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.

Evaluation of a Genetic Risk Score to Improve Risk Prediction for Alzheimer's Disease.

PubMed

Chouraki, Vincent; Reitz, Christiane; Maury, Fleur; Bis, Joshua C; Bellenguez, Celine; Yu, Lei; Jakobsdottir, Johanna; Mukherjee, Shubhabrata; Adams, Hieab H; Choi, Seung Hoan; Larson, Eric B; Fitzpatrick, Annette; Uitterlinden, Andre G; de Jager, Philip L; Hofman, Albert; Gudnason, Vilmundur; Vardarajan, Badri; Ibrahim-Verbaas, Carla; van der Lee, Sven J; Lopez, Oscar; Dartigues, Jean-François; Berr, Claudine; Amouyel, Philippe; Bennett, David A; van Duijn, Cornelia; DeStefano, Anita L; Launer, Lenore J; Ikram, M Arfan; Crane, Paul K; Lambert, Jean-Charles; Mayeux, Richard; Seshadri, Sudha

2016-06-18

Effective prevention of Alzheimer's disease (AD) requires the development of risk prediction tools permitting preclinical intervention. We constructed a genetic risk score (GRS) comprising common genetic variants associated with AD, evaluated its association with incident AD and assessed its capacity to improve risk prediction over traditional models based on age, sex, education, and APOEɛ4. In eight prospective cohorts included in the International Genomics of Alzheimer's Project (IGAP), we derived weighted sum of risk alleles from the 19 top SNPs reported by the IGAP GWAS in participants aged 65 and older without prevalent dementia. Hazard ratios (HR) of incident AD were estimated in Cox models. Improvement in risk prediction was measured by the difference in C-index (Δ-C), the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI>0). Overall, 19,687 participants at risk were included, of whom 2,782 developed AD. The GRS was associated with a 17% increase in AD risk (pooled HR = 1.17; 95% CI =   [1.13-1.21] per standard deviation increase in GRS; p-value =  2.86×10-16). This association was stronger among persons with at least one APOEɛ4 allele (HRGRS = 1.24; 95% CI =   [1.15-1.34]) than in others (HRGRS = 1.13; 95% CI =   [1.08-1.18]; pinteraction = 3.45×10-2). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOEɛ4, and education (Δ-Cindex =  0.0043 [0.0019-0.0067]). Similar patterns were observed for IDI and NRI>0. In conclusion, a risk score incorporating common genetic variation outside the APOEɛ4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.

Cardiovascular risk scores for coronary atherosclerosis.

PubMed

Yalcin, Murat; Kardesoglu, Ejder; Aparci, Mustafa; Isilak, Zafer; Uz, Omer; Yiginer, Omer; Ozmen, Namik; Cingozbay, Bekir Yilmaz; Uzun, Mehmet; Cebeci, Bekir Sitki

2012-10-01

The objective of this study was to compare frequently used cardiovascular risk scores in predicting the presence of coronary artery disease (CAD) and 3-vessel disease. In 350 consecutive patients (218 men and 132 women) who underwent coronary angiography, the cardiovascular risk level was determined using the Framingham Risk Score (FRS), the Modified Framingham Risk Score (MFRS), the Prospective Cardiovascular Münster (PROCAM) score, and the Systematic Coronary Risk Evaluation (SCORE). The area under the curve for receiver operating characteristic curves showed that FRS had more predictive value than the other scores for CAD (area under curve, 0.76, P < or = 0.001), but all scores had good specificity and positive predictive value. For 3-vessel disease, the FRS had better predictive value than the other scores (area under curve, 0.74, P < or = 0.001), but all scores had good specificity and negative predictive value. The risk scores (FRS, MFRS, PROCAM, and SCORE) may predict the presence and severity of coronary atherosclerosis.The FRS had better predictive value than the other scores.

Fall risk: the clinical relevance of falls and how to integrate fall risk with fracture risk.

PubMed

Peeters, G; van Schoor, Natasja M; Lips, Paul

2009-12-01

In old age, 5-10% percent of all falls result in a fracture, and up to 90% of all fractures result from a fall. This article describes the link between fall risk and fracture risk in community-dwelling older persons. Which factors attribute to both the fall risk and the fracture risk? Which falls result in a fracture? Which tools are available to predict falls and fractures? Directions for the use of prediction tools in clinical practice are given. Challenges for future research include further validation of existing prediction tools and evaluation of the cost-effectiveness of treatment after screening.

Gender, TIMI risk score and in-hospital mortality in STEMI patients undergoing primary PCI: results from the Belgian STEMI registry.

PubMed

Gevaert, Sofie A; De Bacquer, Dirk; Evrard, Patrick; Convens, Carl; Dubois, Philippe; Boland, Jean; Renard, Marc; Beauloye, Christophe; Coussement, Patrick; De Raedt, Herbert; de Meester, Antoine; Vandecasteele, Els; Vranckx, Pascal; Sinnaeve, Peter R; Claeys, Marc J

2014-01-22

The relationship between the predictive performance of the TIMI risk score for STEMI and gender has not been evaluated in the setting of primary PCI (pPCI). Here, we compared in-hospital mortality and predictive performance of the TIMI risk score between Belgian women and men undergoing pPCI. In-hospital mortality was analysed in 8,073 (1,920 [23.8%] female and 6,153 [76.2%] male patients) consecutive pPCI-treated STEMI patients, included in the prospective, observational Belgian STEMI registry (January 2007 to February 2011). A multivariable logistic regression model, including TIMI risk score variables and gender, evaluated differences in in-hospital mortality between men and women. The predictive performance of the TIMI risk score according to gender was evaluated in terms of discrimination and calibration. Mortality rates for TIMI scores in women and men were compared. Female patients were older, had more comorbidities and longer ischaemic times. Crude in-hospital mortality was 10.1% in women vs. 4.9% in men (OR 2.2; 95% CI: 1.82-2.66, p<0.001). When adjusting for TIMI risk score variables, mortality remained higher in women (OR 1.47, 95% CI: 1.15-1.87, p=0.002). The TIMI risk score provided a good predictive discrimination and calibration in women as well as in men (c-statistic=0.84 [95% CI: 0.809-0.866], goodness-of-fit p=0.53 and c-statistic=0.89 [95% CI: 0.873-0.907], goodness-of-fit p=0.13, respectively), but mortality prediction for TIMI scores was better in men (p=0.02 for TIMI score x gender interaction). In the Belgian STEMI registry, pPCI-treated women had a higher in-hospital mortality rate even after correcting for TIMI risk score variables. The TIMI risk score was effective in predicting in-hospital mortality but performed slightly better in men. The database was registered with clinicaltrials.gov (NCT00727623).

Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

PubMed Central

Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

2013-01-01

Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening. Primary Funding Source Roy Castle Lung Cancer Foundation. PMID:22910935

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.

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

Evaluation of polygenic risk scores for predicting breast and prostate cancer risk.

PubMed

Machiela, Mitchell J; Chen, Chia-Yen; Chen, Constance; Chanock, Stephen J; Hunter, David J; Kraft, Peter

2011-09-01

Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores. © 2011 Wiley-Liss, Inc.

High Tumor Volume to Fetal Weight Ratio Is Associated with Worse Fetal Outcomes and Increased Maternal Risk in Fetuses with Sacrococcygeal Teratoma.

PubMed

Gebb, Juliana S; Khalek, Nahla; Qamar, Huma; Johnson, Mark P; Oliver, Edward R; Coleman, Beverly G; Peranteau, William H; Hedrick, Holly L; Flake, Alan W; Adzick, N Scott; Moldenhauer, Julie S

2018-03-01

Tumor volume to fetal weight ratio (TFR) > 0.12 before 24 weeks has been associated with poor outcome in fetuses with sacrococcygeal teratoma (SCT). We evaluated TFR in predicting poor fetal outcome and increased maternal operative risk in our cohort of SCT pregnancies. This is a retrospective, single-center review of fetuses seen with SCT from 1997 to 2015. Patients who chose termination of pregnancy (TOP), delivered elsewhere, or had initial evaluation at > 24 weeks were excluded. Receiver operating characteristic (ROC) analysis determined the optimal TFR to predict poor fetal outcome and increased maternal operative risk. Poor fetal outcome included fetal demise, neonatal demise, or fetal deterioration warranting open fetal surgery or delivery < 32 weeks. Increased maternal operative risk included cases necessitating open fetal surgery, classical cesarean delivery, or ex utero intrapartum treatment (EXIT). Of 139 pregnancies with SCT, 27 chose TOP, 14 delivered elsewhere, and 40 had initial evaluation at > 24 weeks. Thus, 58 fetuses were reviewed. ROC analysis revealed that at ≤24 weeks, TFR > 0.095 was predictive of poor fetal outcome and TFR > 0.12 was predictive of increased maternal operative risk. This study supports the use of TFR at ≤24 weeks for risk stratification of pregnancies with SCT. © 2018 S. Karger AG, Basel.

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.

Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.

PubMed

Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L; Schmidt, Marjanka K; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Beckmann, Matthias W; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J; Cox, Angela; Cross, Simon S; Czene, Kamila; Devilee, Peter; Dörk, Thilo; Dumont, Martine; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G; Guénel, Pascal; Haiman, Christopher A; Hamann, Ute; Hooning, Maartje J; Hopper, John L; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M; Johnson, Nichola; Jones, Michael E; Kabisch, Maria; Kibriya, Muhammad; Knight, Julia A; Koppert, Linetta B; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Luben, Robert; Lubinski, Jan; Malone, Kathi E; Mannermaa, Arto; Margolin, Sara; Marme, Frederik; McLean, Catriona; Meijers-Heijboer, Hanne; Meindl, Alfons; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E; Perez, Jose I A; Perkins, Barbara; Peterlongo, Paolo; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J; Schmutzler, Rita K; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J; Southey, Melissa C; Swerdlow, Anthony J; Toland, Amanda E; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B; Verhoef, Senno; Whittemore, Alice S; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Pharoah, Paul; Hunter, David; Easton, Douglas F; Zheng, Wei

2016-08-01

Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31-0.62, p  =  9.91 × 10-8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46-0.71, p  =  1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60-0.84, p   =   1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.

Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

PubMed Central

Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K.; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L.; Schmidt, Marjanka K.; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E.; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L.; Anton-Culver, Hoda; Beckmann, Matthias W.; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V.; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J.; Cross, Simon S.; Czene, Kamila; Dörk, Thilo; Dumont, Martine; Fasching, Peter A.; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G.; Guénel, Pascal; Haiman, Christopher A.; Hamann, Ute; Hooning, Maartje J.; Hopper, John L.; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M.; Johnson, Nichola; Jones, Michael E.; Kabisch, Maria; Knight, Julia A.; Koppert, Linetta B.; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Lubinski, Jan; Malone, Kathi E.; Mannermaa, Arto; Margolin, Sara; McLean, Catriona; Meindl, Alfons; Neuhausen, Susan L.; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E.; Perez, Jose I. A.; Perkins, Barbara; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J.; Schmutzler, Rita K.; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J.; Southey, Melissa C.; Swerdlow, Anthony J.; Toland, Amanda E.; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B.; Verhoef, Senno; Whittemore, Alice S.; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Hunter, David; Easton, Douglas F.; Zheng, Wei

2016-01-01

Background Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31–0.62, p  =  9.91 × 10−8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46–0.71, p  =  1.88 × 10−8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60–0.84, p   =   1.64 × 10−7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. Conclusions BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer. PMID:27551723

Scoring life insurance applicants' laboratory results, blood pressure and build to predict all-cause mortality risk.

PubMed

Fulks, Michael; Stout, Robert L; Dolan, Vera F

2012-01-01

Evaluate the degree of medium to longer term mortality prediction possible from a scoring system covering all laboratory testing used for life insurance applicants, as well as blood pressure and build measurements. Using the results of testing for life insurance applicants who reported a Social Security number in conjunction with the Social Security Death Master File, the mortality associated with each test result was defined by age and sex. The individual mortality scores for each test were combined for each individual and a composite mortality risk score was developed. This score was then tested against the insurance applicant dataset to evaluate its ability to discriminate risk across age and sex. The composite risk score was highly predictive of all-cause mortality risk in a linear manner from the best to worst quintile of scores in a nearly identical fashion for each sex and decade of age. Laboratory studies, blood pressure and build from life insurance applicants can be used to create scoring that predicts all-cause mortality across age and sex. Such an approach may hold promise for preventative health screening as well.

Genetically Predicted Body Mass Index and Alzheimer’s Disease Related Phenotypes in Three Large Samples: Mendelian Randomization Analyses

PubMed Central

Mukherjee, Shubhabrata; Walter, Stefan; Kauwe, John S.K.; Saykin, Andrew J.; Bennett, David A.; Larson, Eric B.; Crane, Paul K.; Glymour, M. Maria

2015-01-01

Observational research shows that higher body mass index (BMI) increases Alzheimer’s disease (AD) risk, but it is unclear whether this association is causal. We applied genetic variants that predict BMI in Mendelian Randomization analyses, an approach that is not biased by reverse causation or confounding, to evaluate whether higher BMI increases AD risk. We evaluated individual level data from the AD Genetics Consortium (ADGC: 10,079 AD cases and 9,613 controls), the Health and Retirement Study (HRS: 8,403 participants with algorithm-predicted dementia status) and published associations from the Genetic and Environmental Risk for AD consortium (GERAD1: 3,177 AD cases and 7,277 controls). No evidence from individual SNPs or polygenic scores indicated BMI increased AD risk. Mendelian Randomization effect estimates per BMI point (95% confidence intervals) were: ADGC OR=0.95 (0.90, 1.01); HRS OR=1.00 (0.75, 1.32); GERAD1 OR=0.96 (0.87, 1.07). One subscore (cellular processes not otherwise specified) unexpectedly predicted lower AD risk. PMID:26079416

Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare

PubMed Central

Wilson, Richard; Goodacre, Steve W; Klingbajl, Marcin; Kelly, Anne-Maree; Rainer, Tim; Coats, Tim; Holloway, Vikki; Townend, Will; Crane, Steve

2014-01-01

Background and objective Risk-adjusted mortality rates can be used as a quality indicator if it is assumed that the discrepancy between predicted and actual mortality can be attributed to the quality of healthcare (ie, the model has attributional validity). The Development And Validation of Risk-adjusted Outcomes for Systems of emergency care (DAVROS) model predicts 7-day mortality in emergency medical admissions. We aimed to test this assumption by evaluating the attributional validity of the DAVROS risk-adjustment model. Methods We selected cases that had the greatest discrepancy between observed mortality and predicted probability of mortality from seven hospitals involved in validation of the DAVROS risk-adjustment model. Reviewers at each hospital assessed hospital records to determine whether the discrepancy between predicted and actual mortality could be explained by the healthcare provided. Results We received 232/280 (83%) completed review forms relating to 179 unexpected deaths and 53 unexpected survivors. The healthcare system was judged to have potentially contributed to 10/179 (8%) of the unexpected deaths and 26/53 (49%) of the unexpected survivors. Failure of the model to appropriately predict risk was judged to be responsible for 135/179 (75%) of the unexpected deaths and 2/53 (4%) of the unexpected survivors. Some 10/53 (19%) of the unexpected survivors died within a few months of the 7-day period of model prediction. Conclusions We found little evidence that deaths occurring in patients with a low predicted mortality from risk-adjustment could be attributed to the quality of healthcare provided. PMID:23605036

Predicting Academics via Behavior within an Elementary Sample: An Evaluation of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS)

ERIC Educational Resources Information Center

Kilgus, Stephen P.; Bowman, Nicollette A.; Christ, Theodore J.; Taylor, Crystal N.

2017-01-01

This study examined the extent to which teacher ratings of student behavior via the "Social, Academic, and Emotional Behavior Risk Screener" (SAEBRS) predicted academic achievement in math and reading. A secondary purpose was to compare the predictive capacity of three SAEBRS subscales corresponding to social, academic, or emotional…

In Search of Black Swans: Identifying Students at Risk of Failing Licensing Examinations.

PubMed

Barber, Cassandra; Hammond, Robert; Gula, Lorne; Tithecott, Gary; Chahine, Saad

2018-03-01

To determine which admissions variables and curricular outcomes are predictive of being at risk of failing the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1), how quickly student risk of failure can be predicted, and to what extent predictive modeling is possible and accurate in estimating future student risk. Data from five graduating cohorts (2011-2015), Schulich School of Medicine & Dentistry, Western University, were collected and analyzed using hierarchical generalized linear models (HGLMs). Area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of predictive models and determine whether they could be used to predict future risk, using the 2016 graduating cohort. Four predictive models were developed to predict student risk of failure at admissions, year 1, year 2, and pre-MCCQE1. The HGLM analyses identified gender, MCAT verbal reasoning score, two preclerkship course mean grades, and the year 4 summative objective structured clinical examination score as significant predictors of student risk. The predictive accuracy of the models varied. The pre-MCCQE1 model was the most accurate at predicting a student's risk of failing (AUC 0.66-0.93), while the admissions model was not predictive (AUC 0.25-0.47). Key variables predictive of students at risk were found. The predictive models developed suggest, while it is not possible to identify student risk at admission, we can begin to identify and monitor students within the first year. Using such models, programs may be able to identify and monitor students at risk quantitatively and develop tailored intervention strategies.

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

PubMed Central

McCarthy, John F.; Katz, Ira R.; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M.; Nielson, Christopher; Schoenbaum, Michael

2015-01-01

Objectives. The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Methods. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Results. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Conclusions. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions. PMID:26066914

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

PubMed

McCarthy, John F; Bossarte, Robert M; Katz, Ira R; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M; Nielson, Christopher; Schoenbaum, Michael

2015-09-01

The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

The ACC/AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease.

PubMed

Jung, Keum Ji; Jang, Yangsoo; Oh, Dong Joo; Oh, Byung-Hee; Lee, Sang Hoon; Park, Seong-Wook; Seung, Ki-Bae; Kim, Hong-Kyu; Yun, Young Duk; Choi, Sung Hee; Sung, Jidong; Lee, Tae-Yong; Kim, Sung Hi; Koh, Sang Baek; Kim, Moon Chan; Chang Kim, Hyeon; Kimm, Heejin; Nam, Chungmo; Park, Sungha; Jee, Sun Ha

2015-09-01

To evaluate the performance of the American College of Cardiology/American Heart Association (ACC/AHA) 2013 Pooled Cohort Equations in the Korean Heart Study (KHS) population and to develop a Korean Risk Prediction Model (KRPM) for atherosclerotic cardiovascular disease (ASCVD) events. The KHS cohort included 200,010 Korean adults aged 40-79 years who were free from ASCVD at baseline. Discrimination, calibration, and recalibration of the ACC/AHA Equations in predicting 10-year ASCVD risk in the KHS cohort were evaluated. The KRPM was derived using Cox model coefficients, mean risk factor values, and mean incidences from the KHS cohort. In the discriminatory analysis, the ACC/AHA Equations' White and African-American (AA) models moderately distinguished cases from non-cases, and were similar to the KRPM: For men, the area under the receiver operating characteristic curve (AUROCs) were 0.727 (White model), 0.725 (AA model), and 0.741 (KRPM); for women, the corresponding AUROCs were 0.738, 0.739, and 0.745. Absolute 10-year ASCVD risk for men in the KHS cohort was overestimated by 56.5% (White model) and 74.1% (AA model), while the risk for women was underestimated by 27.9% (White model) and overestimated by 29.1% (AA model). Recalibration of the ACC/AHA Equations did not affect discriminatory ability but improved calibration substantially, especially in men in the White model. Of the three ASCVD risk prediction models, the KRPM showed best calibration. The ACC/AHA Equations should not be directly applied for ASCVD risk prediction in a Korean population. The KRPM showed best predictive ability for ASCVD risk. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A threshold-free summary index of prediction accuracy for censored time to event data.

PubMed

Yuan, Yan; Zhou, Qian M; Li, Bingying; Cai, Hengrui; Chow, Eric J; Armstrong, Gregory T

2018-05-10

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t 0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors. Copyright © 2018 John Wiley & Sons, Ltd.

Evaluation of the Three Parameter Weibull Distribution Function for Predicting Fracture Probability in Composite Materials

DTIC Science & Technology

1978-03-01

for the risk of rupture for a unidirectionally laminat - ed composite subjected to pure bending. (5D This equation can be simplified further by use of...C EVALUATION OF THE THREE PARAMETER WEIBULL DISTRIBUTION FUNCTION FOR PREDICTING FRACTURE PROBABILITY IN COMPOSITE MATERIALS. THESIS / AFIT/GAE...EVALUATION OF THE THREE PARAMETER WE1BULL DISTRIBUTION FUNCTION FOR PREDICTING FRACTURE PROBABILITY IN COMPOSITE MATERIALS THESIS Presented

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

Murray secretion scale and fiberoptic endoscopic evaluation of swallowing in predicting aspiration in dysphagic patients.

PubMed

Kuo, Chia-Wei; Allen, Clint Tanner; Huang, Chu-Chun; Lee, Chia-Jung

2017-06-01

The objective of this retrospective review is to evaluate the ability of the Murray secretion scale to predict aspiration as determined by fiberoptic endoscopic evaluation of swallowing. Patients with dysphagia undergoing a fiberoptic endoscopic evaluation of swallowing study between January 2013 and November 2015 from a single, tertiary care institution were retrospectively reviewed. The Murray secretion scale and penetration aspiration scale on fiberoptic endoscopic evaluation of swallowing examination were determined. Spearman's correlation analysis, sensitivity, specificity, predictive values, and relative risk evaluating the relationship between the Murray secretion scale and aspiration on fiberoptic endoscopic evaluation of swallowing were calculated. Subgroups of head and neck cancer patients, penetration group, and aspiration group were also analyzed. The mean age of the cases (N = 212) was 62.4 years. Eighty percent were male. There was a strong correlation between Murray secretion scale grade and penetration aspiration scale score (r = 0.785, p < 0.001). The sensitivity and specificity of a Murray secretion scale grade 2 or higher in predicting aspiration were 74 and 90%, respectively. Individuals with a Murray secretion scale grade of 2 or higher were 13.6 times more likely to aspirate than patients with a lower Murray secretion scale grade. All subgroups showed similar trend. Determination of a Murray secretion scale grade, determined by flexible nasopharyngoscopy, may predict patients at high risk for aspiration. In clinical scenarios where more complete assessments of aspiration risk are immediately impossible or impractical, the Murray secretion scale grade may add valuable information to assist in clinical decision-making in patients with dysphagia.

Prognostic implications of serial risk score assessments in patients with pulmonary arterial hypertension: a Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management (REVEAL) analysis.

PubMed

Benza, Raymond L; Miller, Dave P; Foreman, Aimee J; Frost, Adaani E; Badesch, David B; Benton, Wade W; McGoon, Michael D

2015-03-01

Data from the Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management (REVEAL) were used previously to develop a risk score calculator to predict 1-year survival. We evaluated prognostic implications of changes in the risk score and individual risk-score parameters over 12 months. Patients were grouped by decreased, unchanged, or increased risk score from enrollment to 12 months. Kaplan-Meier estimates of subsequent 1-year survival were made based on change in the risk score during the initial 12 months of follow-up. Cox regression was used for multivariable analysis. Of 2,529 patients in the analysis cohort, the risk score was decreased in 800, unchanged in 959, and increased in 770 at 12 months post-enrollment. Six parameters (functional class, systolic blood pressure, heart rate, 6-minute walk distance, brain natriuretic peptide levels, and pericardial effusion) each changed sufficiently over time to improve or worsen risk scores in ≥5% of patients. One-year survival estimates in the subsequent year were 93.7%, 90.3%, and 84.6% in patients with a decreased, unchanged, and increased risk score at 12 months, respectively. Change in risk score significantly predicted future survival, adjusting for risk at enrollment. Considering follow-up risk concurrently with risk at enrollment, follow-up risk was a much stronger predictor, although risk at enrollment maintained a significant effect on future survival. Changes in REVEAL risk scores occur in most patients with pulmonary arterial hypertension over a 12-month period and are predictive of survival. Thus, serial risk score assessments can identify changes in disease trajectory that may warrant treatment modifications. Copyright © 2015 International Society for Heart and Lung Transplantation. All rights reserved.

Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study

PubMed Central

Hahn, Seokyung; Moon, Min Kyong; Park, Kyong Soo; Cho, Young Min

2016-01-01

Background Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters. Methods The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis. Results For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264). Conclusions The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes. PMID:27214034

Quantitative AOP-based predictions for two aromatase inhibitors evaluating the influence of bioaccumulation on prediction accuracy

EPA Science Inventory

The adverse outcome pathway (AOP) framework can be used to support the use of mechanistic toxicology data as a basis for risk assessment. For certain risk contexts this includes defining, quantitative linkages between the molecular initiating event (MIE) and subsequent key events...

Predicting Reading Ability for Bilingual Latino Children Using Dynamic Assessment

ERIC Educational Resources Information Center

Petersen, Douglas B.; Gillam, Ronald B.

2015-01-01

This study investigated the predictive validity of a dynamic assessment designed to evaluate later risk for reading difficulty in bilingual Latino children at risk for language impairment. During kindergarten, 63 bilingual Latino children completed a dynamic assessment nonsense-word recoding task that yielded pretest to posttest gain scores,…

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

PubMed

Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

2018-06-13

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System.

PubMed

Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

Pressure injury risk assessment is the first step toward preventing pressure injuries, but traditional assessment tools are time-consuming, resulting in work overload and fatigue for nurses. The objectives of the study were to build an automated pressure injury risk assessment system (Auto-PIRAS) that can assess pressure injury risk using data, without requiring nurses to collect or input additional data, and to evaluate the validity of this assessment tool. A retrospective case-control study and a system development study were conducted in a 1,355-bed university hospital in Seoul, South Korea. A total of 1,305 pressure injury patients and 5,220 nonpressure injury patients participated for the development of a risk scoring algorithm: 687 and 2,748 for the validation of the algorithm and 237 and 994 for validation after clinical implementation, respectively. A total of 4,211 pressure injury-related clinical variables were extracted from the electronic health record (EHR) systems to develop a risk scoring algorithm, which was validated and incorporated into the EHR. That program was further evaluated for predictive and concurrent validity. Auto-PIRAS, incorporated into the EHR system, assigned a risk assessment score of high, moderate, or low and displayed this on the Kardex nursing record screen. Risk scores were updated nightly according to 10 predetermined risk factors. The predictive validity measures of the algorithm validation stage were as follows: sensitivity = .87, specificity = .90, positive predictive value = .68, negative predictive value = .97, Youden index = .77, and the area under the receiver operating characteristic curve = .95. The predictive validity measures of the Braden Scale were as follows: sensitivity = .77, specificity = .93, positive predictive value = .72, negative predictive value = .95, Youden index = .70, and the area under the receiver operating characteristic curve = .85. The kappa of the Auto-PIRAS and Braden Scale risk classification result was .73. The predictive performance of the Auto-PIRAS was similar to Braden Scale assessments conducted by nurses. Auto-PIRAS is expected to be used as a system that assesses pressure injury risk automatically without additional data collection by nurses.

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

A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

PubMed Central

2018-01-01

Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

PubMed Central

Moreno-Peral, Patricia; Luna, Juan de Dios; Marston, Louise; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Muñoz-Bravo, Carlos; Bellón, Juan Ángel

2014-01-01

Background There are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months. Methods A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces. Results The cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit. Conclusions The predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care. PMID:25184313

[Psychosocial risk factors at work as predictors of mobbing].

PubMed

Meseguer de Pedro, Mariano; Soler Sánchez, María I; García-Izquierdo, Mariano; Sáez Navarro, M C; Sánchez Meca, Julio

2007-05-01

This work analyses the way in which various psychosocial risk indicators may predict mobbing. A sample of 638 workers, 168 men and 470 women, from the fruit-and-vegetable sector was evaluated. An anonymous questionnaire was administered to all employees who were present on the evaluation days in the companies comprising the study. After analysing the data obtained with the mobbing questionnaire NAQ-RE (Sáez, García-Izquierdo, and Llor, 2003) and with the psychosocial risk factors evaluation method of the INSHT (Martín and Pérez, 1997), using canonical regression, we found that several psychosocial factors such as role definition, mental workload, interest in the workers, and supervision / participation predict two types of mobbing: personal mobbing and work-performance-related mobbing.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

PubMed Central

Clyde, Merlise A.; Palmieri Weber, Rachel; Iversen, Edwin S.; Poole, Elizabeth M.; Doherty, Jennifer A.; Goodman, Marc T.; Ness, Roberta B.; Risch, Harvey A.; Rossing, Mary Anne; Terry, Kathryn L.; Wentzensen, Nicolas; Whittemore, Alice S.; Anton-Culver, Hoda; Bandera, Elisa V.; Berchuck, Andrew; Carney, Michael E.; Cramer, Daniel W.; Cunningham, Julie M.; Cushing-Haugen, Kara L.; Edwards, Robert P.; Fridley, Brooke L.; Goode, Ellen L.; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B.; Olson, Sara H.; Pearce, Celeste Leigh; Pike, Malcolm C.; Rothstein, Joseph H.; Sellers, Thomas A.; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J.; Vierkant, Robert A.; Wicklund, Kristine G.; Wu, Anna H.; Ziogas, Argyrios; Tworoger, Shelley S.; Schildkraut, Joellen M.

2016-01-01

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. PMID:27698005

Common carotid artery intima-media thickness is as good as carotid intima-media thickness of all carotid artery segments in improving prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study.

PubMed

Nambi, Vijay; Chambless, Lloyd; He, Max; Folsom, Aaron R; Mosley, Tom; Boerwinkle, Eric; Ballantyne, Christie M

2012-01-01

Carotid intima-media thickness (CIMT) and plaque information can improve coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). However, obtaining adequate images of all carotid artery segments (A-CIMT) may be difficult. Of A-CIMT, the common carotid artery intima-media thickness (CCA-IMT) is relatively more reliable and easier to measure. We evaluated whether CCA-IMT is comparable to A-CIMT when added to TRF and plaque information in improving CHD risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. Ten-year CHD risk prediction models using TRF alone, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque were developed for the overall cohort, men, and women. The area under the receiver operator characteristic curve (AUC), per cent individuals reclassified, net reclassification index (NRI), and model calibration by the Grønnesby-Borgan test were estimated. There were 1722 incident CHD events in 12 576 individuals over a mean follow-up of 15.2 years. The AUC for TRF only, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque models were 0.741, 0.754, and 0.753, respectively. Although there was some discordance when the CCA-IMT + plaque- and A-CIMT + plaque-based risk estimation was compared, the NRI and clinical NRI (NRI in the intermediate-risk group) when comparing the CIMT models with TRF-only model, per cent reclassified, and test for model calibration were not significantly different. Coronary heart disease risk prediction can be improved by adding A-CIMT + plaque or CCA-IMT + plaque information to TRF. Therefore, evaluating the carotid artery for plaque presence and measuring CCA-IMT, which is easier and more reliable than measuring A-CIMT, provide a good alternative to measuring A-CIMT for CHD risk prediction.

Evaluation of potential risks from ash disposal site leachate

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

Mills, W.B.; Loh, J.Y.; Bate, M.C.

1999-04-01

A risk-based approach is used to evaluate potential human health risks associated with a discharge from an ash disposal site into a small stream. The RIVRISK model was used to estimate downstream concentrations and corresponding risks. The modeling and risk analyses focus on boron, the constituent of greatest potential concern to public health at the site investigated, in Riddle Run, Pennsylvania. Prior to performing the risk assessment, the model is validated by comparing observed and predicted results. The comparison is good and an uncertainty analysis is provided to explain the comparison. The hazard quotient (HQ) for boron is predicted tomore » be greater than 1 at presently regulated compliance points over a range of flow rates. The reference dose (RfD) currently recommended by the United States Environmental Protection Agency (US EPA) was used for the analyses. However, the toxicity of boron as expressed by the RfD is now under review by both the U.S. EPA and the World Health Organization. Alternative reference doses being examined would produce predicted boron hazard quotients of less than 1 at nearly all flow conditions.« less

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.

Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators.

PubMed

Nam, Robert K; Kattan, Michael W; Chin, Joseph L; Trachtenberg, John; Singal, Rajiv; Rendon, Ricardo; Klotz, Laurence H; Sugar, Linda; Sherman, Christopher; Izawa, Jonathan; Bell, David; Stanimirovic, Aleksandra; Venkateswaran, Vasundara; Diamandis, Eleftherios P; Yu, Changhong; Loblaw, D Andrew; Narod, Steven A

2011-08-01

Prostate cancer risk calculators incorporate many factors to evaluate an individual's risk for prostate cancer. We validated two common North American-based, prostate cancer risk calculators. We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) -based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models. Of the 2,130 patients, 867 men (40.7%) were found to have cancer, and 1,263 (59.3%) did not have cancer. Of the patients with cancer, 403 (46.5%) had a Gleason score of 7 or more. The area under the [concentration-time] curve (AUC) for the SRC was 0.67 (95% CI, 0.65 to 0.69); the AUC for the PRC was 0.61 (95% CI, 0.59 to 0.64). The AUC was higher for predicting aggressive disease from the SRC (0.72; 95% CI, 0.70 to 0.75) compared with that from the PRC (0.67; 95% CI, 0.64 to 0.70). Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer. The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%. Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer.

Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve.

PubMed

Viallon, Vivian; Latouche, Aurélien

2011-03-01

Finding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC--area under the receiver-operating curve--which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC--AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with the existing ones and assess the effect of model misspecification--when estimating the conditional absolute risk function--on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

PubMed Central

Kerr, Kathleen F.; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G.

2014-01-01

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients’ risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. PMID:24855282

Validation of Fatigue Modeling Predictions in Aviation Operations

NASA Technical Reports Server (NTRS)

Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin

2017-01-01

Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

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).

Stress and anger as contextual factors and preexisting cognitive schemas: predicting parental child maltreatment risk.

PubMed

Rodriguez, Christina M; Richardson, Michael J

2007-11-01

Progress in the child maltreatment field depends on refinements in leading models. This study examines aspects of social information processing theory (Milner, 2000) in predicting physical maltreatment risk in a community sample. Consistent with this theory, selected preexisting schema (external locus-of-control orientation, inappropriate developmental expectations, low empathic perspective-taking ability, and low perceived attachment relationship to child) were expected to predict child abuse risk beyond contextual factors (parenting stress and anger expression). Based on 115 parents' self-report, results from this study support cognitive factors that predict abuse risk (with locus of control, perceived attachment, or empathy predicting different abuse risk measures, but not developmental expectations), although the broad contextual factors involving negative affectivity and stress were consistent predictors across abuse risk markers. Findings are discussed with regard to implications for future model evaluations, with indications the model may apply to other forms of maltreatment, such as psychological maltreatment or neglect.

The development of a plant risk evaluation (PRE) tool for assessing the invasive potential of ornamental plants.

PubMed

Conser, Christiana; Seebacher, Lizbeth; Fujino, David W; Reichard, Sarah; DiTomaso, Joseph M

2015-01-01

Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) tool for ornamental plants. The 19 questions in the final PRE tool were narrowed down from 56 original questions from existing WRA tools. We evaluated the 56 WRA questions by screening 21 known invasive and 14 known non-invasive ornamental plants. After statistically comparing the predictability of each question and the frequency the question could be answered for both invasive and non-invasive species, we eliminated questions that provided no predictive power, were irrelevant in our current model, or could not be answered reliably at a high enough percentage. We also combined many similar questions. The final 19 remaining PRE questions were further tested for accuracy using 56 additional known invasive plants and 36 known non-invasive ornamental species. The resulting evaluation demonstrated that when "needs further evaluation" classifications were not included, the accuracy of the model was 100% for both predicting invasiveness and non-invasiveness. When "needs further evaluation" classifications were included as either false positive or false negative, the model was still 93% accurate in predicting invasiveness and 97% accurate in predicting non-invasiveness, with an overall accuracy of 95%. We conclude that the PRE tool should not only provide growers with a method to accurately screen their current stock and potential new introductions, but also increase the probability of the tool being accepted for use by the industry as the basis for a nursery certification program.

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

Evaluating Micrometeoroid and Orbital Debris Risk Assessments Using Anomaly Data

NASA Technical Reports Server (NTRS)

Squire, Michael

2017-01-01

The accuracy of micrometeoroid and orbital debris (MMOD) risk assessments can be difficult to evaluate. A team from the National Aeronautics and Space Administration (NASA) Engineering and Safety Center (NESC) has completed a study that compared MMOD-related failures on operational satellites to predictions of how many of those failures should occur using NASA's TM"s MMOD risk assessment methodology and tools. The study team used the Poisson probability to quantify the degree of inconsistency between the predicted and reported numbers of failures. Many elements go into a risk assessment, and each of those elements represent a possible source of uncertainty or bias that will influence the end result. There are also challenges in obtaining accurate and useful data on MMOD-related failures.

PETRORISK: a risk assessment framework for petroleum substances.

PubMed

Redman, Aaron D; Parkerton, Thomas F; Comber, Mike H I; Paumen, Miriam Leon; Eadsforth, Charles V; Dmytrasz, Bhodan; King, Duncan; Warren, Christopher S; den Haan, Klaas; Djemel, Nadia

2014-07-01

PETRORISK is a modeling framework used to evaluate environmental risk of petroleum substances and human exposure through these routes due to emissions under typical use conditions as required by the European regulation for the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH). Petroleum substances are often complex substances comprised of hundreds to thousands of individual hydrocarbons. The physicochemical, fate, and effects properties of the individual constituents within a petroleum substance can vary over several orders of magnitude, complicating risk assessment. PETRORISK combines the risk assessment strategies used on single chemicals with the hydrocarbon block approach to model complex substances. Blocks are usually defined by available analytical characterization data on substances that are expressed in terms of mass fractions for different structural chemical classes that are specified as a function of C number or boiling point range. The physicochemical and degradation properties of the blocks are determined by the properties of representative constituents in that block. Emissions and predicted exposure concentrations (PEC) are then modeled using mass-weighted individual representative constituents. Overall risk for various environmental compartments at the regional and local level is evaluated by comparing the PECs for individual representative constituents to corresponding predicted no-effect concentrations (PNEC) derived using the Target Lipid Model. Risks to human health are evaluated using the overall predicted human dose resulting from multimedia environmental exposure to a substance-specific derived no-effect level (DNEL). A case study is provided to illustrate how this modeling approach has been applied to assess the risks of kerosene manufacture and use as a fuel. © 2014 SETAC.

Listeria monocytogenes in Retail Delicatessens: an Interagency Risk Assessment-model and baseline results.

PubMed

Pouillot, Régis; Gallagher, Daniel; Tang, Jia; Hoelzer, Karin; Kause, Janell; Dennis, Sherri B

2015-01-01

The Interagency Risk Assessment-Listeria monocytogenes (Lm) in Retail Delicatessens provides a scientific assessment of the risk of listeriosis associated with the consumption of ready-to-eat (RTE) foods commonly prepared and sold in the delicatessen (deli) of a retail food store. The quantitative risk assessment (QRA) model simulates the behavior of retail employees in a deli department and tracks the Lm potentially present in this environment and in the food. Bacterial growth, bacterial inactivation (following washing and sanitizing actions), and cross-contamination (from object to object, from food to object, or from object to food) are evaluated through a discrete event modeling approach. The QRA evaluates the risk per serving of deli-prepared RTE food for the susceptible and general population, using a dose-response model from the literature. This QRA considers six separate retail baseline conditions and provides information on the predicted risk of listeriosis for each. Among the baseline conditions considered, the model predicts that (i) retail delis without an environmental source of Lm (such as niches), retail delis without niches that do apply temperature control, and retail delis with niches that do apply temperature control lead to lower predicted risk of listeriosis relative to retail delis with niches and (ii) retail delis with incoming RTE foods that are contaminated with Lm lead to higher predicted risk of listeriosis, directly or through cross-contamination, whether the contaminated incoming product supports growth or not. The risk assessment predicts that listeriosis cases associated with retail delicatessens result from a sequence of key events: (i) the contaminated RTE food supports Lm growth; (ii) improper retail and/or consumer storage temperature or handling results in the growth of Lm on the RTE food; and (iii) the consumer of this RTE food is susceptible to listeriosis. The risk assessment model, therefore, predicts that cross-contamination with Lm at retail predominantly results in sporadic cases.

Net Reclassification Indices for Evaluating Risk-Prediction Instruments: A Critical Review

PubMed Central

Kerr, Kathleen F.; Wang, Zheyu; Janes, Holly; McClelland, Robyn L.; Psaty, Bruce M.; Pepe, Margaret S.

2014-01-01

Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit. PMID:24240655

Screening for Malnutrition in Community Dwelling Older Japanese: Preliminary Development and Evaluation of the Japanese Nutritional Risk Screening Tool (NRST).

PubMed

Htun, N C; Ishikawa-Takata, K; Kuroda, A; Tanaka, T; Kikutani, T; Obuchi, S P; Hirano, H; Iijima, K

2016-02-01

Early and effective screening for age-related malnutrition is an essential part of providing optimal nutritional care to older populations. This study was performed to evaluate the adaptation of the original SCREEN II questionnaire (Seniors in the Community: Risk Evaluation for Eating and Nutrition, version II) for use in Japan by examining its measurement properties and ability to predict nutritional risk and sarcopenia in community-dwelling older Japanese people. The ultimate objective of this preliminary validation study is to develop a license granted full Japanese version of the SCREEN II. The measurement properties and predictive validity of the NRST were examined in this cross-sectional study of 1921 community-dwelling older Japanese people. Assessments included medical history, and anthropometric and serum albumin measurements. Questions on dietary habits that corresponded to the original SCREEN II were applied to Nutritional Risk Screening Tool (NRST) scoring system. Nutritional risk was assessed by the Geriatric Nutrition Risk Index (GNRI) and the short form of the Mini-Nutritional Assessment (MNA-SF). Sarcopenia was diagnosed according to the criteria of the European Working Group on Sarcopenia in Older People. The nutritional risk prevalences determined by the GNRI and MNA-SF were 5.6% and 34.7%, respectively. The prevalence of sarcopenia was 13.3%. Mean NRST scores were significantly lower in the nutritionally at-risk than in the well-nourished groups. Concurrent validity analysis showed significant correlations between NRST scores and both nutritional risk parameters (GNRI or MNA-SF) and sarcopenia. The areas under the receiver operating characteristic curves (AUC) of NRST for the prediction of nutritional risk were 0.635 and 0.584 as assessed by GNRI and MNA-SF, respectively. AUCs for the prediction of sarcopenia were 0.602 (NRST), 0.655 (age-integrated NRST), and 0.676 (age and BMI-integrated NRST). These results indicate that the NRST is a promising screening tool for the prediction of malnutrition and sarcopenia in community-dwelling older Japanese people. Further development of a full Japanese version of the SCREEN II is indicated.

Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population

PubMed Central

Yamakado, Minoru; Nagao, Kenji; Imaizumi, Akira; Tani, Mizuki; Toda, Akiko; Tanaka, Takayuki; Jinzu, Hiroko; Miyano, Hiroshi; Yamamoto, Hiroshi; Daimon, Takashi; Horimoto, Katsuhisa; Ishizaka, Yuko

2015-01-01

Plasma free amino acid (PFAA) profile is highlighted in its association with visceral obesity and hyperinsulinemia, and future diabetes. Indeed PFAA profiling potentially can evaluate individuals’ future risks of developing lifestyle-related diseases, in addition to diabetes. However, few studies have been performed especially in Asian populations, about the optimal combination of PFAAs for evaluating health risks. We quantified PFAA levels in 3,701 Japanese subjects, and determined visceral fat area (VFA) and two-hour post-challenge insulin (Ins120 min) values in 865 and 1,160 subjects, respectively. Then, models between PFAA levels and the VFA or Ins120 min values were constructed by multiple linear regression analysis with variable selection. Finally, a cohort study of 2,984 subjects to examine capabilities of the obtained models for predicting four-year risk of developing new-onset lifestyle-related diseases was conducted. The correlation coefficients of the obtained PFAA models against VFA or Ins120 min were higher than single PFAA level. Our models work well for future risk prediction. Even after adjusting for commonly accepted multiple risk factors, these models can predict future development of diabetes, metabolic syndrome, and dyslipidemia. PFAA profiles confer independent and differing contributions to increasing the lifestyle-related disease risks in addition to the currently known factors in a general Japanese population. PMID:26156880

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.

Epidemiologic research using probabilistic outcome definitions.

PubMed

Cai, Bing; Hennessy, Sean; Lo Re, Vincent; Small, Dylan S

2015-01-01

Epidemiologic studies using electronic healthcare data often define the presence or absence of binary clinical outcomes by using algorithms with imperfect specificity, sensitivity, and positive predictive value. This results in misclassification and bias in study results. We describe and evaluate a new method called probabilistic outcome definition (POD) that uses logistic regression to estimate the probability of a clinical outcome using multiple potential algorithms and then uses multiple imputation to make valid inferences about the risk ratio or other epidemiologic parameters of interest. We conducted a simulation to evaluate the performance of the POD method with two variables that can predict the true outcome and compared the POD method with the conventional method. The simulation results showed that when the true risk ratio is equal to 1.0 (null), the conventional method based on a binary outcome provides unbiased estimates. However, when the risk ratio is not equal to 1.0, the traditional method, either using one predictive variable or both predictive variables to define the outcome, is biased when the positive predictive value is <100%, and the bias is very severe when the sensitivity or positive predictive value is poor (less than 0.75 in our simulation). In contrast, the POD method provides unbiased estimates of the risk ratio both when this measure of effect is equal to 1.0 and not equal to 1.0. Even when the sensitivity and positive predictive value are low, the POD method continues to provide unbiased estimates of the risk ratio. The POD method provides an improved way to define outcomes in database research. This method has a major advantage over the conventional method in that it provided unbiased estimates of risk ratios and it is easy to use. Copyright © 2014 John Wiley & Sons, Ltd.

Combined Endoscopic/Sonographic-Based Risk Matrix Model for Predicting One-Year Risk of Surgery: A Prospective Observational Study of a Tertiary Center Severe/Refractory Crohn's Disease Cohort.

PubMed

Rispo, Antonio; Imperatore, Nicola; Testa, Anna; Bucci, Luigi; Luglio, Gaetano; De Palma, Giovanni Domenico; Rea, Matilde; Nardone, Olga Maria; Caporaso, Nicola; Castiglione, Fabiana

2018-03-08

In the management of Crohn's Disease (CD) patients, having a simple score combining clinical, endoscopic and imaging features to predict the risk of surgery could help to tailor treatment more effectively. AIMS: to prospectively evaluate the one-year risk factors for surgery in refractory/severe CD and to generate a risk matrix for predicting the probability of surgery at one year. CD patients needing a disease re-assessment at our tertiary IBD centre underwent clinical, laboratory, endoscopy and bowel sonography (BS) examinations within one week. The optimal cut-off values in predicting surgery were identified using ROC curves for Simple Endoscopic Score for CD (SES-CD), bowel wall thickness (BWT) at BS, and small bowel CD extension at BS. Binary logistic regression and Cox's regression were then carried out. Finally, the probabilities of surgery were calculated for selected baseline levels of covariates and results were arranged in a prediction matrix. Of 100 CD patients, 30 underwent surgery within one year. SES-CD©9 (OR 15.3; p<0.001), BWT©7 mm (OR 15.8; p<0.001), small bowel CD extension at BS©33 cm (OR 8.23; p<0.001) and stricturing/penetrating behavior (OR 4.3; p<0.001) were the only independent factors predictive of surgery at one-year based on binary logistic and Cox's regressions. Our matrix model combined these risk factors and the probability of surgery ranged from 0.48% to 87.5% (sixteen combinations). Our risk matrix combining clinical, endoscopic and ultrasonographic findings can accurately predict the one-year risk of surgery in patients with severe/refractory CD requiring a disease re-evaluation. This tool could be of value in clinical practice, serving as the basis for a tailored management of CD patients.

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 American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator Has a Role in Predicting Discharge to Post-Acute Care in Total Joint Arthroplasty.

PubMed

Goltz, Daniel E; Baumgartner, Billy T; Politzer, Cary S; DiLallo, Marcus; Bolognesi, Michael P; Seyler, Thorsten M

2018-01-01

Patient demand and increasing cost awareness have led to the creation of surgical risk calculators that attempt to predict the likelihood of adverse events and to facilitate risk mitigation. The American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator is an online tool available for a wide variety of surgical procedures, and has not yet been fully evaluated in total joint arthroplasty. A single-center, retrospective review was performed on 909 patients receiving a unilateral primary total knee (496) or hip (413) arthroplasty between January 2012 and December 2014. Patient characteristics were entered into the risk calculator, and predicted outcomes were compared with observed results. Discrimination was evaluated using the receiver-operator area under the curve (AUC) for 90-day readmission, return to operating room (OR), discharge to skilled nursing facility (SNF)/rehab, deep venous thrombosis (DVT), and periprosthetic joint infection (PJI). The risk calculator demonstrated adequate performance in predicting discharge to SNF/rehab (AUC 0.72). Discrimination was relatively limited for DVT (AUC 0.70, P = .2), 90-day readmission (AUC 0.63), PJI (AUC 0.67), and return to OR (AUC 0.59). Risk score differences between those who did and did not experience discharge to SNF/rehab, 90-day readmission, and PJI reached significance (P < .01). Predicted length of stay performed adequately, only overestimating by 0.2 days on average (rho = 0.25, P < .001). The American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator has fair utility in predicting discharge to SNF/rehab, but limited usefulness for 90-day readmission, return to OR, DVT, and PJI. Although length of stay predictions are similar to actual outcomes, statistical correlation remains relatively weak. Copyright © 2017 Elsevier Inc. All rights reserved.

Risk-adjusted performance evaluation in three academic thoracic surgery units using the Eurolung risk models.

PubMed

Pompili, Cecilia; Shargall, Yaron; Decaluwe, Herbert; Moons, Johnny; Chari, Madhu; Brunelli, Alessandro

2018-01-03

The objective of this study was to evaluate the performance of 3 thoracic surgery centres using the Eurolung risk models for morbidity and mortality. This was a retrospective analysis performed on data collected from 3 academic centres (2014-2016). Seven hundred and twenty-one patients in Centre 1, 857 patients in Centre 2 and 433 patients in Centre 3 who underwent anatomical lung resections were analysed. The Eurolung1 and Eurolung2 models were used to predict risk-adjusted cardiopulmonary morbidity and 30-day mortality rates. Observed and risk-adjusted outcomes were compared within each centre. The observed morbidity of Centre 1 was in line with the predicted morbidity (observed 21.1% vs predicted 22.7%, P = 0.31). Centre 2 performed better than expected (observed morbidity 20.2% vs predicted 26.7%, P < 0.001), whereas the observed morbidity of Centre 3 was higher than the predicted morbidity (observed 41.1% vs predicted 24.3%, P < 0.001). Centre 1 had higher observed mortality when compared with the predicted mortality (3.6% vs 2.1%, P = 0.005), whereas Centre 2 had an observed mortality rate significantly lower than the predicted mortality rate (1.2% vs 2.5%, P = 0.013). Centre 3 had an observed mortality rate in line with the predicted mortality rate (observed 1.4% vs predicted 2.4%, P = 0.17). The observed mortality rates in the patients with major complications were 30.8% in Centre 1 (versus predicted mortality rate 3.8%, P < 0.001), 8.2% in Centre 2 (versus predicted mortality rate 4.1%, P = 0.030) and 9.0% in Centre 3 (versus predicted mortality rate 3.5%, P = 0.014). The Eurolung models were successfully used as risk-adjusting instruments to internally audit the outcomes of 3 different centres, showing their applicability for future quality improvement initiatives. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

PubMed

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-04-01

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

Evaluation of waist-to-height ratio to predict 5 year cardiometabolic risk in sub-Saharan African adults.

PubMed

Ware, L J; Rennie, K L; Kruger, H S; Kruger, I M; Greeff, M; Fourie, C M T; Huisman, H W; Scheepers, J D W; Uys, A S; Kruger, R; Van Rooyen, J M; Schutte, R; Schutte, A E

2014-08-01

Simple, low-cost central obesity measures may help identify individuals with increased cardiometabolic disease risk, although it is unclear which measures perform best in African adults. We aimed to: 1) cross-sectionally compare the accuracy of existing waist-to-height ratio (WHtR) and waist circumference (WC) thresholds to identify individuals with hypertension, pre-diabetes, or dyslipidaemia; 2) identify optimal WC and WHtR thresholds to detect CVD risk in this African population; and 3) assess which measure best predicts 5-year CVD risk. Black South Africans (577 men, 942 women, aged >30years) were recruited by random household selection from four North West Province communities. Demographic and anthropometric measures were taken. Recommended diagnostic thresholds (WC > 80 cm for women, >94 cm for men; WHtR > 0.5) were evaluated to predict blood pressure, fasting blood glucose, lipids, and glycated haemoglobin measured at baseline and 5 year follow up. Women were significantly more overweight than men at baseline (mean body mass index (BMI) women 27.3 ± 7.4 kg/m(2), men 20.9 ± 4.3 kg/m(2)); median WC women 81.9 cm (interquartile range 61-103), men 74.7 cm (63-87 cm), all P < 0.001). In women, both WC and WHtR significantly predicted all cardiometabolic risk factors after 5 years. In men, even after adjusting WC threshold based on ROC analysis, WHtR better predicted overall 5-year risk. Neither measure predicted hypertension in men. The WHtR threshold of >0.5 appears to be more consistently supported and may provide a better predictor of future cardiometabolic risk in sub-Saharan Africa. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment of three risk evaluation systems for patients aged ≥70 in East China: performance of SinoSCORE, EuroSCORE II and the STS risk evaluation system.

PubMed

Shan, Lingtong; Ge, Wen; Pu, Yiwei; Cheng, Hong; Cang, Zhengqiang; Zhang, Xing; Li, Qifan; Xu, Anyang; Wang, Qi; Gu, Chang; Zhang, Yangyang

2018-01-01

To assess and compare the predictive ability of three risk evaluation systems (SinoSCORE, EuroSCORE II and the STS risk evaluation system) in patients aged ≥70, and who underwent coronary artery bypass grafting (CABG) in East China. Three risk evaluation systems were applied to 1,946 consecutive patients who underwent isolated CABG from January 2004 to September 2016 in two hospitals. Patients were divided into two subsets according to their age: elderly group (age ≥70) with a younger group (age <70) used for comparison. The outcome of interest in this study was in-hospital mortality. The entire cohort and subsets of patients were analyzed. The calibration and discrimination in total and in subsets were assessed by the Hosmer-Lemeshow and the C statistics respectively. Institutional overall mortality was 2.52%. The expected mortality rates of SinoSCORE, EuroSCORE II and the STS risk evaluation system were 0.78(0.64)%, 1.43(1.14)% and 0.78(0.77)%, respectively. SinoSCORE achieved the best discrimination (the area under the receiver operating characteristic curve (AUC) = 0.829), followed by the STS risk evaluation system (AUC = 0.790) and EuroSCORE II (AUC = 0.769) in the entire cohort. In the elderly group, the observed mortality rate was 4.82% while it was 1.38% in the younger group. SinoSCORE (AUC = .829) also achieved the best discrimination in the elderly group, followed by the STS risk evaluation system (AUC = .730) and EuroSCORE II (AUC = 0.640) while all three risk evaluation systems all had good performances in the younger group. SinoSCORE, EuroSCORE II and the STS risk evaluation system all achieved positive calibrations in the entire cohort and subsets. The performance of the three risk evaluation systems was not ideal in the entire cohort. In the elderly group, SinoSCORE appeared to achieve better predictive efficiency than EuroSCORE II and the STS risk evaluation system.

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

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

Can shoulder dystocia be reliably predicted?

PubMed

Dodd, Jodie M; Catcheside, Britt; Scheil, Wendy

2012-06-01

To evaluate factors reported to increase the risk of shoulder dystocia, and to evaluate their predictive value at a population level. The South Australian Pregnancy Outcome Unit's population database from 2005 to 2010 was accessed to determine the occurrence of shoulder dystocia in addition to reported risk factors, including age, parity, self-reported ethnicity, presence of diabetes and infant birth weight. Odds ratios (and 95% confidence interval) of shoulder dystocia was calculated for each risk factor, which were then incorporated into a logistic regression model. Test characteristics for each variable in predicting shoulder dystocia were calculated. As a proportion of all births, the reported rate of shoulder dystocia increased significantly from 0.95% in 2005 to 1.38% in 2010 (P = 0.0002). Using a logistic regression model, induction of labour and infant birth weight greater than both 4000 and 4500 g were identified as significant independent predictors of shoulder dystocia. The value of risk factors alone and when incorporated into the logistic regression model was poorly predictive of the occurrence of shoulder dystocia. While there are a number of factors associated with an increased risk of shoulder dystocia, none are of sufficient sensitivity or positive predictive value to allow their use clinically to reliably and accurately identify the occurrence of shoulder dystocia. © 2012 The Authors ANZJOG © 2012 The Royal Australian and New Zealand College of Obstetricians and Gynaecologists.

Using single leg standing time to predict the fall risk in elderly.

PubMed

Chang, Chun-Ju; Chang, Yu-Shin; Yang, Sai-Wei

2013-01-01

In clinical evaluation, we used to evaluate the fall risk according to elderly falling experience or the balance assessment tool. Because of the tool limitation, sometimes we could not predict accurately. In this study, we first analyzed 15 healthy elderly (without falling experience) and 15 falling elderly (1~3 time falling experience) balance performance in previous research. After 1 year follow up, there was only 1 elderly fall down during this period. It seemed like that falling experience had a ceiling effect on the falling prediction. But we also found out that using single leg standing time could be more accurately to help predicting the fall risk, especially for the falling elderly who could not stand over 10 seconds by single leg, and with a significant correlation between the falling experience and single leg standing time (r = -0.474, p = 0.026). The results also showed that there was significant body sway just before they falling down, and the COP may be an important characteristic in the falling elderly group.

Validation of risk stratification for children with febrile neutropenia in a pediatric oncology unit in India.

PubMed

Das, Anirban; Trehan, Amita; Oberoi, Sapna; Bansal, Deepak

2017-06-01

The study aims to validate a score predicting risk of complications in pediatric patients with chemotherapy-related febrile neutropenia (FN) and evaluate the performance of previously published models for risk stratification. Children diagnosed with cancer and presenting with FN were evaluated in a prospective single-center study. A score predicting the risk of complications, previously derived in the unit, was validated on a prospective cohort. Performance of six predictive models published from geographically distinct settings was assessed on the same cohort. Complications were observed in 109 (26.3%) of 414 episodes of FN over 15 months. A risk score based on undernutrition (two points), time from last chemotherapy (<7 days = two points), presence of a nonupper respiratory focus of infection (two points), C-reactive protein (>60 mg/l = five points), and absolute neutrophil count (<100 per μl = two points) was used to stratify patients into "low risk" (score <7, n = 208) and assessed using the following parameters: overall performance (Nagelkerke R 2 = 34.4%), calibration (calibration slope = 0.39; P = 0.25 in Hosmer-Lemeshow test), discrimination (c-statistic = 0.81), overall sensitivity (86%), negative predictive value (93%), and clinical net benefit (0.43). Six previously published rules demonstrated inferior performance in this cohort. An indigenous decision rule using five simple predefined variables was successful in identifying children at risk for complications. Prediction models derived in developed nations may not be appropriate for low-middle-income settings and need to be validated before use. © 2016 Wiley Periodicals, Inc.

Hope, Core Self-Evaluations, Emotional Well-Being, Health-Risk Behaviors, and Academic Performance in University Freshmen.

PubMed

Griggs, Stephanie; Crawford, Sybil L

2017-09-01

The purpose of the current online cross-sectional study was to examine the relationship between hope, core self-evaluations (CSE), emotional well-being, health-risk behaviors, and academic performance in students enrolled in their first year of college. Freshmen (N = 495) attending a large public university in the Northeastern United States completed an online survey between February 1 and 13, 2017. Linear regression, path analysis, and structural equation modeling procedures were performed. CSE mediated the relationship between hope and emotional well-being and academic performance. Contrary to the hypotheses, higher hope predicted more sexual risk-taking behaviors and alcohol use. CSE is an important component of Hope Theory, which is useful for predicting emotional well-being and academic performance, but not as useful for predicting drug use, alcohol use, and sexual risk taking. Hope and CSE interventions are needed to improve academic performance and emotional well-being in university freshmen. [Journal of Psychosocial Nursing and Mental Health Services, 55(9), 33-42.]. Copyright 2017, SLACK Incorporated.

Discomfort Intolerance: Evaluation of a Potential Risk Factor for Anxiety Psychopathology

ERIC Educational Resources Information Center

Schmidt, Norman B.; Richey, J. Anthony; Cromer, Kiara R.; Buckner, Julia D.

2007-01-01

Discomfort intolerance, defined as an individual difference in the capacity to tolerate unpleasant bodily sensations, is a construct recently posited as a risk factor for panic and anxiety psychopathology. The present report used a biological challenge procedure to evaluate whether discomfort intolerance predicts fearful responding beyond the…

Temporal and geographical external validation study and extension of the Mayo Clinic prediction model to predict eGFR in the younger population of Swiss ADPKD patients.

PubMed

Girardat-Rotar, Laura; Braun, Julia; Puhan, Milo A; Abraham, Alison G; Serra, Andreas L

2017-07-17

Prediction models in autosomal dominant polycystic kidney disease (ADPKD) are useful in clinical settings to identify patients with greater risk of a rapid disease progression in whom a treatment may have more benefits than harms. Mayo Clinic investigators developed a risk prediction tool for ADPKD patients using a single kidney value. Our aim was to perform an independent geographical and temporal external validation as well as evaluate the potential for improving the predictive performance by including additional information on total kidney volume. We used data from the on-going Swiss ADPKD study from 2006 to 2016. The main analysis included a sample size of 214 patients with Typical ADPKD (Class 1). We evaluated the Mayo Clinic model performance calibration and discrimination in our external sample and assessed whether predictive performance could be improved through the addition of subsequent kidney volume measurements beyond the baseline assessment. The calibration of both versions of the Mayo Clinic prediction model using continuous Height adjusted total kidney volume (HtTKV) and using risk subclasses was good, with R 2 of 78% and 70%, respectively. Accuracy was also good with 91.5% and 88.7% of the predicted within 30% of the observed, respectively. Additional information regarding kidney volume did not substantially improve the model performance. The Mayo Clinic prediction models are generalizable to other clinical settings and provide an accurate tool based on available predictors to identify patients at high risk for rapid disease progression.

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

PubMed

Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2014-08-07

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

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.

Evaluation of the validity of osteoporosis and fracture risk assessment tools (IOF One Minute Test, SCORE, and FRAX) in postmenopausal Palestinian women.

PubMed

Kharroubi, Akram; Saba, Elias; Ghannam, Ibrahim; Darwish, Hisham

2017-12-01

The need for simple self-assessment tools is necessary to predict women at high risk for developing osteoporosis. In this study, tools like the IOF One Minute Test, Fracture Risk Assessment Tool (FRAX), and Simple Calculated Osteoporosis Risk Estimation (SCORE) were found to be valid for Palestinian women. The threshold for predicting women at risk for each tool was estimated. The purpose of this study is to evaluate the validity of the updated IOF (International Osteoporosis Foundation) One Minute Osteoporosis Risk Assessment Test, FRAX, SCORE as well as age alone to detect the risk of developing osteoporosis in postmenopausal Palestinian women. Three hundred eighty-two women 45 years and older were recruited including 131 women with osteoporosis and 251 controls following bone mineral density (BMD) measurement, 287 completed questionnaires of the different risk assessment tools. Receiver operating characteristic (ROC) curves were evaluated for each tool using bone BMD as the gold standard for osteoporosis. The area under the ROC curve (AUC) was the highest for FRAX calculated with BMD for predicting hip fractures (0.897) followed by FRAX for major fractures (0.826) with cut-off values ˃1.5 and ˃7.8%, respectively. The IOF One Minute Test AUC (0.629) was the lowest compared to other tested tools but with sufficient accuracy for predicting the risk of developing osteoporosis with a cut-off value ˃4 total yes questions out of 18. SCORE test and age alone were also as good predictors of risk for developing osteoporosis. According to the ROC curve for age, women ≥64 years had a higher risk of developing osteoporosis. Higher percentage of women with low BMD (T-score ≤-1.5) or osteoporosis (T-score ≤-2.5) was found among women who were not exposed to the sun, who had menopause before the age of 45 years, or had lower body mass index (BMI) compared to controls. Women who often fall had lower BMI and approximately 27% of the recruited postmenopausal Palestinian women had accidents that caused fractures. Simple self-assessment tools like FRAX without BMD, SCORE, and the IOF One Minute Tests were valid for predicting Palestinian postmenopausal women at high risk of developing osteoporosis.

Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

PubMed

Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

2014-05-21

Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.

Comparison of different risk stratification systems in predicting short-term serious outcome of syncope patients.

PubMed

Safari, Saeed; Baratloo, Alireza; Hashemi, Behrooz; Rahmati, Farhad; Forouzanfar, Mohammad Mehdi; Motamedi, Maryam; Mirmohseni, Ladan

2016-01-01

Determining etiologic causes and prognosis can significantly improve management of syncope patients. The present study aimed to compare the values of San Francisco, Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL), Boston, and Risk Stratification of Syncope in the Emergency Department (ROSE) score clinical decision rules in predicting the short-term serious outcome of syncope patients. The present diagnostic accuracy study with 1-week follow-up was designed to evaluate the predictive values of the four mentioned clinical decision rules. Screening performance characteristics of each model in predicting mortality, myocardial infarction (MI), and cerebrovascular accidents (CVAs) were calculated and compared. To evaluate the value of each aforementioned model in predicting the outcome, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated and receiver-operating curve (ROC) curve analysis was done. A total of 187 patients (mean age: 64.2 ± 17.2 years) were enrolled in the study. Mortality, MI, and CVA were seen in 19 (10.2%), 12 (6.4%), and 36 (19.2%) patients, respectively. Area under the ROC curve for OESIL, San Francisco, Boston, and ROSE models in prediction the risk of 1-week mortality, MI, and CVA was in the 30-70% range, with no significant difference among models ( P > 0.05). The pooled model did not show higher accuracy in prediction of mortality, MI, and CVA compared to others ( P > 0.05). This study revealed the weakness of all four evaluated models in predicting short-term serious outcome of syncope patients referred to the emergency department without any significant advantage for one among others.

Novel surgical performance evaluation approximates Standardized Incidence Ratio with high accuracy at simple means.

PubMed

Gabbay, Itay E; Gabbay, Uri

2013-01-01

Excess adverse events may be attributable to poor surgical performance but also to case-mix, which is controlled through the Standardized Incidence Ratio (SIR). SIR calculations can be complicated, resource consuming, and unfeasible in some settings. This article suggests a novel method for SIR approximation. In order to evaluate a potential SIR surrogate measure we predefined acceptance criteria. We developed a new measure - Approximate Risk Index (ARI). "Number Needed for Event" (NNE) is the theoretical number of patients needed "to produce" one adverse event. ARI is defined as the quotient of the group of patients needed for no observed events Ge by total patients treated Ga. Our evaluation compared 2500 surgical units and over 3 million heterogeneous risk surgical patients that were induced through a computerized simulation. Surgical unit's data were computed for SIR and ARI to evaluate compliance with the predefined criteria. Approximation was evaluated by correlation analysis and performance prediction capability by Receiver Operating Characteristics (ROC) analysis. ARI strongly correlates with SIR (r(2) = 0.87, p < 0.05). ARI prediction of excessive risk revealed excellent ROC (Area Under the Curve > 0.9) 87% sensitivity and 91% specificity. ARI provides good approximation of SIR and excellent prediction capability. ARI is simple and cost-effective as it requires thorough risk evaluation of only the adverse events patients. ARI can provide a crucial screening and performance evaluation quality control tool. The ARI method may suit other clinical and epidemiological settings where relatively small fraction of the entire population is affected. Copyright © 2013 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Uptake of Predictive Genetic Testing and Cardiac Evaluation for Children at Risk for an Inherited Arrhythmia or Cardiomyopathy.

PubMed

Christian, Susan; Atallah, Joseph; Clegg, Robin; Giuffre, Michael; Huculak, Cathleen; Dzwiniel, Tara; Parboosingh, Jillian; Taylor, Sherryl; Somerville, Martin

2018-02-01

Predictive genetic testing in minors should be considered when clinical intervention is available. Children who carry a pathogenic variant for an inherited arrhythmia or cardiomyopathy require regular cardiac screening and may be prescribed medication and/or be told to modify their physical activity. Medical genetics and pediatric cardiology charts were reviewed to identify factors associated with uptake of genetic testing and cardiac evaluation for children at risk for long QT syndrome, hypertrophic cardiomyopathy or arrhythmogenic right ventricular cardiomyopathy. The data collected included genetic diagnosis, clinical symptoms in the carrier parent, number of children under 18 years of age, age of children, family history of sudden cardiac arrest/death, uptake of cardiac evaluation and if evaluated, phenotype for each child. We identified 97 at risk children from 58 families found to carry a pathogenic variant for one of these conditions. Sixty six percent of the families pursued genetic testing and 73% underwent cardiac screening when it was recommended. Declining predictive genetic testing was significantly associated with genetic specialist recommendation (p < 0.001) and having an asymptomatic carrier father (p = 0.006). Cardiac evaluation was significantly associated with uptake of genetic testing (p = 0.007). This study provides a greater understanding of factors associated with uptake of genetic testing and cardiac evaluation in children at risk of an inherited arrhythmia or cardiomyopathy. It also identifies a need to educate families about the importance of cardiac evaluation even in the absence of genetic testing.

Credit risk evaluation based on social media.

PubMed

Yang, Yang; Gu, Jing; Zhou, Zongfang

2016-07-01

Social media has been playing an increasingly important role in the sharing of individuals' opinions on many financial issues, including credit risk in investment decisions. This paper analyzes whether these opinions, which are transmitted through social media, can accurately predict enterprises' future credit risk. We consider financial statements oriented evaluation results based on logit and probit approaches as the benchmarks. We then conduct textual analysis to retrieve both posts and their corresponding commentaries published on two of the most popular social media platforms for financial investors in China. Professional advice from financial analysts is also investigated in this paper. We surprisingly find that the opinions extracted from both posts and commentaries surpass opinions of analysts in terms of credit risk prediction. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes.

PubMed

Baker, Stuart G; Kramer, Barnett S

2015-08-01

A surrogate endpoint is an endpoint observed earlier than the true endpoint (a health outcome) that is used to draw conclusions about the effect of treatment on the unobserved true endpoint. A prognostic marker is a marker for predicting the risk of an event given a control treatment; it informs treatment decisions when there is information on anticipated benefits and harms of a new treatment applied to persons at high risk. A predictive marker is a marker for predicting the effect of treatment on outcome in a subgroup of patients or study participants; it provides more rigorous information for treatment selection than a prognostic marker when it is based on estimated treatment effects in a randomized trial. We organized our discussion around a different theme for each topic. "Fundamentally an extrapolation" refers to the non-statistical considerations and assumptions needed when using surrogate endpoints to evaluate a new treatment. "Decision analysis to the rescue" refers to use the use of decision analysis to evaluate an additional prognostic marker because it is not possible to choose between purely statistical measures of marker performance. "The appeal of simplicity" refers to a straightforward and efficient use of a single randomized trial to evaluate overall treatment effect and treatment effect within subgroups using predictive markers. The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers. © The Author(s) 2014.

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.

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.

Evaluation and Enhancement of Calibration in the American College of Surgeons NSQIP Surgical Risk Calculator.

PubMed

Liu, Yaoming; Cohen, Mark E; Hall, Bruce L; Ko, Clifford Y; Bilimoria, Karl Y

2016-08-01

The American College of Surgeon (ACS) NSQIP Surgical Risk Calculator has been widely adopted as a decision aid and informed consent tool by surgeons and patients. Previous evaluations showed excellent discrimination and combined discrimination and calibration, but model calibration alone, and potential benefits of recalibration, were not explored. Because lack of calibration can lead to systematic errors in assessing surgical risk, our objective was to assess calibration and determine whether spline-based adjustments could improve it. We evaluated Surgical Risk Calculator model calibration, as well as discrimination, for each of 11 outcomes modeled from nearly 3 million patients (2010 to 2014). Using independent random subsets of data, we evaluated model performance for the Development (60% of records), Validation (20%), and Test (20%) datasets, where prediction equations from the Development dataset were recalibrated using restricted cubic splines estimated from the Validation dataset. We also evaluated performance on data subsets composed of higher-risk operations. The nonrecalibrated Surgical Risk Calculator performed well, but there was a slight tendency for predicted risk to be overestimated for lowest- and highest-risk patients and underestimated for moderate-risk patients. After recalibration, this distortion was eliminated, and p values for miscalibration were most often nonsignificant. Calibration was also excellent for subsets of higher-risk operations, though observed calibration was reduced due to instability associated with smaller sample sizes. Performance of NSQIP Surgical Risk Calculator models was shown to be excellent and improved with recalibration. Surgeons and patients can rely on the calculator to provide accurate estimates of surgical risk. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Predicting Vulnerabilities of North American Shorebirds to Climate Change

PubMed Central

Galbraith, Hector; DesRochers, David W.; Brown, Stephen; Reed, J. Michael

2014-01-01

Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at–risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners–in–Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower–risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change. PMID:25268907

Risk factors for the treatment outcome of retreated pulmonary tuberculosis patients in China: an optimized prediction model.

PubMed

Wang, X-M; Yin, S-H; Du, J; Du, M-L; Wang, P-Y; Wu, J; Horbinski, C M; Wu, M-J; Zheng, H-Q; Xu, X-Q; Shu, W; Zhang, Y-J

2017-07-01

Retreatment of tuberculosis (TB) often fails in China, yet the risk factors associated with the failure remain unclear. To identify risk factors for the treatment failure of retreated pulmonary tuberculosis (PTB) patients, we analyzed the data of 395 retreated PTB patients who received retreatment between July 2009 and July 2011 in China. PTB patients were categorized into 'success' and 'failure' groups by their treatment outcome. Univariable and multivariable logistic regression were used to evaluate the association between treatment outcome and socio-demographic as well as clinical factors. We also created an optimized risk score model to evaluate the predictive values of these risk factors on treatment failure. Of 395 patients, 99 (25·1%) were diagnosed as retreatment failure. Our results showed that risk factors associated with treatment failure included drug resistance, low education level, low body mass index (6 months), standard treatment regimen, retreatment type, positive culture result after 2 months of treatment, and the place where the first medicine was taken. An Optimized Framingham risk model was then used to calculate the risk scores of these factors. Place where first medicine was taken (temporary living places) received a score of 6, which was highest among all the factors. The predicted probability of treatment failure increases as risk score increases. Ten out of 359 patients had a risk score >9, which corresponded to an estimated probability of treatment failure >70%. In conclusion, we have identified multiple clinical and socio-demographic factors that are associated with treatment failure of retreated PTB patients. We also created an optimized risk score model that was effective in predicting the retreatment failure. These results provide novel insights for the prognosis and improvement of treatment for retreated PTB patients.

Carotid intima media thickness and plaques can predict the occurrence of ischemic cerebrovascular events.

PubMed

Prati, Patrizio; Tosetto, Alberto; Vanuzzo, Diego; Bader, Giovanni; Casaroli, Marco; Canciani, Luigi; Castellani, Sergio; Touboul, Pierre-Jean

2008-09-01

The clinical usefulness of noninvasive measurement of carotid intima media thickness and plaque visualization in the general population is still uncertain. We evaluated the age-specific incidence rates of cerebrovascular events in a cohort of 1348 subjects randomly taken from the census list of San Daniele Township and followed for a mean period of 12.7 years. The association among common carotid intima media thickness, measured at baseline, arterial risk factors, and incidence of ischemic cerebrovascular events was modeled using Poisson regression. The predictive ability of common carotid intima media thickness over arterial risk factors (summarized in the Framingham Stroke Risk Score) was evaluated by receiver operating characteristic curve analysis. During the follow-up, 115 subjects developed nonfatal ischemic stroke, transient ischemic attack, or vascular death, which were the predefined study end points. After adjustment for age and sex, hypertension, diabetes, common carotid intima media thickness above 1 mm, and carotid plaques were all independent risk factors for development of vascular events. Inclusion of carotid findings (presence of common carotid intima media thickness above 1 mm or carotid plaques) resulted in a predictive power higher than Framingham Stroke Risk Score alone only on for those subjects with a Framingham Stroke Risk Score over 20%. Although common carotid intima media thickness and presence of carotid plaques are known to be risk factors for the development of vascular events and to be independent from the conventional risk factors summarized in the Framingham Stroke Risk Score, their contribution to individual risk prediction is limited. Further studies will be required to address the role of carotid ultrasonography in the primary prevention of high-risk subjects.

Predicting vulnerabilities of North American shorebirds to climate change.

PubMed

Galbraith, Hector; DesRochers, David W; Brown, Stephen; Reed, J Michael

2014-01-01

Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at-risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners-in-Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower-risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

PubMed Central

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-01-01

Background Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. Aim To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Design and setting Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Method Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. Results From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The ‘predictAL-10’ risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the ‘predictAL-9’), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. Conclusion The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. PMID:28360074

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.

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

Evaluation of the modified FINDRISC to identify individuals at high risk for diabetes among middle-aged white and black ARIC study participants.

PubMed

Kulkarni, Manjusha; Foraker, Randi E; McNeill, Ann M; Girman, Cynthia; Golden, Sherita H; Rosamond, Wayne D; Duncan, Bruce; Schmidt, Maria Ines; Tuomilehto, Jaakko

2017-09-01

To evaluate a modified Finnish Diabetes Risk Score (FINDRISC) for predicting the risk of incident diabetes among white and black middle-aged participants from the Atherosclerosis Risk in Communities (ARIC) study. We assessed 9754 ARIC cohort participants who were free of diabetes at baseline. Logistic regression and receiver operator characteristic (ROC) curves were used to evaluate a modified FINDRISC for predicting incident diabetes after 9 years of follow-up, overall and by race/gender group. The modified FINDRISC used comprised age, body mass index, waist circumference, blood pressure medication and family history. The mean FINDRISC (range, 2 [lowest risk] to 17 [highest risk]) for black women was higher (9.9 ± 3.6) than that for black men (7.6 ± 3.9), white women (8.0 ± 3.6) and white men (7.6 ± 3.5). The incidence of diabetes increased generally across deciles of FINDRISC for all 4 race/gender groups. ROC curve statistics for the FINDRISC showed the highest area under the curve for white women (0.77) and the lowest for black men (0.70). We used a modified FINDRISC to predict the 9-year risk of incident diabetes in a biracial US population. The modified risk score can be useful for early screening of incident diabetes in biracial populations, which may be helpful for early interventions to delay or prevent diabetes. © 2017 John Wiley & Sons Ltd.

Novel Biomarkers to Improve the Prediction of Cardiovascular Event Risk in Type 2 Diabetes Mellitus.

PubMed

van der Leeuw, Joep; Beulens, Joline W J; van Dieren, Susan; Schalkwijk, Casper G; Glatz, Jan F C; Hofker, Marten H; Verschuren, W M Monique; Boer, Jolanda M A; van der Graaf, Yolanda; Visseren, Frank L J; Peelen, Linda M; van der Schouw, Yvonne T

2016-05-31

We evaluated the ability of 23 novel biomarkers representing several pathophysiological pathways to improve the prediction of cardiovascular event (CVE) risk in patients with type 2 diabetes mellitus beyond traditional risk factors. We used data from 1002 patients with type 2 diabetes mellitus from the Second Manifestations of ARTertial disease (SMART) study and 288 patients from the European Prospective Investigation into Cancer and Nutrition-NL (EPIC-NL). The associations of 23 biomarkers (adiponectin, C-reactive protein, epidermal-type fatty acid binding protein, heart-type fatty acid binding protein, basic fibroblast growth factor, soluble FMS-like tyrosine kinase-1, soluble intercellular adhesion molecule-1 and -3, matrix metalloproteinase [MMP]-1, MMP-3, MMP-9, N-terminal prohormone of B-type natriuretic peptide, osteopontin, osteonectin, osteocalcin, placental growth factor, serum amyloid A, E-selectin, P-selectin, tissue inhibitor of MMP-1, thrombomodulin, soluble vascular cell adhesion molecule-1, and vascular endothelial growth factor) with CVE risk were evaluated by using Cox proportional hazards analysis adjusting for traditional risk factors. The incremental predictive performance was assessed with use of the c-statistic and net reclassification index (NRI; continuous and based on 10-year risk strata 0-10%, 10-20%, 20-30%, >30%). A multimarker model was constructed comprising those biomarkers that improved predictive performance in both cohorts. N-terminal prohormone of B-type natriuretic peptide, osteopontin, and MMP-3 were the only biomarkers significantly associated with an increased risk of CVE and improved predictive performance in both cohorts. In SMART, the combination of these biomarkers increased the c-statistic with 0.03 (95% CI 0.01-0.05), and the continuous NRI was 0.37 (95% CI 0.21-0.52). In EPIC-NL, the multimarker model increased the c-statistic with 0.03 (95% CI 0.00-0.03), and the continuous NRI was 0.44 (95% CI 0.23-0.66). Based on risk strata, the NRI was 0.12 (95% CI 0.03-0.21) in SMART and 0.07 (95% CI -0.04-0.17) in EPIC-NL. Of the 23 evaluated biomarkers from different pathophysiological pathways, N-terminal prohormone of B-type natriuretic peptide, osteopontin, MMP-3, and their combination improved CVE risk prediction in 2 separate cohorts of patients with type 2 diabetes mellitus beyond traditional risk factors. However, the number of patients reclassified to a different risk stratum was limited. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Falls screening and assessment tools used in acute mental health settings: a review of policies in England and Wales

PubMed Central

Narayanan, V.; Dickinson, A.; Victor, C.; Griffiths, C.; Humphrey, D.

2016-01-01

Objectives There is an urgent need to improve the care of older people at risk of falls or who experience falls in mental health settings. The aims of this study were to evaluate the individual falls risk assessment tools adopted by National Health Service (NHS) mental health trusts in England and healthcare boards in Wales, to evaluate the comprehensiveness of these tools and to review their predictive validity. Methods All NHS mental health trusts in England (n = 56) and healthcare boards in Wales (n = 6) were invited to supply their falls policies and other relevant documentation (e.g. local falls audits). In order to check the comprehensiveness of tools listed in policy documents, the risk variables of the tools adopted by the mental health trusts’ policies were compared with the 2004 National Institute for Health and Care Excellence (NICE) falls prevention guidelines. A comprehensive analytical literature review was undertaken to evaluate the predictive validity of the tools used in these settings. Results Falls policies were obtained from 46 mental health trusts. Thirty-five policies met the study inclusion criteria and were included in the analysis. The main falls assessment tools used were the St. Thomas’ Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY), Falls Risk Assessment Scale for the Elderly, Morse Falls Scale (MFS) and Falls Risk Assessment Tool (FRAT). On detailed examination, a number of different versions of the FRAT were evident; validated tools had inconsistent predictive validity and none of them had been validated in mental health settings. Conclusions Falls risk assessment is the most commonly used component of risk prevention strategies, but most policies included unvalidated tools and even well validated tool such as the STRATIFY and the MFS that are reported to have inconsistent predictive accuracy. This raises questions about operational usefulness, as none of these tools have been tested in acute mental health settings. The falls risk assessment tools from only four mental health trusts met all the recommendations of the NICE falls guidelines on multifactorial assessment for prevention of falls. The recent NICE (2013) guidance states that tools predicting risk using numeric scales should no longer be used; however, multifactorial risk assessment and interventions tailored to patient needs is recommended. Trusts will need to update their policies in response to this guidance. PMID:26395210

Uncertainty Considerations for Ballistic Limit Equations

NASA Technical Reports Server (NTRS)

Schonberg, W. P.; Evans, H. J.; Williamsen, J. E.; Boyer, R. L.; Nakayama, G. S.

