreated because they do not meet the absolute risk-intervention threshold of 20% risk over 10 years with the classic model. Recognition of the limitations of classic risk-assessment models led to new guidelines, particularly those of the European Society of Hypertension-European Society of Cardiology. These guidelines view hypertension as one of many risk and disease factors that require treatment to decrease risk. These newer guidelines include a more comprehensive range of risk factors and more finely graded blood pressure ranges to stratify patients by degree of risk. Whether they accurately predict CVD risk in most populations is not known. Evidence from the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) study, which stratified patients by several risk and disease factors, highlights the predictive value of some newer CVD risk assessments. Modern risk assessments, which include blood pressure along with a wide array of modifiable risk factors, may be more accurate than classic models for CVD risk prediction.

Utilization of small changes in serum creatinine with clinical risk factors to assess the risk of AKI in critically lll adults.

PubMed

Cruz, Dinna N; Ferrer-Nadal, Asunción; Piccinni, Pasquale; Goldstein, Stuart L; Chawla, Lakhmir S; Alessandri, Elisa; Belluomo Anello, Clara; Bohannon, Will; Bove, Tiziana; Brienza, Nicola; Carlini, Mauro; Forfori, Francesco; Garzotto, Francesco; Gramaticopolo, Silvia; Iannuzzi, Michele; Montini, Luca; Pelaia, Paolo; Ronco, Claudio

2014-04-01

Disease biomarkers require appropriate clinical context to be used effectively. Combining clinical risk factors, in addition to small changes in serum creatinine, has been proposed to improve the assessment of AKI. This notion was developed in order to identify the risk of AKI early in a patient's clinical course. We set out to assess the performance of this combination approach. A secondary analysis of data from a prospective multicenter intensive care unit cohort study (September 2009 to April 2010) was performed. Patients at high risk using this combination approach were defined as an early increase in serum creatinine of 0.1-0.4 mg/dl, depending on number of clinical factors predisposing to AKI. AKI was defined and staged using the Acute Kidney Injury Network criteria. The primary outcome was evolution to severe AKI (Acute Kidney Injury Network stages 2 and 3) within 7 days in the intensive care unit. Of 506 patients, 214 (42.2%) patients had early creatinine elevation and were deemed at high risk for AKI. This group was more likely to subsequently develop the primary endpoint (16.4% versus 1.0% [not at high risk], P<0.001). The sensitivity of this grouping for severe AKI was 92%, the specificity was 62%, the positive predictive value was 16%, and the negative predictive value was 99%. After adjustment for Sequential Organ Failure Assessment score, serum creatinine, and hazard tier for AKI, early creatinine elevation remained an independent predictor for severe AKI (adjusted relative risk, 12.86; 95% confidence interval, 3.52 to 46.97). Addition of early creatinine elevation to the best clinical model improved prediction of the primary outcome (area under the receiver operating characteristic curve increased from 0.75 to 0.83, P<0.001). Critically ill patients at high AKI risk, based on the combination of clinical factors and early creatinine elevation, are significantly more likely to develop severe AKI. As initially hypothesized, the high-risk combination group methodology can be used to identify patients at low risk for severe AKI in whom AKI biomarker testing may be expected to have low yield. The high risk combination group methodology could potentially allow clinicians to optimize biomarker use.

Validation of 1-hour post-thyroidectomy parathyroid hormone level in predicting hypocalcemia

PubMed Central

2014-01-01

Background Prior work by our group suggested that a single one hour post-thyroidectomy parathyroid hormone (1 hr PTH) level could accurately stratify patients into high and low risk groups for the development of hypocalcemia. This study looks to validate the safety and efficacy of a protocol based on a 1 hr PTH threshold of 12 pg/ml. Study design Retrospective analysis of consecutive cohort treated with standardized protocol. Methods One hundred and twenty five consecutive patients underwent total or completion thyroidectomy and their PTH level was drawn 1-hour post operatively. Based on our previous work, patients were stratified into either a low risk group (PTH < 12 pg/ml) or a high risk group (PTH ≥ 12 pg/ml). Patients in the high risk group were immediately started on prophylactic calcium carbonate (5–10 g/d) and calcitriol (0.5-1.0 mcg/d). The outcomes were then reviewed focusing mainly on how many low risk patients developed hypocalcemia (false negative rate), and how many high risk patients failed prophylactic therapy. Results Thirty one patients (25%) were stratified as high risk, and 94 (75%) as low risk. Five (16%) of the high risk patients became hypocalcemic despite prophylactic therapy. Two of the low risk group became hypocalcemic, (negative predictive value = 98%). None of the hypocalcemic patients had anything more than mild symptoms. Conclusions A single 1-hour post-thyroidectomy PTH level is a very useful way to stratify thyroidectomy patients into high and low risk groups for development of hypocalcemia. Early implementation of oral prophylactic calcium and vitamin D in the high risk patients is a very effective way to prevent serious hypocalcemia. Complex protocols requiring multiple calcium and PTH measurements are not required to guide post-thyroidectomy management. PMID:24476535

[FRAX® thresholds to identify people with high or low risk of osteoporotic fracture in Spanish female population].

PubMed

Azagra, Rafael; Roca, Genís; Martín-Sánchez, Juan Carlos; Casado, Enrique; Encabo, Gloria; Zwart, Marta; Aguyé, Amada; Díez-Pérez, Adolf

2015-01-06

To detect FRAX(®) threshold levels that identify groups of the population that are at high/low risk of osteoporotic fracture in the Spanish female population using a cost-effective assessment. This is a cohort study. Eight hundred and sixteen women 40-90 years old selected from the FRIDEX cohort with densitometry and risk factors for fracture at baseline who received no treatment for osteoporosis during the 10 year follow-up period and were stratified into 3 groups/levels of fracture risk (low<10%, 10-20% intermediate and high>20%) according to the real fracture incidence. The thresholds of FRAX(®) baseline for major osteoporotic fracture were: low risk<5; intermediate ≥ 5 to <7.5 and high ≥ 7.5. The incidence of fracture with these values was: low risk (3.6%; 95% CI 2.2-5.9), intermediate risk (13.7%; 95% CI 7.1-24.2) and high risk (21.4%; 95% CI12.9-33.2). The most cost-effective option was to refer to dual energy X-ray absorptiometry (DXA-scan) for FRAX(®)≥ 5 (Intermediate and high risk) to reclassify by FRAX(®) with DXA-scan at high/low risk. These thresholds select 17.5% of women for DXA-scan and 10% for treatment. With these thresholds of FRAX(®), compared with the strategy of opportunistic case finding isolated risk factors, would improve the predictive parameters and reduce 82.5% the DXA-scan, 35.4% osteoporosis prescriptions and 28.7% cost to detect the same number of women who suffer fractures. The use of FRAX ® thresholds identified as high/low risk of osteoporotic fracture in this calibration (FRIDEX model) improve predictive parameters in Spanish women and in a more cost-effective than the traditional model based on the T-score ≤ -2.5 of DXA scan. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury

PubMed Central

Najafi, Mahdi

2014-01-01

Serum creatinine is still the most important determinant in the assessment of perioperative renal function and in the prediction of adverse outcome in cardiac surgery. Many biomarkers have been studied to date; still, there is no surrogate for serum creatinine measurement in clinical practice because it is feasible and inexpensive. High levels of serum creatinine and its equivalents have been the most important preoperative risk factor for postoperative renal injury. Moreover, creatinine is the mainstay in predicting risk models and risk factor reduction has enhanced its importance in outcome prediction. The future perspective is the development of new definitions and novel tools for the early diagnosis of acute kidney injury largely based on serum creatinine and a panel of novel biomarkers. PMID:25276301

A Habitat-based Wind-Wildlife Collision Model with Application to the Upper Great Plains Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forcey, Greg, M.

Most previous studies on collision impacts at wind facilities have taken place at the site-specific level and have only examined small-scale influences on mortality. In this study, we examine landscape-level influences using a hierarchical spatial model combined with existing datasets and life history knowledge for: Horned Lark, Red-eyed Vireo, Mallard, American Avocet, Golden Eagle, Whooping Crane, red bat, silver-haired bat, and hoary bat. These species were modeled in the central United States within Bird Conservation Regions 11, 17, 18, and 19. For the bird species, we modeled bird abundance from existing datasets as a function of habitat variables known tomore » be preferred by each species to develop a relative abundance prediction for each species. For bats, there are no existing abundance datasets so we identified preferred habitat in the landscape for each species and assumed that greater amounts of preferred habitat would equate to greater abundance of bats. The abundance predictions for bird and bats were modeled with additional exposure factors known to influence collisions such as visibility, wind, temperature, precipitation, topography, and behavior to form a final mapped output of predicted collision risk within the study region. We reviewed published mortality studies from wind farms in our study region and collected data on reported mortality of our focal species to compare to our modeled predictions. We performed a sensitivity analysis evaluating model performance of 6 different scenarios where habitat and exposure factors were weighted differently. We compared the model performance in each scenario by evaluating observed data vs. our model predictions using spearmans rank correlations. Horned Lark collision risk was predicted to be highest in the northwestern and west-central portions of the study region with lower risk predicted elsewhere. Red-eyed Vireo collision risk was predicted to be the highest in the eastern portions of the study region and in the forested areas of the western portion; the lowest risk was predicted in the treeless portions of the northwest portion of the study area. Mallard collision risk was predicted to be highest in the eastern central portion of the prairie potholes and in Iowa which has a high density of pothole wetlands; lower risk was predicted in the more arid portions of the study area. Predicted collision risk for American Avocet was similar to Mallard and was highest in the prairie pothole region and lower elsewhere. Golden Eagle collision risk was predicted to be highest in the mountainous areas of the western portion of the study area and lowest in the eastern portion of the prairie potholes. Whooping Crane predicted collision risk was highest within the migration corridor that the birds follow through in the central portion of the study region; predicted collision risk was much lower elsewhere. Red bat collision risk was highly driven by large tracts of forest and river corridors which made up most of the areas of higher collision risk. Silver-haired bat and hoary bat predicted collision risk were nearly identical and driven largely by forest and river corridors as well as locations with warmer temperatures, and lower average wind speeds. Horned Lark collisions were mostly influenced by abundance and predictions showed a moderate correlation between observed and predicted mortality (r = 0.55). Red bat, silver-haired bat, and hoary bat predictions were much higher and shown a strong correlations with observed mortality with correlations of 0.85, 0.90, and 0.91 respectively. Red bat collisions were influenced primarily by habitat, while hoary bat and silver-haired bat collisions were influenced mainly by exposure variables. Stronger correlations between observed and predicted collision for bats than for Horned Larks can likely be attributed to stronger habitat associations and greater influences of weather on behavior for bats. Although the collision predictions cannot be compared among species, our model outputs provide a convenient and easy landscape-level tool to quickly screen for siting issues at a high level. The model resolution is suitable for state or multi-county siting but users are cautioned against using these models for micrositing. The U.S. Fish and Wildlife Service recently released voluntary land-based wind energy guidelines for assessing impacts of a wind facility to wildlife using a tiered approach. The tiered approach uses an iterative approach for assessing impacts to wildlife in levels of increasing detail from landscape-level screening to site-specific field studies. Our models presented in this paper would be applicable to be used as tools to conduct screening at the tier 1 level and would not be appropriate to complete smaller scale tier 2 and tier 3 level studies. For smaller scale screening ancillary field studies should be conducted at the site-specific level to validate collision predictions.« less

Clinical risk scoring for predicting non-alcoholic fatty liver disease in metabolic syndrome patients (NAFLD-MS score).