2005-01-01

The overall risk for any spacecraft system is typically determined using a Probabilistic Risk Assessment (PRA). A PRA attempts to determine the overall risk associated with a particular mission by factoring in all known risks (and their corresponding uncertainties, if known) to the spacecraft during its mission. The threat to mission and human life posed by the mircro-meteoroid & orbital debris (MMOD) environment is one of the risks. NASA uses the BUMPER II program to provide point estimate predictions of MMOD risk for the Space Shuttle and the International Space Station. However, BUMPER II does not provide uncertainty bounds or confidence intervals for its predictions. With so many uncertainties believed to be present in the models used within BUMPER II, providing uncertainty bounds with BUMPER II results would appear to be essential to properly evaluating its predictions of MMOD risk. The uncertainties in BUMPER II come primarily from three areas: damage prediction/ballistic limit equations, environment models, and failure criteria definitions. In order to quantify the overall uncertainty bounds on MMOD risk predictions, the uncertainties in these three areas must be identified. In this paper, possible approaches through which uncertainty bounds can be developed for the various damage prediction and ballistic limit equations encoded within the shuttle and station versions of BUMPER II are presented and discussed. We begin the paper with a review of the current approaches used by NASA to perform a PRA for the Space Shuttle and the International Space Station, followed by a review of the results of a recent sensitivity analysis performed by NASA using the shuttle version of the BUMPER II code. Following a discussion of the various equations that are encoded in BUMPER II, we propose several possible approaches for establishing uncertainty bounds for the equations within BUMPER II. We conclude with an evaluation of these approaches and present a recommendation regarding which of them would be the most appropriate to follow.

A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

PubMed

Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

2017-04-01

Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

The Development of a Plant Risk Evaluation (PRE) Tool for Assessing the Invasive Potential of Ornamental Plants

PubMed Central

Conser, Christiana; Seebacher, Lizbeth; Fujino, David W.; Reichard, Sarah; DiTomaso, Joseph M.

2015-01-01

Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) tool for ornamental plants. The 19 questions in the final PRE tool were narrowed down from 56 original questions from existing WRA tools. We evaluated the 56 WRA questions by screening 21 known invasive and 14 known non-invasive ornamental plants. After statistically comparing the predictability of each question and the frequency the question could be answered for both invasive and non-invasive species, we eliminated questions that provided no predictive power, were irrelevant in our current model, or could not be answered reliably at a high enough percentage. We also combined many similar questions. The final 19 remaining PRE questions were further tested for accuracy using 56 additional known invasive plants and 36 known non-invasive ornamental species. The resulting evaluation demonstrated that when “needs further evaluation” classifications were not included, the accuracy of the model was 100% for both predicting invasiveness and non-invasiveness. When “needs further evaluation” classifications were included as either false positive or false negative, the model was still 93% accurate in predicting invasiveness and 97% accurate in predicting non-invasiveness, with an overall accuracy of 95%. We conclude that the PRE tool should not only provide growers with a method to accurately screen their current stock and potential new introductions, but also increase the probability of the tool being accepted for use by the industry as the basis for a nursery certification program. PMID:25803830

Healthy eating index and breast cancer risk among Malaysian women.

PubMed

Shahril, Mohd Razif; Sulaiman, Suhaina; Shaharudin, Soraya Hanie; Akmal, Sharifah Noor

2013-07-01

Healthy Eating Index-2005 (HEI-2005), an index-based dietary pattern, has been shown to predict the risk of chronic diseases among Americans. This study aims to examine the ability of HEI-2005 in predicting the probability for risk of premenopausal and postmenopausal breast cancer among Malaysian women. Data from a case-control nutritional epidemiology study among 764 participants including 382 breast cancer cases and 382 healthy women were extracted and scored. Multivariate odds ratios (OR) with 95% confidence intervals (CI) were used to evaluate the relationship between the risk of breast cancer and quartiles (Q) of HEI-2005 total scores and its component, whereas the risk prediction ability of HEI-2005 was investigated using diagnostics analysis. The results of this study showed that there is a significant reduction in the risk of breast cancer, with a higher HEI-2005 total score among premenopausal women (OR Q1 vs. Q4=0.34, 95% CI; 0.15-0.76) and postmenopausal women (OR Q1 vs. Q4=0.20, 95% CI; 0.06-0.63). However, HEI-2005 has a sensitivity of 56-60%, a specificity of 55-60%, and a positive predictive value and negative predictive value of 57-58%, which indicates a moderate ability to predict the risk of breast cancer according to menopausal status. The breast cancer incidence observed poorly agrees with risk outcomes from HEI-2005 as shown by low κ statistics (κ=0.15). In conclusion, although the total HEI-2005 scores were associated with a risk of breast cancer among Malaysian women, the ability of HEI-2005 to predict risk is poor as indicated by the diagnostic analysis. A local index-based dietary pattern, which is disease specific, is required to predict the risk of breast cancer among Malaysian women for early prevention.

Phase angle assessment by bioelectrical impedance analysis and its predictive value for malnutrition risk in hospitalized geriatric patients.

PubMed

Varan, Hacer Dogan; Bolayir, Basak; Kara, Ozgur; Arik, Gunes; Kizilarslanoglu, Muhammet Cemal; Kilic, Mustafa Kemal; Sumer, Fatih; Kuyumcu, Mehmet Emin; Yesil, Yusuf; Yavuz, Burcu Balam; Halil, Meltem; Cankurtaran, Mustafa

2016-12-01

Phase angle (PhA) value determined by bioelectrical impedance analysis (BIA) is an indicator of cell membrane damage and body cell mass. Recent studies have shown that low PhA value is associated with increased nutritional risk in various group of patients. However, there have been only a few studies performed globally assessing the relationship between nutritional risk and PhA in hospitalized geriatric patients. The aim of the study is to evaluate the predictive value of the PhA for malnutrition risk in hospitalized geriatric patients. One hundred and twenty-two hospitalized geriatric patients were included in this cross-sectional study. Comprehensive geriatric assessment tests and BIA measurements were performed within the first 48 h after admission. Nutritional risk state of the patients was determined with NRS-2002. Phase angle values of the patients with malnutrition risk were compared with the patients that did not have the same risk. The independent variables for predicting malnutrition risk were determined. SPSS version 15 was utilized for the statistical analyzes. The patients with malnutrition risk had significantly lower phase angle values than the patients without malnutrition risk (p = 0.003). ROC curve analysis suggested that the optimum PhA cut-off point for malnutrition risk was 4.7° with 79.6 % sensitivity, 64.6 % specificity, 73.9 % positive predictive value, and 73.9 % negative predictive value. BMI, prealbumin, PhA, and Mini Mental State Examination Test scores were the independent variables for predicting malnutrition risk. PhA can be a useful, independent indicator for predicting malnutrition risk in hospitalized geriatric patients.

Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

PubMed

Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

2016-01-01

Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

PubMed Central

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Evaluation of the WinROP system for identifying retinopathy of prematurity in Czech preterm infants.

PubMed

Timkovic, Juraj; Pokryvkova, Martina; Janurova, Katerina; Barinova, Denisa; Polackova, Renata; Masek, Petr

2017-03-01

Retinopathy of Prematurity (ROP) is a potentially serious condition that can afflict preterm infants. Timely and correct identification of individuals at risk of developing a serious form of ROP is therefore of paramount importance. WinROP is an online system for predicting ROP based on birth weight and weight increments. However, the results vary significantly for various populations. It has not been evaluated in the Czech population. This study evaluates the test characteristics (specificity, sensitivity, positive and negative predictive values) of the WinROP system in Czech preterm infants. Data on 445 prematurely born infants included in the ROP screening program at the University Hospital Ostrava, Czech Republic, were retrospectively entered into the WinROP system and the outcomes of the WinROP and regular screening were compared. All 24 infants who developed high-risk (Type 1 or Type 2) ROP were correctly identified by the system. The sensitivity and negative predictive values for this group were 100%. However, the specificity and positive predictive values were substantially lower, resulting in a large number of false positives. Extending the analysis to low risk ROP, the system did not provide such reliable results. The system is a valuable tool for identifying infants who are not likely to develop high-risk ROP and this could help to substantially reduce the number of preterm infants in need of regular ROP screening. It is not suitable for predicting the development of less serious forms of ROP which is however in accordance with the declared aims of the WinROP system.

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.

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

Complete blood count risk score and its components, including RDW, are associated with mortality in the JUPITER trial.

PubMed

Horne, Benjamin D; Anderson, Jeffrey L; Muhlestein, Joseph B; Ridker, Paul M; Paynter, Nina P

2015-04-01

Previously, we showed that sex-specific complete blood count (CBC) risk scores strongly predicted risk of all-cause mortality in multiple sets of general medical patients. This study evaluated the CBC risk score in an independent, well-studied international primary risk population of lower-risk individuals initially free from cardiovascular (CV) disease. Observational secondary analysis of a randomized trial population. The previously derived and validated CBC score was evaluated for association with all-cause mortality among CV disease-free females (n = 6568) and males (n = 10,629) enrolled for up to 5 years in the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Associations of the CBC score with CV mortality and with major CV disease were also tested. The CBC score predicted all-cause mortality, with univariable hazard ratio (HR) 4.83 (95% CI 3.70-6.31) for the third CBC score tertile vs. the first tertile, and HR 2.31 (CI 1.75-3.05) for the second tertile (p trend < 0.001). The CBC score retained significance after adjustment: HR 1.97 (CI 1.46-2.67) and 1.51 (CI 1.13-2.00) for tertiles 3 and 2 vs. 1, respectively (p trend < 0.001). The CBC score also predicted CV mortality (p trend = 0.025) and the primary JUPITER endpoint (p trend = 0.015). c-statistics for mortality were 0.729 among all, and 0.722 and 0.750 for females and males, respectively. The CBC risk score was strongly associated with all-cause mortality among JUPITER trial participants and had good discrimination. It also predicted CV-specific outcomes. This CBC score may be useful in identifying cardiac disease-free individuals at increased risk of mortality. © The European Society of Cardiology 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study.

PubMed

Gajic, Ognjen; Dabbagh, Ousama; Park, Pauline K; Adesanya, Adebola; Chang, Steven Y; Hou, Peter; Anderson, Harry; Hoth, J Jason; Mikkelsen, Mark E; Gentile, Nina T; Gong, Michelle N; Talmor, Daniel; Bajwa, Ednan; Watkins, Timothy R; Festic, Emir; Yilmaz, Murat; Iscimen, Remzi; Kaufman, David A; Esper, Annette M; Sadikot, Ruxana; Douglas, Ivor; Sevransky, Jonathan; Malinchoc, Michael

2011-02-15

Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1-4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78-0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9-5.7). ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Extensions of criteria for evaluating risk prediction models for public health applications.

PubMed

Pfeiffer, Ruth M

2013-04-01

We recently proposed two novel criteria to assess the usefulness of risk prediction models for public health applications. The proportion of cases followed, PCF(p), is the proportion of individuals who will develop disease who are included in the proportion p of individuals in the population at highest risk. The proportion needed to follow-up, PNF(q), is the proportion of the general population at highest risk that one needs to follow in order that a proportion q of those destined to become cases will be followed (Pfeiffer, R.M. and Gail, M.H., 2011. Two criteria for evaluating risk prediction models. Biometrics 67, 1057-1065). Here, we extend these criteria in two ways. First, we introduce two new criteria by integrating PCF and PNF over a range of values of q or p to obtain iPCF, the integrated PCF, and iPNF, the integrated PNF. A key assumption in the previous work was that the risk model is well calibrated. This assumption also underlies novel estimates of iPCF and iPNF based on observed risks in a population alone. The second extension is to propose and study estimates of PCF, PNF, iPCF, and iPNF that are consistent even if the risk models are not well calibrated. These new estimates are obtained from case-control data when the outcome prevalence in the population is known, and from cohort data, with baseline covariates and observed health outcomes. We study the efficiency of the various estimates and propose and compare tests for comparing two risk models, both of which were evaluated in the same validation data.

Effects and Risk Evaluation of Oil Spillage in the Sea Areas of Changxing Island

PubMed Central

Wang, Hanxi; Xu, Jianling; Zhao, Wenkui; Zhang, Jiquan

2014-01-01

This paper evaluated the oil spillage risk in the waters near the island of Changxing in Dalian (China) based on the established risk assessment index. Four wind regimes (windless, northerly wind, westerly wind and southerly wind) were selected as weather conditions for the dynamic prediction of oil drift. If an oil spill occurs near the Koumen (a place near the island of Changxing), the forecast and evaluation are conducted based on a three-dimensional mathematical model of oil spillage, and the results obtained show the scope of the affected area when winds from various directions are applied. The oil spillage would, under various conditions, flow into the northern and western sea area of Changxing Island Bay, namely the Dalian harbor seal National Nature Reserve, and create adverse effects on the marine ecological environment. The rationality of combining the established oil spillage risk comprehensive index system with model prediction is further confirmed. Finally, preventive measures and quick fixes are presented in the case of accidental oil spillages. The most effective method to reduce environment risk is to adopt reasonable preventive measures and quick fixes. PMID:25153473

Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory.

PubMed

Shi, X; Wong, Y D; Li, M Z F; Chai, C

2018-08-01

Accident events are generally unexpected and occur rarely. Pre-accident risk assessment by surrogate indicators is an effective way to identify risk levels and thus boost accident prediction. Herein, the concept of Key Risk Indicator (KRI) is proposed, which assesses risk exposures using hybrid indicators. Seven metrics are shortlisted as the basic indicators in KRI, with evaluation in terms of risk behaviour, risk avoidance, and risk margin. A typical real-world chain-collision accident and its antecedent (pre-crash) road traffic movements are retrieved from surveillance video footage, and a grid remapping method is proposed for data extraction and coordinates transformation. To investigate the feasibility of each indicator in risk assessment, a temporal-spatial case-control is designed. By comparison, Time Integrated Time-to-collision (TIT) performs better in identifying pre-accident risk conditions; while Crash Potential Index (CPI) is helpful in further picking out the severest ones (the near-accident). Based on TIT and CPI, the expressions of KRIs are developed, which enable us to evaluate risk severity with three levels, as well as the likelihood. KRI-based risk assessment also reveals predictive insights about a potential accident, including at-risk vehicles, locations and time. Furthermore, straightforward thresholds are defined flexibly in KRIs, since the impact of different threshold values is found not to be very critical. For better validation, another independent real-world accident sample is examined, and the two results are in close agreement. Hierarchical indicators such as KRIs offer new insights about pre-accident risk exposures, which is helpful for accident assessment and prediction. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

PubMed

Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

2016-12-08

Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

PubMed

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

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.

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.

The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

PubMed

Breisinger, Terry P; Skidmore, Elizabeth R; Niyonkuru, Christian; Terhorst, Lauren; Campbell, Grace B

2014-12-01

To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen. Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital. Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010. Not applicable. Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation. A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p < 0.001), with area under the curve of 0.73, positive predictive value of 0.29, and negative predictive value of 0.94. For the Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively). An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted. © The Author(s) 2014.

Comparison of different risk stratification systems in predicting short-term serious outcome of syncope patients

PubMed Central

Safari, Saeed; Baratloo, Alireza; Hashemi, Behrooz; Rahmati, Farhad; Forouzanfar, Mohammad Mehdi; Motamedi, Maryam; Mirmohseni, Ladan

2016-01-01

Background: Determining etiologic causes and prognosis can significantly improve management of syncope patients. The present study aimed to compare the values of San Francisco, Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL), Boston, and Risk Stratification of Syncope in the Emergency Department (ROSE) score clinical decision rules in predicting the short-term serious outcome of syncope patients. Materials and Methods: The present diagnostic accuracy study with 1-week follow-up was designed to evaluate the predictive values of the four mentioned clinical decision rules. Screening performance characteristics of each model in predicting mortality, myocardial infarction (MI), and cerebrovascular accidents (CVAs) were calculated and compared. To evaluate the value of each aforementioned model in predicting the outcome, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated and receiver-operating curve (ROC) curve analysis was done. Results: A total of 187 patients (mean age: 64.2 ± 17.2 years) were enrolled in the study. Mortality, MI, and CVA were seen in 19 (10.2%), 12 (6.4%), and 36 (19.2%) patients, respectively. Area under the ROC curve for OESIL, San Francisco, Boston, and ROSE models in prediction the risk of 1-week mortality, MI, and CVA was in the 30–70% range, with no significant difference among models (P > 0.05). The pooled model did not show higher accuracy in prediction of mortality, MI, and CVA compared to others (P > 0.05). Conclusion: This study revealed the weakness of all four evaluated models in predicting short-term serious outcome of syncope patients referred to the emergency department without any significant advantage for one among others. PMID:27904602

Predictive validity of the Braden Scale, Norton Scale, and Waterlow Scale in the Czech Republic.

PubMed

Šateková, Lenka; Žiaková, Katarína; Zeleníková, Renáta

2017-02-01

The aim of this study was to determine the predictive validity of the Braden, Norton, and Waterlow scales in 2 long-term care departments in the Czech Republic. Assessing the risk for developing pressure ulcers is the first step in their prevention. At present, many scales are used in clinical practice, but most of them have not been properly validated yet (for example, the Modified Norton Scale in the Czech Republic). In the Czech Republic, only the Braden Scale has been validated so far. This is a prospective comparative instrument testing study. A random sample of 123 patients was recruited. The predictive validity of the pressure ulcer risk assessment scales was evaluated based on sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. The data were collected from April to August 2014. In the present study, the best predictive validity values were observed for the Norton Scale, followed by the Braden Scale and the Waterlow Scale, in that order. We recommended that the above 3 pressure ulcer risk assessment scales continue to be evaluated in the Czech clinical setting. © 2016 John Wiley & Sons Australia, Ltd.

Monitoring risk-adjusted outcomes in congenital heart surgery: does the appropriateness of a risk model change with time?

PubMed

Tsang, Victor T; Brown, Katherine L; Synnergren, Mats Johanssen; Kang, Nicholas; de Leval, Marc R; Gallivan, Steve; Utley, Martin

2009-02-01

Risk adjustment of outcomes in pediatric congenital heart surgery is challenging due to the great diversity in diagnoses and procedures. We have previously shown that variable life-adjusted display (VLAD) charts provide an effective graphic display of risk-adjusted outcomes in this specialty. A question arises as to whether the risk model used remains appropriate over time. We used a recently developed graphic technique to evaluate the performance of an existing risk model among those patients at a single center during 2000 to 2003 originally used in model development. We then compared the distribution of predicted risk among these patients with that among patients in 2004 to 2006. Finally, we constructed a VLAD chart of risk-adjusted outcomes for the latter period. Among 1083 patients between April 2000 and March 2003, the risk model performed well at predicted risks above 3%, underestimated mortality at 2% to 3% predicted risk, and overestimated mortality below 2% predicted risk. There was little difference in the distribution of predicted risk among these patients and among 903 patients between June 2004 and October 2006. Outcomes for the more recent period were appreciably better than those expected according to the risk model. This finding cannot be explained by any apparent bias in the risk model combined with changes in case-mix. Risk models can, and hopefully do, become out of date. There is scope for complacency in the risk-adjusted audit if the risk model used is not regularly recalibrated to reflect changing standards and expectations.

Risk Reduction and Resource Pooling on a Cooperation Task

ERIC Educational Resources Information Center

Pietras, Cynthia J.; Cherek, Don R.; Lane, Scott D.; Tcheremissine, Oleg

2006-01-01

Two experiments investigated choice in adult humans on a simulated cooperation task to evaluate a risk-reduction account of sharing based on the energy-budget rule. The energy-budget rule is an optimal foraging model that predicts risk-averse choices when net energy gains exceed energy requirements (positive energy budget) and risk-prone choices…

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce.

PubMed

Pang, Hao; Lambertini, Elisabetta; Buchanan, Robert L; Schaffner, Donald W; Pradhan, Abani K

2017-02-01

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

A new software for prediction of femoral neck fractures.

PubMed

Testi, Debora; Cappello, Angelo; Sgallari, Fiorella; Rumpf, Martin; Viceconti, Marco

2004-08-01

Femoral neck fractures are an important clinical, social and economic problem. Even if many different attempts have been carried out to improve the accuracy predicting the fracture risk, it was demonstrated in retrospective studies that the standard clinical protocol achieves an accuracy of about 65%. A new procedure was developed including for the prediction not only bone mineral density but also geometric and femoral strength information and achieving an accuracy of about 80% in a previous retrospective study. Aim of the present work was to re-engineer research-based procedures and develop a real-time software for the prediction of the risk for femoral fracture. The result was efficient, repeatable and easy to use software for the evaluation of the femoral neck fracture risk to be inserted in the daily clinical practice providing a useful tool for the improvement of fracture prediction.

How to quantify exposure to traumatic stress? Reliability and predictive validity of measures for cumulative trauma exposure in a post-conflict population.

PubMed

Wilker, Sarah; Pfeiffer, Anett; Kolassa, Stephan; Koslowski, Daniela; Elbert, Thomas; Kolassa, Iris-Tatjana

2015-01-01

While studies with survivors of single traumatic experiences highlight individual response variation following trauma, research from conflict regions shows that almost everyone develops posttraumatic stress disorder (PTSD) if trauma exposure reaches extreme levels. Therefore, evaluating the effects of cumulative trauma exposure is of utmost importance in studies investigating risk factors for PTSD. Yet, little research has been devoted to evaluate how this important environmental risk factor can be best quantified. We investigated the retest reliability and predictive validity of different trauma measures in a sample of 227 Ugandan rebel war survivors. Trauma exposure was modeled as the number of traumatic event types experienced or as a score considering traumatic event frequencies. In addition, we investigated whether age at trauma exposure can be reliably measured and improves PTSD risk prediction. All trauma measures showed good reliability. While prediction of lifetime PTSD was most accurate from the number of different traumatic event types experienced, inclusion of event frequencies slightly improved the prediction of current PTSD. As assessing the number of traumatic events experienced is the least stressful and time-consuming assessment and leads to the best prediction of lifetime PTSD, we recommend this measure for research on PTSD etiology.

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.

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

PubMed

Pan, Ian; Nolan, Laura B; Brown, Rashida R; Khan, Romana; van der Boor, Paul; Harris, Daniel G; Ghani, Rayid

2017-06-01

To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.

Evaluating surrogate endpoints, prognostic markers, and predictive markers — some simple themes

PubMed Central

Baker, Stuart G.; Kramer, Barnett S.

2014-01-01

Background A surrogate endpoint is an endpoint observed earlier than the true endpoint (a health outcome) that is used to draw conclusions about the effect of treatment on the unobserved true endpoint. A prognostic marker is a marker for predicting the risk of an event given a control treatment; it informs treatment decisions when there is information on anticipated benefits and harms of a new treatment applied to persons at high risk. A predictive marker is a marker for predicting the effect of treatment on outcome in a subgroup of patients or study participants; it provides more rigorous information for treatment selection than a prognostic marker when it is based on estimated treatment effects in a randomized trial. Methods We organized our discussion around a different theme for each topic. Results “Fundamentally an extrapolation” refers to the non-statistical considerations and assumptions needed when using surrogate endpoints to evaluate a new treatment. “Decision analysis to the rescue” refers to use the use of decision analysis to evaluate an additional prognostic marker because it is not possible to choose between purely statistical measures of marker performance. “The appeal of simplicity” refers to a straightforward and efficient use of a single randomized trial to evaluate overall treatment effect and treatment effect within subgroups using predictive markers. Conclusion The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers. PMID:25385934

Evaluation of health risks for contaminated aquifers.

PubMed Central

Piver, W T; Jacobs, T L; Medina, M A

1997-01-01

This review focuses on progress in the development of transport models for heterogeneous contaminated aquifers, the use of predicted contaminant concentrations in groundwater for risk assessment for heterogeneous human populations, and the evaluation of aquifer remediation technologies. Major limitations and areas for continuing research for all methods presented in this review are identified. Images Figure 2. PMID:9114282

Evaluation of forest management systems under risk of wildfire

Treesearch

Kari Hyytiainen; Robert G. Haight

2010-01-01

We evaluate the economic efficiency of even- and uneven-aged management systems under risk of wildfire. The management problems are formulated for a mixed-conifer stand and approximations of the optimal solutions are obtained using simulation optimization. The Northern Idaho variant of the Forest Vegetation Simulator and its Fire and Fuels Extension is used to predict...

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.

Which risk models perform best in selecting ever-smokers for lung cancer screening?

Cancer.gov

A new analysis by scientists at NCI evaluates nine different individualized lung cancer risk prediction models based on their selections of ever-smokers for computed tomography (CT) lung cancer screening.

Utility of the Care Dependency Scale in predicting care needs and health risks of elderly patients admitted to a geriatric unit: a cross-sectional study of 200 consecutive patients.

PubMed

Doroszkiewicz, Halina; Sierakowska, Matylda; Muszalik, Marta

2018-01-01

The aim of the study was to evaluate the usefulness of the Polish version of the Care Dependency Scale (CDS) in predicting care needs and health risks of elderly patients admitted to a geriatric unit. This was a cross-sectional study of 200 geriatric patients aged ≥60 years, chronologically admitted to a geriatrics unit in Poland. The study was carried out using the Polish version of the CDS questionnaire to evaluate biopsychosocial needs and the level of care dependency. The mean age of the participating geriatric patients was 81.8±6.6. The mean result of the sum of the CDS index for all the participants was 55.3±15.1. Detailed analysis of the results of evaluation of the respondents' functional condition showed statistically significant differences in the levels of care dependency. Evaluation of the patients' physical performance in terms of the ability to do basic activities of daily living (ADL) and instrumental ADL (I-ADL) showed statistically significant differences between the levels of care dependency. Patients with high dependency were more often prone to pressure ulcers - 13.1±3.3, falls (87.2%), poorer emotional state - 6.9±3.6, mental function - 5.1±2.8, and more often problems with locomotion, vision, and hearing. The results showed that locomotive disability, depression, advanced age, and problem with vision and hearing are connected with increasing care dependency. CDS evaluation of each admitted geriatric patient enables us to predict the care needs and health risks that need to be reduced and the disease states to be improved. CDS evaluation should be accompanied by the use of other instruments and assessments to evaluate pressure ulcer risk, fall risk, and actions toward the improvement of subjective well-being, as well as correction of vision and hearing problems where possible and assistive devices for locomotion.

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

PubMed

Harrison, David A; Brady, Anthony R; Parry, Gareth J; Carpenter, James R; Rowan, Kathy

2006-05-01

To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. Prospective cohort study. A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. None. The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

INTERPRETING SPONTANEOUS RENAL LESIONS IN SAFETY AND RISK ASSESSMENT

EPA Science Inventory

Interpreting Spontaneous Renal Lesions in Safety and Risk Assessment
Douglas C. Wolf, D.V.M., Ph.D.

Introduction

Risk assessment is a process whereby the potential adverse health effects from exposure to a xenobiotic are predicted after evaluation of the availab...

Updating Risk Prediction Tools: A Case Study in Prostate Cancer

PubMed Central

Ankerst, Donna P.; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J.; Feng, Ziding; Sanda, Martin G.; Partin, Alan W.; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M.

2013-01-01

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [−2]proPSA measured on an external case control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. PMID:22095849

Updating risk prediction tools: a case study in prostate cancer.

PubMed

Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

2012-01-01

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk

Treesearch

Frank H. Koch; Denys Yemshanov; Daniel W. McKenney; William D. Smith

2009-01-01

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model’s numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for...

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.

The impact of birth weight on cardiovascular disease risk in the Women's Health Initiative

PubMed Central

Smith, CJ; Ryckman, KK; Barnabei, Vanessa M.; Howard, Barbara; Isasi, Carmen R.; Sarto, Gloria; Tom, Sarah E.; Van Horn, Linda; Wallace, Robert; Robinson, Jennifer G

2016-01-01

Background and Aims Cardiovascular disease (CVD) is among the leading causes of morbidity and mortality worldwide. Traditional risk factors predict 75-80% of an individual's risk of incident CVD. However, the role of early life experiences in future disease risk is gaining attention. The Barker hypothesis proposes fetal origins of adult disease, with consistent evidence demonstrating the deleterious consequences of birth weight outside the normal range. In this study, we investigate the role of birth weight in CVD risk prediction. Methods and Results The Women's Health Initiative (WHI) represents a large national cohort of post-menopausal women with 63 815 participants included in this analysis. Univariable proportional hazards regression analyses evaluated the association of 4 self-reported birth weight categories against 3 CVD outcome definitions, which included indicators of coronary heart disease, ischemic stroke, coronary revascularization, carotid artery disease and peripheral arterial disease. The role of birth weight was also evaluated for prediction of CVD events in the presence of traditional risk factors using 3 existing CVD risk prediction equations: one body mass index (BMI)-based and two laboratory-based models. Low birth weight (LBW) (< 6 lbs.) was significantly associated with all CVD outcome definitions in univariable analyses (HR=1.086, p=0.009). LBW was a significant covariate in the BMI-based model (HR=1.128, p<0.0001) but not in the lipid-based models. Conclusion LBW (<6 lbs.) is independently associated with CVD outcomes in the WHI cohort. This finding supports the role of the prenatal and postnatal environment in contributing to the development of adult chronic disease. PMID:26708645

Validation of the Chinese SAD PERSONS Scale to predict repeated self-harm in emergency attendees in Taiwan

PubMed Central

2014-01-01

Background Past and repeated self-harm are long-term risks to completed suicide. A brief rating scale to assess repetition risk of self-harm is important for high-risk identification and early interventions in suicide prevention. The study aimed to examine the validity of the Chinese SAD PERSONS Scale (CSPS) and to evaluate its feasibility in clinical settings. Methods One hundred and forty-seven patients with self-harm were recruited from the Emergency Department and assessed at baseline and the sixth month. The controls, 284 people without self-harm from the Family Medicine Department in the same hospital were recruited and assessed concurrently. The psychometric properties of the CSPS were examined using baseline and follow-up measurements that assessed a variety of suicide risk factors. Clinical feasibility and applicability of the CSPS were further evaluated by a group of general nurses who used case vignette approach in CSPS risk assessment in clinical settings. An open-ended question inquiring their opinions of scale adaptation to hospital inpatient assessment for suicide risks were also analyzed using content analysis. Results The CSPS was significantly correlated with other scales measuring depression, hopelessness and suicide ideation. A cut-off point of the scale was at 4/5 in predicting 6-month self-harm repetition with the sensitivity and specificity being 65.4% and 58.1%, respectively. Based on the areas under the Receiver Operating Characteristic curves, the predictive validity of the scale showed a better performance than the other scales. Fifty-four nurses, evaluating the scale using case vignette found it a useful tool to raise the awareness of suicide risk and a considerable tool to be adopted into nursing care. Conclusions The Chinese SAD PERSONS Scale is a brief instrument with acceptable psychometric properties for self-harm prediction. However, cautions should be paid to level of therapeutic relationships during assessment, staff workload and adequate training for wider clinical applications. PMID:24533537

Validation of the Chinese SAD PERSONS Scale to predict repeated self-harm in emergency attendees in Taiwan.

PubMed

Wu, Chia-Yi; Huang, Hui-Chun; Wu, Shu-I; Sun, Fang-Ju; Huang, Chiu-Ron; Liu, Shen-Ing

2014-02-17

Past and repeated self-harm are long-term risks to completed suicide. A brief rating scale to assess repetition risk of self-harm is important for high-risk identification and early interventions in suicide prevention. The study aimed to examine the validity of the Chinese SAD PERSONS Scale (CSPS) and to evaluate its feasibility in clinical settings. One hundred and forty-seven patients with self-harm were recruited from the Emergency Department and assessed at baseline and the sixth month. The controls, 284 people without self-harm from the Family Medicine Department in the same hospital were recruited and assessed concurrently. The psychometric properties of the CSPS were examined using baseline and follow-up measurements that assessed a variety of suicide risk factors. Clinical feasibility and applicability of the CSPS were further evaluated by a group of general nurses who used case vignette approach in CSPS risk assessment in clinical settings. An open-ended question inquiring their opinions of scale adaptation to hospital inpatient assessment for suicide risks were also analyzed using content analysis. The CSPS was significantly correlated with other scales measuring depression, hopelessness and suicide ideation. A cut-off point of the scale was at 4/5 in predicting 6-month self-harm repetition with the sensitivity and specificity being 65.4% and 58.1%, respectively. Based on the areas under the Receiver Operating Characteristic curves, the predictive validity of the scale showed a better performance than the other scales. Fifty-four nurses, evaluating the scale using case vignette found it a useful tool to raise the awareness of suicide risk and a considerable tool to be adopted into nursing care. The Chinese SAD PERSONS Scale is a brief instrument with acceptable psychometric properties for self-harm prediction. However, cautions should be paid to level of therapeutic relationships during assessment, staff workload and adequate training for wider clinical applications.

Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

PubMed

Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

2016-04-01

The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infected patients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

PubMed

Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

2016-04-01

Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. 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/

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.

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.

Characteristics of Students at Risk for Mathematics Difficulties Predicting Arithmetic Word Problem Solving Performance: The Role of Attention, Behavior, and Reading

ERIC Educational Resources Information Center

Jitendra, Asha K.; Corroy, Kelly Cozine; Dupuis, Danielle N.

2013-01-01

The purposes of this study were (a) to evaluate differences in arithmetic word problem solving between high and low at-risk students for mathematics difficulties (MD) and (b) to assess the influence of attention, behavior, reading, and socio-economic status (SES) in predicting the word problem solving performance of third-grade students with MD.…

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.

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.

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

HKF-R 10 - screening for predicting chronicity in acute low back pain (LBP): a prospective clinical trial.

PubMed

Neubauer, Eva; Junge, Astrid; Pirron, Peter; Seemann, Hanne; Schiltenwolf, Marcus

2006-08-01

Prospective cohort study. To develop a short instrument to reliably predict chronicity in low back pain (LBP). Health care expenditures on the treatment of low back pain continue to increase. It is therefore important to prevent the development of chronicity. In Germany, there is at present no early risk assessment tool to predict the risk of developing chronic LBP for patients presenting with acute LBP. Undertaken in an orthopedic practice setting, this study examined known risk factors for chronicity. It resulted in the development of a short questionnaire that successfully predicted the course of chronicity with an accuracy of 78%. A cohort of 192 orthopaedic outpatients was assessed for clinical, behavioral, emotional, and cognitive parameters bsed on a self-report test battery of 167 established items predictive for chronicity in LBP. Chronicity was defined as back pain persisting for longer than six months. Logistic regression analysis was performed to evaluate the predictive value of all items significantly associated with the dependent variable. The study found the following items to have the strongest predictive value in the development of chronicity: "How strong was your back pain during the last week when it was most tolerable?" and the question "How much residual pain would you be willing to tolerate while still considering the therapy successful?" These were followed by the variables for "Duration of existing LBP" (more than eight days), the patient's educational level (low levels are related to higher risks of chronicity) and pain being experienced elsewhere in the body. Other significant factors were five items assessing depression (Zung) and the palliative effect of therapeutic massage (where a positive correlation was found). Female patients have a higher risk for chronicity, as do patients with a high total score on the scales assessing "catastrophizing thoughts" and thoughts of "helplessness". Using the items listed above, the study was able to predict a patient's risk of developing chronic LBP with a probability of 78%. These items were assembled in a brief questionnaire and were paired with a corresponding evaluative tool. This enables practitioners to assess an individual patient's risk for chronicity by means of a simple calculator in just a few minutes. A validation study for the questionnaire is currently being prepared. MINI ABSTRACT: The objective of this study was the development of a brief questionnaire to assess the risk for chronicity for LBP.

How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through.

PubMed

Baker, Stuart G; Schuit, Ewoud; Steyerberg, Ewout W; Pencina, Michael J; Vickers, Andrew; Vickers, Andew; Moons, Karel G M; Mol, Ben W J; Lindeman, Karen S

2014-09-28

An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the addition of a marker with a large odds ratio. Because it is not possible on purely statistical grounds to choose between the odds ratio and AUC, we invoke decision analysis, which incorporates costs and benefits. For example, a timely estimate of the risk of later non-elective operative delivery can help a woman in labor decide if she wants an early elective cesarean section to avoid greater complications from possible later non-elective operative delivery. A basic risk prediction model for later non-elective operative delivery involves only antepartum markers. Because adding intrapartum markers to this risk prediction model increases AUC by 0.02, we questioned whether this small improvement is worthwhile. A key decision-analytic quantity is the risk threshold, here the risk of later non-elective operative delivery at which a patient would be indifferent between an early elective cesarean section and usual care. For a range of risk thresholds, we found that an increase in the net benefit of risk prediction requires collecting intrapartum marker data on 68 to 124 women for every correct prediction of later non-elective operative delivery. Because data collection is non-invasive, this test tradeoff of 68 to 124 is clinically acceptable, indicating the value of adding intrapartum markers to the risk prediction model. Copyright © 2014 John Wiley & Sons, Ltd.

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

PubMed Central

Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A.

2017-01-01

Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm (“profile”) incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes. PMID:28890908

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting.

PubMed

Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A; Brenton, Ashley

2017-01-01

Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.

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.

Predictive value of flat-panel CT for haemorrhagic transformations in patients with acute stroke treated with thrombectomy.

PubMed

Rouchaud, Aymeric; Pistocchi, Silvia; Blanc, Raphaël; Engrand, Nicolas; Bartolini, Bruno; Piotin, Michel

2014-03-01

Haemorrhagic transformations are pejorative for patients with acute ischaemic stroke (AIS). We estimated flat-panel CT performances to detect brain parenchymal hyperdense lesions immediately after mechanical thrombectomy directly on the angiography table in patients with AIS, and its ability to predict haemorrhagic transformation. We also evaluated an easy-reading protocol for post-procedure flat-panel CT evaluation by clinicians to enable them to determine the potential risk of haemorrhage. Two neuroradiologists retrospectively reviewed post-procedural flat-panel CT and 24 h follow-up imaging. We evaluated hyperdense lesions on flat-panel CT to predict the occurrence of haemorrhagic transformation within 24 h detected with conventional imaging. Of 63 patients, 60.3% presented post-procedural parenchymal hyperdensity and 54.0% had haemorrhagic transformation. Significantly more patients with hyperdense lesions on post-thrombectomy flat-panel CT presented haemorrhagic transformation (84.2% vs 8.0%; p<0.0001). No significant haemorrhagic transformations were detected for patients without parenchymal hyperdensity. Sensitivity and specificity of hyperdense lesions on flat-panel CT for the prediction of haemorrhagic transformation were 94.1% (80.3-99.3%) and 79.3% (60.3-92.0%), respectively. The positive and negative predictive values for the occurrence of haemorrhage were 84.2% (68.8-94.0%) and 92.0% (74.0-99.0%), respectively. For significant parenchymal haemorrhage type 2, sensitivity and negative predictive values were 100%. We observed good homogeneity between the different readers. Hyperdensity on post-procedural flat-panel CT was associated with a tendency for higher risk of death and lower risk of good clinical outcome. Flat-panel CT appears to be a good tool to detect brain parenchymal hyperdensities after mechanical thrombectomy in patients with AIS and to predict haemorrhagic transformation.

Applying the National Surgical Quality Improvement Program risk calculator to patients undergoing colorectal surgery: theory vs reality.

PubMed

Adegboyega, Titilayo O; Borgert, Andrew J; Lambert, Pamela J; Jarman, Benjamin T

2017-01-01

Discussing potential morbidity and mortality is essential to informed decision-making and consent. The American College of Surgery National Surgical Quality Improvement Program developed an online risk calculator (RC) using patient-specific information to determine operative risk. Colorectal procedures at our independent academic medical center from 2010 to 2011 were evaluated. The RC's predicted outcomes were compared with observed outcomes. Statistical analysis included Brier score, Wilcoxon sign rank test, and standardized event ratio. There were 324 patients included. The RC's Brier score was .24 (.015-.219) for predicting mortality and morbidity, respectively. The observed event rate for surgical site infection and any complication was higher than the RC predicted (standardized event ratio 1.9 CI [1.49 to 2.39] and 1.39 CI [1.14 to 1.68], respectively). The observed length of stay was longer than predicted (5.6 vs 6.6 days, P < .001). The RC underestimated the surgical site infection and overall complication rates. The RC is a valuable tool in predicting risk for adverse outcomes; however, institution-specific trends may influence actual risk. Surgeons and institutions must recognize areas where they are outliers from estimated risks and tailor risk discussions accordingly. Copyright © 2016 Elsevier Inc. All rights reserved.

Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension.

PubMed

Boucly, Athénaïs; Weatherald, Jason; Savale, Laurent; Jaïs, Xavier; Cottin, Vincent; Prevot, Grégoire; Picard, François; de Groote, Pascal; Jevnikar, Mitja; Bergot, Emmanuel; Chaouat, Ari; Chabanne, Céline; Bourdin, Arnaud; Parent, Florence; Montani, David; Simonneau, Gérald; Humbert, Marc; Sitbon, Olivier

2017-08-01

Current European guidelines recommend periodic risk assessment for patients with pulmonary arterial hypertension (PAH). The aim of our study was to determine the association between the number of low-risk criteria achieved within 1 year of diagnosis and long-term prognosis.Incident patients with idiopathic, heritable and drug-induced PAH between 2006 and 2016 were analysed. The number of low-risk criteria present at diagnosis and at first re-evaluation were assessed: World Health Organization (WHO)/New York Heart Association (NYHA) functional class I or II, 6-min walking distance (6MWD) >440 m, right atrial pressure <8 mmHg and cardiac index ≥2.5 L·min -1 ·m -2 1017 patients were included (mean age 57 years, 59% female, 75% idiopathic PAH). After a median follow-up of 34 months, 238 (23%) patients had died. Each of the four low-risk criteria independently predicted transplant-free survival at first re-evaluation. The number of low-risk criteria present at diagnosis (p<0.001) and at first re-evaluation (p<0.001) discriminated the risk of death or lung transplantation. In addition, in a subgroup of 603 patients with brain natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP) measurements, the number of three noninvasive criteria (WHO/NYHA functional class, 6MWD and BNP/NT-proBNP) present at first re-evaluation discriminated prognostic groups (p<0.001).A simplified risk assessment tool that quantifies the number of low-risk criteria present accurately predicted transplant-free survival in PAH. Copyright ©ERS 2017.

Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia.

PubMed

Burkhardt, T; Schmidt, M; Kurmanavicius, J; Zimmermann, R; Schäffer, L

2014-01-01

To evaluate the quality of anthropometric measures to improve the prediction of shoulder dystocia by combining different sonographic biometric parameters. This was a retrospective cohort study of 12,794 vaginal deliveries with complete sonographic biometry data obtained within 7 days before delivery. Receiver-operating characteristics (ROC) curves of various combinations of the biometric parameters, namely, biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference, abdominal diameter (AD), abdominal circumference (AC) and femur length were analyzed. The influences of independent risk factors were calculated and their combination used in a predictive model. The incidence of shoulder dystocia was 1.14%. Different combinations of sonographic parameters showed comparable ROC curves without advantage for a particular combination. The difference between AD and BPD (AD - BPD) (area under the curve (AUC) = 0.704) revealed a significant increase in risk (odds ratio (OR) 7.6 (95% CI 4.2-13.9), sensitivity 8.2%, specificity 98.8%) at a suggested cut-off ≥ 2.6 cm. However, the positive predictive value (PPV) was low (7.5%). The AC as a single parameter (AUC = 0.732) with a cut-off ≥ 35 cm performed worse (OR 4.6 (95% CI 3.3-6.5), PPV 2.6%). BPD/OFD (a surrogate for fetal cranial shape) was not significantly different between those with and those without shoulder dystocia. The combination of estimated fetal weight, maternal diabetes, gender and AD - BPD provided a reasonable estimate of the individual risk. Sonographic fetal anthropometric measures appear not to be a useful tool to screen for the risk of shoulder dystocia due to a low PPV. However, AD - BPD appears to be a relevant risk factor. While risk stratification including different known risk factors may aid in counseling, shoulder dystocia cannot effectively be predicted. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Nurses' short-term prediction of violence in acute psychiatric intensive care.

PubMed

Björkdahl, A; Olsson, D; Palmstierna, T

2006-03-01

To evaluate the short-term predictive capacity of the Brøset Violence Checklist (BVC) when used by nurses in a psychiatric intensive care unit. Seventy-three patients were assessed according to the BVC three times daily. Violent incidents were recorded with the Staff Observation Aggression Scale, revised version. An extended Cox proportional hazards model with multiple events and time-dependent covariates was estimated to evaluate how the highest BVC sum of the last 24 h and its separate items affect the risk for severe violence within the next 24 h. With a BVC sum of one or more, hazard for severe violence was six times higher than if the sum was zero. Four of the six separate items significantly increased the risk for severe violence with hazard ratios between 3.0 and 6.3. Risk for in-patient violence in a short-term perspective can to a high degree be predicted by nurses using the BVC.

Evaluation of critical nuclear power plant electrical cable response to severe thermal fire conditions

NASA Astrophysics Data System (ADS)

Taylor, Gabriel James

The failure of electrical cables exposed to severe thermal fire conditions are a safety concern for operating commercial nuclear power plants (NPPs). The Nuclear Regulatory Commission (NRC) has promoted the use of risk-informed and performance-based methods for fire protection which resulted in a need to develop realistic methods to quantify the risk of fire to NPP safety. Recent electrical cable testing has been conducted to provide empirical data on the failure modes and likelihood of fire-induced damage. This thesis evaluated numerous aspects of the data. Circuit characteristics affecting fire-induced electrical cable failure modes have been evaluated. In addition, thermal failure temperatures corresponding to cable functional failures have been evaluated to develop realistic single point thermal failure thresholds and probability distributions for specific cable insulation types. Finally, the data was used to evaluate the prediction capabilities of a one-dimension conductive heat transfer model used to predict cable failure.

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/

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

Emergency Physician Attitudes, Preferences, and Risk Tolerance for Stroke as a Potential Cause of Dizziness Symptoms.

PubMed

Kene, Mamata V; Ballard, Dustin W; Vinson, David R; Rauchwerger, Adina S; Iskin, Hilary R; Kim, Anthony S

2015-09-01

We evaluated emergency physicians' (EP) current perceptions, practice, and attitudes towards evaluating stroke as a cause of dizziness among emergency department patients. We administered a survey to all EPs in a large integrated healthcare delivery system. The survey included clinical vignettes, perceived utility of historical and exam elements, attitudes about the value of and requisite post-test probability of a clinical prediction rule for dizziness. We calculated descriptive statistics and post-test probabilities for such a clinical prediction rule. The response rate was 68% (366/535). Respondents' median practice tenure was eight years (37% female, 92% emergency medicine board certified). Symptom quality and typical vascular risk factors increased suspicion for stroke as a cause of dizziness. Most respondents reported obtaining head computed tomography (CT) (74%). Nearly all respondents used and felt confident using cranial nerve and limb strength testing. A substantial minority of EPs used the Epley maneuver (49%) and HINTS (head-thrust test, gaze-evoked nystagmus, and skew deviation) testing (30%); however, few EPs reported confidence in these tests' bedside application (35% and 16%, respectively). Respondents favorably viewed applying a properly validated clinical prediction rule for assessment of immediate and 30-day stroke risk, but indicated it would have to reduce stroke risk to <0.5% to be clinically useful. EPs report relying on symptom quality, vascular risk factors, simple physical exam elements, and head CT to diagnose stroke as the cause of dizziness, but would find a validated clinical prediction rule for dizziness helpful. A clinical prediction rule would have to achieve a 0.5% post-test stroke probability for acceptability.

The Diagnostic Accuracy of the Berg Balance Scale in Predicting Falls.

PubMed

Park, Seong-Hi; Lee, Young-Shin

2017-11-01

This study aimed to evaluate the predictive validity of the Berg Balance Scale (BBS) as a screening tool for fall risks among those with varied levels of balance. A total of 21 studies reporting predictive validity of the BBS of fall risk were meta-analyzed. With regard to the overall predictive validity of the BBS, the pooled sensitivity and specificity were 0.72 and 0.73, respectively; the accuracy curve area was 0.84. The findings showed statistical heterogeneity among studies. Among the sub-groups, the age group of those younger than 65 years, those with neuromuscular disease, those with 2+ falls, and those with a cutoff point of 45 to 49 showed better sensitivity with statistically less heterogeneity. The empirical evidence indicates that the BBS is a suitable tool to screen for the risk of falls and shows good predictability when used with the appropriate criteria and applied to those with neuromuscular disease.

Dynamic Risk Assessment of Sexual Offenders: Validity and Dimensional Structure of the Stable-2007.

PubMed

Etzler, Sonja; Eher, Reinhard; Rettenberger, Martin

2018-02-01

In this study, the predictive and incremental validity of the Stable-2007 beyond the Static-99 was evaluated in an updated sample of N = 638 adult male sexual offenders followed-up for an average of M = 8.2 years. Data were collected at the Federal Evaluation Center for Violent and Sexual Offenders (FECVSO) in Austria within a prospective-longitudinal research design. Scores and risk categories of the Static-99 (AUC = .721; p < .001) and of the Stable-2007 (AUC = .623, p = .005) were found to be significantly related to sexual recidivism. The Stable-2007 risk categories contributed incrementally to the prediction of sexual recidivism beyond the Static-99. Analyzing the dimensional structure of the Stable-2007 yielded three factors, named Antisociality, Sexual Deviance, and Hypersexuality. Antisociality and Sexual Deviance were significant predictors for sexual recidivism. Sexual Deviance was negatively associated with non-sexual violent recidivism. Comparisons with latent dimensions of other risk assessment instruments are made and implications for applied risk assessment are discussed.

Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

PubMed

Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

2016-10-01

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a U.K. tri-ethnic prospective cohort study (SABRE--Southall And Brent REvisited).

PubMed

Tillin, Therese; Hughes, Alun D; Whincup, Peter; Mayet, Jamil; Sattar, Naveed; McKeigue, Paul M; Chaturvedi, Nish

2014-01-01

To evaluate QRISK2 and Framingham cardiovascular disease (CVD) risk scores in a tri-ethnic U.K. population. Cohort study. West London. Randomly selected from primary care lists. Follow-up data were available for 87% of traced participants, comprising 1866 white Europeans, 1377 South Asians, and 578 African Caribbeans, aged 40-69 years at baseline (1998-1991). First CVD events: myocardial infarction, coronary revascularisation, angina, transient ischaemic attack or stroke reported by participant, primary care or hospital records or death certificate. During follow-up, 387 CVD events occurred in men (14%) and 78 in women (8%). Both scores underestimated risk in European and South Asian women (ratio of predicted to observed risk: European women: QRISK2: 0.73, Framingham: 0.73; South Asian women: QRISK2: 0.52, Framingham: 0.43). In African Caribbeans, Framingham over-predicted in men and women and QRISK2 over-predicted in women. Framingham classified 28% of participants as high risk, predicting 54% of all such events. QRISK2 classified 19% as high risk, predicting 42% of all such events. Both scores performed poorly in identifying high risk African Caribbeans; QRISK2 and Framingham identified as high risk only 10% and 24% of those who experienced events. Neither score performed consistently well in all ethnic groups. Further validation of QRISK2 in other multi-ethnic datasets, and better methods for identifying high risk African Caribbeans and South Asian women, are required.

The c-index is not proper for the evaluation of $t$-year predicted risks.

PubMed

Blanche, Paul; Kattan, Michael W; Gerds, Thomas A

2018-02-16

We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context.

68Ga-PSMA-617 PET/CT: a promising new technique for predicting risk stratification and metastatic risk of prostate cancer patients.

PubMed

Liu, Chen; Liu, Teli; Zhang, Ning; Liu, Yiqiang; Li, Nan; Du, Peng; Yang, Yong; Liu, Ming; Gong, Kan; Yang, Xing; Zhu, Hua; Yan, Kun; Yang, Zhi

2018-05-02

The purpose of this study was to investigate the performance of 68 Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68 Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68 Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Semi-quantitative analysis indexes of 68 Ga-PSMA-617 PET/CT imaging can be used as "imaging biomarkers" to predict risk stratification and metastatic risk of prostate cancer.

Evaluation of the efficacy of six nutritional screening tools to predict malnutrition in the elderly.

PubMed

Poulia, Kalliopi-Anna; Yannakoulia, Mary; Karageorgou, Dimitra; Gamaletsou, Maria; Panagiotakos, Demosthenes B; Sipsas, Nikolaos V; Zampelas, Antonis

2012-06-01

Malnutrition in the elderly is a multifactorial problem, more prevalent in hospitals and care homes. The absence of a gold standard in evaluating nutritional risk led us to evaluate the efficacy of six nutritional screening tools used in the elderly. Two hundred forty eight elderly patients (129 men, 119 female women, aged 75.2 ± 8.5 years) were examined. Nutritional screening was performed on admission using the following tools: Nutritional Risk Index (NRI), Geriatric Nutritional Risk Index (GNRI), Subjective Global Assessment (SGA), Mini Nutritional Assessment - Screening Form (MNA-SF), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated. Nutritional risk and/or malnutrition varied greatly, ranging from 47.2 to 97.6%, depending on the nutritional screening tool used. MUST was the most valid screening tool (validity coefficient = 0.766, CI 95%: 0.690-0.841), while SGA was in better agreement with the combined index (κ = 0.707, p = 0.000). NRS 2002 although was the highest in sensitivity (99.4%), it was the lowest in specificity (6.1%) and positive predictive value (68.2%). MUST seem to be the most valid in the evaluation of the risk for malnutrition in the elderly upon admission to the hospital. NRS 2002 was found to overestimate nutritional risk in the elderly. Copyright © 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Become the PPUPET Master: Mastering Pressure Ulcer Risk Assessment With the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET).

PubMed

Sterken, David J; Mooney, JoAnn; Ropele, Diana; Kett, Alysha; Vander Laan, Karen J

2015-01-01

Hospital acquired pressure ulcers (HAPU) are serious, debilitating, and preventable complications in all inpatient populations. Despite evidence of the development of pressure ulcers in the pediatric population, minimal research has been done. Based on observations gathered during quarterly HAPU audits, bedside nursing staff recognized trends in pressure ulcer locations that were not captured using current pressure ulcer risk assessment tools. Together, bedside nurses and nursing leadership created and conducted multiple research studies to investigate the validity and reliability of the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET). Copyright © 2015 Elsevier Inc. All rights reserved.

KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors.

PubMed

Lentine, Krista L; Kasiske, Bertram L; Levey, Andrew S; Adams, Patricia L; Alberú, Josefina; Bakr, Mohamed A; Gallon, Lorenzo; Garvey, Catherine A; Guleria, Sandeep; Li, Philip Kam-Tao; Segev, Dorry L; Taler, Sandra J; Tanabe, Kazunari; Wright, Linda; Zeier, Martin G; Cheung, Michael; Garg, Amit X

2017-08-01

The 2017 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors is intended to assist medical professionals who evaluate living kidney donor candidates and provide care before, during and after donation. The guideline development process followed the Grades of Recommendation Assessment, Development, and Evaluation (GRADE) approach and guideline recommendations are based on systematic reviews of relevant studies that included critical appraisal of the quality of the evidence and the strength of recommendations. However, many recommendations, for which there was no evidence or no systematic search for evidence was undertaken by the Evidence Review Team, were issued as ungraded expert opinion recommendations. The guideline work group concluded that a comprehensive approach to risk assessment should replace decisions based on assessments of single risk factors in isolation. Original data analyses were undertaken to produce a "proof-in-concept" risk-prediction model for kidney failure to support a framework for quantitative risk assessment in the donor candidate evaluation and defensible shared decision making. This framework is grounded in the simultaneous consideration of each candidate's profile of demographic and health characteristics. The processes and framework for the donor candidate evaluation are presented, along with recommendations for optimal care before, during, and after donation. Limitations of the evidence are discussed, especially regarding the lack of definitive prospective studies and clinical outcome trials. Suggestions for future research, including the need for continued refinement of long-term risk prediction and novel approaches to estimating donation-attributable risks, are also provided.In citing this document, the following format should be used: Kidney Disease: Improving Global Outcomes (KDIGO) Living Kidney Donor Work Group. KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors. Transplantation. 2017;101(Suppl 8S):S1-S109.

Validation of the Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management (REVEAL) pulmonary hypertension prediction model in a unique population and utility in the prediction of long-term survival.

PubMed

Cogswell, Rebecca; Kobashigawa, Erin; McGlothlin, Dana; Shaw, Robin; De Marco, Teresa

2012-11-01

The Registry to Evaluate Early and Long-Term Pulmonary Arterial (PAH) Hypertension Disease Management (REVEAL) model was designed to predict 1-year survival in patients with PAH. Multivariate prediction models need to be evaluated in cohorts distinct from the derivation set to determine external validity. In addition, limited data exist on the utility of this model in the prediction of long-term survival. REVEAL model performance was assessed to predict 1-year and 5-year outcomes, defined as survival or composite survival or freedom from lung transplant, in 140 patients with PAH. The validation cohort had a higher proportion of human immunodeficiency virus (7.9% vs 1.9%, p < 0.0001), methamphetamine use (19.3% vs 4.9%, p < 0.0001), and portal hypertension PAH (16.4% vs 5.1%, p < 0.0001) compared with the development cohort. The C-index of the model to predict survival was 0.765 at 1 year and 0.712 at 5 years of follow-up. The C-index of the model to predict composite survival or freedom from lung transplant was 0.805 and 0.724 at 1 and 5 years of follow-up, respectively. Prediction by the model, however, was weakest among patients with intermediate-risk predicted survival. The REVEAL model had adequate discrimination to predict 1-year survival in this small but clinically distinct validation cohort. Although the model also had predictive ability out to 5 years, prediction was limited among patients of intermediate risk, suggesting our prediction methods can still be improved. Copyright © 2012. Published by Elsevier Inc.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.

PubMed

Wu, Lang; Shi, Wei; Long, Jirong; Guo, Xingyi; Michailidou, Kyriaki; Beesley, Jonathan; Bolla, Manjeet K; Shu, Xiao-Ou; Lu, Yingchang; Cai, Qiuyin; Al-Ejeh, Fares; Rozali, Esdy; Wang, Qin; Dennis, Joe; Li, Bingshan; Zeng, Chenjie; Feng, Helian; Gusev, Alexander; Barfield, Richard T; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Aronson, Kristan J; Auer, Paul L; Barrdahl, Myrto; Baynes, Caroline; Beckmann, Matthias W; Benitez, Javier; Bermisheva, Marina; Blomqvist, Carl; Bogdanova, Natalia V; Bojesen, Stig E; Brauch, Hiltrud; Brenner, Hermann; Brinton, Louise; Broberg, Per; Brucker, Sara Y; Burwinkel, Barbara; Caldés, Trinidad; Canzian, Federico; Carter, Brian D; Castelao, J Esteban; Chang-Claude, Jenny; Chen, Xiaoqing; Cheng, Ting-Yuan David; Christiansen, Hans; Clarke, Christine L; Collée, Margriet; Cornelissen, Sten; Couch, Fergus J; Cox, David; Cox, Angela; Cross, Simon S; Cunningham, Julie M; Czene, Kamila; Daly, Mary B; Devilee, Peter; Doheny, Kimberly F; Dörk, Thilo; Dos-Santos-Silva, Isabel; Dumont, Martine; Dwek, Miriam; Eccles, Diana M; Eilber, Ursula; Eliassen, A Heather; Engel, Christoph; Eriksson, Mikael; Fachal, Laura; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fritschi, Lin; Gabrielson, Marike; Gago-Dominguez, Manuela; Gapstur, Susan M; García-Closas, Montserrat; Gaudet, Mia M; Ghoussaini, Maya; Giles, Graham G; Goldberg, Mark S; Goldgar, David E; González-Neira, Anna; Guénel, Pascal; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hall, Per; Hallberg, Emily; Hamann, Ute; Harrington, Patricia; Hein, Alexander; Hicks, Belynda; Hillemanns, Peter; Hollestelle, Antoinette; Hoover, Robert N; Hopper, John L; Huang, Guanmengqian; Humphreys, Keith; Hunter, David J; Jakubowska, Anna; Janni, Wolfgang; John, Esther M; Johnson, Nichola; Jones, Kristine; Jones, Michael E; Jung, Audrey; Kaaks, Rudolf; Kerin, Michael J; Khusnutdinova, Elza; Kosma, Veli-Matti; Kristensen, Vessela N; Lambrechts, Diether; Le Marchand, Loic; Li, Jingmei; Lindström, Sara; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Lubinski, Jan; Luccarini, Craig; Lux, Michael P; MacInnis, Robert J; Maishman, Tom; Kostovska, Ivana Maleva; Mannermaa, Arto; Manson, JoAnn E; Margolin, Sara; Mavroudis, Dimitrios; Meijers-Heijboer, Hanne; Meindl, Alfons; Menon, Usha; Meyer, Jeffery; Mulligan, Anna Marie; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F; Nordestgaard, Børge G; Olopade, Olufunmilayo I; Olson, Janet E; Olsson, Håkan; Peterlongo, Paolo; Peto, Julian; Plaseska-Karanfilska, Dijana; Prentice, Ross; Presneau, Nadege; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rennert, Gad; Rennert, Hedy S; Rhenius, Valerie; Romero, Atocha; Romm, Jane; Rudolph, Anja; Saloustros, Emmanouil; Sandler, Dale P; Sawyer, Elinor J; Schmidt, Marjanka K; Schmutzler, Rita K; Schneeweiss, Andreas; Scott, Rodney J; Scott, Christopher G; Seal, Sheila; Shah, Mitul; Shrubsole, Martha J; Smeets, Ann; Southey, Melissa C; Spinelli, John J; Stone, Jennifer; Surowy, Harald; Swerdlow, Anthony J; Tamimi, Rulla M; Tapper, William; Taylor, Jack A; Terry, Mary Beth; Tessier, Daniel C; Thomas, Abigail; Thöne, Kathrin; Tollenaar, Rob A E M; Torres, Diana; Truong, Thérèse; Untch, Michael; Vachon, Celine; Van Den Berg, David; Vincent, Daniel; Waisfisz, Quinten; Weinberg, Clarice R; Wendt, Camilla; Whittemore, Alice S; Wildiers, Hans; Willett, Walter C; Winqvist, Robert; Wolk, Alicja; Xia, Lucy; Yang, Xiaohong R; Ziogas, Argyrios; Ziv, Elad; Dunning, Alison M; Pharoah, Paul D P; Simard, Jacques; Milne, Roger L; Edwards, Stacey L; Kraft, Peter; Easton, Douglas F; Chenevix-Trench, Georgia; Zheng, Wei

2018-06-18

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10 -6 , including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

The Application of a Residual Risk Evaluation Technique Used for Expendable Launch Vehicles

NASA Technical Reports Server (NTRS)

Latimer, John A.

2009-01-01

This presentation provides a Residual Risk Evaluation Technique (RRET) developed by Kennedy Space Center (KSC) Safety and Mission Assurance (S&MA) Launch Services Division. This technique is one of many procedures used by S&MA at KSC to evaluate residual risks for each Expendable Launch Vehicle (ELV) mission. RRET is a straight forward technique that incorporates the proven methodology of risk management, fault tree analysis, and reliability prediction. RRET derives a system reliability impact indicator from the system baseline reliability and the system residual risk reliability values. The system reliability impact indicator provides a quantitative measure of the reduction in the system baseline reliability due to the identified residual risks associated with the designated ELV mission. An example is discussed to provide insight into the application of RRET.

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.

Risk model for estimating the 1-year risk of deferred lesion intervention following deferred revascularization after fractional flow reserve assessment.

PubMed

Depta, Jeremiah P; Patel, Jayendrakumar S; Novak, Eric; Gage, Brian F; Masrani, Shriti K; Raymer, David; Facey, Gabrielle; Patel, Yogesh; Zajarias, Alan; Lasala, John M; Amin, Amit P; Kurz, Howard I; Singh, Jasvindar; Bach, Richard G

2015-02-21

Although lesions deferred revascularization following fractional flow reserve (FFR) assessment have a low risk of adverse cardiac events, variability in risk for deferred lesion intervention (DLI) has not been previously evaluated. The aim of this study was to develop a prediction model to estimate 1-year risk of DLI for coronary lesions where revascularization was not performed following FFR assessment. A prediction model for DLI was developed from a cohort of 721 patients with 882 coronary lesions where revascularization was deferred based on FFR between 10/2002 and 7/2010. Deferred lesion intervention was defined as any revascularization of a lesion previously deferred following FFR. The final DLI model was developed using stepwise Cox regression and validated using bootstrapping techniques. An algorithm was constructed to predict the 1-year risk of DLI. During a mean (±SD) follow-up period of 4.0 ± 2.3 years, 18% of lesions deferred after FFR underwent DLI; the 1-year incidence of DLI was 5.3%, while the predicted risk of DLI varied from 1 to 40%. The final Cox model included the FFR value, age, current or former smoking, history of coronary artery disease (CAD) or prior percutaneous coronary intervention, multi-vessel CAD, and serum creatinine. The c statistic for the DLI prediction model was 0.66 (95% confidence interval, CI: 0.61-0.70). Patients deferred revascularization based on FFR have variation in their risk for DLI. A clinical prediction model consisting of five clinical variables and the FFR value can help predict the risk of DLI in the first year following FFR assessment. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

EPA perspective - exposure and effects prediction and monitoring

EPA Science Inventory

Risk-based decisions for environmental chemicals often rely on estimates of human exposure and biological response. Biomarkers have proven a useful empirical tool for evaluating exposure and hazard predictions. In the United States, the Centers for Disease Control and Preventio...

Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model.

PubMed

Dhana, Klodian; Ikram, M Arfan; Hofman, Albert; Franco, Oscar H; Kavousi, Maryam

2015-03-01

Body mass index (BMI) has been used to simplify cardiovascular risk prediction models by substituting total cholesterol and high-density lipoprotein cholesterol. In the elderly, the ability of BMI as a predictor of cardiovascular disease (CVD) declines. We aimed to find the most predictive anthropometric measure for CVD risk to construct a non-laboratory-based model and to compare it with the model including laboratory measurements. The study included 2675 women and 1902 men aged 55-79 years from the prospective population-based Rotterdam Study. We used Cox proportional hazard regression analysis to evaluate the association of BMI, waist circumference, waist-to-hip ratio and a body shape index (ABSI) with CVD, including coronary heart disease and stroke. The performance of the laboratory-based and non-laboratory-based models was evaluated by studying the discrimination, calibration, correlation and risk agreement. Among men, ABSI was the most informative measure associated with CVD, therefore ABSI was used to construct the non-laboratory-based model. Discrimination of the non-laboratory-based model was not different than laboratory-based model (c-statistic: 0.680-vs-0.683, p=0.71); both models were well calibrated (15.3% observed CVD risk vs 16.9% and 17.0% predicted CVD risks by the non-laboratory-based and laboratory-based models, respectively) and Spearman rank correlation and the agreement between non-laboratory-based and laboratory-based models were 0.89 and 91.7%, respectively. Among women, none of the anthropometric measures were independently associated with CVD. Among middle-aged and elderly where the ability of BMI to predict CVD declines, the non-laboratory-based model, based on ABSI, could predict CVD risk as accurately as the laboratory-based model among men. 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.

Preclinical Torsades-de-Pointes screens: advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk.

PubMed

Gintant, Gary A

2008-08-01

The successful development of novel drugs requires the ability to detect (and avoid) compounds that may provoke Torsades-de-Pointes (TdeP) arrhythmia while endorsing those compounds with minimal torsadogenic risk. As TdeP is a rare arrhythmia not readily observed during clinical or post-marketing studies, numerous preclinical models are employed to assess delayed or altered ventricular repolarization (surrogate markers linked to enhanced proarrhythmic risk). This review evaluates the advantages and limitations of selected preclinical models (ranging from the simplest cellular hERG current assay to the more complex in vitro perfused ventricular wedge and Langendorff heart preparations and in vivo chronic atrio-ventricular (AV)-node block model). Specific attention is paid to the utility of concentration-response relationships and "risk signatures" derived from these studies, with the intention of moving beyond predicting clinical QT prolongation and towards prediction of TdeP risk. While the more complex proarrhythmia models may be suited to addressing questionable or conflicting proarrhythmic signals obtained with simpler preclinical assays, further benchmarking of proarrhythmia models is required for their use in the robust evaluation of safety margins. In the future, these models may be able to reduce unwarranted attrition of evolving compounds while becoming pivotal in the balanced integrated risk assessment of advancing compounds.

Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Camarinha, P. I. M.; Canavesi, V.; Alvalá, R. C. S.

2013-10-01

In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

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.

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier.

PubMed

Miranda, Eka; Irwansyah, Edy; Amelga, Alowisius Y; Maribondang, Marco M; Salim, Mulyadi

2016-07-01

The number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults. The process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3. The evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease. The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

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.

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-01-01

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. PMID:19036144

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.

PubMed

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-11-26

Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.

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 crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Biomarkers of exposure, sensitivity and disease

NASA Technical Reports Server (NTRS)

Brooks, A. L.

1999-01-01

PURPOSE: This review is to evaluate the use of biomarkers as an indication of past exposure to radiation or other environmental insults, individual sensitivity and risk for the development of late occurring disease. OVERVIEW: Biomarkers can be subdivided depending on their applications. Markers of exposure and dose can be used to reconstruct and predict past accidental or occupational exposures when limited or no physical measurements were available. Markers of risk or susceptibility can help identify sensitivity individuals that are at increased risk for development of spontaneous disease and may help predict the increased risk in sensitive individuals associated with environmental or therapeutic radiation exposures. Markers of disease represent the initial cellular or molecular changes that occur during disease development. Each of these types of biomarkers serves a unique purpose. OUTLINE: This paper concentrates on biomarkers of dose and exposure and provides a brief review of biomarkers of sensitivity and disease. The review of biomarkers of dose and exposure will demonstrate the usefulness of biomarkers in evaluation of physical factors associated with radiation exposure, such as LET, doserate and dose distribution. It will also evaluate the use of biomarkers to establish relationships that exist between exposure parameters such as energy deposition, environmental concentration of radioactive materials, alpha traversals and dose. In addition, the importance of biological factors on the magnitude of the biomarker response will be reviewed. Some of the factors evaluated will be the influence of species, tissue, cell types and genetic background. The review will demonstrate that markers of sensitivity and disease often have little usefulness in dose-reconstruction and, by the same token, many markers of dose or exposure may not be applicable for prediction of sensitivity or risk.

Risk Score for Predicting Treatment-Requiring Retinopathy of Prematurity (ROP) in the Telemedicine Approaches to Evaluating Acute-Phase ROP Study.

PubMed

Ying, Gui-Shuang; VanderVeen, Deborah; Daniel, Ebenezer; Quinn, Graham E; Baumritter, Agnieshka

2016-10-01

To develop a risk score for predicting treatment-requiring retinopathy of prematurity (TR-ROP) in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) study. Second analyses of an observational cohort study. Infants with birth weight (BW) <1251 g who had ≥1 imaging session by 34 weeks of postmenstrual age (PMA) and ≥1 subsequent retinopathy of prematurity (ROP) examination for determining TR-ROP by study-certified ophthalmologists. Nonphysician trained readers evaluated wide-field retinal image sets for characteristics of ROP, pre-plus/plus disease, and retinal hemorrhage. Risk score points for predicting TR-ROP were derived from the regression coefficients of significant predictors in a multivariate logistic regression model. TR-ROP. Eighty-five of 771 infants (11.0%) developed TR-ROP. In a multivariate model, significant predictors for TR-ROP were gestational age (GA) (odds ratio [OR], 5.7; 95% confidence interval [CI], 1.7-18.9 for ≤25 vs. ≥28 weeks), need for respiratory support (OR, 7.0; 95% CI, 1.3-37.1 for high-frequency oscillatory ventilation vs. no respiratory support), slow weight gain (OR, 2.4; 95% CI, 1.2-4.6 for weight gain ≤12 g/day vs. >15 g/day), and image findings at the first image session including number of quadrants with pre-plus (OR, 3.8; 95% CI, 1.5-9.7 for 4 pre-plus quadrants vs. no pre-plus), stage and zone of ROP (OR, 4.7; 95% CI, 2.1-11.8 for stage 1-2 zone I, OR, 5.9; 95% CI, 2.1-16.6 for stage 3 zone I vs. no ROP), and presence of blot hemorrhage (OR, 3.1; 95% CI, 1.4-6.7). Image findings predicted TR-ROP better than GA (area under receiver operating characteristic curve [AUC] = 0.82 vs. 0.75, P = 0.03). The risk of TR-ROP steadily increased with higher risk score and predicted TR-ROP well (AUC = 0.88; 95% CI, 0.85-0.92). Risk score ≥3 points for predicting TR-ROP had a sensitivity of 98.8%, specificity of 40.1%, and positive and negative predictive values of 17.0% and 99.6%, respectively. Image characteristics at 34 PMA weeks or earlier independently predict TR-ROP. If externally validated in other infants, risk score, calculated from image findings, GA, weight gain, and respiratory support, enables early identification of infants in need of increased surveillance for TR-ROP. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

NASA Technical Reports Server (NTRS)

Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

2015-01-01

Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

Predicting Future Suicide Attempts Among Adolescent and Emerging Adult Psychiatric Emergency Patients

PubMed Central

Horwitz, Adam G.; Czyz, Ewa K.; King, Cheryl A.

2014-01-01

Objective The purpose of this study was to longitudinally examine specific characteristics of suicidal ideation in combination with histories of suicide attempts and non-suicidal self-injury (NSSI) to best evaluate risk for a future attempt among high-risk adolescents and emerging adults. Method Participants in this retrospective medical record review study were 473 (53% female; 69% Caucasian) consecutive patients, ages 15–24 years (M = 19.4 years) who presented for psychiatric emergency (PE) services during a 9-month period. These patients’ medical records, including a clinician-administered Columbia-Suicide Severity Rating Scale, were coded at the index visit and at future visits occurring within the next 18 months. Logistic regression models were used to predict suicide attempts during this period. Results SES, suicidal ideation severity (i.e., intent, method), suicidal ideation intensity (i.e., frequency, controllability), a lifetime history of suicide attempt, and a lifetime history of NSSI were significant independent predictors of a future suicide attempt. Suicidal ideation added incremental validity to the prediction of future suicide attempts above and beyond the influence of a past suicide attempt, whereas a lifetime history of NSSI did not. Sex moderated the relationship between the duration of suicidal thoughts and future attempts (predictive for males, but not females). Conclusions Results suggest value in incorporating both past behaviors and current thoughts into suicide risk formulation. Furthermore, suicidal ideation duration warrants additional examination as a potential critical factor for screening assessments evaluating suicide risk among high-risk samples, particularly for males. PMID:24871489

Influences on emergency department length of stay for older people.

PubMed

Street, Maryann; Mohebbi, Mohammadreza; Berry, Debra; Cross, Anthony; Considine, Julie

2018-02-14

The aim of this study was to examine the influences on emergency department (ED) length of stay (LOS) for older people and develop a predictive model for an ED LOS more than 4 h. This retrospective cohort study used organizational data linkage at the patient level from a major Australian health service. The study population was aged 65 years or older, attending an ED during the 2013/2014 financial year. We developed and internally validated a clinical prediction rule. Discriminatory performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. An integer-based risk score was developed using multivariate logistic regression. The risk score was evaluated using ROC analysis. There were 33 926 ED attendances: 57.5% (n=19 517) had an ED LOS more than 4 h. The area under ROC for age, usual accommodation, triage category, arrival by ambulance, arrival overnight, imaging, laboratory investigations, overcrowding, time to be seen by doctor, ED visits with admission and access block relating to ED LOS more than 4 h was 0.796, indicating good performance. In the validation set, area under ROC was 0.80, P-value was 0.36 and prediction mean square error was 0.18, indicating good calibration. The risk score value attributed to each risk factor ranged from 2 to 68 points. The clinical prediction rule stratified patients into five levels of risk on the basis of the total risk score. Objective identification of older people at intermediate and high risk of an ED LOS more than 4 h early in ED care enables targeted approaches to streamline the patient journey, decrease ED LOS and optimize emergency care for older people.

Risk factors for lung function decline in a large cohort of young cystic fibrosis patients.

PubMed

Cogen, Jonathan; Emerson, Julia; Sanders, Don B; Ren, Clement; Schechter, Michael S; Gibson, Ronald L; Morgan, Wayne; Rosenfeld, Margaret

2015-08-01

To identify novel risk factors and corroborate previously identified risk factors for mean annual decline in FEV1% predicted in a large, contemporary, United States cohort of young cystic fibrosis (CF) patients. Retrospective observational study of participants in the EPIC Observational Study, who were Pseudomonas-negative and ≤12 years of age at enrollment in 2004-2006. The associations between potential demographic, clinical, and environmental risk factors evaluated during the baseline year and subsequent mean annual decline in FEV1 percent predicted were evaluated using generalized estimating equations. The 946 participants in the current analysis were followed for a mean of 6.2 (SD 1.3) years. Mean annual decline in FEV1% predicted was 1.01% (95%CI 0.85-1.17%). Children with one or no F508del mutations had a significantly smaller annual decline in FEV1 compared to F508del homozygotes. In a multivariable model, risk factors during the baseline year associated with a larger subsequent mean annual lung function decline included female gender, frequent or productive cough, low BMI (<66th percentile, median in the cohort), ≥1 pulmonary exacerbation, high FEV1 (≥115% predicted, in the top quartile), and respiratory culture positive for methicillin-sensitive Staphylococcus aureus, methicillin-resistant S. aureus, or Stenotrophomonas maltophilia. We have identified a range of risk factors for FEV1 decline in a large cohort of young, CF patients who were Pa negative at enrollment, including novel as well as previously identified characteristics. These results could inform the design of a clinical trial in which rate of FEV1 decline is the primary endpoint and identify high-risk groups that may benefit from closer monitoring. © 2015 Wiley Periodicals, Inc.

Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker

PubMed Central

Iasonos, Alexia; Chapman, Paul B.; Satagopan, Jaya M.

2016-01-01

There is an increased interest in finding predictive biomarkers that can guide treatment options for both mutation carriers and non-carriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time to event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a Proportional Hazards regression model is commonly used as a measure of variation in treatment benefit. While this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus non-carriers. We illustrate the use and interpretation of the proposed measures using data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. PMID:27141007

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.

Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

PubMed Central

Sorbe, Bengt

2012-01-01

Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

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.

Cardiovascular risk assessment in elderly adults using SCORE OP model in a Latin American population: The experience from Ecuador.

PubMed

Sisa, Ivan

2018-02-09

Cardiovascular disease (CVD) mortality is predicted to increase in Latin America countries due to their rapidly aging population. However, there is very little information about CVD risk assessment as a primary preventive measure in this high-risk population. We predicted the national risk of developing CVD in Ecuadorian elderly population using the Systematic COronary Risk Evaluation in Older Persons (SCORE OP) High and Low models by risk categories/CVD risk region in 2009. Data on national cardiovascular risk factors were obtained from the Encuesta sobre Salud, Bienestar y Envejecimiento. We computed the predicted 5-year risk of CVD risk and compared the extent of agreement and reclassification in stratifying high-risk individuals between SCORE OP High and Low models. Analyses were done by risk categories, CVD risk region, and sex. In 2009, based on SCORE OP Low model almost 42% of elderly adults living in Ecuador were at high risk of suffering CVD over a 5-year period. The extent of agreement between SCORE OP High and Low risk prediction models was moderate (Cohen's kappa test of 0.5), 34% of individuals approximately were reclassified into different risk categories and a third of the population would benefit from a pharmacologic intervention to reduce the CVD risk. Forty-two percent of elderly Ecuadorians were at high risk of suffering CVD over a 5-year period, indicating an urgent need to tailor primary preventive measures for this vulnerable and high-risk population. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Can we improve clinical prediction of at-risk older drivers?

PubMed Central

Bowers, Alex R.; Anastasio, R. Julius; Sheldon, Sarah S.; O’Connor, Margaret G.; Hollis, Ann M.; Howe, Piers D.; Horowitz, Todd S.

2013-01-01

Objectives To conduct a pilot study to evaluate the predictive value of the Montreal Cognitive Assessment test (MoCA) and a brief test of multiple object tracking (MOT) relative to other tests of cognition and attention in identifying at-risk older drivers, and to determine which combination of tests provided the best overall prediction. Methods Forty-seven currently-licensed drivers (58 to 95 years), primarily from a clinical driving evaluation program, participated. Their performance was measured on: (1) a screening test battery, comprising MoCA, MOT, MiniMental State Examination (MMSE), Trail-Making Test, visual acuity, contrast sensitivity, and Useful Field of View (UFOV); and (2) a standardized road test. Results Eighteen participants were rated at-risk on the road test. UFOV subtest 2 was the best single predictor with an area under the curve (AUC) of .84. Neither MoCA nor MOT was a better predictor of the at-risk outcome than either MMSE or UFOV, respectively. The best four-test combination (MMSE, UFOV subtest 2, visual acuity and contrast sensitivity) was able to identify at-risk drivers with 95% specificity and 80% sensitivity (.91 AUC). Conclusions Although the best four-test combination was much better than a single test in identifying at-risk drivers, there is still much work to do in this field to establish test batteries that have both high sensitivity and specificity. PMID:23954688

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

PubMed

Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

2011-02-01

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

Risk Prediction Models in Psychiatry: Toward a New Frontier for the Prevention of Mental Illnesses.

PubMed

Bernardini, Francesco; Attademo, Luigi; Cleary, Sean D; Luther, Charles; Shim, Ruth S; Quartesan, Roberto; Compton, Michael T

2017-05-01

We conducted a systematic, qualitative review of risk prediction models designed and tested for depression, bipolar disorder, generalized anxiety disorder, posttraumatic stress disorder, and psychotic disorders. Our aim was to understand the current state of research on risk prediction models for these 5 disorders and thus future directions as our field moves toward embracing prediction and prevention. Systematic searches of the entire MEDLINE electronic database were conducted independently by 2 of the authors (from 1960 through 2013) in July 2014 using defined search criteria. Search terms included risk prediction, predictive model, or prediction model combined with depression, bipolar, manic depressive, generalized anxiety, posttraumatic, PTSD, schizophrenia, or psychosis. We identified 268 articles based on the search terms and 3 criteria: published in English, provided empirical data (as opposed to review articles), and presented results pertaining to developing or validating a risk prediction model in which the outcome was the diagnosis of 1 of the 5 aforementioned mental illnesses. We selected 43 original research reports as a final set of articles to be qualitatively reviewed. The 2 independent reviewers abstracted 3 types of data (sample characteristics, variables included in the model, and reported model statistics) and reached consensus regarding any discrepant abstracted information. Twelve reports described models developed for prediction of major depressive disorder, 1 for bipolar disorder, 2 for generalized anxiety disorder, 4 for posttraumatic stress disorder, and 24 for psychotic disorders. Most studies reported on sensitivity, specificity, positive predictive value, negative predictive value, and area under the (receiver operating characteristic) curve. Recent studies demonstrate the feasibility of developing risk prediction models for psychiatric disorders (especially psychotic disorders). The field must now advance by (1) conducting more large-scale, longitudinal studies pertaining to depression, bipolar disorder, anxiety disorders, and other psychiatric illnesses; (2) replicating and carrying out external validations of proposed models; (3) further testing potential selective and indicated preventive interventions; and (4) evaluating effectiveness of such interventions in the context of risk stratification using risk prediction models. © Copyright 2017 Physicians Postgraduate Press, Inc.

Candida colonization as a risk marker for invasive candidiasis in mixed medical-surgical intensive care units: development and evaluation of a simple, standard protocol.

PubMed

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

2015-04-01

Colonization with Candida species is an independent risk factor for invasive candidiasis (IC), but the minimum and most practicable parameters for prediction of IC have not been optimized. We evaluated Candida colonization in a prospective cohort of 6,015 nonneutropenic, critically ill patients. Throat, perineum, and urine were sampled 72 h post-intensive care unit (ICU) admission and twice weekly until discharge or death. Specimens were cultured onto chromogenic agar, and a subset underwent molecular characterization. Sixty-three (86%) patients who developed IC were colonized prior to infection; 61 (97%) tested positive within the first two time points. The median time from colonization to IC was 7 days (range, 0 to 35). Colonization at any site was predictive of IC, with the risk of infection highest for urine colonization (relative risk [RR]=2.25) but with the sensitivity highest (98%) for throat and/or perineum colonization. Colonization of ≥2 sites and heavy colonization of ≥1 site were significant independent risk factors for IC (RR=2.25 and RR=3.7, respectively), increasing specificity to 71% to 74% but decreasing sensitivity to 48% to 58%. Molecular testing would have prompted a resistance-driven decision to switch from fluconazole treatment in only 11% of patients infected with C. glabrata, based upon species-level identification alone. Positive predictive values (PPVs) were low (2% to 4%) and negative predictive values (NPVs) high (99% to 100%) regardless of which parameters were applied. In the Australian ICU setting, culture of throat and perineum within the first two time points after ICU admission captures 84% (61/73 patients) of subsequent IC cases. These optimized parameters, in combination with clinical risk factors, should strengthen development of a setting-specific risk-predictive model for IC. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review.

PubMed

Muskett, Hannah; Shahin, Jason; Eyres, Gavin; Harvey, Sheila; Rowan, Kathy; Harrison, David

2011-01-01

Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review

PubMed Central

2011-01-01

Introduction Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. Methods An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Results Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. Conclusions This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation. PMID:22126425

Can risk assessment predict suicide in secondary mental healthcare? Findings from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register.