PubMed

Saokaew, Surasak; Kanchanasuwan, Shada; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Charatcharoenwitthaya, Phunchai; Phisalprapa, Pochamana; Chaiyakunapruk, Nathorn

2017-10-01

Non-alcoholic fatty liver disease (NAFLD) can progress from simple steatosis to hepatocellular carcinoma. None of tools have been developed specifically for high-risk patients. This study aimed to develop a simple risk scoring to predict NAFLD in patients with metabolic syndrome (MetS). A total of 509 patients with MetS were recruited. All were diagnosed by clinicians with ultrasonography-confirmed whether they were patients with NAFLD. Patients were randomly divided into derivation (n=400) and validation (n=109) cohort. To develop the risk score, clinical risk indicators measured at the time of recruitment were built by logistic regression. Regression coefficients were transformed into item scores and added up to a total score. A risk scoring scheme was developed from clinical predictors: BMI ≥25, AST/ALT ≥1, ALT ≥40, type 2 diabetes mellitus and central obesity. The scoring scheme was applied in validation cohort to test the performance. The scheme explained, by area under the receiver operating characteristic curve (AuROC), 76.8% of being NAFLD with good calibration (Hosmer-Lemeshow χ 2 =4.35; P=.629). The positive likelihood ratio of NAFLD in patients with low risk (scores below 3) and high risk (scores 5 and over) were 2.32 (95% CI: 1.90-2.82) and 7.77 (95% CI: 2.47-24.47) respectively. When applied in validation cohort, the score showed good performance with AuROC 76.7%, and illustrated 84%, and 100% certainty in low- and high-risk groups respectively. A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

PubMed Central

Pfeiffer, Ruth M.; Miglioretti, Diana L.; Kerlikowske, Karla; Tice, Jeffery; Vacek, Pamela M.; Gierach, Gretchen L.

2016-01-01

Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown. Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (<1%; 1–1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1–13 years; median: 5.4–5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35–44; 45–54; 55–69; 70–89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years. Results Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75–0.90); BCRAT: HR = 0.72 (95% CI = 0.65–0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75–0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55–69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35–44 years. Conclusions Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively. PMID:27560501

An integrative model of risk for high school disordered eating.

PubMed

Davis, Heather A; Smith, Gregory T

2018-06-21

Binge eating and purging behaviors are associated with significant harm and distress among adolescents. The process by which these behaviors develop (often in the high school years) is not fully understood. We tested the Acquired Preparedness (AP) model of risk involving transactions among biological, personality, and psychosocial factors to predict binge eating and purging behavior in a sample of 1,906 children assessed in the spring of 5th grade (the last year of elementary school), the fall of 6th grade (the first year of middle school), spring of 6th grade, and spring of 10th grade (second year of high school). Pubertal onset in spring of 5th grade predicted increases in negative urgency, but not negative affect, in the fall of 6th grade. Negative urgency in the fall of 6th grade predicted increases in expectancies for reinforcement from eating in the spring of 6th grade, which in turn predicted increases in binge eating behavior in the spring of 10th grade. Negative affect in the fall of 6th grade predicted increases in thinness expectancies in the spring of 6th grade, which in turn predicted increases in purging in the spring of 10th grade. Results demonstrate similarities and differences in the development of these two different bulimic behaviors. Intervention efforts targeting the risk factors evident in this model may prove fruitful in the treatment of eating disorders characterized by binge eating and purging. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Improving antenatal risk assessment in women exposed to high risks.

PubMed

Perry, Natasha; Newman, Louise K; Hunter, Mick; Dunlop, Adrian

2015-01-01

Antenatal substance use and related psychosocial risk factors are known to increase the likelihood of child protection involvement; less is known about the predictive nature of maternal reflective functioning (RF) in this population. This preliminary study assessed psychosocial and psychological risk factors for a group of substance dependent women exposed to high risks in pregnancy, and their impact on child protection involvement. Pregnant women on opiate substitution treatment (n = 11) and a comparison group (n = 15) were recruited during their third trimester to complete measures of RF (Pregnancy Interview), childhood trauma, mental health and psychosocial assessments. At postnatal follow-up, RF was reassessed (Parent Development Interview - Revised Short Version) and mother-infant dyads were videotaped to assess emotional availability (EA). Child protection services were contacted to determine if any concerns had been raised for infant safety. Significant between-group differences were observed for demographics, psychosocial factors, trauma and mental health symptoms. Unexpectedly, no significant differences were found for RF or EA between groups. Eight women in the 'exposed to high risks' group became involved with child protection services. Reflective functioning was not significantly associated with psychosocial risk factors, and therefore did not mediate the outcome of child protection involvement. Women 'exposed to high risks' were equally able to generate a model of their own and their infants' mental states and should not be seen within a deficit perspective. Further research is required to better understand the range of risk factors that predict child protection involvement in high risk groups. © The Author(s) 2013.

Review of fall risk assessment in geriatric populations using inertial sensors

PubMed Central

2013-01-01

Background Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population. Methods Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes. Results Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%). Conclusions Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls. PMID:23927446

Using genetic prediction from known complex disease Loci to guide the design of next-generation sequencing experiments.

PubMed

Jostins, Luke; Levine, Adam P; Barrett, Jeffrey C

2013-01-01

A central focus of complex disease genetics after genome-wide association studies (GWAS) is to identify low frequency and rare risk variants, which may account for an important fraction of disease heritability unexplained by GWAS. A profusion of studies using next-generation sequencing are seeking such risk alleles. We describe how already-known complex trait loci (largely from GWAS) can be used to guide the design of these new studies by selecting cases, controls, or families who are most likely to harbor undiscovered risk alleles. We show that genetic risk prediction can select unrelated cases from large cohorts who are enriched for unknown risk factors, or multiply-affected families that are more likely to harbor high-penetrance risk alleles. We derive the frequency of an undiscovered risk allele in selected cases and controls, and show how this relates to the variance explained by the risk score, the disease prevalence and the population frequency of the risk allele. We also describe a new method for informing the design of sequencing studies using genetic risk prediction in large partially-genotyped families using an extension of the Inside-Outside algorithm for inference on trees. We explore several study design scenarios using both simulated and real data, and show that in many cases genetic risk prediction can provide significant increases in power to detect low-frequency and rare risk alleles. The same approach can also be used to aid discovery of non-genetic risk factors, suggesting possible future utility of genetic risk prediction in conventional epidemiology. Software implementing the methods in this paper is available in the R package Mangrove.

Neurobiological and memory models of risky decision making in adolescents versus young adults.

PubMed

Reyna, Valerie F; Estrada, Steven M; DeMarinis, Jessica A; Myers, Regina M; Stanisz, Janine M; Mills, Britain A

2011-09-01

Predictions of fuzzy-trace theory and neurobiological approaches are examined regarding risk taking in a classic decision-making task--the framing task--as well as in the context of real-life risk taking. We report the 1st study of framing effects in adolescents versus adults, varying risk and reward, and relate choices to individual differences, sexual behavior, and behavioral intentions. As predicted by fuzzy-trace theory, adolescents modulated risk taking according to risk and reward. Adults showed standard framing, reflecting greater emphasis on gist-based (qualitative) reasoning, but adolescents displayed reverse framing when potential gains for risk taking were high, reflecting greater emphasis on verbatim-based (quantitative) reasoning. Reverse framing signals a different way of thinking compared with standard framing (reverse framing also differs from simply choosing the risky option). Measures of verbatim- and gist-based reasoning about risk, sensation seeking, behavioral activation, and inhibition were used to extract dimensions of risk proneness: Sensation seeking increased and then decreased, whereas inhibition increased from early adolescence to young adulthood, predicted by neurobiological theories. Two additional dimensions, verbatim- and gist-based reasoning about risk, loaded separately and predicted unique variance in risk taking. Importantly, framing responses predicted real-life risk taking. Reasoning was the most consistent predictor of real-life risk taking: (a) Intentions to have sex, sexual behavior, and number of partners decreased when gist-based reasoning was triggered by retrieval cues in questions about perceived risk, whereas (b) intentions to have sex and number of partners increased when verbatim-based reasoning was triggered by different retrieval cues in questions about perceived risk. (c) 2011 APA, all rights reserved.

Beyond D'Amico risk classes for predicting recurrence after external beam radiotherapy for prostate cancer: the Candiolo classifier.

PubMed

Gabriele, Domenico; Jereczek-Fossa, Barbara A; Krengli, Marco; Garibaldi, Elisabetta; Tessa, Maria; Moro, Gregorio; Girelli, Giuseppe; Gabriele, Pietro

2016-02-24

The aim of this work is to develop an algorithm to predict recurrence in prostate cancer patients treated with radical radiotherapy, getting up to a prognostic power higher than traditional D'Amico risk classification. Two thousand four hundred ninety-three men belonging to the EUREKA-2 retrospective multi-centric database on prostate cancer and treated with external-beam radiotherapy as primary treatment comprised the study population. A Cox regression time to PSA failure analysis was performed in univariate and multivariate settings, evaluating the predictive ability of age, pre-treatment PSA, clinical-radiological staging, Gleason score and percentage of positive cores at biopsy (%PC). The accuracy of this model was checked with bootstrapping statistics. Subgroups for all the variables' combinations were combined to classify patients into five different "Candiolo" risk-classes for biochemical Progression Free Survival (bPFS); thereafter, they were also applied to clinical PFS (cPFS), systemic PFS (sPFS) and Prostate Cancer Specific Survival (PCSS), and compared to D'Amico risk grouping performances. The Candiolo classifier splits patients in 5 risk-groups with the following 10-years bPFS, cPFS, sPFS and PCSS: for very-low-risk 90 %, 94 %, 100 % and 100 %; for low-risk 74 %, 88 %, 94 % and 98 %; for intermediate-risk 60 %, 82 %, 91 % and 92 %; for high-risk 43 %, 55 %, 80 % and 89 % and for very-high-risk 14 %, 38 %, 56 % and 70 %. Our classifier outperforms D'Amico risk classes for all the end-points evaluated, with concordance indexes of 71.5 %, 75.5 %, 80 % and 80.5 % versus 63 %, 65.5 %, 69.5 % and 69 %, respectively. Our classification tool, combining five clinical and easily available parameters, seems to better stratify patients in predicting prostate cancer recurrence after radiotherapy compared to the traditional D'Amico risk classes.

Frailty Versus Stopping Elderly Accidents, Deaths and Injuries Initiative Fall Risk Score: Ability to Predict Future Falls.

PubMed

Crow, Rebecca S; Lohman, Matthew C; Pidgeon, Dawna; Bruce, Martha L; Bartels, Stephen J; Batsis, John A

2018-03-01

To compare the ability of frailty status to predict fall risk with that of community fall risk screening tools. Analysis of cross-sectional and longitudinal data from NHATS. National Health and Aging Trend Study (NHATS) 2011-2015. Individuals aged 65 and older (N = 7,392). Fall risk was defined according to the Stopping Elderly Accidents, Deaths and Injuries (STEADI) initiative. Frailty was defined as exhaustion, weight loss, low activity, slow gait speed, and weak grip strength. Robust was defined as meeting 0 criteria, prefrailty as 1 or 2 criteria, and frailty as 3 or more criteria. Falls were self-reported and ascertained using NHATS subsequent rounds (2012-2015). We compared the ability of frailty to predict future falls with that of STEADI score, adjusting for age, race, sex, education, comorbidities, hearing and vision impairment, and disability. Of the 7,392 participants (58.5% female), there 3,545 (48.0%) were classified as being at low risk of falling, 2,966 (40.1%) as being at moderate risk, and 881 (11.9%) as being at high risk. The adjusted risk of falling over the 4 subsequent years was 2.5 times as great for the moderate-risk group (hazard ratio (HR) = 2.50, 95% confidence interval (CI) = 2.16-2.89) and almost 4 times as great (HR = 3.79, 95% CI = 2.76-5.21) for the high-risk group as for the low-risk group. Risk of falling was greater for those who were prefrail (HR = 1.34, 95% CI 1.16-1.55) and frail (HR = 1.20, 95% CI = 0.94-1.54) than for those who were robust. STEADI score is a strong predictor of future falls. Addition of frailty status does not improve the ability of the STEADI measure to predict future falls. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

High fracture probability predicts fractures in a 4-year follow-up in women from the RAC-OST-POL study.

PubMed

Pluskiewicz, W; Adamczyk, P; Czekajło, A; Grzeszczak, W; Drozdzowska, B

2015-12-01

In 770 postmenopausal women, the fracture incidence during a 4-year follow-up was analyzed in relation to the fracture probability (FRAX risk assessment tool) and risk (Garvan risk calculator) predicted at baseline. Incident fractures occurred in 62 subjects with a higher prevalence in high-risk subgroups. Prior fracture, rheumatoid arthritis, femoral neck T-score and falls increased independent of fracture incidence. The aim of the study was to analyze the incidence of fractures during a 4-year follow-up in relation to the baseline fracture probability and risk. Enrolled in the study were 770 postmenopausal women with a mean age of 65.7 ± 7.3 years. Bone mineral density (BMD) at the proximal femur, clinical data, and fracture probability using the FRAX tool and risk using the Garvan calculator were determined. Each subject was asked yearly by phone call about the incidence of fracture during the follow-up period. Of the 770 women, 62 had a fracture during follow-up, and 46 had a major fracture. At baseline, BMD was significantly lower, and fracture probability and fracture risk were significantly higher in women who had a fracture. Among women with a major fracture, the percentage with a high baseline fracture probability (>10 %) was significantly higher than among those without a fracture (p < 0.01). Fracture incidence during follow-up was significantly higher among women with a high baseline fracture probability (12.7 % vs. 5.2 %) and a high fracture risk (9.2 vs. 5.3 %) so that the "fracture-free survival" curves were significantly different (p < 0.05). The number of clinical risk factors noted at baseline was significantly associated with fracture incidence (chi-squared = 20.82, p < 0.01). Prior fracture, rheumatoid arthritis, and femoral neck T-score were identified as significant risk factors for major fractures (for any fractures, the influence of falls was also significant). During follow-up, fracture incidence was predicted by baseline fracture probability (FRAX risk assessment tool) and risk (Garvan risk calculator). A number of clinical risk factors and a prior fracture, rheumatoid arthritis, femoral neck T-score, and falls were independently associated with an increased incidence of fractures. [Corrected

Common polygenic variation enhances risk prediction for Alzheimer’s disease

PubMed Central

Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D.; Amouyel, Philippe

2015-01-01

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. PMID:26490334

Close Approach Prediction Analysis of the Earth Science Constellation with the Fengyun-1C Debris

NASA Technical Reports Server (NTRS)

Duncan, Matthew; Rand, David K.

2008-01-01

Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. Each day, close approach predictions are generated by a U.S. Department of Defense Joint Space Operations Center Orbital Safety Analyst using the high accuracy Space Object Catalog maintained by the Air Force's 1" Space Control Squadron. Prediction results and other ancillary data such as state vector information are sent to NASAJGoddard Space Flight Center's (GSFC's) Collision Risk Assessment analysis team for review. Collision analysis is performed and the GSFC team works with the ESC member missions to develop risk reduction strategies as necessary. This paper presents various close approach statistics for the ESC. The ESC missions have been affected by debris from the recent anti-satellite test which destroyed the Chinese Fengyun- 1 C satellite. The paper also presents the percentage of close approach events induced by the Fengyun-1C debris, and presents analysis results which predict the future effects on the ESC caused by this event. Specifically, the Fengyun-1C debris is propagated for twenty years using high-performance computing technology and close approach predictions are generated for the ESC. The percent increase in the total number of conjunction events is considered to be an estimate of the collision risk due to the Fengyun-1C break- UP.

Computer-based analysis of general movements reveals stereotypies predicting cerebral palsy.

PubMed

Philippi, Heike; Karch, Dominik; Kang, Keun-Sun; Wochner, Katarzyna; Pietz, Joachim; Dickhaus, Hartmut; Hadders-Algra, Mijna

2014-10-01

To evaluate a kinematic paradigm of automatic general movements analysis in comparison to clinical assessment in 3-month-old infants and its prediction for neurodevelopmental outcome. Preterm infants at high risk (n=49; 26 males, 23 females) and term infants at low risk (n=18; eight males, 10 females) of developmental impairment were recruited from hospitals around Heidelberg, Germany. Kinematic analysis of general movements by magnet tracking and clinical video-based assessment of general movements were performed at 3 months of age. Neurodevelopmental outcome was evaluated at 2 years. By comparing the general movements of small samples of children with and without cerebral palsy (CP), we developed a kinematic paradigm typical for infants at risk of developing CP. We tested the validity of this paradigm as a tool to predict CP and neurodevelopmental impairment. Clinical assessment correctly identified almost all infants with neurodevelopmental impairment including CP, but did not predict if the infant would be affected by CP or not. The kinematic analysis, in particular the stereotypy score of arm movements, was an excellent predictor of CP, whereas stereotyped repetitive movements of the legs predicted any neurodevelopmental impairment. The automatic assessment of the stereotypy score by magnet tracking in 3-month-old spontaneously moving infants at high risk of developmental abnormalities allowed a valid detection of infants affected and unaffected by CP. © 2014 Mac Keith Press.

Birth-Weight, Pregnancy Term, Pre-Natal and Natal Complications Related to Child's Dental Anomalies.

PubMed

Prokocimer, T; Amir, E; Blumer, S; Peretz, B

2015-01-01

This cross-sectional study was aimed at determining whether certain pre-natal and natal conditions can predict specific dental anomalies. The conditions observed were: low birth-weight, preterm birth, pre-natal & natal complications. The dental anomalies observed were: enamel defects, total number of decayed, missing and filled teeth (total DMFT), disturbances in the tooth shape and disturbances in the number of teeth. Out of more than 2000 medical files of children aged 2-17 years old which were reviewed, 300 files met the selection criteria. Information recorded from the files included: age, gender, health status (the ASA physical status classification system by the American Society of Anesthesiologists), birth week, birth weight, total DMFT, hypomineralization, abnormal tooth shape, abnormal number of teeth and hypoplasia. Twenty one children out of 300 (7%) were born after a high-risk pregnancy, 25 children (8.3%) were born after high-risk birth, 20 children (6.7%) were born preterm - before week 37, and 29 children (9.7%) were born with a low birth weight (LBW) - 2500 grams or less. A relationship between a preterm birth and LBW to hypomineralization was found. And a relationship between a preterm birth and high-risk pregnancy to abnormal number of teeth was found. No relationship was found between birth (normal/high-risk) and the other parameters inspected. Preterm birth and LBW may predict hypomineralization in both primary and permanent dentitions. Furthermore, the study demonstrated that preterm birth and high-risk pregnancy may predict abnormal number of teeth in both dentitions.

Performance Characteristics of the Cepheid Xpert vanA Assay for Rapid Identification of Patients at High Risk for Carriage of Vancomycin-Resistant Enterococci

PubMed Central

Gilhuley, Kathleen; Cianciminio-Bordelon, Diane; Tang, Yi-Wei

2012-01-01

We compared the performance characteristics of culture and the Cepheid Xpert vanA assay for routine surveillance of vancomycin-resistant enterococci (VRE) from rectal swabs in patients at high risk for VRE carriage. The Cepheid Xpert vanA assay had a limit of detection of 100 CFU/ml and correctly detected 101 well-characterized clinical VRE isolates with no cross-reactivity in 27 non-VRE and related culture isolates. The clinical sensitivity, specificity, positive predictive value, and negative predictive value of the Xpert vanA PCR assay were 100%, 96.9%, 91.3%, and 100%, respectively, when tested on 300 consecutively collected rectal swabs. This assay provides excellent predictive values for prompt identification of VRE-colonized patients in hospitals with relatively high rates of VRE carriage. PMID:22972822

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity.