PubMed

Lopez-Morinigo, Javier-David; Fernandes, Andrea C; Shetty, Hitesh; Ayesa-Arriola, Rosa; Bari, Ashraful; Stewart, Robert; Dutta, Rina

2018-06-02

The predictive value of suicide risk assessment in secondary mental healthcare remains unclear. This study aimed to investigate the extent to which clinical risk assessment ratings can predict suicide among people receiving secondary mental healthcare. Retrospective inception cohort study (n = 13,758) from the South London and Maudsley NHS Foundation Trust (SLaM) (London, UK) linked with national mortality data (n = 81 suicides). Cox regression models assessed survival from the last suicide risk assessment and ROC curves evaluated the performance of risk assessment total scores. Hopelessness (RR = 2.24, 95% CI 1.05-4.80, p = 0.037) and having a significant loss (RR = 1.91, 95% CI 1.03-3.55, p = 0.041) were significantly associated with suicide in the multivariable Cox regression models. However, screening statistics for the best cut-off point (4-5) of the risk assessment total score were: sensitivity 0.65 (95% CI 0.54-0.76), specificity 0.62 (95% CI 0.62-0.63), positive predictive value 0.01 (95% CI 0.01-0.01) and negative predictive value 0.99 (95% CI 0.99-1.00). Although suicide was linked with hopelessness and having a significant loss, risk assessment performed poorly to predict such an uncommon outcome in a large case register of patients receiving secondary mental healthcare.

A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model.

PubMed

Harrison, David A; Parry, Gareth J; Carpenter, James R; Short, Alasdair; Rowan, Kathy

2007-04-01

To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. Prospective cohort study. The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. Patients were 216,626 critical care admissions. None. The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission-age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission-to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro's R 0.665, mean Brier's score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK.

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.

Simulation for Prediction of Entry Article Demise (SPEAD): An Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

NASA Technical Reports Server (NTRS)

Ling, Lisa

2014-01-01

For the purpose of performing safety analysis and risk assessment for a potential off-nominal atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. The software and methodology have been validated against actual flights, telemetry data, and validated software, and safety/risk analyses were performed for various programs using SPEAD. This report discusses the capabilities, modeling, validation, and application of the SPEAD analysis tool.

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 3: An Evaluation of SNP Incorporated National Health Information System of Turkey for Prostate Cancer

PubMed Central

Beyan, Timur

2014-01-01

Background A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey. Objective As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer. Methods In the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches. Results Through the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture. Conclusions In order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these parameters. Finally, categorization using similar, individual characteristics (SNP patterns, exposure history, etc) may be an effective way to predict disease risks, but this approach needs to be concretized and supported with new studies. PMID:25600087

Pedophilia: an evaluation of diagnostic and risk prediction methods.

PubMed

Wilson, Robin J; Abracen, Jeffrey; Looman, Jan; Picheca, Janice E; Ferguson, Meaghan

2011-06-01

One hundred thirty child sexual abusers were diagnosed using each of following four methods: (a) phallometric testing, (b) strict application of Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision [DSM-IV-TR]) criteria, (c) Rapid Risk Assessment of Sex Offender Recidivism (RRASOR) scores, and (d) "expert" diagnoses rendered by a seasoned clinician. Comparative utility and intermethod consistency of these methods are reported, along with recidivism data indicating predictive validity for risk management. Results suggest that inconsistency exists in diagnosing pedophilia, leading to diminished accuracy in risk assessment. Although the RRASOR and DSM-IV-TR methods were significantly correlated with expert ratings, RRASOR and DSM-IV-TR were unrelated to each other. Deviant arousal was not associated with any of the other methods. Only the expert ratings and RRASOR scores were predictive of sexual recidivism. Logistic regression analyses showed that expert diagnosis did not add to prediction of sexual offence recidivism over and above RRASOR alone. Findings are discussed within a context of encouragement of clinical consistency and evidence-based practice regarding treatment and risk management of those who sexually abuse children.

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.

Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people.

PubMed

Molinaro, Annette M; Ferrucci, Leah M; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J; Bale, Allen E; Mayne, Susan T

2015-06-01

Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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.

Rapid Chemical Exposure and Dose Research

EPA Pesticide Factsheets

EPA evaluates the potential risks of the manufacture and use of thousands of chemicals. To assist with this evaluation, EPA scientists developed a rapid, automated model using off the shelf technology that predicts exposures for thousands of chemicals.

The contribution of changes in diet, exercise, and stress management to changes in coronary risk in women and men in the multisite cardiac lifestyle intervention program.

PubMed

Daubenmier, Jennifer J; Weidner, Gerdi; Sumner, Michael D; Mendell, Nancy; Merritt-Worden, Terri; Studley, Joli; Ornish, Dean

2007-02-01

The relative contribution of health behaviors to coronary risk factors in multicomponent secondary coronary heart disease (CHD) prevention programs is largely unknown. Our purpose is to evaluate the additive and interactive effects of 3-month changes in health behaviors (dietary fat intake, exercise, and stress management) on 3-month changes in coronary risk and psychosocial factors among 869 nonsmoking CHD patients (34% female) enrolled in the health insurance-based Multisite Cardiac Lifestyle Intervention Program. Analyses of variance for repeated measures were used to analyze health behaviors, coronary risk factors, and psychosocial factors at baseline and 3 months. Multiple regression analyses evaluated changes in dietary fat intake and hours per week of exercise and stress management as predictors of changes in coronary risk and psychosocial factors. Significant overall improvement in coronary risk was observed. Reductions in dietary fat intake predicted reductions in weight, total cholesterol, low-density lipoprotein cholesterol, and interacted with increased exercise to predict reductions in perceived stress. Increases in exercise predicted improvements in total cholesterol and exercise capacity (for women). Increased stress management was related to reductions in weight, total cholesterol/high-density lipoprotein cholesterol (for men), triglycerides, hemoglobin A1c (in patients with diabetes), and hostility. Improvements in dietary fat intake, exercise, and stress management were individually, additively and interactively related to coronary risk and psychosocial factors, suggesting that multicomponent programs focusing on diet, exercise, and stress management may benefit patients with CHD.

[Perioperative mortality. Risk factors associated with anaesthesia].

PubMed

Zajac, Krzysztof; Zajac, Małgorzata

2005-01-01

Perioperative mortality associated with anaesthesia has been closely monitored throughout half of the century. The breakthrough in anaesthesia safety occurred in the 80-ties and 90-ties of the last century, when we could witness 5-folded reduction in mortality associated with anaesthesia, i.e. from 1 death:2680 operations/anaesthetic procedures (the 50-ties of the 20 h century) to 1:10,000 (and even 20-folded reduction within the ASA 1 and 2 groups of the patients--1 death:185,000 procedures). However, the more detailed analysis showed that the perioperative mortality is significantly higher, namely 1 death: approximately 500 procedures, and in the ASA 5 group of patients 1:4.5 procedures; what is more meaningful, the numbers have not been changed since 50 years. This phenomenon supports the thesis of anaesthesia safety, however, it indicates the drawbacks within the models and scoring systems evaluating operative risk. Several available scoring scales which can predict death rate, at the same time are not able to assess the extent of the other than biological risk factors. The "extra-biological risk" (i.e. process of therapy) may in some cases increase the operative risk as a whole. The value of the operative risk, as the fraction given by predicted death rate, is located between the numbers 0 and 1 (or between survival and death in the binary model of the probability theory). Recognition of the "extrabiological risk" value depends however on the high sensitivity of the scales evaluating prediction of death rate.

Risk stratification for arrhythmic death in an emergency department cohort: a new method of nonlinear PD2i analysis of the ECG

PubMed Central

Skinner, James E; Meyer, Michael; Dalsey, William C; Nester, Brian A; Ramalanjaona, George; O’Neil, Brian J; Mangione, Antoinette; Terregino, Carol; Moreyra, Abel; Weiss, Daniel N; Anchin, Jerry M; Geary, Una; Taggart, Pamela

2008-01-01

Heart rate variability (HRV) reflects both cardiac autonomic function and risk of sudden arrhythmic death (AD). Indices of HRV based on linear stochastic models are independent risk factors for AD in postmyocardial infarction (MI) cohorts. Indices based on nonlinear deterministic models have a higher sensitivity and specificity for predicting AD in retrospective data. A new nonlinear deterministic model, the automated Point Correlation Dimension (PD2i), was prospectively evaluated for prediction of AD. Patients were enrolled (N = 918) in 6 emergency departments (EDs) upon presentation with chest pain and being determined to be at risk of acute MI (AMI) >7%. Brief digital ECGs (>1000 heartbeats, ∼15 min) were recorded and automated PD2i results obtained. Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. All-cause mortality at 1 year was 6.2%, with 3.5% being ADs. Of the AD fatalities, 34% were without previous history of MI or diagnosis of AMI. The PD2i prediction of AD had sensitivity = 96%, specificity = 85%, negative predictive value = 99%, and relative risk >24.2 (p ≤ 0.001). HRV analysis by the time-dependent nonlinear PD2i algorithm can accurately predict risk of AD in an ED cohort and may have both life-saving and resource-saving implications for individual risk assessment. PMID:19209249

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.

New directions in diagnostic evaluation of insect allergy.

PubMed

Golden, David B K

2014-08-01

Diagnosis of insect sting allergy and prediction of risk of sting anaphylaxis are often difficult because tests for venom-specific IgE antibodies have a limited positive predictive value and do not reliably predict the severity of sting reactions. Component-resolved diagnosis using recombinant venom allergens has shown promise in improving the specificity of diagnostic testing for insect sting allergy. Basophil activation tests have been explored as more sensitive assays for identification of patients with insect allergy and for prediction of clinical outcomes. Measurement of mast cell mediators reflects the underlying risk for more severe reactions and limited clinical response to treatment. Measurement of IgE to recombinant venom allergens can distinguish cross-sensitization from dual sensitization to honeybee and vespid venoms, thus helping to limit venom immunotherapy to a single venom instead of multiple venoms in many patients. Basophil activation tests can detect venom allergy in patients who show no detectable venom-specific IgE in standard diagnostic tests and can predict increased risk of systemic reactions to venom immunotherapy, and to stings during and after stopping venom immunotherapy. The risk of severe or fatal anaphylaxis to stings can also be predicted by measurement of baseline serum tryptase or other mast cell mediators.

Approaches for the Application of Physiologically Based ...

EPA Pesticide Factsheets

EPA released the final report, Approaches for the Application of Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment as announced in a September 22 2006 Federal Register Notice.This final report addresses the application and evaluation of PBPK models for risk assessment purposes. These models represent an important class of dosimetry models that are useful for predicting internal dose at target organs for risk assessment applications. EPA is releasing a final report describing the evaluation and applications of physiologically based pharmacokinetic (PBPK) models in health risk assessment. This was announced in the September 22 2006 Federal Register Notice.

Chapter 4. Predicting post-fire erosion and sedimentation risk on a landscape scale

USGS Publications Warehouse

MacDonald, L.H.; Sampson, R.; Brady, D.; Juarros, L.; Martin, Deborah

2000-01-01

Historic fire suppression efforts have increased the likelihood of large wildfires in much of the western U.S. Post-fire soil erosion and sedimentation risks are important concerns to resource managers. In this paper we develop and apply procedures to predict post-fire erosion and sedimentation risks on a pixel-, catchment-, and landscape-scale in central and western Colorado.Our model for predicting post-fire surface erosion risk is conceptually similar to the Revised Universal Soil Loss Equation (RUSLE). One key addition is the incorporation of a hydrophobicity risk index (HY-RISK) based on vegetation type, predicted fire severity, and soil texture. Post-fire surface erosion risk was assessed for each 90-m pixel by combining HYRISK, slope, soil erodibility, and a factor representing the likely increase in soil wetness due to removal of the vegetation. Sedimentation risk was a simple function of stream gradient. Composite surface erosion and sedimentation risk indices were calculated and compared across the 72 catchments in the study area.When evaluated on a catchment scale, two-thirds of the catchments had relatively little post-fire erosion risk. Steeper catchments with higher fuel loadings typically had the highest post-fire surface erosion risk. These were generally located along the major north-south mountain chains and, to a lesser extent, in west-central Colorado. Sedimentation risks were usually highest in the eastern part of the study area where a higher proportion of streams had lower gradients. While data to validate the predicted erosion and sedimentation risks are lacking, the results appear reasonable and are consistent with our limited field observations. The models and analytic procedures can be readily adapted to other locations and should provide useful tools for planning and management at both the catchment and landscape scale.

A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

PubMed

Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

2016-07-01

Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value <0.25 in the univariate analysis were further evaluated in multivariate models using backward elimination. Discrimination was assessed using receiver operating curve. Calibration was evaluated using the McFadden's R2. Net reclassification index (NRI) associated with incorporation of laboratory results was calculated. Results were internally validated. A model similar to existing CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

Emergency Physician Attitudes, Preferences, and Risk Tolerance for Stroke as a Potential Cause of Dizziness Symptoms

PubMed Central

Kene, Mamata V.; Ballard, Dustin W.; Vinson, David R.; Rauchwerger, Adina S.; Iskin, Hilary R.; Kim, Anthony S.

2015-01-01

Introduction We evaluated emergency physicians’ (EP) current perceptions, practice, and attitudes towards evaluating stroke as a cause of dizziness among emergency department patients. Methods We administered a survey to all EPs in a large integrated healthcare delivery system. The survey included clinical vignettes, perceived utility of historical and exam elements, attitudes about the value of and requisite post-test probability of a clinical prediction rule for dizziness. We calculated descriptive statistics and post-test probabilities for such a clinical prediction rule. Results The response rate was 68% (366/535). Respondents’ median practice tenure was eight years (37% female, 92% emergency medicine board certified). Symptom quality and typical vascular risk factors increased suspicion for stroke as a cause of dizziness. Most respondents reported obtaining head computed tomography (CT) (74%). Nearly all respondents used and felt confident using cranial nerve and limb strength testing. A substantial minority of EPs used the Epley maneuver (49%) and HINTS (head-thrust test, gaze-evoked nystagmus, and skew deviation) testing (30%); however, few EPs reported confidence in these tests’ bedside application (35% and 16%, respectively). Respondents favorably viewed applying a properly validated clinical prediction rule for assessment of immediate and 30-day stroke risk, but indicated it would have to reduce stroke risk to <0.5% to be clinically useful. Conclusion EPs report relying on symptom quality, vascular risk factors, simple physical exam elements, and head CT to diagnose stroke as the cause of dizziness, but would find a validated clinical prediction rule for dizziness helpful. A clinical prediction rule would have to achieve a 0.5% post-test stroke probability for acceptability. PMID:26587108

Development of a risk prediction model among professional hockey players with visible signs of concussion.

PubMed

Bruce, Jared M; Echemendia, Ruben J; Meeuwisse, Willem; Hutchison, Michael G; Aubry, Mark; Comper, Paul

2017-04-04

Little research examines how to best identify concussed athletes. The purpose of the present study was to develop a preliminary risk decision model that uses visible signs (VS) and mechanisms of injury (MOI) to predict the likelihood of subsequent concussion diagnosis. Coders viewed and documented VS and associated MOI for all NHL games over the course of the 2013-2014 and 2014-2015 regular seasons. After coding was completed, player concussions were identified from the NHL injury surveillance system and it was determined whether players exhibiting VS were subsequently diagnosed with concussions by club medical staff as a result of the coded event. Among athletes exhibiting VS, suspected loss of consciousness, motor incoordination or balance problems, being in a fight, having an initial hit from another player's shoulder and having a secondary hit on the ice were all associated with increased risk of subsequent concussion diagnosis. In contrast, having an initial hit with a stick was associated with decreased risk of subsequent concussion diagnosis. A risk prediction model using a combination of the above VS and MOI was superior to approaches that relied on individual VS and associated MOI (sensitivity=81%, specificity=72%, positive predictive value=26%). Combined use of VS and MOI significantly improves a clinician's ability to identify players who need to be evaluated for possible concussion. A preliminary concussion prediction log has been developed from these data. Pending prospective validation, the use of these methods may improve early concussion detection and evaluation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

The Validity and Reliability of the Violence Risk Scale-Sexual Offender Version: Assessing Sex Offender Risk and Evaluating Therapeutic Change

ERIC Educational Resources Information Center

Olver, Mark E.; Wong, Stephen C. P.; Nicholaichuk, Terry; Gordon, Audrey

2007-01-01

The Violence Risk Scale-Sexual Offender version (VRS-SO) is a rating scale designed to assess risk and predict sexual recidivism, to measure and link treatment changes to sexual recidivism, and to inform the delivery of sexual offender treatment. The VRS-SO comprises 7 static and 17 dynamic items empirically or conceptually linked to sexual…

Implementation and Effects of Risk-Dependent Obstetric Care in the Netherlands (Expect Study II): Protocol for an Impact Study.

PubMed

van Montfort, Pim; Willemse, Jessica Ppm; Dirksen, Carmen D; van Dooren, Ivo Ma; Meertens, Linda Je; Spaanderman, Marc Ea; Zelis, Maartje; Zwaan, Iris M; Scheepers, Hubertina Cj; Smits, Luc Jm

2018-05-04

Recently, validated risk models predicting adverse obstetric outcomes combined with risk-dependent care paths have been made available for early antenatal care in the southeastern part of the Netherlands. This study will evaluate implementation progress and impact of the new approach in obstetric care. The objective of this paper is to describe the design of a study evaluating the impact of implementing risk-dependent care. Validated first-trimester prediction models are embedded in daily clinical practice and combined with risk-dependent obstetric care paths. A multicenter prospective cohort study consisting of women who receive risk-dependent care is being performed from April 2017 to April 2018 (Expect Study II). Obstetric risk profiles will be calculated using a Web-based tool, the Expect prediction tool. The primary outcomes are the adherence of health care professionals and compliance of women. Secondary outcomes are patient satisfaction and cost-effectiveness. Outcome measures will be established using Web-based questionnaires. The secondary outcomes of the risk-dependent care cohort (Expect II) will be compared with the outcomes of a similar prospective cohort (Expect I). Women of this similar cohort received former care-as-usual and were prospectively included between July 1, 2013 and December 31, 2015 (Expect I). Currently, women are being recruited for the Expect Study II, and a total of 300 women are enrolled. This study will provide information about the implementation and impact of a new approach in obstetric care using prediction models and risk-dependent obstetric care paths. Netherlands Trial Register NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9). ©Pim van Montfort, Jessica PPM Willemse, Carmen D Dirksen, Ivo MA van Dooren, Linda JE Meertens, Marc EA Spaanderman, Maartje Zelis, Iris M Zwaan, Hubertina CJ Scheepers, Luc JM Smits. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 04.05.2018.

Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)

EPA Pesticide Factsheets

EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.

Coming Attractions.

ERIC Educational Resources Information Center

Datta, Lois-Ellin

2001-01-01

Forecasts the future of evaluation, predicting an era of stability or expansion for the profession, with increased communication among evaluators and a flowering of evaluation theories. Among the unknowns that give pause are the possibility of underprepared users and the risks of promising more than can be delivered. Some suggestions are made for…

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

FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

PubMed

Cattelani, Luca; Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Becker, Clemens; Chesani, Federico; Chiari, Lorenzo

2015-02-18

About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

PubMed

Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

2009-09-01

Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF.

Environmental risk assessment of selected organic chemicals based on TOC test and QSAR estimation models.

PubMed

Chi, Yulang; Zhang, Huanteng; Huang, Qiansheng; Lin, Yi; Ye, Guozhu; Zhu, Huimin; Dong, Sijun

2018-02-01

Environmental risks of organic chemicals have been greatly determined by their persistence, bioaccumulation, and toxicity (PBT) and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor (BCF) and octanol-water partition coefficient (Kow), which frequently displays a substantial correlation with the sediment sorption coefficient (Koc). Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship (QSAR) estimation models are indispensable for predicting environmental fate and health effects in the absence of field- or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon (TOC) degradation, and two widely accepted and highly used QSAR estimation models (i.e., EPI Suite and PBT Profiler) were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment. However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants. Copyright © 2016. Published by Elsevier B.V.

Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review

PubMed Central

Bao, Wei; Hu, Frank B.; Rong, Shuang; Rong, Ying; Bowers, Katherine; Schisterman, Enrique F.; Liu, Liegang; Zhang, Cuilin

2013-01-01

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker–based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55–0.68), which did not differ appreciably by study design, sample size, participants’ race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor–based models (median AUC, 0.79 (range, 0.63–0.91) vs. median AUC, 0.78 (range, 0.63–0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants’ race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance. PMID:24008910

Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

PubMed

Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

2018-05-01

The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p < 0.001 for both; C-statistic = 0.815 for ASCERT and 0.781 for PROM). Prolonged ventilation, stroke, and hospital length of stay were also predictive of long-term death. The ASCERT survival probability calculator was externally validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Accuracy of the Atherosclerotic Cardiovascular Risk Equation in a Large Contemporary, Multiethnic Real-World Population

PubMed Central

Rana, Jamal S.; Tabada, Grace H.; Solomon, Matthew D.; Lo, Joan C.; Jaffe, Marc G.; Sung, Sue Hee; Ballantyne, Christie M.; Go, Alan S.

2016-01-01

Background The accuracy of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) risk equation for atherosclerotic cardiovascular disease (ASCVD) events in contemporary and ethnically diverse populations is not well understood. Objectives We sought to evaluate the accuracy of the 2013 ACC/AHA risk equation within a large, multiethnic population in clinical care. Methods The target population for consideration of cholesterol-lowering therapy in a large, integrated health care delivery system population was identified in 2008 and followed through 2013. The main analyses excluded those with known ASCVD, diabetes mellitus, low-density lipoprotein cholesterol levels <70 or ≥190 mg/dl, prior statin use, or incomplete 5-year follow-up. Patient characteristics were obtained from electronic medical records and ASCVD events were ascertained using validated algorithms for hospitalization databases and death certificates. We compared predicted versus observed 5-year ASCVD risk, overall and by sex and race/ethnicity. We additionally examined predicted versus observed risk in patients with diabetes mellitus. Results Among 307,591 eligible adults without diabetes between 40 and 75 years of age, 22,283 were black, 52,917 Asian/Pacific Islander, and 18,745 Hispanic. We observed 2,061 ASCVD events during 1,515,142 person-years. In each 5-year predicted ASCVD risk category, observed 5-year ASCVD risk was substantially lower: 0.20% for predicted risk <2.50%; 0.65% for predicted risk 2.50 to 3.74%; 0.90% for predicted risk 3.75 to 4.99%; and 1.85% for predicted risk ≥5.00%, with C: 0.74. Similar ASCVD risk overestimation and poor calibration with moderate discrimination (C: 0.68 to 0.74) was observed in sex, racial/ethnic, and socioeconomic status subgroups, and in sensitivity analyses among patients receiving statins for primary prevention. Calibration among 4,242 eligible adults with diabetes was improved, but discrimination was worse (C: 0.64). Conclusions In a large, contemporary “real-world” population, the ACC/AHA Pooled Cohort risk equation substantially overestimated actual 5-year risk in adults without diabetes, overall and across sociodemographic subgroups. PMID:27151343

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

Screening for Reading Problems: The Utility of SEARCH.

ERIC Educational Resources Information Center

Morrison, Delmont; And Others

1988-01-01

The accuracy of SEARCH for identifying children at risk for developing learning disabilities was evaluated with 1,107 kindergarten children. Children identified as at risk were of average intelligence. SEARCH scores were significantly correlated with sequential and simultaneous information processing skills. SEARCH predicted adequacy of…

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

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

PubMed

Karstoft, Karen-Inge; Galatzer-Levy, Isaac R; Statnikov, Alexander; Li, Zhiguo; Shalev, Arieh Y

2015-03-16

Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

Evaluating biomarkers to model cancer risk post cosmic ray exposure

PubMed Central

Sridhara, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

2017-01-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens. PMID:27345199

Evaluating biomarkers to model cancer risk post cosmic ray exposure

NASA Astrophysics Data System (ADS)

Sridharan, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

2016-06-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens.

Evaluating biomarkers to model cancer risk post cosmic ray exposure.

PubMed

Sridharan, Deepa M; Asaithamby, Aroumougame; Blattnig, Steve R; Costes, Sylvain V; Doetsch, Paul W; Dynan, William S; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D; Peterson, Leif E; Plante, Ianik; Ponomarev, Artem L; Saha, Janapriya; Snijders, Antoine M; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M

2016-06-01

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens. Copyright © 2016 The Committee on Space Research (COSPAR). All rights reserved.

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

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

PubMed Central

Bellón, Juan Ángel; Moreno-Küstner, Berta; Torres-González, Francisco; Montón-Franco, Carmen; GildeGómez-Barragán, María Josefa; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; de Dios Luna, Juan; Cervilla, Jorge A; Gutierrez, Blanca; Martínez-Cañavate, María Teresa; Oliván-Blázquez, Bárbara; Vázquez-Medrano, Ana; Sánchez-Artiaga, María Soledad; March, Sebastia; Motrico, Emma; Ruiz-García, Victor Manuel; Brangier-Wainberg, Paulette Renée; del Mar Muñoz-García, María; Nazareth, Irwin; King, Michael

2008-01-01

Background The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care. PMID:18657275

[Early prediction of the neurological result at 12 months in newborns at neurological risk].

PubMed

Herbón, F; Garibotti, G; Moguilevsky, J

2015-08-01

The aim of this study was to evaluate the Amiel-Tison neurological examination (AT) and cranial ultrasound at term for predicting the neurological result at 12 months in newborns with neurological risk. The study included 89 newborns with high risk of neurological damage, who were discharged from the Neonatal Intensive Care of the Hospital Zonal Bariloche, Argentina. The assessment consisted of a neurological examination and cranial ultrasound at term, and neurological examination and evaluation of development at 12 months. The sensitivity, specificity, positive and negative predictor value was calculated. The relationship between perinatal factors and neurodevelopment at 12 month of age was also calculated using logistic regression models. Seventy children completed the follow-up. At 12 months of age, 14% had an abnormal neurological examination, and 17% abnormal development. The neurological examination and the cranial ultrasound at term had low sensitivity to predict abnormal neurodevelopment. At 12 months, 93% of newborns with normal AT showed normal neurological results, and 86% normal development. Among newborns with normal cranial ultrasound the percentages were 90 and 81%, respectively. Among children with three or more perinatal risk factors, the frequency of abnormalities in the neurological response was 5.4 times higher than among those with fewer risk factors, and abnormal development was 3.5 times more frequent. The neurological examination and cranial ultrasound at term had low sensitivity but high negative predictive value for the neurodevelopment at 12 months. Three or more perinatal risk factors were associated with neurodevelopment abnormalities at 12 months of age. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Evaluation of markers and risk prediction models: Overview of relationships between NRI and decision-analytic measures

PubMed Central

Calster, Ben Van; Vickers, Andrew J; Pencina, Michael J; Baker, Stuart G; Timmerman, Dirk; Steyerberg, Ewout W

2014-01-01

BACKGROUND For the evaluation and comparison of markers and risk prediction models, various novel measures have recently been introduced as alternatives to the commonly used difference in the area under the ROC curve (ΔAUC). The Net Reclassification Improvement (NRI) is increasingly popular to compare predictions with one or more risk thresholds, but decision-analytic approaches have also been proposed. OBJECTIVE We aimed to identify the mathematical relationships between novel performance measures for the situation that a single risk threshold T is used to classify patients as having the outcome or not. METHODS We considered the NRI and three utility-based measures that take misclassification costs into account: difference in Net Benefit (ΔNB), difference in Relative Utility (ΔRU), and weighted NRI (wNRI). We illustrate the behavior of these measures in 1938 women suspect of ovarian cancer (prevalence 28%). RESULTS The three utility-based measures appear transformations of each other, and hence always lead to consistent conclusions. On the other hand, conclusions may differ when using the standard NRI, depending on the adopted risk threshold T, prevalence P and the obtained differences in sensitivity and specificity of the two models that are compared. In the case study, adding the CA-125 tumor marker to a baseline set of covariates yielded a negative NRI yet a positive value for the utility-based measures. CONCLUSIONS The decision-analytic measures are each appropriate to indicate the clinical usefulness of an added marker or compare prediction models, since these measures each reflect misclassification costs. This is of practical importance as these measures may thus adjust conclusions based on purely statistical measures. A range of risk thresholds should be considered in applying these measures. PMID:23313931

Persistent hemifacial spasm after microvascular decompression: a risk assessment model.

PubMed

Shah, Aalap; Horowitz, Michael

2017-06-01

Microvascular decompression (MVD) for hemifacial spasm (HFS) provides resolution of disabling symptoms such as eyelid twitching and muscle contractions of the entire hemiface. The primary aim of this study was to evaluate the predictive value of patient demographics and spasm characteristics on long-term outcomes, with or without intraoperative lateral spread response (LSR) as an additional variable in a risk assessment model. A retrospective study was undertaken to evaluate the associations of pre-operative patient characteristics, as well as intraoperative LSR and need for a staged procedure on the presence of persistent or recurrent HFS at the time of hospital discharge and at follow-up. A risk assessment model was constructed with the inclusion of six clinically or statistically significant variables from the univariate analyses. A receiving operator characteristic curve was generated, and area under the curve was calculated to determine the strength of the predictive model. A risk assessment model was first created consisting of significant pre-operative variables (Model 1) (age >50, female gender, history of botulinum toxin use, platysma muscle involvement). This model demonstrated borderline predictive value for persistent spasm at discharge (AUC .60; p=.045) and fair predictive value at follow-up (AUC .75; p=.001). Intraoperative variables (e.g. LSR persistence) demonstrated little additive value (Model 2) (AUC .67). Patients with a higher risk score (three or greater) demonstrated greater odds of persistent HFS at the time of discharge (OR 1.5 [95%CI 1.16-1.97]; p=.035), as well as greater odds of persistent or recurrent spasm at the time of follow-up (OR 3.0 [95%CI 1.52-5.95]; p=.002) Conclusions: A risk assessment model consisting of pre-operative clinical characteristics is useful in prognosticating HFS persistence at follow-up.

Geriatric Assessment and Tools for Predicting Treatment Toxicity in Older Adults With Cancer.

PubMed

Li, Daneng; Soto-Perez-de-Celis, Enrique; Hurria, Arti

Cancer is a disease of older adults, and the majority of new cancer cases and deaths occur in people 65 years or older. However, fewer data are available regarding the risks and benefits of cancer treatment in older adults, and commonly used assessments in oncology fail to adequately evaluate factors that affect treatment efficacy and outcomes in the older patients. The geriatric assessment is a multidisciplinary evaluation that provides detailed information about a patient's functional status, comorbidities, psychological state, social support, nutritional status, and cognitive function. Among older patients with cancer, geriatric assessment has been shown to identify patients at risk of poorer overall survival, and geriatric assessment-based tools are significantly more effective in predicting chemotherapy toxicity than other currently utilized measures. In this review, we summarize the components of the geriatric assessment and provide information about existing tools used to predict treatment toxicity in older patients with cancer.

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.

The American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator Does Not Accurately Predict Risk of 30-Day Complications Among Patients Undergoing Microvascular Head and Neck Reconstruction.

PubMed

Arce, Kevin; Moore, Eric J; Lohse, Christine M; Reiland, Matthew D; Yetzer, Jacob G; Ettinger, Kyle S

2016-09-01

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) is a novel universal risk calculator designed to aid in risk stratification of patients undergoing various types of major surgery. The purpose of this study was to assess the validity of the ACS NSQIP SRC in predicting postoperative complications in patients undergoing microvascular head and neck reconstruction. A retrospective cohort study of patients undergoing head and neck microvascular reconstruction with fibular free flaps at a single institution was completed. The NSQIP SRC was used to compute complication risk estimates and length of stay (LOS) estimates for all patients under study. Associations between complication risk estimates generated by the SRC and actual rates of observed complications were evaluated using logistic regression models. Logistic regression models also were used to evaluate the SRC estimates for LOS duration compared with the actual observed LOS after surgery. Of 153 patients under study, 46 (30%) developed a postoperative complication corresponding to those defined by NSQIP SRC. Thirty-eight patients (25%) developed a postoperative complication categorized as severe in the parameters of the NSQIP SRC. None of the SRC complication estimates showed a statistically relevant association with the corresponding observed rates of complications. The mean LOS predicted by the SRC was 8.0 days (median, 7.5 days; interquartile range [IQR], 6.5 to 9; range, 5.0 to 18.5 days). The mean observed LOS for the study group was 9.6 days (median, 7.0 days; IQR, 6 to 9; range, 5 to 67 days). Lin's (Biometrics 45:255, 1989) concordance correlation coefficient to measure agreement between observed and predicted LOS was 0.10, indicating only slight agreement between the 2 values. The ACS NSQIP SRC is not a useful risk-stratifying metric for patients undergoing major head and neck reconstruction with microvascular fibular free flaps. The SRC also does not accurately predict hospital LOS for this same patient cohort. Copyright © 2016 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Can the ACS-NSQIP surgical risk calculator predict post-operative complications in patients undergoing flap reconstruction following soft tissue sarcoma resection?

PubMed

Slump, Jelena; Ferguson, Peter C; Wunder, Jay S; Griffin, Anthony; Hoekstra, Harald J; Bagher, Shaghayegh; Zhong, Toni; Hofer, Stefan O P; O'Neill, Anne C

2016-10-01

The ACS-NSQIP surgical risk calculator is an open-access on-line tool that estimates the risk of adverse post-operative outcomes for a wide range of surgical procedures. Wide surgical resection of soft tissue sarcoma (STS) often requires complex reconstructive procedures that can be associated with relatively high rates of complications. This study evaluates the ability of this calculator to identify patients with STS at risk for post-operative complications following flap reconstruction. Clinical details of 265 patients who underwent flap reconstruction following STS resection were entered into the online calculator. The predicted rates of complications were compared to the observed rates. The calculator model was validated using measures of prediction and discrimination. The mean predicted rate of any complication was 15.35 ± 5.6% which differed significantly from the observed rate of 32.5% (P = 0.009). The c-statistic was relatively low at 0.626 indicating poor discrimination between patients who are at risk of complications and those who are not. The Brier's score of 0.242 was significantly different from 0 (P < 0.001) indicating poor correlation between the predicted and actual probability of complications. The ACS-NSQIP universal risk calculator did not maintain its predictive value in patients undergoing flap reconstruction following STS resection. J. Surg. Oncol. 2016;114:570-575. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

[Determination of prognostic value of the OESIL risk score at 6 months in a Colombian cohort with syncope evaluated in the emergency department; first Latin American experience].

PubMed

Díaz-Tribaldos, Diana Carolina; Mora, Guillermo; Olaya, Alejandro; Marín, Jorge; Sierra Matamoros, Fabio

2017-07-14

To establish the prognostic value, with sensitivity, specificity, positive predictive value, and negative predictive value for the OESIL syncope risk score to predict the presentation of severe outcomes (death, invasive interventions, and readmission) after 6 months of observation in adults who consulted the emergency department due to syncope. Observational, prospective, and multicentre study with enrolment of subjects older than 18 years, who consulted in the emergency department due to syncope. A record was mad of the demographic and clinical information of all patients. The OESIL risk score was calculated, and severe patient outcomes were followed up during a 6 month period using telephone contact. A total of 161 patients met the inclusion criteria and were followed up for 6 months. A score above or equal to 2 in the risk score, classified as high risk, was present in 72% of the patients. The characteristics of the risk score to predict the combined outcome of mortality, invasive interventions, and readmission for a score above or equal to 2 were 75.7, 30.5, 43.1, and 64.4% for sensitivity, specificity, positive predictive value, and negative predictive value, respectively. A score above or equal to 2 in the OESIL risk score applied in Colombian population was of limited use to predict the studied severe outcomes. This score will be unable to discriminate between patients that benefit of early admission and further clinical studies. Copyright © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

PubMed

Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

2016-11-08

The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

Systems-based accident analysis in the led outdoor activity domain: application and evaluation of a risk management framework.

PubMed

Salmon, P; Williamson, A; Lenné, M; Mitsopoulos-Rubens, E; Rudin-Brown, C M

2010-08-01

Safety-compromising accidents occur regularly in the led outdoor activity domain. Formal accident analysis is an accepted means of understanding such events and improving safety. Despite this, there remains no universally accepted framework for collecting and analysing accident data in the led outdoor activity domain. This article presents an application of Rasmussen's risk management framework to the analysis of the Lyme Bay sea canoeing incident. This involved the development of an Accimap, the outputs of which were used to evaluate seven predictions made by the framework. The Accimap output was also compared to an analysis using an existing model from the led outdoor activity domain. In conclusion, the Accimap output was found to be more comprehensive and supported all seven of the risk management framework's predictions, suggesting that it shows promise as a theoretically underpinned approach for analysing, and learning from, accidents in the led outdoor activity domain. STATEMENT OF RELEVANCE: Accidents represent a significant problem within the led outdoor activity domain. This article presents an evaluation of a risk management framework that can be used to understand such accidents and to inform the development of accident countermeasures and mitigation strategies for the led outdoor activity domain.

Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

PubMed

Jering, Monika Zdenka; Marolen, Khensani N; Shotwell, Matthew S; Denton, Jason N; Sandberg, Warren S; Ehrenfeld, Jesse Menachem

2015-11-01

The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

A new scoring system (DAIGA) for predicting bleeding complications in atrial fibrillation patients after drug-eluting stent implantation with triple antithrombotic therapy.

PubMed

Kobayashi, Norihiro; Yamawaki, Masahiro; Nakano, Masatsugu; Hirano, Keisuke; Araki, Motoharu; Takimura, Hideyuki; Sakamoto, Yasunari; Mori, Shinsuke; Tsutsumi, Masakazu; Ito, Yoshiaki

2016-11-15

No scoring system for evaluating the bleeding risk of atrial fibrillation (AF) patients after drug-eluting stent (DES) implantation with triple antithrombotic therapy (TAT) is available. We aimed to develop a new scoring system for predicting bleeding complications in AF patients after DES implantation with TAT. Between April 2007 and April 2014, 227 AF patients undergoing DES implantation with TAT were enrolled. Bleeding incidence defined as Bleeding Academic Research Consortium criteria≥2 was investigated and predictors of bleeding complications were evaluated using multivariate analysis. Bleeding complications occurred in 58 patients (25.6%) during follow-up. Multivariate analysis revealed dual antiplatelet therapy (DAPT) continuation (OR 3.33, P=0.01), age>75 (OR 2.14, P=0.037), international normalized ratio>2.2 (OR 5.82, P<0.001), gastrointestinal ulcer history (OR 3.06, P=0.037), and anemia (OR 2.15, P=0.042) as predictors of major bleeding complications. A score was created using the weighted points proportional to the beta regression coefficient of each variable. The DAIGA score showed better predictive ability for bleeding complications than the HAS-BLED score (AUC: 0.79 vs. 0.62, P=0.0003). Bleeding incidence was well stratified: 17.8% in low-risk (scores 0-1), 55.5% in moderate-risk (2-3), and 83.0% in high-risk (4-7) patients (P<0.001). This scoring system is useful for predicting bleeding complications and risk stratification of AF patients after DES implantation with TAT. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

New equations for predicting postoperative risk in patients with hip fracture.

PubMed

Hirose, Jun; Ide, Junji; Irie, Hiroki; Kikukawa, Kenshi; Mizuta, Hiroshi

2009-12-01

Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

Detecting aberrant opioid behavior in the emergency department: a prospective study using the screener and Opioid Assessment for Patients with Pain-Revised (SOAPP®-R), Current Opioid Misuse Measure (COMM)™, and provider gestalt.

PubMed

Varney, Shawn M; Perez, Crystal A; Araña, Allyson A; Carey, Katherine R; Ganem, Victoria J; Zarzabal, Lee A; Ramos, Rosemarie G; Bebarta, Vikhyat S

2018-03-03

Emergency department (ED) providers have limited time to evaluate patients at risk for opioid misuse. A validated tool to assess the risk for aberrant opioid behavior may mitigate adverse sequelae associated with prescription opioid misuse. We sought to determine if SOAPP-R, COMM, and provider gestalt were able to identify patients at risk for prescription opioid misuse as determined by pharmacy records at 12 months. We conducted a prospective observational study of adult patients in a high volume US ED. Patients completed the SOAPP-R and COMM, and treating EM providers evaluated patients' opioid misuse risk. We performed variable-centered, person-centered, and hierarchical cluster analyses to determine whether provider gestalt, SOAPP-R, or COMM, or a combination, predicted higher misuse risk. The primary outcome was the number of opioid prescriptions at 12 months according to pharmacy records. For 169 patients (mean age 43 years, 51% female, 73% white), correlation analysis showed a strong relationship between SOAPP-R and COMM with predicting the number of opioid prescriptions dispensed at 12 months. Provider scores estimating opioid misuse were not related to SOAPP-R and only weakly associated with COMM. In our adjusted regression models, provider gestalt and SOAPP-R uniquely predicted opioid prescriptions at 6 and 12 months. Using designated cutoff scores, only SOAPP-R detected a difference in the number of opioid prescriptions. Cluster analysis revealed that provider gestalt, SOAPP-R, and COMM scores jointly predicted opioid prescriptions. Provider gestalt and self-report instruments uniquely predicted the number of opioid prescriptions in ED patients. A combination of gestalt and self-assessment scores can be used to identify at-risk patients who otherwise miss the cutoff scores for SOAPP-R and COMM.

Two criteria for evaluating risk prediction models

PubMed Central

Pfeiffer, R.M.; Gail, M.H.

2010-01-01

SUMMARY We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF(q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF(p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data. PMID:21155746

A fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment.

PubMed

Bell, P M; Crumpton, L

1997-08-01

This research presents the development and evaluation of a fuzzy linguistic model designated to predict the risk of carpal tunnel syndrome (CTS) in an occupational setting. CTS has become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In addition, practitioners are interested in identifying accurate methods for evaluating the risk of CTS in an occupational setting. It is hypothesized that many factors impact an individual's likelihood of developing CTS and the eventual development of CTS. This disparity in the occurrence of CTS for workers with similar backgrounds and work activities has confused researchers and has been a stumbling block in the development of a model for widespread use in evaluating the development of CTS. Thus this research is an attempt to develop a method that can be used to predict the likelihood of CTS risk in a variety of environments. The intent is that this model will be applied eventually in an occupational setting, thus model development was focused on a method that provided a usable interface and the desired system inputs can also be obtained without the benefit of a medical practitioner. The methodology involves knowledge acquisition to identify and categorize a holistic set of risk factors that include task-related, personal, and organizational categories. The determination of relative factor importance was accomplished using analytic hierarchy processing (AHP) analysis. Finally a mathematical representation of the CTS risk was accomplished by utilizing fuzzy set theory in order to quantify linguistic input parameters. An evaluation of the model including determination of sensitivity and specificity is conducted and the results of the model indicate that the results are fairly accurate and this method has the potential for widespread use. A significant aspect of this research is the comparison of this technique to other methods for assessing presence of CTS. The results of this evaluation technique are compared with more traditional methods for assessing the presence of CTS.

Decision curve analysis and external validation of the postoperative Karakiewicz nomogram for renal cell carcinoma based on a large single-center study cohort.

PubMed

Zastrow, Stefan; Brookman-May, Sabine; Cong, Thi Anh Phuong; Jurk, Stanislaw; von Bar, Immanuel; Novotny, Vladimir; Wirth, Manfred

2015-03-01

To predict outcome of patients with renal cell carcinoma (RCC) who undergo surgical therapy, risk models and nomograms are valuable tools. External validation on independent datasets is crucial for evaluating accuracy and generalizability of these models. The objective of the present study was to externally validate the postoperative nomogram developed by Karakiewicz et al. for prediction of cancer-specific survival. A total of 1,480 consecutive patients with a median follow-up of 82 months (IQR 46-128) were included into this analysis with 268 RCC-specific deaths. Nomogram-estimated survival probabilities were compared with survival probabilities of the actual cohort, and concordance indices were calculated. Calibration plots and decision curve analyses were used for evaluating calibration and clinical net benefit of the nomogram. Concordance between predictions of the nomogram and survival rates of the cohort was 0.911 after 12, 0.909 after 24 months and 0.896 after 60 months. Comparison of predicted probabilities and actual survival estimates with calibration plots showed an overestimation of tumor-specific survival based on nomogram predictions of high-risk patients, although calibration plots showed a reasonable calibration for probability ranges of interest. Decision curve analysis showed a positive net benefit of nomogram predictions for our patient cohort. The postoperative Karakiewicz nomogram provides a good concordance in this external cohort and is reasonably calibrated. It may overestimate tumor-specific survival in high-risk patients, which should be kept in mind when counseling patients. A positive net benefit of nomogram predictions was proven.

Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study†

PubMed Central

Quinlivan, Leah; Cooper, Jayne; Meehan, Declan; Longson, Damien; Potokar, John; Hulme, Tom; Marsden, Jennifer; Brand, Fiona; Lange, Kezia; Riseborough, Elena; Page, Lisa; Metcalfe, Chris; Davies, Linda; O'Connor, Rory; Hawton, Keith; Gunnell, David; Kapur, Nav

2017-01-01

Background Scales are widely used in psychiatric assessments following self-harm. Robust evidence for their diagnostic use is lacking. Aims To evaluate the performance of risk scales (Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS scale, Modified SAD PERSONS scale, Barratt Impulsiveness Scale); and patient and clinician estimates of risk in identifying patients who repeat self-harm within 6 months. Method A multisite prospective cohort study was conducted of adults aged 18 years and over referred to liaison psychiatry services following self-harm. Scale a priori cut-offs were evaluated using diagnostic accuracy statistics. The area under the curve (AUC) was used to determine optimal cut-offs and compare global accuracy. Results In total, 483 episodes of self-harm were included in the study. The episode-based 6-month repetition rate was 30% (n = 145). Sensitivity ranged from 1% (95% CI 0–5) for the SAD PERSONS scale, to 97% (95% CI 93–99) for the Manchester Self-Harm Rule. Positive predictive values ranged from 13% (95% CI 2–47) for the Modified SAD PERSONS Scale to 47% (95% CI 41–53) for the clinician assessment of risk. The AUC ranged from 0.55 (95% CI 0.50–0.61) for the SAD PERSONS scale to 0.74 (95% CI 0.69–0.79) for the clinician global scale. The remaining scales performed significantly worse than clinician and patient estimates of risk (P<0.001). Conclusions Risk scales following self-harm have limited clinical utility and may waste valuable resources. Most scales performed no better than clinician or patient ratings of risk. Some performed considerably worse. Positive predictive values were modest. In line with national guidelines, risk scales should not be used to determine patient management or predict self-harm. PMID:28302702

Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study.

PubMed

Quinlivan, Leah; Cooper, Jayne; Meehan, Declan; Longson, Damien; Potokar, John; Hulme, Tom; Marsden, Jennifer; Brand, Fiona; Lange, Kezia; Riseborough, Elena; Page, Lisa; Metcalfe, Chris; Davies, Linda; O'Connor, Rory; Hawton, Keith; Gunnell, David; Kapur, Nav

2017-06-01

Background Scales are widely used in psychiatric assessments following self-harm. Robust evidence for their diagnostic use is lacking. Aims To evaluate the performance of risk scales (Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS scale, Modified SAD PERSONS scale, Barratt Impulsiveness Scale); and patient and clinician estimates of risk in identifying patients who repeat self-harm within 6 months. Method A multisite prospective cohort study was conducted of adults aged 18 years and over referred to liaison psychiatry services following self-harm. Scale a priori cut-offs were evaluated using diagnostic accuracy statistics. The area under the curve (AUC) was used to determine optimal cut-offs and compare global accuracy. Results In total, 483 episodes of self-harm were included in the study. The episode-based 6-month repetition rate was 30% ( n = 145). Sensitivity ranged from 1% (95% CI 0-5) for the SAD PERSONS scale, to 97% (95% CI 93-99) for the Manchester Self-Harm Rule. Positive predictive values ranged from 13% (95% CI 2-47) for the Modified SAD PERSONS Scale to 47% (95% CI 41-53) for the clinician assessment of risk. The AUC ranged from 0.55 (95% CI 0.50-0.61) for the SAD PERSONS scale to 0.74 (95% CI 0.69-0.79) for the clinician global scale. The remaining scales performed significantly worse than clinician and patient estimates of risk ( P <0.001). Conclusions Risk scales following self-harm have limited clinical utility and may waste valuable resources. Most scales performed no better than clinician or patient ratings of risk. Some performed considerably worse. Positive predictive values were modest. In line with national guidelines, risk scales should not be used to determine patient management or predict self-harm. © The Royal College of Psychiatrists 2017.

Inclusion of Endogenous Hormone Levels in Risk Prediction Models of Postmenopausal Breast Cancer

PubMed Central

Tworoger, Shelley S.; Zhang, Xuehong; Eliassen, A. Heather; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Rosner, Bernard A.; Kraft, Peter; Hankinson, Susan E.

2014-01-01

Purpose Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone–binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. Methods We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Results Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor–positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Conclusion Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. PMID:25135988

Superiority of delayed risk stratification in differentiated thyroid cancer after total thyroidectomy and radioactive iodine ablation.

PubMed

Hong, Chae Moon; Lee, Won Kee; Jeong, Shin Young; Lee, Sang-Woo; Ahn, Byeong-Cheol; Lee, Jaetae

2014-11-01

The aim of this study was to validate the effectiveness of delayed risk stratification (DRS) in predicting structural progression and compare the predictive value of American Thyroid Association (ATA) risk stratification with that of DRS in patients with differentiated thyroid cancer (DTC). A total of 398 patients with DTC who underwent surgery followed by radioactive iodine ablation were enrolled. Patients were categorized as having excellent response, acceptable response, biochemical incomplete response, or structural incomplete response at 8-15 months' evaluation after radioactive iodine ablation for DRS. Effectiveness of DRS was evaluated according to structural progression-free survival (PFS; median follow-up, 10.7 years). A total of 229 patients (57.5%) were classified as having excellent response, 78 (19.6%) as having acceptable response, 62 (15.6%) as having biochemical incomplete response, and 29 patients (7.3%) as having structural incomplete response. After DRS, 60.2% of intermediate-risk patients and 20.5% of high-risk patients were shifted to the excellent response category. Sixty-nine patients (17.3%) showed structural progression. DRS showed statistical difference in PFS (hazard ratio, 4.268; 95% confidence interval, 3.258-5.477; P<0.001). In multivariate analysis of ATA risk stratification and DRS, DRS was significantly associated with PFS (hazard ratio, 4.383; 95% confidence interval, 3.250-5.912; P<0.001), but ATA risk stratification was not. There was no significant difference in deviances between the use of DRS alone and the use of both DRS and ATA risk stratification (χ=0.103, d.f.=1, P=0.748). DRS is superior to ATA risk stratification in predicting structural disease progression for DTC patients.

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.

Scores for post-myocardial infarction risk stratification in the community.

PubMed

Singh, Mandeep; Reeder, Guy S; Jacobsen, Steven J; Weston, Susan; Killian, Jill; Roger, Véronique L

2002-10-29

Several scores, most of which were derived from clinical trials, have been proposed for stratifying risk after myocardial infarctions (MIs). Little is known about their generalizability to the community, their respective advantages, and whether the ejection fraction (EF) adds prognostic information to the scores. The purpose of this study is to evaluate the Thrombolysis in Myocardial Infarction (TIMI) and Predicting Risk of Death in Cardiac Disease Tool (PREDICT) scores in a geographically defined MI cohort and determine the incremental value of EF for risk stratification. MIs occurring in Olmsted County were validated with the use of standardized criteria and stratified with the ECG into ST-segment elevation (STEMI) and non-ST-segment elevation (NSTEMI) MI. Logistic regression examined the discriminant accuracy of the TIMI and PREDICT scores to predict death and recurrent MI and assessed the incremental value of the EF. After 6.3+/-4.7 years, survival was similar for the 562 STEMIs and 717 NSTEMIs. The discriminant accuracy of the TIMI score was good in STEMI but only fair in NSTEMI. Across time and end points, irrespective of reperfusion therapy, the discriminant accuracy of the PREDICT score was consistently superior to that of the TIMI scores, largely because PREDICT includes comorbidity; EF provided incremental information over that provided by the scores and comorbidity. In the community, comorbidity and EF convey important prognostic information and should be included in approaches for stratifying risk after MI.

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).

Second- to third-trimester longitudinal growth assessment for prediction of small-for-gestational age and late fetal growth restriction.

PubMed

Caradeux, J; Eixarch, E; Mazarico, E; Basuki, T R; Gratacós, E; Figueras, F

2018-02-01

Detection of fetal growth restriction (FGR) remains poor and most screening strategies rely on cross-sectional evaluation of fetal size during the third trimester. A longitudinal and individualized approach has been proposed as an alternative method of evaluation. The aim of this study was to compare second- to third-trimester longitudinal growth assessment to cross-sectional evaluation in the third trimester for the prediction of small-for-gestational age (SGA) and late FGR in low-risk singleton pregnancy. This was a prospective cohort study of 2696 unselected consecutive low-risk singleton pregnancies scanned at 21 ± 2 and 32 ± 2 weeks. For cross-sectional growth assessment, abdominal circumference (AC) measurements were transformed to z-values according the 21st-INTERGROWTH standards. Longitudinal growth assessment was performed by calculating the AC z-velocity and the second- to third-trimester AC conditional growth centile. Longitudinal assessment was compared with cross-sectional assessment at 32 weeks. Association of cross-sectional and longitudinal evaluations with SGA and late FGR was assessed by logistic regression analysis. Predictive performance was determined by receiver-operating characteristics curve analysis. In total, 210 (7.8%) newborns were classified as SGA and 103 (3.8%) as late FGR. Neither longitudinal measurement improved the association with SGA or late FGR provided by cross-sectional evaluation of AC z-score at 32 weeks. Areas under the curves of AC z-velocity and conditional AC growth were significantly smaller than those of cross-sectional AC z-scores (P < 0.001), although AC z-velocity performed significantly better than did conditional AC growth (P < 0.001). Longitudinal assessment of fetal growth from the second to third trimester has a low predictive capacity for SGA and late FGR in low-risk singleton pregnancy compared with cross-sectional growth evaluation. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Potential role of liver enzymes levels as predictor markers of glucose metabolism disorders in Tunisian population.

PubMed

Bouhajja, Houda; Abdelhedi, Rania; Amouri, Ali; Hadj Kacem, Faten; Marrakchi, Rim; Safi, Wajdi; Mrabet, Houcem; Chtourou, Lassaad; Charfi, Nadia; Fourati, Mouna; Bensassi, Salwa; Jamoussi, Kamel; Abid, Mohamed; Ayadi, Hammadi; Feki, Mouna Mnif; Elleuch, Noura Bougacha

2018-03-10

The relationship between liver enzymes and type 2 diabetes (T2D) risk is inconclusive. We aimed to evaluate the association between liver markers and risk of carbohydrate metabolism disorders and their discriminatory power for T2D prediction. This cross-sectional study enrolled 216 participants classified as normoglycemic, prediabetes, newly-diagnosed diabetes and diagnosed diabetes. All participants underwent anthropometric and biochemical measurements. The relationship between hepatic enzymes and glucose metabolism markers was evaluated by ANCOVA analyses. The associations between liver enzymes and incident carbohydrate metabolism disorders were analyzed through logistic regression and their discriminatory capacity for T2D by receiver operating characteristic (ROC) analysis. High alkaline phosphatase (AP), alanine aminotransferase (ALT), γ-glutamyltransferase (γGT) and aspartate aminotrasferase (AST) levels were independently related to decreased insulin sensitivity. Interestingly, higher AP level was significantly associated with increased risk of prediabetes (p=0.017), newly-diagnosed diabetes (p=0.004) and T2D (p=0.007). Elevated γGT level was an independent risk factor for T2D (p=0.032) and undiagnosed-T2D (p=0.010) in prediabetic and normoglycemic subjects, respectively. In ROC analysis, AP was a powerful predictor of incident diabetes and significantly improved T2D prediction. Liver enzymes within normal range, specifically AP levels, are associated with increased risk of carbohydrate metabolism disorders and significantly improved T2D prediction.

Life history and spatial traits predict extinction risk due to climate change

NASA Astrophysics Data System (ADS)

Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

2014-03-01

There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

Predictive value of late decelerations for fetal acidemia in unselective low-risk pregnancies.

PubMed

Sameshima, Hiroshi; Ikenoue, Tsuyomu

2005-01-01

We evaluated the clinical significance of late decelerations (LD) of intrapartum fetal heart rate (FHR) monitoring to detect low pH (< 7.1) in low-risk pregnancies. We selected two secondary and two tertiary-level institutions where 10,030 women delivered. Among them, 5522 were low-risk pregnancies. The last 2 hours of FHR patterns before delivery were interpreted according to the guidelines of the National Institute of Child Health and Human Development. The correlation between the incidence of LD (occasional, < 50%; recurrent, > or = 50%) and severity (reduced baseline FHR accelerations and variability) of LD, and low pH (< 7.1) were evaluated. Statistical analyses included a contingency table with chi2 and the Fisher test, and one-way analysis of variance with the Bonferroni/Dunn test. In the 5522 low-risk pregnancies, 301 showed occasional LD and 99 showed recurrent LD. Blood gases and pH values deteriorated as the incidence of LD increased and as baseline accelerations or variability was decreased. Positive predictive value for low pH (< 7.1) was exponentially elevated from 0% at no deceleration, 1% in occasional LD, and > 50% in recurrent LD with no baseline FHR accelerations and reduced variability. In low-risk pregnancies, information on LD combined with acceleration and baseline variability enables us to predict the potential incidence of fetal acidemia.

The FIM instrument to identify patients at risk of falling in geriatric wards: a 10-year retrospective study.

PubMed

Petitpierre, Nicolas Julien; Trombetti, Andrea; Carroll, Iain; Michel, Jean-Pierre; Herrmann, François Richard

2010-05-01

the main objective was to evaluate if the admission functional independence measure (FIM) score could be used to predict the risk of falls in geriatric inpatients. a 10-year retrospective study was performed. the study was conducted in a 298-bed geriatric teaching hospital in Geneva, Switzerland. all patients discharged from the hospital from 1 January 1997 to 31 December 2006 were selected. measures used were FIM scores at admission using the FIM instrument and number of falls extracted from the institution's fall report forms. during the study period, there were 23,966 hospital stays. A total of 8,254 falls occurred. Of these, 7,995 falls were linked to 4,651 stays. Falls were recorded in 19.4% of hospital stays, with a mean incidence of 7.84 falls per 1,000 patients-days. Although there was a statistically significant relationship between total FIM score, its subscales, and the risk of falling, the sensitivity, specificity, positive predictive value and negative predictive value obtained with receiver operating characteristic curves were insufficient to permit fall prediction. This might be due in part to a non-linear relationship between FIM score and fall risk. in this study, the FIM instrument was found to be unable to predict risk of falls in general geriatric wards.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Paleontological baselines for evaluating extinction risk in the modern oceans

NASA Astrophysics Data System (ADS)

Finnegan, Seth; Anderson, Sean C.; Harnik, Paul G.; Simpson, Carl; Tittensor, Derek P.; Byrnes, Jarrett E.; Finkel, Zoe V.; Lindberg, David R.; Liow, Lee Hsiang; Lockwood, Rowan; Lotze, Heike K.; McClain, Craig R.; McGuire, Jenny L.; O'Dea, Aaron; Pandolfi, John M.

2015-05-01

Marine taxa are threatened by anthropogenic impacts, but knowledge of their extinction vulnerabilities is limited. The fossil record provides rich information on past extinctions that can help predict biotic responses. We show that over 23 million years, taxonomic membership and geographic range size consistently explain a large proportion of extinction risk variation in six major taxonomic groups. We assess intrinsic risk—extinction risk predicted by paleontologically calibrated models—for modern genera in these groups. Mapping the geographic distribution of these genera identifies coastal biogeographic provinces where fauna with high intrinsic risk are strongly affected by human activity or climate change. Such regions are disproportionately in the tropics, raising the possibility that these ecosystems may be particularly vulnerable to future extinctions. Intrinsic risk provides a prehuman baseline for considering current threats to marine biodiversity.

Risk-Assessment Score and Patient Optimization as Cost Predictors for Ventral Hernia Repair.

PubMed

Saleh, Sherif; Plymale, Margaret A; Davenport, Daniel L; Roth, John Scott

2018-04-01

Ventral hernia repair (VHR) is associated with complications that significantly increase healthcare costs. This study explores the associations between hospital costs for VHR and surgical complication risk-assessment scores, need for cardiac or pulmonary evaluation, and smoking or obesity counseling. An IRB-approved retrospective study of patients having undergone open VHR over 3 years was performed. Ventral Hernia Risk Score (VHRS) for surgical site occurrence and surgical site infection, and the Ventral Hernia Working Group grade were calculated for each case. Also recorded were preoperative cardiology or pulmonary evaluations, smoking cessation and weight reduction counseling, and patient goal achievement. Hospital costs were obtained from the cost accounting system for the VHR hospitalization stratified by major clinical cost drivers. Univariate regression analyses were used to compare the predictive power of the risk scores. Multivariable analysis was performed to develop a cost prediction model. The mean cost of index VHR hospitalization was $20,700. Total and operating room costs correlated with increasing CDC wound class, VHRS surgical site infection score, VHRS surgical site occurrence score, American Society of Anesthesiologists class, and Ventral Hernia Working Group (all p < 0.01). The VHRS surgical site infection scores correlated negatively with contribution margin (-280; p < 0.01). Multivariable predictors of total hospital costs for the index hospitalization included wound class, hernia defect size, age, American Society of Anesthesiologists class 3 or 4, use of biologic mesh, and 2+ mesh pieces; explaining 73% of the variance in costs (p < 0.001). Weight optimization significantly reduced direct and operating room costs (p < 0.05). Cardiac evaluation was associated with increased costs. Ventral hernia repair hospital costs are more accurately predicted by CDC wound class than VHR risk scores. A straightforward 6-factor model predicted most cost variation for VHR. Copyright © 2018 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Does the Surgical Apgar Score Measure Intraoperative Performance?

PubMed Central

Regenbogen, Scott E.; Lancaster, R. Todd; Lipsitz, Stuart R.; Greenberg, Caprice C.; Hutter, Matthew M.; Gawande, Atul A.

2008-01-01

Objective To evaluate whether Surgical Apgar Scores measure the relationship between intraoperative care and surgical outcomes. Summary Background Data With preoperative risk-adjustment now well-developed, the role of intraoperative performance in surgical outcomes may be considered. We previously derived and validated a ten-point Surgical Apgar Score—based on intraoperative blood loss, heart rate, and blood pressure—that effectively predicts major postoperative complications within 30 days of general and vascular surgery. This study evaluates whether the predictive value of this score comes solely from patients’ preoperative risk, or also measures care in the operating room. Methods Among a systematic sample of 4,119 general and vascular surgery patients at a major academic hospital, we constructed a detailed risk-prediction model including 27 patient-comorbidity and procedure-complexity variables, and computed patients’ propensity to suffer a major postoperative complication. We evaluated the prognostic value of patients’ Surgical Apgar Scores before and after adjustment for this preoperative risk. Results After risk-adjustment, the Surgical Apgar Score remained strongly correlated with postoperative outcomes (p<0.0001). Odds of major complications among average-scoring patients (scores 7–8) were equivalent to preoperative predictions (likelihood ratio (LR) 1.05, 95%CI 0.78–1.41), significantly decreased for those who achieved the best scores of 9–10 (LR 0.52, 95%CI 0.35–0.78), and were significantly poorer for those with low scores—LRs 1.60 (1.12–2.28) for scores 5–6, and 2.80 (1.50–5.21) for scores 0–4. Conclusions Even after accounting for fixed preoperative risk—due to patients’ acute condition, comorbidities and/or operative complexity—the Surgical Apgar Score appears to detect differences in intraoperative management that reduce odds of major complications by half, or increase them by nearly three-fold. PMID:18650644

Evaluation of a Social Contextual Model of Delinquency: A Cross-Study Replication.

ERIC Educational Resources Information Center

Scaramella, Laura V.; Conger, Rand D.; Spoth, Richard; Simons, Ronald L.

2002-01-01

Examined three theories for predicting risk for delinquency during adolescence with sixth- and seventh-grade students: an individual difference perspective, social interactional model, and social contextual approach. Found that lack of nurturant and involved parenting indirectly predicted delinquency by increasing antisocial behavior and deviant…

Predictive factors for intrauterine growth restriction.

PubMed

Albu, A R; Anca, A F; Horhoianu, V V; Horhoianu, I A

2014-06-15

Reduced fetal growth is seen in about 10% of the pregnancies but only a minority has a pathological background and is known as intrauterine growth restriction or fetal growth restriction (IUGR / FGR). Increased fetal and neonatal mortality and morbidity as well as adult pathologic conditions are often associated to IUGR. Risk factors for IUGR are easy to assess but have poor predictive value. For the diagnostic purpose, biochemical serum markers, ultrasound and Doppler study of uterine and spiral arteries, placental volume and vascularization, first trimester growth pattern are object of assessment today. Modern evaluations propose combined algorithms using these strategies, all with the goal of a better prediction of risk pregnancies.

Prediction of human population responses to toxic compounds by a collaborative competition.

PubMed

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Knowledge of heart disease risk in a multicultural community sample of people with diabetes.

PubMed

Wagner, Julie; Lacey, Kimberly; Abbott, Gina; de Groot, Mary; Chyun, Deborah

2006-06-01

Prevention of coronary heart disease (CHD) is a primary goal of diabetes management. Unfortunately, CHD risk knowledge is poor among people with diabetes. The objective is to determine predictors of CHD risk knowledge in a community sample of people with diabetes. A total of 678 people with diabetes completed the Heart Disease Facts Questionnaire (HDFQ), a valid and reliable measure of knowledge about the relationship between diabetes and heart disease. In regression analysis with demographics predicting HDFQ scores, sex, annual income, education, and health insurance status predicted HDFQ scores. In a separate regression analysis, having CHD risk factors did not predict HDFQ scores, however, taking medication for CHD risk factors did predict higher HDFQ scores. An analysis of variance showed significant differences between ethnic groups for HDFQ scores; Whites (M = 20.9) showed more CHD risk knowledge than African Americans (M = 19.6), who in turn showed more than Latinos (M = 18.2). Asians scored near Whites (M = 20.4) but did not differ significantly from any other group. Controlling for numerous demographic, socioeconomic, health care, diabetes, and cardiovascular health variables, the magnitude of ethnic differences was attenuated, but persisted. Education regarding modifiable risk factors must be delivered in a timely fashion so that lifestyle modification can be implemented and evaluated before pharmacotherapy is deemed necessary. African Americans and Latinos with diabetes are in the greatest need of education regarding CHD risk.

Making predictions of mangrove deforestation: a comparison of two methods in Kenya.

PubMed

Rideout, Alasdair J R; Joshi, Neha P; Viergever, Karin M; Huxham, Mark; Briers, Robert A

2013-11-01

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined. © 2013 Blackwell Publishing Ltd.

Skinfold reference curves and their use in predicting metabolic syndrome risk in children.

PubMed

Andaki, Alynne C R; Quadros, Teresa M B de; Gordia, Alex P; Mota, Jorge; Tinôco, Adelson L A; Mendes, Edmar L

To draw skinfold (SF) reference curves (subscapular, suprailiac, biceps, triceps) and to determine SF cutoff points for predicting the risk of metabolic syndrome (MetS) in children aged 6-10 years old. This was a cross-sectional study with a random sample of 1480 children aged 6-10 years old, 52.2% females, from public and private schools located in the urban and rural areas of the municipality of Uberaba (MG). Anthropometry, blood pressure, and fasting blood samples were taken at school, following specific protocols. The LMS method was used to draw the reference curves and ROC curve analysis to determine the accuracy and cutoff points for the evaluated skinfolds. The four SF evaluated (subscapular, suprailiac, biceps, and triceps) and their sum (∑4SF) were accurate in predicting MetS for both girls and boys. Additionally, cutoffs have been proposed and percentile curves (p5, p10, p25, p50, p75, p90, and p95) were outlined for the four SF and ∑4SF, for both genders. SF measurements were accurate in predicting metabolic syndrome in children aged 6-10 years old. Age- and gender-specific smoothed percentiles curves of SF provide a reference for the detection of risk for MetS in children. Copyright © 2017. Published by Elsevier Editora Ltda.

Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.

PubMed

Iasonos, Alexia; Chapman, Paul B; Satagopan, Jaya M

2016-05-01

An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR. ©2016 American Association for Cancer Research.

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

Relationship between Anthropometric Measures and Cardiovascular Risk Factors in Children and Adolescents

PubMed Central

Burgos, Miria Suzana; Burgos, Leandro Tibiriçá; Camargo, Marcelo Dias; Franke, Silvia Isabel Rech; Prá, Daniel; da Silva, Antônio Marcos Vargas; Borges, Tássia Silvana; Todendi, Pâmela Ferreira; Reckziegel, Miriam Beatris; Reuter, Cézane Priscila

2013-01-01

Background Obesity has been identified as an important risk factor in the development of cardiovascular diseases; however, other factors, combined or not with obesity, can influence cardiovascular risk and should be considered in cardiovascular risk stratification in pediatrics. Objective To analyze the association between anthropometry measures and cardiovascular risk factors, to investigate the determinants to changes in blood pressure (BP), and to propose a prediction equation to waist circumference (WC) in children and adolescents. Methods We evaluated 1,950 children and adolescents, aged 7 to 18 years. Visceral fat was assessed by WC and waist hip relationship, BP and body mass index (BMI). In a randomly selected subsample of these volunteers (n = 578), total cholesterol, glucose and triglycerides levels were evaluated. Results WC was positively correlated with BMI (r = 0.85; p < 0.001) and BP (SBP r = 0.45 and DBP = 0.37; p < 0.001). Glycaemia and triglycerides showed a weak correlation with WC (r = 0.110; p = 0.008 e r = 0.201; p < 0.001, respectively). Total cholesterol did not correlate with any of the variables. Age, BMI and WC were significant predictors on the regression models for BP (p < 0.001). We propose a WC prediction equation for children and adolescents: boys: y = 17.243 + 0.316 (height in cm); girls: y = 25.197 + 0.256 (height in cm). Conclusion WC is associated with cardiovascular risk factors and presents itself as a risk factor predictor of hypertension in children and adolescents. The WC prediction equation proposed by us should be tested in future studies. PMID:23979777

Translation and validation of the Canadian diabetes risk assessment questionnaire in China.

PubMed

Guo, Jia; Shi, Zhengkun; Chen, Jyu-Lin; Dixon, Jane K; Wiley, James; Parry, Monica

2018-01-01

To adapt the Canadian Diabetes Risk Assessment Questionnaire for the Chinese population and to evaluate its psychometric properties. A cross-sectional study was conducted with a convenience sample of 194 individuals aged 35-74 years from October 2014 to April 2015. The Canadian Diabetes Risk Assessment Questionnaire was adapted and translated for the Chinese population. Test-retest reliability was conducted to measure stability. Criterion and convergent validity of the adapted questionnaire were assessed using 2-hr 75 g oral glucose tolerance tests and the Finnish Diabetes Risk Scores, respectively. Sensitivity and specificity were evaluated to establish its predictive validity. The test-retest reliability was 0.988. Adequate validity of the adapted questionnaire was demonstrated by positive correlations found between the scores and 2-hr 75 g oral glucose tolerance tests (r = .343, p < .001) and with the Finnish Diabetes Risk Scores (r = .738, p < .001). The area under receiver operating characteristic curve was 0.705 (95% CI .632, .778), demonstrating moderate diagnostic value at a cutoff score of 30. The sensitivity was 73%, with a positive predictive value of 57% and negative predictive value of 78%. Our results provided evidence supporting the translation consistency, content validity, convergent validity, criterion validity, sensitivity, and specificity of the translated Canadian Diabetes Risk Assessment Questionnaire with minor modifications. This paper provides clinical, practical, and methodological information on how to adapt a diabetes risk calculator between cultures for public health nurses. © 2017 Wiley Periodicals, Inc.

Can theory predict the process of suicide on entry to prison? Predicting dynamic risk factors for suicide ideation in a high-risk prison population.

PubMed

Slade, Karen; Edelman, Robert

2014-01-01

Each year approximately 110,000 people are imprisoned in England and Wales and new prisoners remain one of the highest risk groups for suicide across the world. The reduction of suicide in prisoners remains difficult as assessments and interventions tend to rely on static risk factors with few theoretical or integrated models yet evaluated. To identify the dynamic factors that contribute to suicide ideation in this population based on Williams and Pollock's (2001) Cry of Pain (CoP) model. New arrivals (N = 198) into prison were asked to complete measures derived from the CoP model plus clinical and prison-specific factors. It was hypothesized that the factors of the CoP model would be predictive of suicide ideation. Support was provided for the defeat and entrapment aspects of the CoP model with previous self-harm, repeated times in prison, and suicide-permissive cognitions also key in predicting suicide ideation for prisoners on entry to prison. An integrated and dynamic model was developed that has utility in predicting suicide in early-stage prisoners. Implications for both theory and practice are discussed along with recommendations for future research.

KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors

PubMed Central

Lentine, Krista L.; Kasiske, Bertram L.; Levey, Andrew S.; Adams, Patricia L.; Alberú, Josefina; Bakr, Mohamed A.; Gallon, Lorenzo; Garvey, Catherine A.; Guleria, Sandeep; Li, Philip Kam-Tao; Segev, Dorry L.; Taler, Sandra J.; Tanabe, Kazunari; Wright, Linda; Zeier, Martin G.; Cheung, Michael; Garg, Amit X.

2017-01-01

Abstract The 2017 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors is intended to assist medical professionals who evaluate living kidney donor candidates and provide care before, during and after donation. The guideline development process followed the Grades of Recommendation Assessment, Development, and Evaluation (GRADE) approach and guideline recommendations are based on systematic reviews of relevant studies that included critical appraisal of the quality of the evidence and the strength of recommendations. However, many recommendations, for which there was no evidence or no systematic search for evidence was undertaken by the Evidence Review Team, were issued as ungraded expert opinion recommendations. The guideline work group concluded that a comprehensive approach to risk assessment should replace decisions based on assessments of single risk factors in isolation. Original data analyses were undertaken to produce a “proof-in-concept” risk-prediction model for kidney failure to support a framework for quantitative risk assessment in the donor candidate evaluation and defensible shared decision making. This framework is grounded in the simultaneous consideration of each candidate's profile of demographic and health characteristics. The processes and framework for the donor candidate evaluation are presented, along with recommendations for optimal care before, during, and after donation. Limitations of the evidence are discussed, especially regarding the lack of definitive prospective studies and clinical outcome trials. Suggestions for future research, including the need for continued refinement of long-term risk prediction and novel approaches to estimating donation-attributable risks, are also provided. In citing this document, the following format should be used: Kidney Disease: Improving Global Outcomes (KDIGO) Living Kidney Donor Work Group. KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors. Transplantation. 2017;101(Suppl 8S):S1–S109. PMID:28742762

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.« less

Value of Progression of Coronary Artery Calcification for Risk Prediction of Coronary and Cardiovascular Events: Result of the HNR Study (Heinz Nixdorf Recall).

PubMed

Lehmann, Nils; Erbel, Raimund; Mahabadi, Amir A; Rauwolf, Michael; Möhlenkamp, Stefan; Moebus, Susanne; Kälsch, Hagen; Budde, Thomas; Schmermund, Axel; Stang, Andreas; Führer-Sakel, Dagmar; Weimar, Christian; Roggenbuck, Ulla; Dragano, Nico; Jöckel, Karl-Heinz

2018-02-13

Computed tomography (CT) allows estimation of coronary artery calcium (CAC) progression. We evaluated several progression algorithms in our unselected, population-based cohort for risk prediction of coronary and cardiovascular events. In 3281 participants (45-74 years of age), free from cardiovascular disease until the second visit, risk factors, and CTs at baseline (b) and after a mean of 5.1 years (5y) were measured. Hard coronary and cardiovascular events, and total cardiovascular events including revascularization, as well, were recorded during a follow-up time of 7.8±2.2 years after the second CT. The added predictive value of 10 CAC progression algorithms on top of risk factors including baseline CAC was evaluated by using survival analysis, C-statistics, net reclassification improvement, and integrated discrimination index. A subgroup analysis of risk in CAC categories was performed. We observed 85 (2.6%) hard coronary, 161 (4.9%) hard cardiovascular, and 241 (7.3%) total cardiovascular events. Absolute CAC progression was higher with versus without subsequent coronary events (median, 115 [Q1-Q3, 23-360] versus 8 [0-83], P <0.0001; similar for hard/total cardiovascular events). Some progression algorithms added to the predictive value of baseline CT and risk assessment in terms of C-statistic or integrated discrimination index, especially for total cardiovascular events. However, CAC progression did not improve models including CAC 5y and 5-year risk factors. An excellent prognosis was found for 921 participants with double-zero CAC b =CAC 5y =0 (10-year coronary and hard/total cardiovascular risk: 1.4%, 2.0%, and 2.8%), which was for participants with incident CAC 1.8%, 3.8%, and 6.6%, respectively. When CAC b progressed from 1 to 399 to CAC 5y ≥400, coronary and total cardiovascular risk were nearly 2-fold in comparison with subjects who remained below CAC 5y =400. Participants with CAC b ≥400 had high rates of hard coronary and hard/total cardiovascular events (10-year risk: 12.0%, 13.5%, and 30.9%, respectively). CAC progression is associated with coronary and cardiovascular event rates, but adds only weakly to risk prediction. What counts is the most recent CAC value and risk factor assessment. Therefore, a repeat scan >5 years after the first scan may be of additional value, except when a double-zero CT scan is present or when the subjects are already at high risk. © 2017 The Authors.

Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

NASA Astrophysics Data System (ADS)

Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

2014-03-01

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

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.

Application of high-density data for hazard prediction, safety assesment, and risk characterization

EPA Science Inventory

There are long lists of chemicals that require some level of evaluation for safety determination. These include the European Union’s Registration, Evaluation, Authorization and Restriction of Chemical substances (REACH) program, Environment Canada’s existing substances evaluatio...

Comparison of Measured and Predicted Bioconcentration Estimates of Pharmaceuticals in Fish Plasma and Prediction of Chronic Risk.

PubMed

Nallani, Gopinath; Venables, Barney; Constantine, Lisa; Huggett, Duane

2016-05-01

Evaluation of the environmental risk of human pharmaceuticals is now a mandatory component in all new drug applications submitted for approval in EU. With >3000 drugs currently in use, it is not feasible to test each active ingredient, so prioritization is key. A recent review has listed nine prioritization approaches including the fish plasma model (FPM). The present paper focuses on comparison of measured and predicted fish plasma bioconcentration factors (BCFs) of four common over-the-counter/prescribed pharmaceuticals: norethindrone (NET), ibuprofen (IBU), verapamil (VER) and clozapine (CLZ). The measured data were obtained from the earlier published fish BCF studies. The measured BCF estimates of NET, IBU, VER and CLZ were 13.4, 1.4, 0.7 and 31.2, while the corresponding predicted BCFs (based log Kow at pH 7) were 19, 1.0, 7.6 and 30, respectively. These results indicate that the predicted BCFs matched well the measured values. The BCF estimates were used to calculate the human: fish plasma concentration ratios of each drug to predict potential risk to fish. The plasma ratio results show the following order of risk potential for fish: NET > CLZ > VER > IBU. The FPM has value in prioritizing pharmaceutical products for ecotoxicological assessments.

DEVELOPMENT AND EVALUATION OF NOVEL DOSE-RESPONSE MODELS FOR USE IN MICROBIAL RISK ASSESSMENT

EPA Science Inventory

This document contains a description of dose-response modeling methods designed to provide a robust approach under uncertainty for predicting human population risk from exposure to pathogens in drinking water.

The purpose of this document is to describe a body of literatu...

Femoral and carotid subclinical atherosclerosis association with risk factors and coronary calcium: the AWHS study

USDA-ARS?s Scientific Manuscript database

BACKGROUND: Early subclinical atherosclerosis has been mainly researched in carotid arteries. The potential value of femoral arteries for improving the predictive capacity of traditional risk factors is an understudied area. OBJECTIVES: This study sought to evaluate the association of subclinical ca...

DESIGN AND PERFORMANCE OF A XENOBIOTIC METABOLISM DATABASE MANAGER FOR METABOLIC SIMULATOR ENHANCEMENT AND CHEMICAL RISK ANALYSIS

EPA Science Inventory

A major uncertainty that has long been recognized in evaluating chemical toxicity is accounting for metabolic activation of chemicals resulting in increased toxicity. In silico approaches to predict chemical metabolism and to subsequently screen and prioritize chemicals for risk ...

Predictors of Well-Being among College Students

ERIC Educational Resources Information Center

Ridner, S. Lee; Newton, Karen S.; Staten, Ruth R.; Crawford, Timothy N.; Hall, Lynne A.

2016-01-01

Objectives: Identification of health-related risk behaviors associated with well-being in college students is essential to guide the development of health promotion strategies for this population. The purposes were to evaluate well-being among undergraduate students and to identify health-related risk behaviors that predict well-being in this…

The cortisol reactivity threshold model: Direction of trait rumination and cortisol reactivity association varies with stressor severity.

PubMed

Vrshek-Schallhorn, Suzanne; Avery, Bradley M; Ditcheva, Maria; Sapuram, Vaibhav R

2018-06-01

Various internalizing risk factors predict, in separate studies, both augmented and reduced cortisol responding to lab-induced stress. Stressor severity appears key: We tested whether heightened trait-like internalizing risk (here, trait rumination) predicts heightened cortisol reactivity under modest objective stress, but conversely predicts reduced reactivity under more robust objective stress. Thus, we hypothesized that trait rumination would interact with a curvilinear (quadratic) function of stress severity to predict cortisol reactivity. Evidence comes from 85 currently non-depressed emerging adults who completed either a non-stressful control protocol (n = 29), an intermediate difficulty Trier Social Stress Test (TSST; n = 26), or a robustly stressful negative evaluative TSST (n = 30). Latent growth curve models evaluated relationships between trait rumination and linear and quadratic effects of stressor severity on the change in cortisol and negative affect over time. Among other findings, a significant Trait Rumination x Quadratic Stress Severity interaction effect for cortisol's Quadratic Trend of Time (i.e., reactivity, B = .125, p = .017) supported the hypothesis. Rumination predicted greater cortisol reactivity to intermediate stress (r p  = .400, p = .043), but blunted reactivity to more robust negative evaluative stress (r p  = -0.379, p = 0.039). Contrasting hypotheses, negative affective reactivity increased independently of rumination as stressor severity increased (B = .453, p = 0.044). The direction of the relationship between an internalizing risk factor (trait rumination) and cortisol reactivity varies as a function of stressor severity. We propose the Cortisol Reactivity Threshold Model, which may help reconcile several divergent reactivity literatures and has implications for internalizing psychopathology, particularly depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Limited Clinical Utility of Testosterone, Estradiol, and Sex Hormone Binding Globulin Measurements in the Prediction of Fracture Risk and Bone Loss in Older Men.

PubMed

Orwoll, Eric S; Lapidus, Jodi; Wang, Patty Y; Vandenput, Liesbeth; Hoffman, Andrew; Fink, Howard A; Laughlin, Gail A; Nethander, Maria; Ljunggren, Östen; Kindmark, Andreas; Lorentzon, Mattias; Karlsson, Magnus K; Mellström, Dan; Kwok, Anthony; Khosla, Sundeep; Kwok, Timothy; Ohlsson, Claes

2017-03-01

Measurement of serum testosterone (T) levels is recommended in the evaluation of osteoporosis in older men and estradiol (E2) and sex hormone binding globulin (SHBG) levels are associated with the rate of bone loss and fractures, but the clinical utility of sex steroid and SHBG measurements for the evaluation of osteoporosis in men has not been examined. To evaluate whether measurements of T, E2, and/or SHBG are useful for the prediction of fracture risk or the rate of bone loss in older men, we analyzed longitudinal data from 5487 community-based men participating in the Osteoporotic Fractures in Men (MrOS) study in the United States, Sweden, and Hong Kong. Serum T, E2, and SHBG levels were assessed at baseline; incident fractures were self-reported at 4-month intervals with radiographic verification (US), or ascertained via national health records (Sweden, Hong Kong). Rate of bone loss was assessed by serial measures of hip bone mineral density (BMD). We used receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to assess improvement in prediction. Mean age at baseline was 72 to 75 years and the prevalence of low T levels (<300 ng/dL) was 7.6% to 21.3% in the three cohorts. There were 619 incident major osteoporotic and 266 hip fractures during follow-up of approximately 10 years. Based on ROC curves, there were no improvements in fracture risk discrimination for any biochemical measure when added to models, including the Fracture Risk Assessment Tool (FRAX) with BMD. Although minor improvements in NRI were observed for the dichotomous parameters low bioavailable E2 (BioE2) (<11.4 pg/mL) and high SHBG (>59.1 nM), neither sex steroids nor SHBG provided clinically useful improvement in fracture risk discrimination. Similarly, they did not contribute to the prediction of BMD change. In conclusion, there is limited clinical utility of serum E2, T, and SHBG measures for the evaluation of osteoporosis risk in elderly men. © 2016 American Society for Bone and Mineral Research. © 2016 American Society for Bone and Mineral Research.