PubMed

Wardenaar, K J; van Loo, H M; Cai, T; Fava, M; Gruber, M J; Li, J; de Jonge, P; Nierenberg, A A; Petukhova, M V; Rose, S; Sampson, N A; Schoevers, R A; Wilcox, M A; Alonso, J; Bromet, E J; Bunting, B; Florescu, S E; Fukao, A; Gureje, O; Hu, C; Huang, Y Q; Karam, A N; Levinson, D; Medina Mora, M E; Posada-Villa, J; Scott, K M; Taib, N I; Viana, M C; Xavier, M; Zarkov, Z; Kessler, R C

2014-11-01

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question. Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes. Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors. Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Gene-Environment Correlation in the Development of Adolescent Substance Abuse: Selection Effects of Child Personality and Mediation via Contextual Risk Factors

PubMed Central

Hicks, Brian M.; Johnson, Wendy; Durbin, C. Emily; Blonigen, Daniel M.; Iacono, William G.; McGue, Matt

2012-01-01

We used a longitudinal twin design to examine selection effects of personality traits at age 11 on high-risk environmental contexts at age 14, and the extent to which these contexts mediated risk for substance abuse at age 17. Socialization at age 11—willingness to follow rules and endorse conventional values—predicted exposure to contextual risk at age 14. Contextual risk partially mediated the effect of socialization on substance abuse, though socialization also had a direct effect. In contrast, boldness at age 11—social engagement and assurance, thrill-seeking, and stress resilience— also predicted substance abuse directly, but was unrelated to contextual risk. There was substantial overlap in the genetic and shared environmental influences on socialization and contextual risk, and genetic risk in socialization contributed to substance abuse indirectly via increased exposure to contextual risk. This suggests that active gene-environment correlations related to individual differences in socialization contributed to an early, high-risk developmental trajectory for adolescent substance abuse. In contrast, boldness appeared to index an independent and direct genetic risk factor for adolescent substance abuse. PMID:23398757

Radiation-Induced Leukemia at Doses Relevant to Radiation Therapy: Modeling Mechanisms and Estimating Risks

NASA Technical Reports Server (NTRS)

Shuryak, Igor; Sachs, Rainer K.; Hlatky, Lynn; Mark P. Little; Hahnfeldt, Philip; Brenner, David J.

2006-01-01

Because many cancer patients are diagnosed earlier and live longer than in the past, second cancers induced by radiation therapy have become a clinically significant issue. An earlier biologically based model that was designed to estimate risks of high-dose radiation induced solid cancers included initiation of stem cells to a premalignant state, inactivation of stem cells at high radiation doses, and proliferation of stem cells during cellular repopulation after inactivation. This earlier model predicted the risks of solid tumors induced by radiation therapy but overestimated the corresponding leukemia risks. Methods: To extend the model to radiation-induced leukemias, we analyzed in addition to cellular initiation, inactivation, and proliferation a repopulation mechanism specific to the hematopoietic system: long-range migration through the blood stream of hematopoietic stem cells (HSCs) from distant locations. Parameters for the model were derived from HSC biologic data in the literature and from leukemia risks among atomic bomb survivors v^ ho were subjected to much lower radiation doses. Results: Proliferating HSCs that migrate from sites distant from the high-dose region include few preleukemic HSCs, thus decreasing the high-dose leukemia risk. The extended model for leukemia provides risk estimates that are consistent with epidemiologic data for leukemia risk associated with radiation therapy over a wide dose range. For example, when applied to an earlier case-control study of 110000 women undergoing radiotherapy for uterine cancer, the model predicted an excess relative risk (ERR) of 1.9 for leukemia among women who received a large inhomogeneous fractionated external beam dose to the bone marrow (mean = 14.9 Gy), consistent with the measured ERR (2.0, 95% confidence interval [CI] = 0.2 to 6.4; from 3.6 cases expected and 11 cases observed). As a corresponding example for brachytherapy, the predicted ERR of 0.80 among women who received an inhomogeneous low-dose-rate dose to the bone marrow (mean = 2.5 Gy) was consistent with the measured ERR (0.62, 95% Cl =-0.2 to 1.9). Conclusions: An extended, biologically based model for leukemia that includes HSC initiation, inactivation, proliferation, and, uniquely for leukemia, long-range HSC migration predicts, %Kith reasonable accuracy, risks for radiationinduced leukemia associated with exposure to therapeutic doses of radiation.

A novel risk score model for prediction of contrast-induced nephropathy after emergent percutaneous coronary intervention.

PubMed

Lin, Kai-Yang; Zheng, Wei-Ping; Bei, Wei-Jie; Chen, Shi-Qun; Islam, Sheikh Mohammed Shariful; Liu, Yong; Xue, Lin; Tan, Ning; Chen, Ji-Yan

2017-03-01

A few studies developed simple risk model for predicting CIN with poor prognosis after emergent PCI. The study aimed to develop and validate a novel tool for predicting the risk of contrast-induced nephropathy (CIN) in patients undergoing emergent percutaneous coronary intervention (PCI). 692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n=461) and a validation dataset (n=231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration. The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age >75years old, baseline serum creatinine (SCr)>1.5mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score=0), 1.0%, moderate risk (score:1-2), 13.4%, high risk (score≥3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population. Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

External validation of the Garvan nomograms for predicting absolute fracture risk: the Tromsø study.

PubMed

Ahmed, Luai A; Nguyen, Nguyen D; Bjørnerem, Åshild; Joakimsen, Ragnar M; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R; Eisman, John A; Nguyen, Tuan V; Emaus, Nina

2014-01-01

Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.

Predictive Validity of the HKT-R Risk Assessment Tool: Two and 5-Year Violent Recidivism in a Nationwide Sample of Dutch Forensic Psychiatric Patients.

PubMed

Bogaerts, Stefan; Spreen, Marinus; Ter Horst, Paul; Gerlsma, Coby

2018-06-01

This study has examined the predictive validity of the Historical Clinical Future [ Historisch Klinisch Toekomst] Revised risk assessment scheme in a cohort of 347 forensic psychiatric patients, which were discharged between 2004 and 2008 from any of 12 highly secure forensic centers in the Netherlands. Predictive validity was measured 2 and 5 years after release. Official reconviction data obtained from the Dutch Ministry of Security and Justice were used as outcome measures. Violent reoffending within 2 and 5 years after discharge was assessed. With regard to violent reoffending, results indicated that the predictive validity of the Historical domain was modest for 2 (area under the curve [AUC] = .75) and 5 (AUC = .74) years. The predictive validity of the Clinical domain was marginal for 2 (admission: AUC = .62; discharge: AUC = .63) and 5 (admission: AUC = .69; discharge: AUC = .62) years after release. The predictive validity of the Future domain was modest (AUC = .71) for 2 years and low for 5 (AUC = .58) years. The total score of the instrument was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .68) years. Finally, the Final Risk Judgment was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .63) years time at risk. It is concluded that this risk assessment instrument appears to be a satisfactory instrument for risk assessment.

Early Warning System for West Nile Virus Risk Areas, California, USA

PubMed Central

Ahearn, Sean C.; McConchie, Alan; Glaser, Carol; Jean, Cynthia; Barker, Chris; Park, Bborie; Padgett, Kerry; Parker, Erin; Aquino, Ervic; Kramer, Vicki

2011-01-01

The Dynamic Continuous-Area Space-Time (DYCAST) system is a biologically based spatiotemporal model that uses public reports of dead birds to identify areas at high risk for West Nile virus (WNV) transmission to humans. In 2005, during a statewide epidemic of WNV (880 cases), the California Department of Public Health prospectively implemented DYCAST over 32,517 km2 in California. Daily risk maps were made available online and used by local agencies to target public education campaigns, surveillance, and mosquito control. DYCAST had 80.8% sensitivity and 90.6% specificity for predicting human cases, and κ analysis indicated moderate strength of chance-adjusted agreement for >4 weeks. High-risk grid cells (populations) were identified an average of 37.2 days before onset of human illness; relative risk for disease was >39× higher than for low-risk cells. Although prediction rates declined in subsequent years, results indicate DYCAST was a timely and effective early warning system during the severe 2005 epidemic. PMID:21801622

Adding A Measure Of Patient Self-Management Capability To Risk Assessment Can Improve Prediction Of High Costs.

PubMed

Hibbard, Judith H; Greene, Jessica; Sacks, Rebecca; Overton, Valerie; Parrotta, Carmen D

2016-03-01

We explored whether supplementing a clinical risk score with a behavioral measure could improve targeting of the patients most in need of supports that reduce their risk of costly service utilization. Using data from a large health system that determines patient self-management capability using the Patient Activation Measure, we examined utilization of hospital and emergency department care by the 15 percent of patients with the highest clinical risk scores. After controlling for risk scores and placing patients within segments based on their level of activation in 2011, we found that the lower the activation level, the higher the utilization and cost of hospital services in each of the following three years. These findings demonstrate that adding a measure of patient self-management capability to a risk assessment can improve prediction of high care costs and inform actions to better meet patient needs. Project HOPE—The People-to-People Health Foundation, Inc.

Transplantation for myelodysplastic syndromes: who, when, and which conditioning regimens.

PubMed

Saber, Wael; Horowitz, Mary M

2016-12-02

Allogeneic hematopoietic stem cell transplantation (HCT) is the only curative therapy for myelodysplastic syndrome (MDS). Broad application is hindered by high risks of transplant-related morbidity and mortality, especially in the older age range represented by the MDS population. However, recent advances in strategies to minimize regimen-related toxicity make HCT a viable option for many more patients. Appropriate selection of patients involves consideration of patient factors, including use of geriatric assessment tools and comorbidity scales, that predict risks of regimen-related toxicity as well as disease factors, including genetic markers, which predict survival with both non-HCT and HCT therapy. Optimal timing of HCT for fit patients must consider MDS risk scores and life-years to be gained, with earlier transplantation indicated for patients with intermediate-2 and high-risk disease but judicious delay for lower risk patients. Selection of suitable conditioning regimens must balance risks of toxicity with opportunity for maximum disease control. © 2016 by The American Society of Hematology. All rights reserved.

Childhood psychopathology and adolescent cigarette smoking: a prospective survival analysis in children at high risk for substance use disorders.