Development of a Frost Risk Assessment Tool in Agriculture for a Mediterranean ecosystem Utilizing MODIS satellite observations Geomatics and Surface Data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Papanikolaou, Ioannis; Petropoulos, George; Migiros, George; Tsiros, Ioannis

2014-05-01

Frost risk in Mediterranean countries is a critical factor in agricultural planning and management. Nowadays, the rapid technological developments in Earth Observation (EO) technology have improved dramatically our ability to map the spatiotemporal distribution of frost conditions over a given area and evaluate its impacts on the environment and society. In this study, a frost risk model for agricultural crops cultivated in a Mediterranean environment has been developed, based primarily on Earth Observation (EO) data from MODIS sensor and ancillary spatial and point data. The ability of the model to predict frost conditions has been validated for selected days on which frost conditions had been observed for a region in Northwestern Greece according to ground observations obtained by the Agricultural Insurance Organization (ELGA). An extensive evaluation of the frost risk model predictions has been performed herein to evaluate objectively its ability to predict the spatio-temporal distribution of frost risk in the studied region, including comparisons against physiographical factors of the study area. The topographical characteristics that were taken under consideration were latitude, altitude, slope steepness, topographic convergence and the extend of the areas influenced by water bodies (such as lake and sea) existing in the study area. Additional data were also used concerning land use data and vegetation classification (type and density). Our results showed that the model was able to produce reasonably the spatio-temporal distribution of the frost conditions in our study area, following largely explainable patterns in respect to the study site and local weather conditions characteristics. All in all, the methodology implemented herein proved capable in obtaining rapidly and cost-effectively cartography of the frost risk in a Mediterranean environment, making it potentially a very useful tool for agricultural management and planning. The model presented here has also a potential to enhance conventional field-based surveying for monitoring frost changes over long timescales. KEYWORDS: Earth Observation, MODIS, frost, risk assessment, Greece

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.

Assessing Breast Cancer Risk with an Artificial Neural Network

PubMed

Sepandi, Mojtaba; Taghdir, Maryam; Rezaianzadeh, Abbas; Rahimikazerooni, Salar

2018-04-25

Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imaging methods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neural network (ANN) technique was used on a retrospectively collected dataset including mammographic results, risk factors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: The network incorporating the selected features performed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90. In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANN has potential applications as a decision-support tool to help underperforming practitioners to improve the positive predictive value of biopsy recommendations. Creative Commons Attribution License

Potential ecological risk assessment and prediction of soil heavy metal pollution around coal gangue dump

NASA Astrophysics Data System (ADS)

Jiang, X.; Lu, W. X.; Yang, Q. C.; Yang, Z. P.

2014-03-01

Aim of the present study is to evaluate the potential ecological risk and predict the trend of soil heavy metal pollution around a~coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy metal pollution. The potential ecological risk in an order of E(Cd) > E(Pb) > E(Cu) > E(Cr) > E(Zn) have been obtained, which showed that Cd was the most important factor led to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, and the fixed number of years exceeding standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metal, and the relationship between sampling points and variables. These findings provide some useful insights for making appropriate management strategies to prevent and decrease heavy metal pollution around coal gangue dump in Yangcaogou coal mine and other similar areas elsewhere.

Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models: Stop Developing, Start Evaluating.

PubMed

van Giessen, Anoukh; Peters, Jaime; Wilcher, Britni; Hyde, Chris; Moons, Carl; de Wit, Ardine; Koffijberg, Erik

2017-04-01

Although health economic evaluations (HEEs) are increasingly common for therapeutic interventions, they appear to be rare for the use of risk prediction models (PMs). To evaluate the current state of HEEs of PMs by performing a comprehensive systematic review. Four databases were searched for HEEs of PM-based strategies. Two reviewers independently selected eligible articles. A checklist was compiled to score items focusing on general characteristics of HEEs of PMs, model characteristics and quality of HEEs, evidence on PMs typically used in the HEEs, and the specific challenges in performing HEEs of PMs. After screening 791 abstracts, 171 full texts, and reference checking, 40 eligible HEEs evaluating 60 PMs were identified. In these HEEs, PM strategies were compared with current practice (n = 32; 80%), to other stratification methods for patient management (n = 19; 48%), to an extended PM (n = 9; 23%), or to alternative PMs (n = 5; 13%). The PMs guided decisions on treatment (n = 42; 70%), further testing (n = 18; 30%), or treatment prioritization (n = 4; 7%). For 36 (60%) PMs, only a single decision threshold was evaluated. Costs of risk prediction were ignored for 28 (46%) PMs. Uncertainty in outcomes was assessed using probabilistic sensitivity analyses in 22 (55%) HEEs. Despite the huge number of PMs in the medical literature, HEE of PMs remains rare. In addition, we observed great variety in their quality and methodology, which may complicate interpretation of HEE results and implementation of PMs in practice. Guidance on HEE of PMs could encourage and standardize their application and enhance methodological quality, thereby improving adequate use of PM strategies. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Stratifying risk factors for multidrug-resistant pathogens in hospitalized patients coming from the community with pneumonia.

PubMed

Aliberti, Stefano; Di Pasquale, Marta; Zanaboni, Anna Maria; Cosentini, Roberto; Brambilla, Anna Maria; Seghezzi, Sonia; Tarsia, Paolo; Mantero, Marco; Blasi, Francesco

2012-02-15

 Not all risk factors for acquiring multidrug-resistant (MDR) organisms are equivalent in predicting pneumonia caused by resistant pathogens in the community. We evaluated risk factors for acquiring MDR bacteria in patients coming from the community who were hospitalized with pneumonia. Our evaluation was based on actual infection with a resistant pathogen and clinical outcome during hospitalization.  An observational, prospective study was conducted on consecutive patients coming from the community who were hospitalized with pneumonia. Data on admission and during hospitalization were collected. Logistic regression models were used to evaluate risk factors for acquiring MDR bacteria independently associated with the actual presence of a resistant pathogen and in-hospital mortality.  Among the 935 patients enrolled in the study, 473 (51%) had at least 1 risk factor for acquiring MDR bacteria on admission. Of all risk factors, hospitalization in the preceding 90 days (odds ratio [OR], 4.87 95% confidence interval {CI}, 1.90-12.4]; P = .001) and residency in a nursing home (OR, 3.55 [95% CI, 1.12-11.24]; P = .031) were independent predictors for an actual infection with a resistant pathogen. A score able to predict pneumonia caused by a resistant pathogen was computed, including comorbidities and risk factors for MDR. Hospitalization in the preceding 90 days and residency in a nursing home were also independent predictors for in-hospital mortality.  Risk factors for acquiring MDR bacteria should be weighted differently, and a probabilistic approach to identifying resistant pathogens among patients coming from the community with pneumonia should be embraced.

A MELD-based model to determine risk of mortality among patients with acute variceal bleeding.

PubMed

Reverter, Enric; Tandon, Puneeta; Augustin, Salvador; Turon, Fanny; Casu, Stefania; Bastiampillai, Ravin; Keough, Adam; Llop, Elba; González, Antonio; Seijo, Susana; Berzigotti, Annalisa; Ma, Mang; Genescà, Joan; Bosch, Jaume; García-Pagán, Joan Carles; Abraldes, Juan G

2014-02-01

Patients with cirrhosis with acute variceal bleeding (AVB) have high mortality rates (15%-20%). Previously described models are seldom used to determine prognoses of these patients, partially because they have not been validated externally and because they include subjective variables, such as bleeding during endoscopy and Child-Pugh score, which are evaluated inconsistently. We aimed to improve determination of risk for patients with AVB. We analyzed data collected from 178 patients with cirrhosis (Child-Pugh scores of A, B, and C: 15%, 57%, and 28%, respectively) and esophageal AVB who received standard therapy from 2007 through 2010. We tested the performance (discrimination and calibration) of previously described models, including the model for end-stage liver disease (MELD), and developed a new MELD calibration to predict the mortality of patients within 6 weeks of presentation with AVB. MELD-based predictions were validated in cohorts of patients from Canada (n = 240) and Spain (n = 221). Among study subjects, the 6-week mortality rate was 16%. MELD was the best model in terms of discrimination; it was recalibrated to predict the 6-week mortality rate with logistic regression (logit, -5.312 + 0.207 • MELD; bootstrapped R(2), 0.3295). MELD values of 19 or greater predicted 20% or greater mortality, whereas MELD scores less than 11 predicted less than 5% mortality. The model performed well for patients from Canada at all risk levels. In the Spanish validation set, in which all patients were treated with banding ligation, MELD predictions were accurate up to the 20% risk threshold. We developed a MELD-based model that accurately predicts mortality among patients with AVB, based on objective variables available at admission. This model could be useful to evaluate the efficacy of new therapies and stratify patients in randomized trials. Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.

Development and evaluation of an automated fall risk assessment system.

PubMed

Lee, Ju Young; Jin, Yinji; Piao, Jinshi; Lee, Sun-Mi

2016-04-01

Fall risk assessment is the first step toward prevention, and a risk assessment tool with high validity should be used. This study aimed to develop and validate an automated fall risk assessment system (Auto-FallRAS) to assess fall risks based on electronic medical records (EMRs) without additional data collected or entered by nurses. This study was conducted in a 1335-bed university hospital in Seoul, South Korea. The Auto-FallRAS was developed using 4211 fall-related clinical data extracted from EMRs. Participants included fall patients and non-fall patients (868 and 3472 for the development study; 752 and 3008 for the validation study; and 58 and 232 for validation after clinical application, respectively). The system was evaluated for predictive validity and concurrent validity. The final 10 predictors were included in the logistic regression model for the risk-scoring algorithm. The results of the Auto-FallRAS were shown as high/moderate/low risk on the EMR screen. The predictive validity analyzed after clinical application of the Auto-FallRAS was as follows: sensitivity = 0.95, NPV = 0.97 and Youden index = 0.44. The validity of the Morse Fall Scale assessed by nurses was as follows: sensitivity = 0.68, NPV = 0.88 and Youden index = 0.28. This study found that the Auto-FallRAS results were better than were the nurses' predictions. The advantage of the Auto-FallRAS is that it automatically analyzes information and shows patients' fall risk assessment results without requiring additional time from nurses. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

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.

Use of sexually transmitted disease risk assessment algorithms for selection of intrauterine device candidates.

PubMed

Morrison, C S; Sekadde-Kigondu, C; Miller, W C; Weiner, D H; Sinei, S K

1999-02-01

Sexually transmitted diseases (STD) are an important contraindication for intrauterine device (IUD) insertion. Nevertheless, laboratory testing for STD is not possible in many settings. The objective of this study is to evaluate the use of risk assessment algorithms to predict STD and subsequent IUD-related complications among IUD candidates. Among 615 IUD users in Kenya, the following algorithms were evaluated: 1) an STD algorithm based on US Agency for International Development (USAID) Technical Working Group guidelines: 2) a Centers for Disease Control and Prevention (CDC) algorithm for management of chlamydia; and 3) a data-derived algorithm modeled from study data. Algorithms were evaluated for prediction of chlamydial and gonococcal infection at 1 month and complications (pelvic inflammatory disease [PID], IUD removals, and IUD expulsions) over 4 months. Women with STD were more likely to develop complications than women without STD (19% vs 6%; risk ratio = 2.9; 95% CI 1.3-6.5). For STD prediction, the USAID algorithm was 75% sensitive and 48% specific, with a positive likelihood ratio (LR+) of 1.4. The CDC algorithm was 44% sensitive and 72% specific, LR+ = 1.6. The data-derived algorithm was 91% sensitive and 56% specific, with LR+ = 2.0 and LR- = 0.2. Category-specific LR for this algorithm identified women with very low (< 1%) and very high (29%) infection probabilities. The data-derived algorithm was also the best predictor of IUD-related complications. These results suggest that use of STD algorithms may improve selection of IUD users. Women at high risk for STD could be counseled to avoid IUD, whereas women at moderate risk should be monitored closely and counseled to use condoms.

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

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.

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.

External Validation of the Garvan Nomograms for Predicting Absolute Fracture Risk: The Tromsø Study

PubMed Central

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

Background 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. Methods 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. Results 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. Conclusions 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. PMID:25255221

External Validation of a Tool Predicting 7-Year Risk of Developing Cardiovascular Disease, Type 2 Diabetes or Chronic Kidney Disease.

PubMed

Rauh, Simone P; Rutters, Femke; van der Heijden, Amber A W A; Luimes, Thomas; Alssema, Marjan; Heymans, Martijn W; Magliano, Dianna J; Shaw, Jonathan E; Beulens, Joline W; Dekker, Jacqueline M

2018-02-01

Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk. We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases. The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance. The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]). Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

PubMed Central

Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian

2018-01-01

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908

FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community

PubMed Central

Cattelani, Luca; Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Becker, Clemens; Chiari, Lorenzo

2015-01-01

Background About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. Objective The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. Methods FRAT-up is based on the assumption that a subject’s fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. Results The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. Conclusions FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. Trial Registration ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR). PMID:25693419

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.

A model to predict the risk of lethal nasopharyngeal necrosis after re-irradiation with intensity-modulated radiotherapy in nasopharyngeal carcinoma patients.

PubMed

Yu, Ya-Hui; Xia, Wei-Xiong; Shi, Jun-Li; Ma, Wen-Juan; Li, Yong; Ye, Yan-Fang; Liang, Hu; Ke, Liang-Ru; Lv, Xing; Yang, Jing; Xiang, Yan-Qun; Guo, Xiang

2016-06-29

For patients with nasopharyngeal carcinoma (NPC) who undergo re-irradiation with intensity-modulated radiotherapy (IMRT), lethal nasopharyngeal necrosis (LNN) is a severe late adverse event. The purpose of this study was to identify risk factors for LNN and develop a model to predict LNN after radical re-irradiation with IMRT in patients with recurrent NPC. Patients who underwent radical re-irradiation with IMRT for locally recurrent NPC between March 2001 and December 2011 and who had no evidence of distant metastasis were included in this study. Clinical characteristics, including recurrent carcinoma conditions and dosimetric features, were evaluated as candidate risk factors for LNN. Logistic regression analysis was used to identify independent risk factors and construct the predictive scoring model. Among 228 patients enrolled in this study, 204 were at risk of developing LNN based on risk analysis. Of the 204 patients treated, 31 (15.2%) developed LNN. Logistic regression analysis showed that female sex (P = 0.008), necrosis before re-irradiation (P = 0.008), accumulated total prescription dose to the gross tumor volume (GTV) ≥145.5 Gy (P = 0.043), and recurrent tumor volume ≥25.38 cm(3) (P = 0.009) were independent risk factors for LNN. A model to predict LNN was then constructed that included these four independent risk factors. A model that includes sex, necrosis before re-irradiation, accumulated total prescription dose to GTV, and recurrent tumor volume can effectively predict the risk of developing LNN in NPC patients who undergo radical re-irradiation with IMRT.

Extinctions. Paleontological baselines for evaluating extinction risk in the modern oceans.

PubMed

Finnegan, Seth; Anderson, Sean C; Harnik, Paul G; Simpson, Carl; Tittensor, Derek P; Byrnes, Jarrett E; Finkel, Zoe V; Lindberg, David R; Liow, Lee Hsiang; Lockwood, Rowan; Lotze, Heike K; McClain, Craig R; McGuire, Jenny L; O'Dea, Aaron; Pandolfi, John M

2015-05-01

Marine taxa are threatened by anthropogenic impacts, but knowledge of their extinction vulnerabilities is limited. The fossil record provides rich information on past extinctions that can help predict biotic responses. We show that over 23 million years, taxonomic membership and geographic range size consistently explain a large proportion of extinction risk variation in six major taxonomic groups. We assess intrinsic risk-extinction risk predicted by paleontologically calibrated models-for modern genera in these groups. Mapping the geographic distribution of these genera identifies coastal biogeographic provinces where fauna with high intrinsic risk are strongly affected by human activity or climate change. Such regions are disproportionately in the tropics, raising the possibility that these ecosystems may be particularly vulnerable to future extinctions. Intrinsic risk provides a prehuman baseline for considering current threats to marine biodiversity. Copyright © 2015, American Association for the Advancement of Science.

How Space Radiation Risk from Galactic Cosmic Rays at the International Space Station Relates to Nuclear Cross Sections

NASA Technical Reports Server (NTRS)

Lin, Zi-Wei; Adams, J. H., Jr.

2005-01-01

Space radiation risk to astronauts is a major obstacle for long term human space explorations. Space radiation transport codes have thus been developed to evaluate radiation effects at the International Space Station (ISS) and in missions to the Moon or Mars. We study how nuclear fragmentation processes in such radiation transport affect predictions on the radiation risk from galactic cosmic rays. Taking into account effects of the geomagnetic field on the cosmic ray spectra, we investigate the effects of fragmentation cross sections at different energies on the radiation risk (represented by dose-equivalent) from galactic cosmic rays behind typical spacecraft materials. These results tell us how the radiation risk at the ISS is related to nuclear cross sections at different energies, and consequently how to most efficiently reduce the physical uncertainty in our predictions on the radiation risk at the ISS.

Scoring annual earthquake predictions in China

NASA Astrophysics Data System (ADS)

Zhuang, Jiancang; Jiang, Changsheng

2012-02-01

The Annual Consultation Meeting on Earthquake Tendency in China is held by the China Earthquake Administration (CEA) in order to provide one-year earthquake predictions over most China. In these predictions, regions of concern are denoted together with the corresponding magnitude range of the largest earthquake expected during the next year. Evaluating the performance of these earthquake predictions is rather difficult, especially for regions that are of no concern, because they are made on arbitrary regions with flexible magnitude ranges. In the present study, the gambling score is used to evaluate the performance of these earthquake predictions. Based on a reference model, this scoring method rewards successful predictions and penalizes failures according to the risk (probability of being failure) that the predictors have taken. Using the Poisson model, which is spatially inhomogeneous and temporally stationary, with the Gutenberg-Richter law for earthquake magnitudes as the reference model, we evaluate the CEA predictions based on 1) a partial score for evaluating whether issuing the alarmed regions is based on information that differs from the reference model (knowledge of average seismicity level) and 2) a complete score that evaluates whether the overall performance of the prediction is better than the reference model. The predictions made by the Annual Consultation Meetings on Earthquake Tendency from 1990 to 2003 are found to include significant precursory information, but the overall performance is close to that of the reference model.

Using exposure prediction tools to link exposure and dosimetry for risk-based decisions: A case study with phthalates

EPA Science Inventory

A few different exposure prediction tools were evaluated for use in the new in vitro-based safety assessment paradigm using di-2-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DnBP) as case compounds. Daily intake of each phthalate was estimated using both high-throughput (HT...

Does Illness Perception Predict Posttraumatic Stress Disorder in Patients with Myocardial Infarction?

PubMed

Oflaz, Serap; Yüksel, Şahika; Şen, Fatma; Özdemiroğlu, Filiz; Kurt, Ramazan; Oflaz, Hüseyin; Kaşikcioğlu, Erdem

2014-06-01

Myocardial infarction (MI) as a life-threatening event, carrying high risk of recurrence and chronic disabling complications, increases the risk of developing acute stress disorder (ASD), posttraumatic stress disorder (PTSD), or both. The aim of this study was to investigate the relationship between illness perceptions and having ASD, PTSD, or both in patients after MI. Seventy-six patients diagnosed with acute MI were enrolled into our prospective study. We evaluated patients during the first week and six months after MI. Patients were assessed by using the Clinician Administered PTSD Scale (CAPS), the Hamilton Depression Rating Scale (HDRS), the Hamilton Anxiety Rating Scale (HARS), the Brief Illness Perception Questionnaire (BIPQ), and a semi-structured interview for socio-demographic characteristics during both the first and second evaluations. Acute stress disorder (ASD) developed in 9.2% of patients and PTSD developed in 11.9% of patients with MI. Illness perception factors of 'consequences, identity and concern' predicted the occurrence of both ASD and PTSD, whereas 'emotion' predicted only PTSD. The factors of illness perceptions predicted the induction of ASD and PTSD in patients who had acute MI.

Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study

PubMed Central

Kohane, Isaac S; Mandl, Kenneth D

2009-01-01

Objective To determine whether longitudinal data in patients’ historical records, commonly available in electronic health record systems, can be used to predict a patient’s future risk of receiving a diagnosis of domestic abuse. Design Bayesian models, known as intelligent histories, used to predict a patient’s risk of receiving a future diagnosis of abuse, based on the patient’s diagnostic history. Retrospective evaluation of the model’s predictions using an independent testing set. Setting A state-wide claims database covering six years of inpatient admissions to hospital, admissions for observation, and encounters in emergency departments. Population All patients aged over 18 who had at least four years between their earliest and latest visits recorded in the database (561 216 patients). Main outcome measures Timeliness of detection, sensitivity, specificity, positive predictive values, and area under the ROC curve. Results 1.04% (5829) of the patients met the narrow case definition for abuse, while 3.44% (19 303) met the broader case definition for abuse. The model achieved sensitive, specific (area under the ROC curve of 0.88), and early (10-30 months in advance, on average) prediction of patients’ future risk of receiving a diagnosis of abuse. Analysis of model parameters showed important differences between sexes in the risks associated with certain diagnoses. Conclusions Commonly available longitudinal diagnostic data can be useful for predicting a patient’s future risk of receiving a diagnosis of abuse. This modelling approach could serve as the basis for an early warning system to help doctors identify high risk patients for further screening. PMID:19789406

Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.

PubMed

Levin, Scott; Toerper, Matthew; Hamrock, Eric; Hinson, Jeremiah S; Barnes, Sean; Gardner, Heather; Dugas, Andrea; Linton, Bob; Kirsch, Tom; Kelen, Gabor

2018-05-01

Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation. A multisite, retrospective, cross-sectional study of 172,726 ED visits from urban and community EDs was conducted. E-triage is composed of a random forest model applied to triage data (vital signs, chief complaint, and active medical history) that predicts the need for critical care, an emergency procedure, and inpatient hospitalization in parallel and translates risk to triage level designations. Predicted outcomes and secondary outcomes of elevated troponin and lactate levels were evaluated and compared with the Emergency Severity Index (ESI). E-triage predictions had an area under the curve ranging from 0.73 to 0.92 and demonstrated equivalent or improved identification of clinical patient outcomes compared with ESI at both EDs. E-triage provided rationale for risk-based differentiation of the more than 65% of ED visits triaged to ESI level 3. Matching the ESI patient distribution for comparisons, e-triage identified more than 10% (14,326 patients) of ESI level 3 patients requiring up triage who had substantially increased risk of critical care or emergency procedure (1.7% ESI level 3 versus 6.2% up triaged) and hospitalization (18.9% versus 45.4%) across EDs. E-triage more accurately classifies ESI level 3 patients and highlights opportunities to use predictive analytics to support triage decisionmaking. Further prospective validation is needed. Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

T wave alternans as a predictor of recurrent ventricular tachyarrhythmias in ICD recipients: prospective comparison with conventional risk markers

NASA Technical Reports Server (NTRS)

Hohnloser, S. H.; Klingenheben, T.; Li, Y. G.; Zabel, M.; Peetermans, J.; Cohen, R. J.

1998-01-01

INTRODUCTION: The current standard for arrhythmic risk stratification is electrophysiologic (EP) testing, which, due to its invasive nature, is limited to patients already known to be at high risk. A number of noninvasive tests, such as determination of left ventricular ejection fraction (LVEF) or heart rate variability, have been evaluated as additional risk stratifiers. Microvolt T wave alternans (TWA) is a promising new risk marker. Prospective evaluation of noninvasive risk markers in low- or moderate-risk populations requires studies involving very large numbers of patients, and in such studies, documentation of the occurrence of ventricular tachyarrhythmias is difficult. In the present study, we identified a high-risk population, recipients of an implantable cardioverter defibrillator (ICD), and prospectively compared microvolt TWA with invasive EP testing and other risk markers with respect to their ability to predict recurrence of ventricular tachyarrhythmias as documented by ICD electrograms. METHODS AND RESULTS: Ninety-five patients with a history of ventricular tachyarrhythmias undergoing implantation of an ICD underwent EP testing, assessment of TWA, as well as determination of LVEF, baroreflex sensitivity, signal-averaged ECG, analysis of 24-hour Holter monitoring, and QT dispersion from the 12-lead surface ECG. The endpoint of the study was first appropriate ICD therapy for electrogram-documented ventricular fibrillation or tachycardia during follow-up. Kaplan-Meier survival analysis revealed that TWA (P < 0.006) and LVEF (P < 0.04) were the only significant univariate risk stratifiers. EP testing was not statistically significant (P < 0.2). Multivariate Cox regression analysis revealed that TWA was the only statistically significant independent risk factor. CONCLUSIONS: Measurement of microvolt TWA compared favorably with both invasive EP testing and other currently used noninvasive risk assessment methods in predicting recurrence of ventricular tachyarrhythmias in ICD recipients. This study suggests that TWA might also be a powerful tool for risk stratification in low- or moderate-risk patients, and needs to be prospectively evaluated in such populations.

Accuracy of the Atherosclerotic Cardiovascular Risk Equation in a Large Contemporary, Multiethnic Population.

PubMed

Rana, Jamal S; Tabada, Grace H; Solomon, Matthew D; Lo, Joan C; Jaffe, Marc G; Sung, Sue Hee; Ballantyne, Christie M; Go, Alan S

2016-05-10

The accuracy of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equation for atherosclerotic cardiovascular disease (ASCVD) events in contemporary and ethnically diverse populations is not well understood. The goal of this study was to evaluate the accuracy of the 2013 ACC/AHA Pooled Cohort Risk Equation within a large, multiethnic population in clinical care. The target population for consideration of cholesterol-lowering therapy in a large, integrated health care delivery system population was identified in 2008 and followed up through 2013. The main analyses excluded those with known ASCVD, diabetes mellitus, low-density lipoprotein cholesterol levels <70 or ≥190 mg/dl, prior lipid-lowering therapy use, or incomplete 5-year follow-up. Patient characteristics were obtained from electronic medical records, and ASCVD events were ascertained by using validated algorithms for hospitalization databases and death certificates. We compared predicted versus observed 5-year ASCVD risk, overall and according to sex and race/ethnicity. We additionally examined predicted versus observed risk in patients with diabetes mellitus. Among 307,591 eligible adults without diabetes between 40 and 75 years of age, 22,283 were black, 52,917 were Asian/Pacific Islander, and 18,745 were Hispanic. We observed 2,061 ASCVD events during 1,515,142 person-years. In each 5-year predicted ASCVD risk category, observed 5-year ASCVD risk was substantially lower: 0.20% for predicted risk <2.50%; 0.65% for predicted risk 2.50% to <3.75%; 0.90% for predicted risk 3.75% to <5.00%; and 1.85% for predicted risk ≥5.00% (C statistic: 0.74). Similar ASCVD risk overestimation and poor calibration with moderate discrimination (C statistic: 0.68 to 0.74) were observed in sex, racial/ethnic, and socioeconomic status subgroups, and in sensitivity analyses among patients receiving statins for primary prevention. Calibration among 4,242 eligible adults with diabetes was improved, but discrimination was worse (C statistic: 0.64). In a large, contemporary "real-world" population, the ACC/AHA Pooled Cohort Risk Equation substantially overestimated actual 5-year risk in adults without diabetes, overall and across sociodemographic subgroups. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

The proposed 'concordance-statistic for benefit' provided a useful metric when modeling heterogeneous treatment effects.

PubMed

van Klaveren, David; Steyerberg, Ewout W; Serruys, Patrick W; Kent, David M

2018-02-01

Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit-the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The 'c-for-benefit' represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke-thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics. Copyright © 2017 Elsevier Inc. All rights reserved.

Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

PubMed

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

Evaluating performance of risk identification methods through a large-scale simulation of observational data.

PubMed

Ryan, Patrick B; Schuemie, Martijn J

2013-10-01

There has been only limited evaluation of statistical methods for identifying safety risks of drug exposure in observational healthcare data. Simulations can support empirical evaluation, but have not been shown to adequately model the real-world phenomena that challenge observational analyses. To design and evaluate a probabilistic framework (OSIM2) for generating simulated observational healthcare data, and to use this data for evaluating the performance of methods in identifying associations between drug exposure and health outcomes of interest. Seven observational designs, including case-control, cohort, self-controlled case series, and self-controlled cohort design were applied to 399 drug-outcome scenarios in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. Longitudinal data for 10 million simulated patients were generated using a model derived from an administrative claims database, with associated demographics, periods of drug exposure derived from pharmacy dispensings, and medical conditions derived from diagnoses on medical claims. Simulation validation was performed through descriptive comparison with real source data. Method performance was evaluated using Area Under ROC Curve (AUC), bias, and mean squared error. OSIM2 replicates prevalence and types of confounding observed in real claims data. When simulated data are injected with relative risks (RR) ≥ 2, all designs have good predictive accuracy (AUC > 0.90), but when RR < 2, no methods achieve 100 % predictions. Each method exhibits a different bias profile, which changes with the effect size. OSIM2 can support methodological research. Results from simulation suggest method operating characteristics are far from nominal properties.

Cardiovascular risk prediction tools for populations in Asia.

PubMed

Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

2007-02-01

Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian populations as tools developed from data on local cohorts.

Repeated Measurement of the Intermountain Risk Score Enhances Prognostication for Mortality

PubMed Central

Horne, Benjamin D.; Lappé, Donald L.; Muhlestein, Joseph B.; May, Heidi T.; Ronnow, Brianna S.; Brunisholz, Kimberly D.; Kfoury, Abdallah G.; Bunch, T. Jared; Alharethi, Rami; Budge, Deborah; Whisenant, Brian K.; Bair, Tami L.; Jensen, Kurt R.; Anderson, Jeffrey L.

2013-01-01

Background The Intermountain Risk Score (IMRS), composed of the complete blood count (CBC) and basic metabolic profile (BMP), predicts mortality and morbidity in medical and general populations. Whether longitudinal repeated measurement of IMRS is useful for prognostication is an important question for its clinical applicability. Methods Females (N = 5,698) and males (N = 5,437) with CBC and BMP panels measured 6 months to 2.0 years apart (mean 1.0 year) had baseline and follow-up IMRS computed. Survival analysis during 4.0±2.5 years (maximum 10 years) evaluated mortality (females: n = 1,255 deaths; males: n = 1,164 deaths) and incident major events (myocardial infarction, heart failure [HF], and stroke). Results Both baseline and follow-up IMRS (categorized as high-risk vs. low-risk) were independently associated with mortality (all p<0.001) in bivariable models. For females, follow-up IMRS had hazard ratio (HR) = 5.23 (95% confidence interval [CI] = 4.11, 6.64) and baseline IMRS had HR = 3.66 (CI = 2.94, 4.55). Among males, follow-up IMRS had HR = 4.28 (CI = 3.51, 5.22) and baseline IMRS had HR = 2.32 (CI = 1.91, 2.82). IMRS components such as RDW, measured at both time points, also predicted mortality. Baseline and follow-up IMRS strongly predicted incident HF in both genders. Conclusions Repeated measurement of IMRS at baseline and at about one year of follow-up were independently prognostic for mortality and incident HF among initially hospitalized patients. RDW and other CBC and BMP values were also predictive of outcomes. Further research should evaluate the utility of IMRS as a tool for clinical risk adjustment. PMID:23874899

Cardiac risk stratification: Role of the coronary calcium score

PubMed Central

Sharma, Rakesh K; Sharma, Rajiv K; Voelker, Donald J; Singh, Vibhuti N; Pahuja, Deepak; Nash, Teresa; Reddy, Hanumanth K

2010-01-01

Coronary artery calcium (CAC) is an integral part of atherosclerotic coronary heart disease (CHD). CHD is the leading cause of death in industrialized nations and there is a constant effort to develop preventative strategies. The emphasis is on risk stratification and primary risk prevention in asymptomatic patients to decrease cardiovascular mortality and morbidity. The Framingham Risk Score predicts CHD events only moderately well where family history is not included as a risk factor. There has been an exploration for new tests for better risk stratification and risk factor modification. While the Framingham Risk Score, European Systematic Coronary Risk Evaluation Project, and European Prospective Cardiovascular Munster study remain excellent tools for risk factor modification, the CAC score may have additional benefit in risk assessment. There have been several studies supporting the role of CAC score for prediction of myocardial infarction and cardiovascular mortality. It has been shown to have great scope in risk stratification of asymptomatic patients in the emergency room. Additionally, it may help in assessment of progression or regression of coronary artery disease. Furthermore, the CAC score may help differentiate ischemic from nonischemic cardiomyopathy. PMID:20730016

Preoperative biomedical risk and depressive symptoms are differently associated with reduced health-related quality of life in patients 1year after cardiac surgery.

PubMed

Patron, Elisabetta; Messerotti Benvenuti, Simone; Palomba, Daniela

2016-01-01

To examine whether preoperative biomedical risk and depressive symptoms were associated with physical and mental components of health-related quality of life (HRQoL) in patients 1year after cardiac surgery. Seventy-five patients completed a psychological evaluation, including the Center for Epidemiological Study of Depression scale, the 12-item Short-Form Physical Component Scale (SF-12-PCS) and Mental Component Scale (SF-12-MCS), the Instrumental Activities of Daily Living questionnaire for depressive symptoms and HRQoL, respectively, before surgery and at 1-year follow-up. Preoperative depressive symptoms predicted the SF-12-PCS (beta=-.22, P<.05) and SF-12-MCS (beta=-.30, P<.04) scores in patients 1year after cardiac surgery, whereas the European System for Cardiac Operative Risk Evaluation was associated with SF-12-PCS (beta=-.28, P<.02), but not SF-12-MCS (beta=.01, P=.97) scores postoperatively. The current findings showed that preoperative depressive symptoms are associated with poor physical and mental components of HRQoL, whereas high biomedical risk predicts reduced physical, but not mental, functioning in patients postoperatively. This study suggests that a preoperative assessment of depressive symptoms in addition to the evaluation of common biomedical risk factors is essential to anticipate which patients are likely to show poor HRQoL after cardiac surgery. Copyright © 2016 Elsevier Inc. All rights reserved.

Empirical evaluation of spatial and non-spatial European-scale multimedia fate models: results and implications for chemical risk assessment.

PubMed

Armitage, James M; Cousins, Ian T; Hauck, Mara; Harbers, Jasper V; Huijbregts, Mark A J

2007-06-01

Multimedia environmental fate models are commonly-applied tools for assessing the fate and distribution of contaminants in the environment. Owing to the large number of chemicals in use and the paucity of monitoring data, such models are often adopted as part of decision-support systems for chemical risk assessment. The purpose of this study was to evaluate the performance of three multimedia environmental fate models (spatially- and non-spatially-explicit) at a European scale. The assessment was conducted for four polycyclic aromatic hydrocarbons (PAHs) and hexachlorobenzene (HCB) and compared predicted and median observed concentrations using monitoring data collected for air, water, sediments and soils. Model performance in the air compartment was reasonable for all models included in the evaluation exercise as predicted concentrations were typically within a factor of 3 of the median observed concentrations. Furthermore, there was good correspondence between predictions and observations in regions that had elevated median observed concentrations for both spatially-explicit models. On the other hand, all three models consistently underestimated median observed concentrations in sediment and soil by 1-3 orders of magnitude. Although regions with elevated median observed concentrations in these environmental media were broadly identified by the spatially-explicit models, the magnitude of the discrepancy between predicted and median observed concentrations is of concern in the context of chemical risk assessment. These results were discussed in terms of factors influencing model performance such as the steady-state assumption, inaccuracies in emission estimates and the representativeness of monitoring data.

The impact of missing trauma data on predicting massive transfusion

PubMed Central

Trickey, Amber W.; Fox, Erin E.; del Junco, Deborah J.; Ning, Jing; Holcomb, John B.; Brasel, Karen J.; Cohen, Mitchell J.; Schreiber, Martin A.; Bulger, Eileen M.; Phelan, Herb A.; Alarcon, Louis H.; Myers, John G.; Muskat, Peter; Cotton, Bryan A.; Wade, Charles E.; Rahbar, Mohammad H.

2013-01-01

INTRODUCTION Missing data are inherent in clinical research and may be especially problematic for trauma studies. This study describes a sensitivity analysis to evaluate the impact of missing data on clinical risk prediction algorithms. Three blood transfusion prediction models were evaluated utilizing an observational trauma dataset with valid missing data. METHODS The PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study included patients requiring ≥ 1 unit of red blood cells (RBC) at 10 participating U.S. Level I trauma centers from July 2009 – October 2010. Physiologic, laboratory, and treatment data were collected prospectively up to 24h after hospital admission. Subjects who received ≥ 10 RBC units within 24h of admission were classified as massive transfusion (MT) patients. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation. A sensitivity analysis for missing data was conducted to determine the upper and lower bounds for correct classification percentages. RESULTS PROMMTT enrolled 1,245 subjects. MT was received by 297 patients (24%). Missing percentage ranged from 2.2% (heart rate) to 45% (respiratory rate). Proportions of complete cases utilized in the MT prediction models ranged from 41% to 88%. All models demonstrated similar correct classification percentages using complete case analysis and multiple imputation. In the sensitivity analysis, correct classification upper-lower bound ranges per model were 4%, 10%, and 12%. Predictive accuracy for all models using PROMMTT data was lower than reported in the original datasets. CONCLUSIONS Evaluating the accuracy clinical prediction models with missing data can be misleading, especially with many predictor variables and moderate levels of missingness per variable. The proposed sensitivity analysis describes the influence of missing data on risk prediction algorithms. Reporting upper/lower bounds for percent correct classification may be more informative than multiple imputation, which provided similar results to complete case analysis in this study. PMID:23778514

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.

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

PubMed

Yokota, Shinichiroh; Ohe, Kazuhiko

2016-04-01

To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data. The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data. The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value. Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear. © 2016 Japan Academy of Nursing Science.

Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.

PubMed

Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

2015-03-01

Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort. © 2014, The International Biometric Society.

Claims-based risk model for first severe COPD exacerbation.

PubMed

Stanford, Richard H; Nag, Arpita; Mapel, Douglas W; Lee, Todd A; Rosiello, Richard; Schatz, Michael; Vekeman, Francis; Gauthier-Loiselle, Marjolaine; Merrigan, J F Philip; Duh, Mei Sheng

2018-02-01

To develop and validate a predictive model for first severe chronic obstructive pulmonary disease (COPD) exacerbation using health insurance claims data and to validate the risk measure of controller medication to total COPD treatment (controller and rescue) ratio (CTR). A predictive model was developed and validated in 2 managed care databases: Truven Health MarketScan database and Reliant Medical Group database. This secondary analysis assessed risk factors, including CTR, during the baseline period (Year 1) to predict risk of severe exacerbation in the at-risk period (Year 2). Patients with COPD who were 40 years or older and who had at least 1 COPD medication dispensed during the year following COPD diagnosis were included. Subjects with severe exacerbations in the baseline year were excluded. Risk factors in the baseline period were included as potential predictors in multivariate analysis. Performance was evaluated using C-statistics. The analysis included 223,824 patients. The greatest risk factors for first severe exacerbation were advanced age, chronic oxygen therapy usage, COPD diagnosis type, dispensing of 4 or more canisters of rescue medication, and having 2 or more moderate exacerbations. A CTR of 0.3 or greater was associated with a 14% lower risk of severe exacerbation. The model performed well with C-statistics, ranging from 0.711 to 0.714. This claims-based risk model can predict the likelihood of first severe COPD exacerbation. The CTR could also potentially be used to target populations at greatest risk for severe exacerbations. This could be relevant for providers and payers in approaches to prevent severe exacerbations and reduce costs.

Neurological Gait Abnormalities And Risk Of Falls In Older Adults

PubMed Central

Verghese, Joe; Ambrose, Anne F; Lipton, Richard B; Wang, Cuiling

2009-01-01

Objective To estimate the validity of neurological gait evaluations in predicting falls in older adults. Methods We studied 632 adults age 70 and over (mean age 80.6 years, 62% women) enrolled in the Einstein Aging Study whose walking patterns were evaluated by study clinicians using a clinical gait rating scale. Association of neurological gaits and six subtypes (hemiparetic, frontal, Parkinsonian, unsteady, neuropathic, and spastic) with incident falls was studied using generalized estimation equation procedures adjusted for potential confounders, and reported as risk ratio with 95% confidence intervals (CI). Results Over a mean follow-up of 21 months, 244 (39%) subjects fell. Mean fall rate was 0.47 falls per person year. At baseline, 120 subjects were diagnosed with neurological gaits. Subjects with neurological gaits were at increased risk of falls (risk ratio 1.49, 95% CI 1.11 – 2.00). Unsteady (risk ratio 1.52, 95% CI 1.04 – 2.22), and neuropathic gait (risk ratio 1.94, 95% CI 1.07 – 3.11) were the two gait subtypes that predicted risk of falls. The results remained significant after accounting for disability and cognitive status, and also with injurious falls as the outcome. Conclusions Neurological gaits and subtypes are independent predictors of falls in older adults. Neurological gait assessments will help clinicians identify and institute preventive measures in older adults at high risk for falls. PMID:19784714

Evaluating the vulnerability of Maine forests to wind damage

Treesearch

Thomas E. Perry; Jeremy S. Wilson

2010-01-01

Numerous factors, some of which cannot be controlled, are continually interacting with the forest resource, introducing risk to management, and making consistent predictable management outcomes uncertain. Included in these factors are threats or hazards such as windstorms and wildfire. Factors influencing the probability (risk) of windthrow or windsnap occurring can be...

Is It Safe? Reliability and Validity of Structured versus Unstructured Child Safety Judgments

ERIC Educational Resources Information Center

Bartelink, Cora; de Kwaadsteniet, Leontien; ten Berge, Ingrid J.; Witteman, Cilia L. M.