PubMed

Clark, Duncan B; Cornelius, Jack

2004-06-01

Children of parents with substance use disorders (SUDs) have been shown to demonstrate an increased risk for cigarette smoking in adolescence. In this prospective study, we hypothesized that adolescent cigarette smoking risk would be accounted for by childhood disruptive behavior disorders and parent cigarette smoking. Preadolescent children (ages 10-12 years) of fathers with SUD considered at high average risk (HAR; n=274) and children of fathers without SUD or major psychopathology considered at low average risk (LAR; n=298) participated in structured interviews to determine mental disorder diagnoses and substance use history. Both parents were assessed. The age of onset of daily tobacco use was determined in three follow-up assessments conducted through late adolescence. Conduct disorder (CD) and parental smoking predicted earlier daily cigarette smoking, and mediated the relationship between risk status and offspring daily cigarette smoking. Through the identification of childhood characteristics predicting daily cigarette smoking in adolescence, these results may facilitate targeting of early childhood preventive interventions.

Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

PubMed

Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

2018-04-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Updating and prospective validation of a prognostic model for high sickness absence.

PubMed

Roelen, C A M; Heymans, M W; Twisk, J W R; van Rhenen, W; Pallesen, S; Bjorvatn, B; Moen, B E; Magerøy, N

2015-01-01

To further develop and validate a Dutch prognostic model for high sickness absence (SA). Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by adding person-related (age, gender, marital status, children at home, and coping strategies), health-related (BMI, physical activity, smoking, and caffeine and alcohol intake), and work-related (job satisfaction, job demands, decision latitude, social support at work, and both work-to-family and family-to-work spillover) variables. The updated model was then prospectively validated for predictions at wave 3. 1,557 (77 %) nurses had complete data at wave 2 and 1,342 (65 %) at wave 3. The risk of high SA was under-estimated by the Dutch model, but discrimination between high-risk and low-risk nurses was fair after re-calibration to the Norwegian data. Gender, marital status, BMI, physical activity, smoking, alcohol intake, job satisfaction, job demands, decision latitude, support at the workplace, and work-to-family spillover were identified as potential predictors of high SA. However, these predictors did not improve the model's discriminative ability, which remained fair at wave 3. The prognostic model correctly identifies 73 % of Norwegian nurses at risk of high SA, although additional predictors are needed before the model can be used to screen working populations for risk of high SA.

TH and DCX mRNAs in peripheral blood and bone marrow predict outcome in metastatic neuroblastoma patients.

PubMed

Yáñez, Yania; Hervás, David; Grau, Elena; Oltra, Silvestre; Pérez, Gema; Palanca, Sarai; Bermúdez, Mar; Márquez, Catalina; Cañete, Adela; Castel, Victoria

2016-03-01

In metastatic neuroblastoma (NB) patients, accurate risk stratification and disease monitoring would reduce relapse probabilities. This study aims to evaluate the independent prognostic significance of detecting tyrosine hydroxylase (TH) and doublecortin (DCX) mRNAs by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) in peripheral blood (PB) and bone marrow (BM) samples from metastatic NB patients. RT-qPCR was performed on PB and BM samples from metastatic NB patients at diagnosis, post-induction therapy and at the end of treatment for TH and DCX mRNAs detection. High levels of TH and DCX mRNAs when detected in PB and BM at diagnosis independently predicted worse outcome in a cohort of 162 metastatic NB. In the subgroup of high-risk metastatic NB, TH mRNA detected in PB remained as independent predictor of EFS and OS at diagnosis. After the induction therapy, high levels of TH mRNA in PB and DCX mRNA in BM independently predicted poor EFS and OS. Furthermore TH mRNA when detected in BM predicted worse EFS. TH mRNA in PB samples at the end of treatment is an independent predictor of worse outcome. TH and DCX mRNAs levels in PB and BM assessed by RT-qPCR should be considered in new pre-treatment risk stratification strategies to reliable estimate outcome differences in metastatic NB patients. In those high-risk metastatic NB, TH and DCX mRNA quantification could be used for the assessment of response to treatment and for early detection of progressive disease or relapses.

Amino-terminal pro-B-type natriuretic peptide and high-sensitivity C-reactive protein but not cystatin C predict cardiovascular events in male patients with peripheral artery disease independently of ambulatory pulse pressure.

PubMed

Skoglund, Per H; Arpegård, Johannes; Ostergren, Jan; Svensson, Per

2014-03-01

Patients with peripheral arterial disease (PAD) are at high risk for cardiovascular (CV) events. We have previously shown that ambulatory pulse pressure (APP) predicts CV events in PAD patients. The biomarkers amino-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and cystatin C are related to a worse outcome in patients with CV disease, but their predictive values have not been studied in relation to APP. Blood samples and 24-hour measurements of ambulatory blood pressure were examined in 98 men referred for PAD evaluation during 1998-2001. Patients were followed for a median of 71 months. The outcome variable was CV events defined as either CV mortality or any hospitalization for myocardial infarction, stroke, or coronary revascularization. The predictive values of log(NT-proBNP), log(hs-CRP), and log(cystatin C) alone and together with APP were assessed by multivariable Cox regression. Area under the curve (AUC) and net reclassification improvement (NRI) were calculated compared with a model containing other significant risk factors. During follow-up, 36 patients had at least 1 CV event. APP, log(NT-proBNP), and log(hs-CRP) all predicted CV events in univariable analysis, whereas log(cystatin C) did not. In multivariable analysis log(NT-proBNP) (hazard ratio (HR) = 1.62; 95% confidence interval (CI) = 1.05-2.51) and log(hs-CRP) (HR = 1.63; 95% CI = 1.19-2.24) predicted events independently of 24-hour PP. The combination of log(NT-proBNP), log(hs-CRP), and average day PP improved risk discrimination (AUC = 0.833 vs. 0.736; P < 0.05) and NRI (37%; P < 0.01) when added to other significant risk factors. NT-proBNP and hs-CRP predict CV events independently of APP and the combination of hs-CRP, NT-proBNP, and day PP improves risk discrimination in PAD patients.

Traditional Cardiovascular Risk Factors as Predictors of Cardiovascular Events in the U.S. Astronaut Corps

NASA Technical Reports Server (NTRS)

Halm, M. K.; Clark, A.; Wear, M. L.; Murray, J. D.; Polk, J. D.; Amirian, E.

2009-01-01

Risk prediction equations from the Framingham Heart Study are commonly used to predict the absolute risk of myocardial infarction (MI) and coronary heart disease (CHD) related death. Predicting CHD-related events in the U.S. astronaut corps presents a monumental challenge, both because astronauts tend to live healthier lifestyles and because of the unique cardiovascular stressors associated with being trained for and participating in space flight. Traditional risk factors may not hold enough predictive power to provide a useful indicator of CHD risk in this unique population. It is important to be able to identify individuals who are at higher risk for CHD-related events so that appropriate preventive care can be provided. This is of special importance when planning long duration missions since the ability to provide advanced cardiac care and perform medical evacuation is limited. The medical regimen of the astronauts follows a strict set of clinical practice guidelines in an effort to ensure the best care. The purpose of this study was to evaluate the utility of the Framingham risk score (FRS), low-density lipoprotein (LDL) and high-density lipoprotein levels, blood pressure, and resting pulse as predictors of CHD-related death and MI in the astronaut corps, using Cox regression. Of these factors, only two, LDL and pulse at selection, were predictive of CHD events (HR(95% CI)=1.12 (1.00-1.25) and HR(95% CI)=1.70 (1.05-2.75) for every 5-unit increase in LDL and pulse, respectively). Since traditional CHD risk factors may lack the specificity to predict such outcomes in astronauts, the development of a new predictive model, using additional measures such as electron-beam computed tomography and carotid intima-media thickness ultrasound, is planned for the future.

Normal-Weight Central Obesity and Mortality Risk in Older Adults With Coronary Artery Disease.

PubMed

Sharma, Saurabh; Batsis, John A; Coutinho, Thais; Somers, Virend K; Hodge, David O; Carter, Rickey E; Sochor, Ondrej; Kragelund, Charlotte; Kanaya, Alka M; Zeller, Marianne; Park, Jong-Seon; Køber, Lars; Torp-Pedersen, Christian; Lopez-Jimenez, Francisco

2016-03-01

To study the relationship between body mass index (BMI) and central obesity and mortality in elderly patients with coronary artery disease (CAD). We identified 7057 patients 65 years or older from 5 cohort studies assessing mortality risk using either waist circumference (WC) or waist-hip ratio (WHR) in patients with CAD from January 1, 1980, to December 31, 2008. Normal weight, overweight, and obesity were defined using standard BMI cutoffs. High WHR was defined as 0.85 or more for women and 0.90 or more for men. High WC was defined as 88 cm or more for women and 102 cm or more for men. Separate models examined WC or WHR in combination with BMI (6 categories each) as the primary predictor (referent = normal BMI and normal WC or WHR). Cox proportional hazards models investigated the relationship between these obesity categories and mortality. Patients' mean age was 73.0±6.0 years (3741 [53%] women). The median censor time was 7.1 years. A normal BMI with central obesity (high WHR or high WC) demonstrated highest mortality risk (hazard ratio [HR], 1.29; 95% CI, 1.14-1.46; HR, 1.29; 95% CI, 1.12-1.50, respectively). High WHR was also predictive of mortality in the overall (HR, 2.14; 95% CI, 1.93-2.38) as well as in the sex-specific cohort. In the overall cohort, high WC was not predictive of mortality (HR, 1.04; 95% CI, 0.97-1.12); however, it predicted higher risk in men (HR, 1.12; 95% CI, 1.01-1.24). In older adults with CAD, normal-weight central obesity defined using either WHR or WC is associated with high mortality risk, highlighting a need to combine measures in adiposity-related risk assessment. Copyright © 2016. Published by Elsevier Inc.

Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand's planted forests.

PubMed

Moore, John R; Watt, Michael S

2015-08-01

Wind is the major abiotic disturbance in New Zealand's planted forests, but little is known about how the risk of wind damage may be affected by future climate change. We linked a mechanistic wind damage model (ForestGALES) to an empirical growth model for radiata pine (Pinus radiata D. Don) and a process-based growth model (cenw) to predict the risk of wind damage under different future emissions scenarios and assumptions about the future wind climate. The cenw model was used to estimate site productivity for constant CO2 concentration at 1990 values and for assumed increases in CO2 concentration from current values to those expected during 2040 and 2090 under the B1 (low), A1B (mid-range) and A2 (high) emission scenarios. Stand development was modelled for different levels of site productivity, contrasting silvicultural regimes and sites across New Zealand. The risk of wind damage was predicted for each regime and emission scenario combination using the ForestGALES model. The sensitivity to changes in the intensity of the future wind climate was also examined. Results showed that increased tree growth rates under the different emissions scenarios had the greatest impact on the risk of wind damage. The increase in risk was greatest for stands growing at high stand density under the A2 emissions scenario with increased CO2 concentration. The increased productivity under this scenario resulted in increased tree height, without a corresponding increase in diameter, leading to more slender trees that were predicted to be at greater risk from wind damage. The risk of wind damage was further increased by the modest increases in the extreme wind climate that are predicted to occur. These results have implications for the development of silvicultural regimes that are resilient to climate change and also indicate that future productivity gains may be offset by greater losses from disturbances. © 2015 John Wiley & Sons Ltd.

Anthropometric measures and absolute cardiovascular risk estimates in the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study.

PubMed

Chen, Lei; Peeters, Anna; Magliano, Dianna J; Shaw, Jonathan E; Welborn, Timothy A; Wolfe, Rory; Zimmet, Paul Z; Tonkin, Andrew M

2007-12-01

Framingham risk functions are widely used for prediction of future cardiovascular disease events. They do not, however, include anthropometric measures of overweight or obesity, now considered a major cardiovascular disease risk factor. We aimed to establish the most appropriate anthropometric index and its optimal cutoff point for use as an ancillary measure in clinical practice when identifying people with increased absolute cardiovascular risk estimates. Analysis of a population-based, cross-sectional survey was carried out. The 1991 Framingham prediction equations were used to compute 5 and 10-year risks of cardiovascular or coronary heart disease in 7191 participants from the Australian Diabetes, Obesity and Lifestyle Study (1999-2000). Receiver operating characteristic curve analysis was used to compare measures of body mass index (BMI), waist circumference, and waist-to-hip ratio in identifying participants estimated to be at 'high', or at 'intermediate or high' absolute risk. After adjustment for BMI and age, waist-to-hip ratio showed stronger correlation with absolute risk estimates than waist circumference. The areas under the receiver operating characteristic curve for waist-to-hip ratio (0.67-0.70 in men, 0.64-0.74 in women) were greater than those for waist circumference (0.60-0.65, 0.59-0.71) or BMI (0.52-0.59, 0.53-0.66). The optimal cutoff points of BMI, waist circumference and waist-to-hip ratio to predict people at 'high', or at 'intermediate or high' absolute risk estimates were 26 kg/m2, 95 cm and 0.90 in men, and 25-26 kg/m2, 80-85 cm and 0.80 in women, respectively. Measurement of waist-to-hip ratio is more useful than BMI or waist circumference in the identification of individuals estimated to be at increased risk for future primary cardiovascular events.

The Effect of Parenting Style on Social Smiling in Infants at High and Low Risk for ASD.

PubMed

Harker, Colleen M; Ibañez, Lisa V; Nguyen, Thanh P; Messinger, Daniel S; Stone, Wendy L

2016-07-01

This study examined how parenting style at 9 months predicts growth in infant social engagement (i.e., social smiling) between 9 and 18 months during a free-play interaction in infants at high (HR-infants) and low (LR-infants) familial risk for autism spectrum disorder (ASD). Results indicated that across all infants, higher levels of maternal responsiveness were concurrently associated with higher levels of social smiling, while higher levels of maternal directiveness predicted slower growth in social smiling. When accounting for maternal directiveness, which was higher in mothers of HR-infants, HR-infants exhibited greater growth in social smiling than LR-infants. Overall, each parenting style appears to make a unique contribution to the development of social engagement in infants at high- and low-risk for ASD.

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system.

PubMed

Moon, Kyoung-Ja; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

2018-01-01

A key component of the delirium management is prevention and early detection. To develop an automated delirium risk assessment system (Auto-DelRAS) that automatically alerts health care providers of an intensive care unit (ICU) patient's delirium risk based only on data collected in an electronic health record (EHR) system, and to evaluate the clinical validity of this system. Cohort and system development designs were used. Medical and surgical ICUs in two university hospitals in Seoul, Korea. A total of 3284 patients for the development of Auto-DelRAS, 325 for external validation, 694 for validation after clinical applications. The 4211 data items were extracted from the EHR system and delirium was measured using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). The potential predictors were selected and a logistic regression model was established to create a delirium risk scoring algorithm to construct the Auto-DelRAS. The Auto-DelRAS was evaluated at three months and one year after its application to clinical practice to establish the predictive validity of the system. Eleven predictors were finally included in the logistic regression model. The results of the Auto-DelRAS risk assessment were shown as high/moderate/low risk on a Kardex screen. The predictive validity, analyzed after the clinical application of Auto-DelRAS after one year, showed a sensitivity of 0.88, specificity of 0.72, positive predictive value of 0.53, negative predictive value of 0.94, and a Youden index of 0.59. A relatively high level of predictive validity was maintained with the Auto-DelRAS system, even one year after it was applied to clinical practice. Copyright © 2017. Published by Elsevier Ltd.

Clinical predictors of conversion to bipolar disorder in a prospective longitudinal familial high-risk sample: focus on depressive features.

PubMed

Frankland, Andrew; Roberts, Gloria; Holmes-Preston, Ellen; Perich, Tania; Levy, Florence; Lenroot, Rhoshel; Hadzi-Pavlovic, Dusan; Breakspear, Michael; Mitchell, Philip B

2017-11-07

Identifying clinical features that predict conversion to bipolar disorder (BD) in those at high familial risk (HR) would assist in identifying a more focused population for early intervention. In total 287 participants aged 12-30 (163 HR with a first-degree relative with BD and 124 controls (CONs)) were followed annually for a median of 5 years. We used the baseline presence of DSM-IV depressive, anxiety, behavioural and substance use disorders, as well as a constellation of specific depressive symptoms (as identified by the Probabilistic Approach to Bipolar Depression) to predict the subsequent development of hypo/manic episodes. At baseline, HR participants were significantly more likely to report ⩾4 Probabilistic features (40.4%) when depressed than CONs (6.7%; p < .05). Nineteen HR subjects later developed either threshold (n = 8; 4.9%) or subthreshold (n = 11; 6.7%) hypo/mania. The presence of ⩾4 Probabilistic features was associated with a seven-fold increase in the risk of 'conversion' to threshold BD (hazard ratio = 6.9, p < .05) above and beyond the fourteen-fold increase in risk related to major depressive episodes (MDEs) per se (hazard ratio = 13.9, p < .05). Individual depressive features predicting conversion were psychomotor retardation and ⩾5 MDEs. Behavioural disorders only predicted conversion to subthreshold BD (hazard ratio = 5.23, p < .01), while anxiety and substance disorders did not predict either threshold or subthreshold hypo/mania. This study suggests that specific depressive characteristics substantially increase the risk of young people at familial risk of BD going on to develop future hypo/manic episodes and may identify a more targeted HR population for the development of early intervention programs.

Prediction of diabetes based on baseline metabolic characteristics in individuals at high risk.

PubMed

Defronzo, Ralph A; Tripathy, Devjit; Schwenke, Dawn C; Banerji, Maryann; Bray, George A; Buchanan, Thomas A; Clement, Stephen C; Henry, Robert R; Kitabchi, Abbas E; Mudaliar, Sunder; Ratner, Robert E; Stentz, Frankie B; Musi, Nicolas; Reaven, Peter D; Gastaldelli, Amalia

2013-11-01

Individuals with impaired glucose tolerance (IGT) are at high risk for developing type 2 diabetes mellitus (T2DM). We examined which characteristics at baseline predicted the development of T2DM versus maintenance of IGT or conversion to normal glucose tolerance. We studied 228 subjects at high risk with IGT who received treatment with placebo in ACT NOW and who underwent baseline anthropometric measures and oral glucose tolerance test (OGTT) at baseline and after a mean follow-up of 2.4 years. In a univariate analysis, 45 of 228 (19.7%) IGT individuals developed diabetes. After adjusting for age, sex, and center, increased fasting plasma glucose, 2-h plasma glucose, G0-120 during OGTT, HbA1c, adipocyte insulin resistance index, ln fasting plasma insulin, and ln I0-120, as well as family history of diabetes and presence of metabolic syndrome, were associated with increased risk of diabetes. At baseline, higher insulin secretion (ln [I0-120/G0-120]) during the OGTT was associated with decreased risk of diabetes. Higher β-cell function (insulin secretion/insulin resistance or disposition index; ln [I0-120/G0-120 × Matsuda index of insulin sensitivity]; odds ratio 0.11; P < 0.0001) was the variable most closely associated with reduced risk of diabetes. In a stepwise multiple-variable analysis, only HbA1c and β-cell function (ln insulin secretion/insulin resistance index) predicted the development of diabetes (r = 0.49; P < 0.0001).

The Impact of Magnetic Resonance Imaging on Prediction of Extraprostatic Extension and Prostatectomy Outcome in Patients with Low-, Intermediate- and High-Risk Prostate Cancer: Try to Find a Standard.

PubMed

Radtke, Jan Philipp; Hadaschik, Boris A; Wolf, Maya B; Freitag, Martin T; Schwab, Constantin; Alt, Celine; Roth, Wilfried; Duensing, Stefan; Pahernik, Sascha A; Roethke, Matthias C; Schlemmer, Heinz-Peter; Hohenfellner, Markus; Teber, Dogu

2015-12-01

To investigate the value of multiparametric magnetic resonance imaging (mpMRI) and to predict extracapsular extension (ECE), seminal vesicle (SV) infiltration, and a negative surgical margin (SM) status at radical prostatectomy (RP) for different prostate cancer (PC) risk groups. In the study, 805 men underwent 3 tesla mpMRI without endorectal coil before MRI/transrectal ultrasonography-fusion guided prostate biopsy. MRIs were analyzed using the prostate imaging reporting and data system. The cohort was classified into risk groups according to National Comprehensive Cancer Network (NCCN) criteria. Of 132 men who subsequently underwent RP, pathologic stage and SM status at RP were used as reference. Retrospectively, we investigated a European Society of Urogenital Radiology (ESUR) score for ECE and SV-infiltration. Statistical analyses included regression analyses, receiver operating characteristics (ROC), and Youden Index to assess an ESUR-score cutoff. Area under the curve in ROC curve analyses was 0.82 for ESUR-ECE score to detect pT(3a)-disease and 0.77 for ESUR-SV score for pT(3b). Using a cutoff of 4 for ECE and of 2 for SV, the positive predictive value of the ECE-score for harboring pT(3) was 50.0%, 90.0%, and 88.8% for the low-, intermediate- and high-risk cohort. Retrospectively, the use of the ESUR-ECE score preoperatively would have changed the initial surgical plan, according to NCCN criteria, in 31.1% of patients. In the high-risk subgroup, 9/35 (25.7%) patients were correctly assessed as not harboring pT(3) by imaging (ECE score <4), and would have allowed secure robot-assisted radical prostatectomy and nerve-sparing surgery (NSS). When T3 suspicion on preoperative MRI would be taken into account, intraoperative frozen-sections (IFS) might avoid positive SM in 12/18 high-risk patients and an oncologic secure NSS in 8/20 intermediate-risk patients. Prediction of pT(3) disease is crucial to plan NSS and to achieve negative SM in RP. Standardized ECE scoring on mpMRI is an independent predictor of pT(3) and may help to plan RP with oncologic security, even in high-risk patients. In addition, it allows more accurate selection of a subgroup of patients for systematic and MRI-guided IFS.

Prediction of severe retinopathy of prematurity using the WINROP algorithm in a cohort from Malopolska. A retrospective, single-center study.

PubMed

Jagła, Mateusz; Peterko, Anna; Olesińska, Katarzyna; Szymońska, Izabela; Kwinta, Przemko

2017-01-01

Retinopathy of prematurity (ROP) is one of the leading avoidable causes of blindness in childhood in developed countries. Accurate diagnosis and treatment are essential for preventing the loss of vision. WINROP (https://www.winrop.com) is an online monitoring system which predicts the risk for ROP requiring treatment based on gestational age, birth weight, and body weight gain. To validate diagnostic accuracy of the WINROP algorithm for the detection of severe ROP in a single centre cohort of Polish, high-risk preterm infant population. Medical records of neonates born before 32 weeks of gestation admitted to the third level neonatal centre in a 2-year retrospective investigation 79 patients were included in the study: their gestational age, birth weight and body weight gain were set in the WINROP system. The algorithm evaluated the risk for ROP divided into low or high-risk of disease and identified infants with high risk of developing severe ROP (type 1 ROP). Out of 79 patients 37 received a high-risk alarm, of whom 22 developed severe ROP. Low-risk alarm was triggered in 42 infants; five of them developed type 1 ROP. The sensitivity of the WINROP was found to be 81.5% (95% CI 61.9-93.7), specificity 71.2% (95% CI 56.9-82.9), negative predictive value (NPV) 88.1% (95% CI 76.7-94.3), and positive predictive value (PPV) 59.5 (95% CI 48.1-69.9), respectively. The accuracy of the test significantly increased after combined WINROP and surfactant therapy as an additional factor - sensitivity 96.3% (95% CI 81.0-99.9), specificity 63.5% (95% CI 49.0-76.4), NPV 97.1% (95% CI 82.3-99.6), and PPV 57.8 (95% CI 48.7-66.4). The WINROP algorithm sensitivity from the Polish cohort was not as high as that reported in developed countries. However, combined with additional factors (e.g. surfactant treatment) it can be useful for identifying the risk groups of sight-threatening ROP. The accuracy of the WINROP algorithm should be validated in a large multi-center prospective study in a Polish population of preterm infants.

Discrimination of health risk by combined body mass index and waist circumference.

PubMed

Ardern, Christopher I; Katzmarzyk, Peter T; Janssen, Ian; Ross, Robert

2003-01-01

NIH Clinical Guidelines (1998) recommend the measurement of waist circumference (WC, centimeters) within body mass index (BMI, kilograms per square meter) categories as a screening tool for increased health risk. The Canada Heart Health Surveys (1986 through 1992) were used to describe the prevalence of the metabolic syndrome in Canada and to test the use of the NIH guidelines for predicting metabolic risk factors. The sample included 7981 participants ages 20 to 74 years who had complete data for WC, BMI, high-density lipoprotein-cholesterol, triglycerides, diabetic status, and systolic and diastolic blood pressures. National Cholesterol Education Program Adult Treatment Panel III risk categories were used to identify the metabolic syndrome and associated risk factors. Logistic regression was used to test the hypothesis that WC improves the prediction of the metabolic syndrome, within overweight (25 to 29.9 kg/m(2)) and obese I (30 to 34.9 kg/m(2)) BMI categories. The prevalence of the metabolic syndrome was 17.0% in men and 13.2% in women. The odds ratios (OR) for the prediction of the metabolic syndrome were elevated in overweight [OR, 1.85; 95% confidence interval (95%CI), 1.02 to 3.35] and obese (OR, 2.35; 95%CI, 1.25 to 4.42) women with a high WC compared with overweight and obese women with a low WC, respectively. On the other hand, WC was not predictive of the metabolic syndrome or component risk factors in men, within BMI categories. In women already at increased health risk because of an elevated BMI, the additional measurement of WC may help identify cardiovascular risk.

Biomechanics laboratory-based prediction algorithm to identify female athletes with high knee loads that increase risk of ACL injury

PubMed Central

Myer, Gregory D; Ford, Kevin R; Khoury, Jane; Succop, Paul; Hewett, Timothy E

2014-01-01

Objective Knee abduction moment (KAM) during landing predicts non-contact anterior cruciate ligament (ACL) injury risk with high sensitivity and specificity in female athletes. The purpose of this study was to employ sensitive laboratory (lab-based) tools to determine predictive mechanisms that underlie increased KAM during landing. Methods Female basketball and soccer players (N=744) from a single county public school district were recruited to participate in testing of anthropometrics, maturation, laxity/flexibility, strength and landing biomechanics. Linear regression was used to model KAM, and logistic regression was used to examine high (>25.25 Nm of KAM) versus low KAM as surrogate for ACL injury risk. Results The most parsimonious model included independent predictors (β±1 SE) (1) peak knee abduction angle (1.78±0.05; p<0.001), (2) peak knee extensor moment (0.17±0.01; p<0.001), (3) knee flexion range of motion (0.15±0.03; p<0.01), (4) body mass index (BMI) Z-score (−1.67±0.36; p<0.001) and (5) tibia length (−0.50±0.14; p<0.001) and accounted for 78% of the variance in KAM during landing. The logistic regression model that employed these same variables predicted high KAM status with 85% sensitivity and 93% specificity and a C-statistic of 0.96. Conclusions Increased knee abduction angle, quadriceps recruitment, tibia length and BMI with decreased knee flexion account for 80% of the measured variance in KAM during a drop vertical jump. Clinical relevance Females who demonstrate increased KAM are more responsive and more likely to benefit from neuromuscular training. These findings should significantly enhance the identification of those at increased risk and facilitate neuromuscular training targeted to this important risk factor (high KAM) for ACL injury. PMID:20558526

The novel EuroSCORE II algorithm predicts the hospital mortality of thoracic aortic surgery in 461 consecutive Japanese patients better than both the original additive and logistic EuroSCORE algorithms.

PubMed

Nishida, Takahiro; Sonoda, Hiromichi; Oishi, Yasuhisa; Tanoue, Yoshihisa; Nakashima, Atsuhiro; Shiokawa, Yuichi; Tominaga, Ryuji

2014-04-01

The European System for Cardiac Operative Risk Evaluation (EuroSCORE) II was developed to improve the overestimation of surgical risk associated with the original (additive and logistic) EuroSCOREs. The purpose of this study was to evaluate the significance of the EuroSCORE II by comparing its performance with that of the original EuroSCOREs in Japanese patients undergoing surgery on the thoracic aorta. We have calculated the predicted mortalities according to the additive EuroSCORE, logistic EuroSCORE and EuroSCORE II algorithms in 461 patients who underwent surgery on the thoracic aorta during a period of 20 years (1993-2013). The actual in-hospital mortality rates in the low- (additive EuroSCORE of 3-6), moderate- (7-11) and high-risk (≥11) groups (followed by overall mortality) were 1.3, 6.2 and 14.4% (7.2% overall), respectively. Among the three different risk groups, the expected mortality rates were 5.5 ± 0.6, 9.1 ± 0.7 and 13.5 ± 0.2% (9.5 ± 0.1% overall) by the additive EuroSCORE algorithm, 5.3 ± 0.1, 16 ± 0.4 and 42.4 ± 1.3% (19.9 ± 0.7% overall) by the logistic EuroSCORE algorithm and 1.6 ± 0.1, 5.2 ± 0.2 and 18.5 ± 1.3% (7.4 ± 0.4% overall) by the EuroSCORE II algorithm, indicating poor prediction (P < 0.0001) of the mortality in the high-risk group, especially by the logistic EuroSCORE. The areas under the receiver operating characteristic curves of the additive EuroSCORE, logistic EuroSCORE and EuroSCORE II algorithms were 0.6937, 0.7169 and 0.7697, respectively. Thus, the mortality expected by the EuroSCORE II more closely matched the actual mortality in all three risk groups. In contrast, the mortality expected by the logistic EuroSCORE overestimated the risks in the moderate- (P = 0.0002) and high-risk (P < 0.0001) patient groups. Although all of the original EuroSCOREs and EuroSCORE II appreciably predicted the surgical mortality for thoracic aortic surgery in Japanese patients, the EuroSCORE II best predicted the mortalities in all risk groups.

Factors predicting high estimated 10-year stroke risk: thai epidemiologic stroke study.

PubMed

Hanchaiphiboolkul, Suchat; Puthkhao, Pimchanok; Towanabut, Somchai; Tantirittisak, Tasanee; Wangphonphatthanasiri, Khwanrat; Termglinchan, Thanes; Nidhinandana, Samart; Suwanwela, Nijasri Charnnarong; Poungvarin, Niphon

2014-08-01

The purpose of the study was to determine the factors predicting high estimated 10-year stroke risk based on a risk score, and among the risk factors comprising the risk score, which factors had a greater impact on the estimated risk. Thai Epidemiologic Stroke study was a community-based cohort study, which recruited participants from the general population from 5 regions of Thailand. Cross-sectional baseline data of 16,611 participants aged 45-69 years who had no history of stroke were included in this analysis. Multiple logistic regression analysis was used to identify the predictors of high estimated 10-year stroke risk based on the risk score of the Japan Public Health Center Study, which estimated the projected 10-year risk of incident stroke. Educational level, low personal income, occupation, geographic area, alcohol consumption, and hypercholesterolemia were significantly associated with high estimated 10-year stroke risk. Among these factors, unemployed/house work class had the highest odds ratio (OR, 3.75; 95% confidence interval [CI], 2.47-5.69) followed by illiterate class (OR, 2.30; 95% CI, 1.44-3.66). Among risk factors comprising the risk score, the greatest impact as a stroke risk factor corresponded to age, followed by male sex, diabetes mellitus, systolic blood pressure, and current smoking. Socioeconomic status, in particular, unemployed/house work and illiterate class, might be good proxy to identify the individuals at higher risk of stroke. The most powerful risk factors were older age, male sex, diabetes mellitus, systolic blood pressure, and current smoking. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Hybrid Capture II detection of oncogenic human papillomavirus: a useful tool when evaluating men who have sex with men with atypical squamous cells of undetermined significance on anal cytology.

PubMed

Goldstone, Stephen E; Kawalek, Adam Z; Goldstone, Robert N; Goldstone, Andrew B

2008-07-01

In the cervix and anus, patients with atypical squamous cells of undetermined significance often do not have high-grade squamous intraepithelial lesions. In women with atypical squamous cells of undetermined significance, Hybrid-Capture II testing for oncogenic high-risk human papillomavirus is performed and those without high-risk human papillomavirus often are observed. We endeavored to determine whether Hybrid-Capture II testing would be beneficial in men who have sex with men with atypical squamous cells of undetermined significance. We performed a retrospective chart review of men who have sex with men with atypical squamous cells of undetermined significance who had high-resolution anoscopy and Hybrid-Capture II. A total of 290 men were identified (mean age, 42 years), and 212 (73 percent) were HIV-negative. High-grade squamous intraepithelial lesions were found in 50 (17 percent): 23 (10 percent) who were HIV-negative and 27 (35 percent) who were HIV-positive men. High-risk human papillomavirus was found in 138 (48 percent); 91 (43 percent) of HIV-negative and 47 (60 percent) of HIV-positive men. The sensitivity, specificity, positive predictive value, and negative predictive value of atypical cells of undetermined significance cytology combined with Hybrid-Capture II were 84, 60, 30, and 95 percent, respectively. There was no significant difference between all men vs. those who were HIV-positive or HIV-negative except for the positive predictive value. Hybrid-Capture II testing for high-risk human papillomavirus in men who have sex with men with atypical cells of undetermined significance and referring only those with high-risk human papillomavirus reduces the number who require high-resolution anoscopy by more than half. Five percent with high-grade squamous intraepithelial lesions would be missed.

Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis.

PubMed

Crowson, Cynthia S; Rollefstad, Silvia; Kitas, George D; van Riel, Piet L C M; Gabriel, Sherine E; Semb, Anne Grete

2017-01-01

Cardiovascular disease (CVD) risk calculators designed for use in the general population do not accurately predict the risk of CVD among patients with rheumatoid arthritis (RA), who are at increased risk of CVD. The process of developing risk prediction models involves numerous issues. Our goal was to develop a CVD risk calculator for patients with RA. Thirteen cohorts of patients with RA originating from 10 different countries (UK, Norway, Netherlands, USA, Sweden, Greece, South Africa, Spain, Canada and Mexico) were combined. CVD risk factors and RA characteristics at baseline, in addition to information on CVD outcomes were collected. Cox models were used to develop a CVD risk calculator, considering traditional CVD risk factors and RA characteristics. Model performance was assessed using measures of discrimination and calibration with 10-fold cross-validation. A total of 5638 RA patients without prior CVD were included (mean age: 55 [SD: 14] years, 76% female). During a mean follow-up of 5.8 years (30139 person years), 389 patients developed a CVD event. Event rates varied between cohorts, necessitating inclusion of high and low risk strata in the models. The multivariable analyses revealed 2 risk prediction models including either a disease activity score including a 28 joint count and erythrocyte sedimentation rate (DAS28ESR) or a health assessment questionnaire (HAQ) along with age, sex, presence of hypertension, current smoking and ratio of total cholesterol to high-density lipoprotein cholesterol. Unfortunately, performance of these models was similar to general population CVD risk calculators. Efforts to develop a specific CVD risk calculator for patients with RA yielded 2 potential models including RA disease characteristics, but neither demonstrated improved performance compared to risk calculators designed for use in the general population. Challenges encountered and lessons learned are discussed in detail.

A simple model for prediction postpartum PTSD in high-risk pregnancies.

PubMed

Shlomi Polachek, Inbal; Dulitzky, Mordechai; Margolis-Dorfman, Lilia; Simchen, Michal J

2016-06-01

This study aimed to examine the prevalence and possible antepartum risk factors of complete and partial post-traumatic stress disorder (PTSD) among women with complicated pregnancies and to define a predictive model for postpartum PTSD in this population. Women attending the high-risk pregnancy outpatient clinics at Sheba Medical Center completed the Edinburgh Postnatal Depression Scale (EPDS) and a questionnaire regarding demographic variables, history of psychological and psychiatric treatment, previous trauma, previous childbirth, current pregnancy medical and emotional complications, fears from childbirth, and expected pain. One month after delivery, women were requested to repeat the EPDS and complete the Post-traumatic Stress Diagnostic Scale (PDS) via telephone interview. The prevalence rates of postpartum PTSD (9.9 %) and partial PTSD (11.9 %) were relatively high. PTSD and partial PTSD were associated with sadness or anxiety during past pregnancy or childbirth, previous very difficult birth experiences, preference for cesarean section in future childbirth, emotional crises during pregnancy, increased fear of childbirth, higher expected intensity of pain, and depression during pregnancy. We created a prediction model for postpartum PTSD which shows a linear growth in the probability for developing postpartum PTSD when summing these seven antenatal risk factors. Postpartum PTSD is extremely prevalent after complicated pregnancies. A simple questionnaire may aid in identifying at-risk women before childbirth. This presents a potential for preventing or minimizing postpartum PTSD in this population.

Longitudinal Relations Among Parental Monitoring Strategies, Knowledge, and Adolescent Delinquency in a Racially Diverse At-Risk Sample.

PubMed

Bendezú, Jason J; Pinderhughes, Ellen E; Hurley, Sean M; McMahon, Robert J; Racz, Sarah J

2016-04-04

Parents raising youth in high-risk communities at times rely on active, involved monitoring strategies in order to increase both knowledge about youth activities and the likelihood that adolescents will abstain from problem behavior. Key monitoring literature suggests that some of these active monitoring strategies predict increases in adolescent problem behavior rather than protect against it. However, this literature has studied racially homogenous, low-risk samples, raising questions about generalizability. With a diverse sample of youth (N = 753; 58% male; 46% Black) and families living in high-risk neighborhoods, bidirectional longitudinal relations were examined among three aspects of monitoring (parental discussions of daily activities, parental curfew rules, and adolescent communication with parents), parental knowledge, and youth delinquency. Parental discussion of daily activities was the strongest predictor of parental knowledge, which negatively predicted delinquency. However, these aspects of monitoring did not predict later delinquency. Findings were consistent across gender and race/urbanicity. Results highlight the importance of active and involved parental monitoring strategies in contexts where they are most needed.

Number of Psychosocial Strengths Predicts Reduced HIV Sexual Risk Behaviors Above and Beyond Syndemic Problems Among Gay and Bisexual Men.

PubMed

Hart, Trevor A; Noor, Syed W; Adam, Barry D; Vernon, Julia R G; Brennan, David J; Gardner, Sandra; Husbands, Winston; Myers, Ted

2017-10-01

Syndemics research shows the additive effect of psychosocial problems on high-risk sexual behavior among gay and bisexual men (GBM). Psychosocial strengths may predict less engagement in high-risk sexual behavior. In a study of 470 ethnically diverse HIV-negative GBM, regression models were computed using number of syndemic psychosocial problems, number of psychosocial strengths, and serodiscordant condomless anal sex (CAS). The number of syndemic psychosocial problems correlated with serodiscordant CAS (RR = 1.51, 95% CI 1.18-1.92; p = 0.001). When adding the number of psychosocial strengths to the model, the effect of syndemic psychosocial problems became non-significant, but the number of strengths-based factors remained significant (RR = 0.67, 95% CI 0.53-0.86; p = 0.002). Psychosocial strengths may operate additively in the same way as syndemic psychosocial problems, but in the opposite direction. Consistent with theories of resilience, psychosocial strengths may be an important set of variables predicting sexual risk behavior that is largely missing from the current HIV behavioral literature.

Risk Assessment Among Prostate Cancer Patients Receiving Primary Androgen Deprivation Therapy

PubMed Central

Cooperberg, Matthew R.; Hinotsu, Shiro; Namiki, Mikio; Ito, Kazuto; Broering, Jeanette; Carroll, Peter R.; Akaza, Hideyuki

2009-01-01

Purpose Prostate cancer epidemiology has been marked overall by a downward risk migration over time. However, in some populations, both in the United States and abroad, many men are still diagnosed with high-risk and/or advanced disease. Primary androgen deprivation therapy (PADT) is frequently offered to these patients, and disease risk prediction is not well-established in this context. We compared risk features between large disease registries from the United States and Japan, and aimed to build and validate a risk prediction model applicable to PADT patients. Methods Data were analyzed from 13,740 men in the United States community-based Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry and 19,265 men in the Japan Study Group of Prostate Cancer (J-CaP) database, a national Japanese registry of men receiving androgen deprivation therapy. Risk distribution was compared between the two datasets using three well-described multivariable instruments. A novel instrument (Japan Cancer of the Prostate Risk Assessment [J-CAPRA]) was designed and validated to be specifically applicable to PADT patients, and more relevant to high-risk patients than existing instruments. Results J-CaP patients are more likely than CaPSURE patients to be diagnosed with high-risk features; 43% of J-CaP versus 5% of CaPSURE patients had locally advanced or metastatic disease that could not be stratified with the standard risk assessment tools. J-CAPRA—scored 0 to 12 based on Gleason score, prostate-specific antigen level, and clinical stage—predicts progression-free survival among PADT patients in J-CaP with a c-index of 0.71, and cancer-specific survival among PADT patients in CaPSURE with a c-index of 0.84. Conclusion The novel J-CAPRA is the first risk instrument developed and validated for patients undergoing PADT. It is applicable to those with both localized and advanced disease, and performs well in diverse populations. PMID:19667269