2017-01-01

Background: The LIRIK, an instrument for the assessment of child safety and risk, is designed to improve assessments by guiding professionals through a structured evaluation of relevant signs, risk factors, and protective factors. Objective: We aimed to assess the interrater agreement and the predictive validity of professionals' judgments made…

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

PubMed

Karaismailoğlu, Eda; Dikmen, Zeliha Günnur; Akbıyık, Filiz; Karaağaoğlu, Ahmet Ergun

2018-04-30

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB) are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, time- dependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where disease status can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. We also obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A web tool was developed to estimate time intervals in risk. Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath were included. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac death and to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significant biomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youden index was used to determine optimal cut-off values. Results : Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves of myoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 during the first 180 h. Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to be statistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web tool can be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

Mathematical model to estimate risk of calcium-containing renal stones

NASA Technical Reports Server (NTRS)

Pietrzyk, R. A.; Feiveson, A. H.; Whitson, P. A.

1999-01-01

BACKGROUND/AIMS: Astronauts exposed to microgravity during the course of spaceflight undergo physiologic changes that alter the urinary environment so as to increase the risk of renal stone formation. This study was undertaken to identify a simple method with which to evaluate the potential risk of renal stone development during spaceflight. METHOD: We used a large database of urinary risk factors obtained from 323 astronauts before and after spaceflight to generate a mathematical model with which to predict the urinary supersaturation of calcium stone forming salts. RESULT: This model, which involves the fewest possible analytical variables (urinary calcium, citrate, oxalate, phosphorus, and total volume), reliably and accurately predicted the urinary supersaturation of the calcium stone forming salts when compared to results obtained from a group of 6 astronauts who collected urine during flight. CONCLUSIONS: The use of this model will simplify both routine medical monitoring during spaceflight as well as the evaluation of countermeasures designed to minimize renal stone development. This model also can be used for Earth-based applications in which access to analytical resources is limited.

Evaluation of prenatal risk factors for prediction of outcome in right heart lesions: CVP score in fetal right heart defects.

PubMed

Neves, Ana Luisa; Mathias, Leigh; Wilhm, Marilyn; Leshko, Jennifer; Linask, Kersti K; Henriques-Coelho, Tiago; Areias, José C; Huhta, James C

2014-09-01

To determine the prenatal variables predicting the risk of perinatal death in congenital right heart defects. Retrospective analysis of 28 fetuses with right heart defects was performed. Logistic regression analyses were performed to obtain odds ratios (OR) for the relationship between the risk of death and echocardiographic parameters. The parameters that correlated with the outcome were incorporated in an attempt to devise a disease-specific cardiovascular profile score. Fetal echocardiograms (143) from 28 patients were analyzed. The cardiovascular profile score predicted the risk of death. A lower right ventricle (RV) pressure was associated with mortality (OR 0.959; 95% confidence intervals (CI) 0.940-0.978). Higher peak aortic velocity through the aortic valve (OR 0.104; 95% CI 0.020-0.529) was associated with a better outcome. These cardiac function parameters were incorporated in a modified disease-specific CVP Score. Patients with a mean modified cardiovascular profile score of ≤ 6 were over 3.7 times more likely to die than those with scores of 7-10. The original Cardiovascular Profile Score predicted the risk of death in right heart defects. The modified score was not validated as a good prediction tool by this study. Fetal RV pressure estimate and peak aortic velocity can be used as independent prognostic predictors.

A simulation model for risk assessment of turbine wheels

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.; Hage, Richard T.

1991-01-01

A simulation model has been successfully developed to evaluate the risk of the Space Shuttle auxiliary power unit (APU) turbine wheels for a specific inspection policy. Besides being an effective tool for risk/reliability evaluation, the simulation model also allows the analyst to study the trade-offs between wheel reliability, wheel life, inspection interval, and rejection crack size. For example, in the APU application, sensitivity analysis results showed that the wheel life limit has the least effect on wheel reliability when compared to the effect of the inspection interval and the rejection crack size. In summary, the simulation model developed represents a flexible tool to predict turbine wheel reliability and study the risk under different inspection policies.

A simulation model for risk assessment of turbine wheels

NASA Astrophysics Data System (ADS)

Safie, Fayssal M.; Hage, Richard T.

A simulation model has been successfully developed to evaluate the risk of the Space Shuttle auxiliary power unit (APU) turbine wheels for a specific inspection policy. Besides being an effective tool for risk/reliability evaluation, the simulation model also allows the analyst to study the trade-offs between wheel reliability, wheel life, inspection interval, and rejection crack size. For example, in the APU application, sensitivity analysis results showed that the wheel life limit has the least effect on wheel reliability when compared to the effect of the inspection interval and the rejection crack size. In summary, the simulation model developed represents a flexible tool to predict turbine wheel reliability and study the risk under different inspection policies.

A decision model to predict the risk of the first fall onset.

PubMed

Deschamps, Thibault; Le Goff, Camille G; Berrut, Gilles; Cornu, Christophe; Mignardot, Jean-Baptiste

2016-08-01

Miscellaneous features from various domains are accepted to be associated with the risk of falling in the elderly. However, only few studies have focused on establishing clinical tools to predict the risk of the first fall onset. A model that would objectively and easily evaluate the risk of a first fall occurrence in the coming year still needs to be built. We developed a model based on machine learning, which might help the medical staff predict the risk of the first fall onset in a one-year time window. Overall, 426 older adults who had never fallen were assessed on 73 variables, comprising medical, social and physical outcomes, at t0. Each fall was recorded at a prospective 1-year follow-up. A decision tree was built on a randomly selected training subset of the cohort (80% of the full-set) and validated on an independent test set. 82 participants experienced a first fall during the follow-up. The machine learning process independently extracted 13 powerful parameters and built a model showing 89% of accuracy for the overall classification with 83%-82% of true positive fallers and 96%-61% of true negative non-fallers (training set vs. independent test set). This study provides a pilot tool that could easily help the gerontologists refine the evaluation of the risk of the first fall onset and prioritize the effective prevention strategies. The study also offers a transparent framework for future, related investigation that would validate the clinical relevance of the established model by independently testing its accuracy on larger cohort. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Building a genome analysis pipeline to predict disease risk and prevent disease.

PubMed

Bromberg, Y

2013-11-01

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice. © 2013. Published by Elsevier Ltd. All rights reserved.

Predicting the risk of suicide by analyzing the text of clinical notes.

PubMed

Poulin, Chris; Shiner, Brian; Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

PubMed Central

Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

Management of complicated gallstones: results of an alternative approach to difficult cholecystectomies.

PubMed

Lirici, Marco Maria; Califano, Andrea

2010-10-01

Laparoscopic cholecystectomy (LC) is the gold standard treatment of gallstones. Nevertheless, the incidence of conversion and injuries to the biliary tract is still high in difficult cholecystectomies. In this study we sought to determine how using operative risk predictive scores (PSs) and the Nassar scale to grade the difficulty of LC would optimize the perioperative management of complicated gallstone patients. We also evaluated whether the "fundus-first" approach to LC combined with ultrasonic dissection minimizes the risk of conversion and biliary injury in difficult cholecystectomies, and avoids routine intraoperative cholangiography. A prospective non-randomized study was carried out from 2005 to 2007 including 237 patients referred for gallbladder diseases. All patients were evaluated using an operative risk PS. The LC grade of difficulty was assessed according to Nassar. Diagnostic accuracy, sensitivity, and specificity of PS were calculated. LC in difficult cases was accomplished with a fundus-first approach. Outcome measures included: Conversion rate, bile duct (BD) injury rate, and postoperative complications according to Clavien. In 178 out of 237 patients, a higher risk of conversion and complication was predicted. In 146 out of these 178 cases, intra-operative grading confirmed the difficulty of the procedure. The PS diagnostic accuracy was 0.865, sensitivity was 100%, and specificity 65%. Positive predictive value and negative predictive value were 0.82 and 1, respectively. Conversion rate was 2.7%. Mean operating time and postoperative length of hospital stay were 75 minutes and 3.5 days. Intra-operative cholangiography was necessary in five cases, and one intraoperative biliary complication occurred with an uneventful postoperative course. Overall, postoperative complications were 2.7% with a mortality rate of 0.68% (1 myocardial infarction). Fundus-first LC by ultrasonic dissection is safe and minimizes the risk of conversion and biliary injuries in difficult cases. Difficult cholecystectomies may be predicted preoperatively; in these cases the fundus-first approach and ultrasound dissection may be advised.

Performance during internal medicine residency training and subsequent disciplinary action by state licensing boards.

PubMed

Papadakis, Maxine A; Arnold, Gerald K; Blank, Linda L; Holmboe, Eric S; Lipner, Rebecca S

2008-06-03

Physicians who are disciplined by state licensing boards are more likely to have demonstrated unprofessional behavior in medical school. Information is limited on whether similar performance measures taken during residency can predict performance as practicing physicians. To determine whether performance measures during residency predict the likelihood of future disciplinary actions against practicing internists. Retrospective cohort study. State licensing board disciplinary actions against physicians from 1990 to 2006. 66,171 physicians who entered internal medicine residency training in the United States from 1990 to 2000 and became diplomates. Predictor variables included components of the Residents' Annual Evaluation Summary ratings and American Board of Internal Medicine (ABIM) certification examination scores. 2 performance measures independently predicted disciplinary action. A low professionalism rating on the Residents' Annual Evaluation Summary predicted increased risk for disciplinary action (hazard ratio, 1.7 [95% CI, 1.3 to 2.2]), and high performance on the ABIM certification examination predicted decreased risk for disciplinary action (hazard ratio, 0.7 [CI, 0.60 to 0.70] for American or Canadian medical school graduates and 0.9 [CI, 0.80 to 1.0] for international medical school graduates). Progressively better professionalism ratings and ABIM certification examination scores were associated with less risk for subsequent disciplinary actions; the risk ranged from 4.0% for the lowest professionalism rating to 0.5% for the highest and from 2.5% for the lowest examination scores to 0.0% for the highest. The study was retrospective. Some diplomates may have practiced outside of the United States. Nondiplomates were excluded. Poor performance on behavioral and cognitive measures during residency are associated with greater risk for state licensing board actions against practicing physicians at every point on a performance continuum. These findings support the Accreditation Council for Graduate Medical Education standards for professionalism and cognitive performance and the development of best practices to remediate these deficiencies.

Integration and Assessment of Component Health Prognostics in Supervisory Control Systems

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

Ramuhalli, Pradeep; Bonebrake, Christopher A.; Dib, Gerges

Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize real-time information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant.more » ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation.« less

Risk scores for outcome in bacterial meningitis: Systematic review and external validation study.

PubMed

Bijlsma, Merijn W; Brouwer, Matthijs C; Bossuyt, Patrick M; Heymans, Martijn W; van der Ende, Arie; Tanck, Michael W T; van de Beek, Diederik

2016-11-01

To perform an external validation study of risk scores, identified through a systematic review, predicting outcome in community-acquired bacterial meningitis. MEDLINE and EMBASE were searched for articles published between January 1960 and August 2014. Performance was evaluated in 2108 episodes of adult community-acquired bacterial meningitis from two nationwide prospective cohort studies by the area under the receiver operating characteristic curve (AUC), the calibration curve, calibration slope or Hosmer-Lemeshow test, and the distribution of calculated risks. Nine risk scores were identified predicting death, neurological deficit or death, or unfavorable outcome at discharge in bacterial meningitis, pneumococcal meningitis and invasive meningococcal disease. Most studies had shortcomings in design, analyses, and reporting. Evaluation showed AUCs of 0.59 (0.57-0.61) and 0.74 (0.71-0.76) in bacterial meningitis, 0.67 (0.64-0.70) in pneumococcal meningitis, and 0.81 (0.73-0.90), 0.82 (0.74-0.91), 0.84 (0.75-0.93), 0.84 (0.76-0.93), 0.85 (0.75-0.95), and 0.90 (0.83-0.98) in meningococcal meningitis. Calibration curves showed adequate agreement between predicted and observed outcomes for four scores, but statistical tests indicated poor calibration of all risk scores. One score could be recommended for the interpretation and design of bacterial meningitis studies. None of the existing scores performed well enough to recommend routine use in individual patient management. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

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.

Predicting Abused Women With Children Who Return to a Shelter: Development and Use of a Rapid Assessment Triage Tool.

PubMed

McFarlane, Judith; Pennings, Jacquelyn; Liu, Fuqin; Gilroy, Heidi; Nava, Angeles; Maddoux, John A; Montalvo-Liendo, Nora; Paulson, René

2016-02-01

To develop a tool to predict risk for return to a shelter, 150 women with children, exiting a domestic violence shelter, were evaluated every 4 months for 24 months to determine risk factors for returning to a shelter. The study identified four risk factors, including danger for murder, woman's age (i.e., older women), tangible support (i.e., access to money, transportation), and child witness to verbal abuse of the mother. An easy to use, quick triage tool with a weighted score was derived, which can identify with 90% accuracy abused women with children most likely to return to shelters. © The Author(s) 2015.

Systematic review of fall risk screening tools for older patients in acute hospitals.

PubMed

Matarese, Maria; Ivziku, Dhurata; Bartolozzi, Francesco; Piredda, Michela; De Marinis, Maria Grazia

2015-06-01

To determine the most accurate fall risk screening tools for predicting falls among patients aged 65 years or older admitted to acute care hospitals. Falls represent a serious problem in older inpatients due to the potential physical, social, psychological and economic consequences. Older inpatients present with risk factors associated with age-related physiological and psychological changes as well as multiple morbidities. Thus, fall risk screening tools for older adults should include these specific risk factors. There are no published recommendations addressing what tools are appropriate for older hospitalized adults. Systematic review. MEDLINE, CINAHL and Cochrane electronic databases were searched between January 1981-April 2013. Only prospective validation studies reporting sensitivity and specificity values were included. Recommendations of the Cochrane Handbook of Diagnostic Test Accuracy Reviews have been followed. Three fall risk assessment tools were evaluated in seven articles. Due to the limited number of studies, meta-analysis was carried out only for the STRATIFY and Hendrich Fall Risk Model II. In the combined analysis, the Hendrich Fall Risk Model II demonstrated higher sensitivity than STRATIFY, while the STRATIFY showed higher specificity. In both tools, the Youden index showed low prognostic accuracy. The identified tools do not demonstrate predictive values as high as needed for identifying older inpatients at risk for falls. For this reason, no tool can be recommended for fall detection. More research is needed to evaluate fall risk screening tools for older inpatients. © 2014 John Wiley & Sons Ltd.

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.

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/.

Predicting neutropenia risk in patients with cancer using electronic data.

PubMed

Pawloski, Pamala A; Thomas, Avis J; Kane, Sheryl; Vazquez-Benitez, Gabriela; Shapiro, Gary R; Lyman, Gary H

2017-04-01

Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test = 0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort. © 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

Evaluation of nutritional status as an independent predictor of post-operative complications and morbidity after gastro-intestinal surgery.

PubMed

van der Kroft, G; Janssen-Heijnen, M L G; van Berlo, C L H; Konsten, J L M

2015-08-01

Nutritional Risk Screening-2002 (NRS-2002) and the Malnutrition Universal Screening Tool (MUST) are screening tools for nutritional risk that have also been used to predict post-operative complications and morbidity, though not all studies confirm the reliability of nutritional screening. Our study aims to evaluate the independent predictive value of nutritional risk screening in addition to currently documented medical, surgical and anesthesiological risk factors for post-operative complications, as well as length of hospital stay. This study is a prospective observational cohort study of 129 patients undergoing elective gastro-intestinal-surgery. Patients were screened for nutritional risk upon admission using both MUST and NRS-2002 screening tools. Univariate and multivariate analyses were performed to investigate the independent predictive value of nutritional risk for post-operative complications and length of hospital stay. MUST ≥2 (OR 2.87; 95% CI 1.05-7.87) and peri-operative transfusion (OR 2.78; 95% CI 1.05-7.40) were significant independent predictors for the occurrence of post-operative complications. Peri-operative transfusion (HR 2.40; 95% CI 1.45-4.00), age ≥70 (HR 1.50; 95% CI 1.05-2.16) and open surgery versus laparoscopic surgery (HR 1.39; 95% CI 0.94-2.05) were independent predictors for increased length of hospital stay, whereas American Society of Anesthesiology Score (ASA) and MUST were not. Nutritional risk screening (MUST ≥2) is an independent predictor for post-operative complications, but not for increased length of hospital stay. Copyright © 2015 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.

Risk of ultrasound-detected neonatal brain abnormalities in intrauterine growth-restricted fetuses born between 28 and 34 weeks' gestation: relationship with gestational age at birth and fetal Doppler parameters.

PubMed

Cruz-Martinez, R; Tenorio, V; Padilla, N; Crispi, F; Figueras, F; Gratacos, E

2015-10-01

To estimate the value of gestational age at birth and fetal Doppler parameters in predicting the risk of neonatal cranial abnormalities in intrauterine growth-restricted (IUGR) fetuses born between 28 and 34 weeks' gestation. Fetal Doppler parameters including umbilical artery (UA), middle cerebral artery (MCA), aortic isthmus, ductus venosus and myocardial performance index were evaluated in a cohort of 90 IUGR fetuses with abnormal UA Doppler delivered between 28 and 34 weeks' gestation and in 90 control fetuses matched for gestational age. The value of gestational age at birth and fetal Doppler parameters in predicting the risk of ultrasound-detected cranial abnormalities (CUA), including intraventricular hemorrhage, periventricular leukomalacia and basal ganglia lesions, was analyzed. Overall, IUGR fetuses showed a significantly higher incidence of CUA than did control fetuses (40.0% vs 12.2%, respectively; P < 0.001). Within the IUGR group, all predictive variables were associated individually with the risk of CUA, but fetal Doppler parameters rather than gestational age at birth were identified as the best predictor. MCA Doppler distinguished two groups with different degrees of risk of CUA (48.5% vs 13.6%, respectively; P < 0.01). In the subgroup with MCA vasodilation, presence of aortic isthmus retrograde net blood flow, compared to antegrade flow, allowed identification of a subgroup of cases with the highest risk of CUA (66.7% vs 38.6%, respectively; P < 0.05). Evaluation of fetal Doppler parameters, rather than gestational age at birth, allows identification of IUGR preterm fetuses at risk of neonatal brain abnormalities. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Positive impact of infection prevention on the management of nosocomial outbreaks at an academic hospital.

PubMed

Dik, Jan-Willem H; Sinha, Bhanu; Lokate, Mariëtte; Lo-Ten-Foe, Jerome R; Dinkelacker, Ariane G; Postma, Maarten J; Friedrich, Alexander W

2016-10-01

Infection prevention (IP) measures are vital to prevent (nosocomial) outbreaks. Financial evaluations of these are scarce. An incremental cost analysis for an academic IP unit was performed. On a yearly basis, we evaluated: IP measures; costs thereof; numbers of patients at risk for causing nosocomial outbreaks; predicted outbreak patients; and actual outbreak patients. IP costs rose on average yearly with €150,000; however, more IP actions were undertaken. Numbers of patients colonized with high-risk microorganisms increased. The trend of actual outbreak patients remained stable. Predicted prevented outbreak patients saved costs, leading to a positive return on investment of 1.94. This study shows that investments in IP can prevent outbreak cases, thereby saving enough money to earn back these investments.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Combined impact of lead, cadmium, polychlorinated biphenyls and non-chemical risk factors on blood pressure in NHANES

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

Peters, Junenette L., E-mail: petersj@bu.edu; Patricia Fabian, M., E-mail: pfabian@bu.edu; Levy, Jonathan I., E-mail: jonlevy@bu.edu

High blood pressure is associated with exposure to multiple chemical and non-chemical risk factors, but epidemiological analyses to date have not assessed the combined effects of both chemical and non-chemical stressors on human populations in the context of cumulative risk assessment. We developed a novel modeling approach to evaluate the combined impact of lead, cadmium, polychlorinated biphenyls (PCBs), and multiple non-chemical risk factors on four blood pressure measures using data for adults aged ≥20 years from the National Health and Nutrition Examination Survey (1999–2008). We developed predictive models for chemical and other stressors. Structural equation models were applied to accountmore » for complex associations among predictors of stressors as well as blood pressure. Models showed that blood lead, serum PCBs, and established non-chemical stressors were significantly associated with blood pressure. Lead was the chemical stressor most predictive of diastolic blood pressure and mean arterial pressure, while PCBs had a greater influence on systolic blood pressure and pulse pressure, and blood cadmium was not a significant predictor of blood pressure. The simultaneously fit exposure models explained 34%, 43% and 52% of the variance for lead, cadmium and PCBs, respectively. The structural equation models were developed using predictors available from public data streams (e.g., U.S. Census), which would allow the models to be applied to any U.S. population exposed to these multiple stressors in order to identify high risk subpopulations, direct intervention strategies, and inform public policy. - Highlights: • We evaluated joint impact of chemical and non-chemical stressors on blood pressure. • We built predictive models for lead, cadmium and polychlorinated biphenyls (PCBs). • Our approach allows joint evaluation of predictors from population-specific data. • Lead, PCBs and established non-chemical stressors were related to blood pressure. • Framework allows cumulative risk assessment in specific geographic settings.« less

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

POSSUM and P-POSSUM for risk assessment in general surgery in the elderly.

PubMed

Igari, Kimihiro; Ochiai, Takanori; Yamazaki, Shigeru

2013-09-01

The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and Portsmouth POSSUM (P-POSSUM) use preoperative and intraoperative factors to evaluate risk. We examined our surgical results to investigate predictive factors for morbidity and mortality, and evaluate the accuracy of the POSSUM and P-POSSUM. Patients (n = 593) aged ≥80 years, undergoing general surgical procedures were enrolled. Logistic regression analysis was used to determine the independent predictors. The predicted outcomes using POSSUM and P-POSSUM were also compared with actual outcomes. Physiological score (PS) and operative severity score (OS) were independent predictors of morbidity and mortality. Using POSSUM, the observed/expected (O/E) morbidity ratio was 1.44 and O/E mortality ratio was 0.98. Using P-POSSUM, the O/E mortality ratio was 1.0. Even though POSSUM tended to underestimate the morbidity rate, POSSUM and P-POSSUM accurately predicted the mortality rate after general surgical procedures.

Development and evaluation of a composite risk score to predict kidney transplant failure.

PubMed

Moore, Jason; He, Xiang; Shabir, Shazia; Hanvesakul, Rajesh; Benavente, David; Cockwell, Paul; Little, Mark A; Ball, Simon; Inston, Nicholas; Johnston, Atholl; Borrows, Richard

2011-05-01

Although risk factors for kidney transplant failure are well described, prognostic risk scores to estimate risk in prevalent transplant recipients are limited. Development and validation of risk-prediction instruments. The development data set included 2,763 prevalent patients more than 12 months posttransplant enrolled into the LOTESS (Long Term Efficacy and Safety Surveillance) Study. The validation data set included 731 patients who underwent transplant at a single UK center. Estimated glomerular filtration rate (eGFR) and other risk factors were evaluated using Cox regression. Scores for death-censored and overall transplant failure were based on the summed hazard ratios for baseline predictor variables. Predictive performance was assessed using calibration (Hosmer-Lemeshow statistic), discrimination (C statistic), and clinical reclassification (net reclassification improvement) compared with eGFR alone. In the development data set, 196 patients died and another 225 experienced transplant failure. eGFR, recipient age, race, serum urea and albumin levels, declining eGFR, and prior acute rejection predicted death-censored transplant failure. eGFR, recipient age, sex, serum urea and albumin levels, and declining eGFR predicted overall transplant failure. In the validation data set, 44 patients died and another 101 experienced transplant failure. The weighted scores comprising these variables showed adequate discrimination and calibration for death-censored (C statistic, 0.83; 95% CI, 0.75-0.91; Hosmer-Lemeshow χ(2)P = 0.8) and overall (C statistic, 0.70; 95% CI, 0.64-0.77; Hosmer-Lemeshow χ(2)P = 0.5) transplant failure. However, the scores failed to reclassify risk compared with eGFR alone (net reclassification improvements of 7.6% [95% CI, -0.2 to 13.4; P = 0.09] and 4.3% [95% CI, -2.7 to 11.8; P = 0.3] for death-censored and overall transplant failure, respectively). Retrospective analysis of predominantly cyclosporine-treated patients; limited study size and categorization of variables may limit power to detect effect. Although the scores performed well regarding discrimination and calibration, clinically relevant risk reclassification over eGFR alone was not evident, emphasizing the stringent requirements for such scores. Further studies are required to develop and refine this process. Copyright © 2011 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

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

Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal).

PubMed

Collins, G S; Altman, D G

2012-07-10

Early identification of colorectal cancer is an unresolved challenge and the predictive value of single symptoms is limited. We evaluated the performance of QCancer (Colorectal) prediction model for predicting the absolute risk of colorectal cancer in an independent UK cohort of patients from general practice records. A total of 2.1 million patients registered with a general practice surgery between 01 January 2000 and 30 June 2008, aged 30-84 years (3.7 million person-years) with 3712 colorectal cancer cases were included in the analysis. Colorectal cancer was defined as incident diagnosis of colorectal cancer during the 2 years after study entry. The results from this independent and external validation of QCancer (Colorectal) prediction model demonstrated good performance data on a large cohort of general practice patients. QCancer (Colorectal) had very good discrimination with an area under the ROC curve of 0.92 (women) and 0.91 (men), and explained 68% (women) and 66% (men) of the variation. QCancer (Colorectal) was well calibrated across all tenths of risk and over all age ranges with predicted risks closely matching observed risks. QCancer (Colorectal) appears to be a useful tool for identifying undetected cases of undiagnosed colorectal cancer in primary care in the United Kingdom.

Evaluation of the National Surgical Quality Improvement Program Universal Surgical Risk Calculator for a gynecologic oncology service.

PubMed

Szender, J Brian; Frederick, Peter J; Eng, Kevin H; Akers, Stacey N; Lele, Shashikant B; Odunsi, Kunle

2015-03-01

The National Surgical Quality Improvement Program is aimed at preventing perioperative complications. An online calculator was recently published, but the primary studies used limited gynecologic surgery data. The purpose of this study was to evaluate the performance of the National Surgical Quality Improvement Program Universal Surgical Risk Calculator (URC) on the patients of a gynecologic oncology service. We reviewed 628 consecutive surgeries performed by our gynecologic oncology service between July 2012 and June 2013. Demographic data including diagnosis and cancer stage, if applicable, were collected. Charts were reviewed to determine complication rates. Specific complications were as follows: death, pneumonia, cardiac complications, surgical site infection (SSI) or urinary tract infection, renal failure, or venous thromboembolic event. Data were compared with modeled outcomes using Brier scores and receiver operating characteristic curves. Significance was declared based on P < 0.05. The model accurately predicated death and venous thromboembolic event, with Brier scores of 0.004 and 0.003, respectively. Predicted risk was 50% greater than experienced for urinary tract infection; the experienced SSI and pneumonia rates were 43% and 36% greater than predicted. For any complication, the Brier score 0.023 indicates poor performance of the model. In this study of gynecologic surgeries, we could not verify the predictive value of the URC for cardiac complications, SSI, and pneumonia. One disadvantage of applying a URC to multiple subspecialties is that with some categories, complications are not accurately estimated. Our data demonstrate that some predicted risks reported by the calculator need to be interpreted with reservation.

Brain injury prediction: assessing the combined probability of concussion using linear and rotational head acceleration.

PubMed

Rowson, Steven; Duma, Stefan M

2013-05-01

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

Evaluating the Zebrafish Embryo Toxicity Test for Pesticide Hazard Screening

EPA Science Inventory

Given the numerous chemicals used in society, it is critical to develop tools for accurate and efficient evaluation of potential risks to human and ecological receptors. Fish embryo acute toxicity tests are 1 tool that has been shown to be highly predictive of standard, more reso...

Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

PubMed

Marino, Miguel; Li, Yi; Pencina, Michael J; D'Agostino, Ralph B; Berkman, Lisa F; Buxton, Orfeu M

2014-08-01

Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories. We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013. HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Quantifying Cardiometabolic Risk Using Modifiable Non–Self-Reported Risk Factors

PubMed Central

Marino, Miguel; Li, Yi; Pencina, Michael J.; D’Agostino, Ralph B.; Berkman, Lisa F.; Buxton, Orfeu M.

2014-01-01

Background Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. Purpose To develop and validate a cumulative general cardiometabolic risk score that focuses on non–self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut off points for risk categories. Methods We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14–year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender–specific Cox proportional hazards models were considered to evaluate the effects of non–self-reported modifiable risk factors (blood pressure, total cholesterol, high–density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10–year general cardiometabolic risk score functions and evaluated its predictive performance in 2012–2013. Results HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit χ2=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). Conclusions This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk based on modifiable risk factors that can motivate an individual’s commitment to prevention and intervention. PMID:24951039

Using the Lorenz Curve to Characterize Risk Predictiveness and Etiologic Heterogeneity

PubMed Central

Mauguen, Audrey; Begg, Colin B.

2017-01-01

The Lorenz curve is a graphical tool that is used widely in econometrics. It represents the spread of a probability distribution, and its traditional use has been to characterize population distributions of wealth or income, or more specifically, inequalities in wealth or income. However, its utility in public health research has not been broadly established. The purpose of this article is to explain its special usefulness for characterizing the population distribution of disease risks, and in particular for identifying the precise disease burden that can be predicted to occur in segments of the population that are known to have especially high (or low) risks, a feature that is important for evaluating the yield of screening or other disease prevention initiatives. We demonstrate that, although the Lorenz curve represents the distribution of predicted risks in a population at risk for the disease, in fact it can be estimated from a case–control study conducted in the population without the need for information on absolute risks. We explore two different estimation strategies and compare their statistical properties using simulations. The Lorenz curve is a statistical tool that deserves wider use in public health research. PMID:27096256

The New York risk score for in-hospital and 30-day mortality for coronary artery bypass graft surgery.

PubMed

Hannan, Edward L; Farrell, Louise Szypulski; Wechsler, Andrew; Jordan, Desmond; Lahey, Stephen J; Culliford, Alfred T; Gold, Jeffrey P; Higgins, Robert S D; Smith, Craig R

2013-01-01

Simplified risk scores for coronary artery bypass graft surgery are frequently in lieu of more complicated statistical models and are valuable for informed consent and choice of intervention. Previous risk scores have been based on in-hospital mortality, but a substantial number of patients die within 30 days of the procedure. These deaths should also be accounted for, so we have developed a risk score based on in-hospital and 30-day mortality. New York's Cardiac Surgery Reporting System was used to develop an in-hospital and 30-day logistic regression model for patients undergoing coronary artery bypass graft surgery in 2009, and this model was converted into a simple linear risk score that provides estimated in-hospital and 30-day mortality rates for different values of the score. The accuracy of the risk score in predicting mortality was tested. This score was also validated by applying it to 2008 New York coronary artery bypass graft data. Subsequent analyses evaluated the ability of the risk score to predict complications and length of stay. The overall in-hospital and 30-day mortality rate for the 10,148 patients in the study was 1.79%. There are seven risk factors comprising the score, with risk factor scores ranging from 1 to 5, and the highest possible total score is 23. The score accurately predicted mortality in 2009 as well as in 2008, and was strongly correlated with complications and length of stay. The risk score is a simple way of estimating short-term mortality that accurately predicts mortality in the year the model was developed as well as in the previous year. Perioperative complications and length of stay are also well predicted by the risk score. Copyright © 2013 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT)

PubMed Central

Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

2016-01-01

Introduction Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. Objective To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Design Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. Participants The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Main outcome measures Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Statistical analysis Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Ethics and dissemination Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. Trial registration number NCT02779309, Pre-results. PMID:27909039

Modeling and predicting historical volatility in exchange rate markets

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Volatility modeling and forecasting of currency exchange rate is an important task in several business risk management tasks; including treasury risk management, derivatives pricing, and portfolio risk evaluation. The purpose of this study is to present a simple and effective approach for predicting historical volatility of currency exchange rate. The approach is based on a limited set of technical indicators as inputs to the artificial neural networks (ANN). To show the effectiveness of the proposed approach, it was applied to forecast US/Canada and US/Euro exchange rates volatilities. The forecasting results show that our simple approach outperformed the conventional GARCH and EGARCH with different distribution assumptions, and also the hybrid GARCH and EGARCH with ANN in terms of mean absolute error, mean of squared errors, and Theil's inequality coefficient. Because of the simplicity and effectiveness of the approach, it is promising for US currency volatility prediction tasks.

The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese.

PubMed

Abe, Makiko; Ito, Hidemi; Oze, Isao; Nomura, Masatoshi; Ogawa, Yoshihiro; Matsuo, Keitaro

2017-12-01

Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication. 558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination. Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group. We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for effective individualized prevention for East Asian.

Prognostic Value of the Thrombolysis in Myocardial Infarction Risk Score in ST-Elevation Myocardial Infarction Patients With Left Ventricular Dysfunction (from the EPHESUS Trial).

PubMed

Popovic, Batric; Girerd, Nicolas; Rossignol, Patrick; Agrinier, Nelly; Camenzind, Edoardo; Fay, Renaud; Pitt, Bertram; Zannad, Faiez

2016-11-15

The Thrombolysis in Myocardial Infarction (TIMI) risk score remains a robust prediction tool for short-term and midterm outcome in the patients with ST-elevation myocardial infarction (STEMI). However, the validity of this risk score in patients with STEMI with reduced left ventricular ejection fraction (LVEF) remains unclear. A total of 2,854 patients with STEMI with early coronary revascularization participating in the randomized EPHESUS (Epleronone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study) trial were analyzed. TIMI risk score was calculated at baseline, and its predictive value was evaluated using C-indexes from Cox models. The increase in reclassification of other variables in addition to TIMI score was assessed using the net reclassification index. TIMI risk score had a poor predictive accuracy for all-cause mortality (C-index values at 30 days and 1 year ≤0.67) and recurrent myocardial infarction (MI; C-index values ≤0.60). Among TIMI score items, diabetes/hypertension/angina, heart rate >100 beats/min, and systolic blood pressure <100 mm Hg were inconsistently associated with survival, whereas none of the TIMI score items, aside from age, were significantly associated with MI recurrence. Using a constructed predictive model, lower LVEF, lower estimated glomerular filtration rate (eGFR), and previous MI were significantly associated with all-cause mortality. The predictive accuracy of this model, which included LVEF and eGFR, was fair for both 30-day and 1-year all-cause mortality (C-index values ranging from 0.71 to 0.75). In conclusion, TIMI risk score demonstrates poor discrimination in predicting mortality or recurrent MI in patients with STEMI with reduced LVEF. LVEF and eGFR are major factors that should not be ignored by predictive risk scores in this population. Copyright © 2016 Elsevier Inc. All rights reserved.

Impact of Hydrogeological Uncertainty on Estimation of Environmental Risks Posed by Hydrocarbon Transportation Networks

NASA Astrophysics Data System (ADS)

Ciriello, V.; Lauriola, I.; Bonvicini, S.; Cozzani, V.; Di Federico, V.; Tartakovsky, Daniel M.

2017-11-01

Ubiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous-phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics-based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk-related contamination scenarios. For a typical oil-spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture-dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision-maker, the extent of contamination and the correspondent remediation costs.

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.

Mixture risk assessment: a case study of Monsanto experiences.

PubMed

Nair, R S; Dudek, B R; Grothe, D R; Johannsen, F R; Lamb, I C; Martens, M A; Sherman, J H; Stevens, M W

1996-01-01

Monsanto employs several pragmatic approaches for evaluating the toxicity of mixtures. These approaches are similar to those recommended by many national and international agencies. When conducting hazard and risk assessments, priority is always given to using data collected directly on the mixture of concern. To provide an example of the first tier of evaluation, actual data on acute respiratory irritation studies on mixtures were evaluated to determine whether the principle of additivity was applicable to the mixture evaluated. If actual data on the mixture are unavailable, extrapolation across similar mixtures is considered. Because many formulations are quite similar in composition, the toxicity data from one mixture can be extended to a closely related mixture in a scientifically justifiable manner. An example of a family of products where such extrapolations have been made is presented to exemplify this second approach. Lastly, if data on similar mixtures are unavailable, data on component fractions are used to predict the toxicity of the mixture. In this third approach, process knowledge and scientific judgement are used to determine how the known toxicological properties of the individual fractions affect toxicity of the mixture. Three examples of plant effluents where toxicological data on fractions were used to predict the toxicity of the mixture are discussed. The results of the analysis are used to discuss the predictive value of each of the above mentioned toxicological approaches for evaluating chemical mixtures.

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 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.

[Cesarean after labor induction: Risk factors and prediction score].

PubMed

Branger, B; Dochez, V; Gervier, S; Winer, N

2018-05-01

The objective of the study is to determine the risk factors for caesarean section at the time of labor induction, to establish a prediction algorithm, to evaluate its relevance and to compare the results with observation. A retrospective study was carried out over a year at Nantes University Hospital with 941 cervical ripening and labor inductions (24.1%) terminated by 167 caesarean sections (17.8%). Within the cohort, a case-control study was conducted with 147 caesarean sections and 148 vaginal deliveries. A multivariate analysis was carried out with a logistic regression allowing the elaboration of an equation of prediction and an ROC curve and the confrontation between the prediction and the reality. In univariate analysis, six variables were significant: nulliparity, small size of the mother, history of scarried uterus, use of prostaglandins as a mode of induction, unfavorable Bishop score<6, variety of posterior release. In multivariate analysis, five variables were significant: nulliparity, maternal size, maternal BMI, scar uterus and Bishop score. The most predictive model corresponded to an area under the curve of 0.86 (0.82-0.90) with a correct prediction percentage ("well classified") of 67.6% for a caesarean section risk of 80%. The prediction criteria would make it possible to inform the woman and the couple about the potential risk of Caesarean section in urgency or to favor a planned Caesarean section or a low-lying attempt on more objective, repeatable and transposable arguments in a medical team. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

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.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

NASA Astrophysics Data System (ADS)

Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

2015-03-01

Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

Context matters: The impact of neighborhood crime and paranoid symptoms on psychosis risk assessment.

PubMed

Wilson, Camille; Smith, Melissa Edmondson; Thompson, Elizabeth; Demro, Caroline; Kline, Emily; Bussell, Kristin; Pitts, Steven C; DeVylder, Jordan; Reeves, Gloria; Schiffman, Jason

2016-03-01

Psychosis risk assessment measures probe for paranoid thinking, persecutory ideas of reference, and suspiciousness as part of a psychosis risk construct. However, in some cases, these symptoms may reflect a normative, realistic, and even adaptive response to environmental stressors rather than psychopathology. Neighborhood characteristics, dangerousness for instance, are linked to levels of fear and suspiciousness that can be theoretically unrelated to psychosis. Despite this potential confound, psychosis-risk assessments do not explicitly evaluate neighborhood factors that might (adaptively) increase suspiciousness. In such cases, interviewers run the risk of misinterpreting adaptive suspiciousness as a psychosis-risk symptom. Ultimately, the degree to which neighborhood factors contribute to psychosis-risk assessment remains unclear. The current study examined the relation between neighborhood crime and suspiciousness as measured by the SIPS among predominantly African American help-seeking adolescents (N=57) living in various neighborhoods in Baltimore City. Uniform Crime Reports, including violent and property crime for Baltimore City, were used to calculate a proxy of neighborhood crime. This crime index correlated with SIPS suspiciousness (r(55)=.32, p=.02). Multiple regression analyses demonstrated that increased neighborhood crime significantly predicted suspiciousness over and above the influence of the other SIPS positive symptoms in predicting suspiciousness. Findings suggest that neighborhood crime may in some cases account for suspiciousness ascertained as part of a psychosis risk assessment, and therefore sensitivity to contextual factors is important when evaluating risk for psychosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Landscape structure and management alter the outcome of a pesticide ERA: Evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model.

PubMed

Topping, Chris J; Dalby, Lars; Skov, Flemming

2016-01-15

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data. Ten different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably. The study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Geographic Profiling to Assess the Risk of Rare Plant Poaching in Natural Areas

NASA Astrophysics Data System (ADS)

Young, John A.; van Manen, Frank T.; Thatcher, Cindy A.

2011-09-01

We demonstrate the use of an expert-assisted spatial model to examine geographic factors influencing the poaching risk of a rare plant (American ginseng, Panax quinquefolius L.) in Shenandoah National Park, Virginia, USA. Following principles of the analytic hierarchy process (AHP), we identified a hierarchy of 11 geographic factors deemed important to poaching risk and requested law enforcement personnel of the National Park Service to rank those factors in a series of pair-wise comparisons. We used those comparisons to determine statistical weightings of each factor and combined them into a spatial model predicting poaching risk. We tested the model using 69 locations of previous poaching incidents recorded by law enforcement personnel. These locations occurred more frequently in areas predicted by the model to have a higher risk of poaching than random locations. The results of our study can be used to evaluate resource protection strategies and to target law enforcement activities.

A Risk Score Model for Evaluation and Management of Patients with Thyroid Nodules.

PubMed

Zhang, Yongwen; Meng, Fanrong; Hong, Lianqing; Chu, Lanfang

2018-06-12

The study is aimed to establish a simplified and practical tool for analyzing thyroid nodules. A novel risk score model was designed, risk factors including patient history, patient characteristics, physical examination, symptoms of compression, thyroid function, ultrasonography (US) of thyroid and cervical lymph nodes were evaluated and classified into high risk factors, intermediate risk factors, and low risk factors. A total of 243 thyroid nodules in 162 patients were assessed with risk score system and Thyroid Imaging-Reporting and Data System (TI-RADS). The diagnostic performance of risk score system and TI-RADS was compared. The accuracy in the diagnosis of thyroid nodules was 89.3% for risk score system, 74.9% for TI-RADS respectively. The specificity, accuracy and positive predictive value (PPV) of risk score system were significantly higher than the TI-RADS system (χ 2 =26.287, 17.151, 11.983; p <0.05), statistically significant differences were not observed in the sensitivity and negative predictive value (NPV) between the risk score system and TI-RADS (χ 2 =1.276, 0.290; p>0.05). The area under the curve (AUC) for risk score diagnosis system was 0.963, standard error 0.014, 95% confidence interval (CI)=0.934-0.991, the AUC for TI-RADS diagnosis system was 0.912 with standard error 0.021, 95% CI=0.871-0.953, the AUC for risk score system was significantly different from that of TI-RADS (Z=2.02; p <0.05). Risk score model is a reliable, simplified and cost-effective diagnostic tool used in diagnosis of thyroid cancer. The higher the score is, the higher the risk of malignancy will be. © Georg Thieme Verlag KG Stuttgart · New York.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

PubMed

Chi, Chih-Lin; Zeng, Wenjun; Oh, Wonsuk; Borson, Soo; Lenskaia, Tatiana; Shen, Xinpeng; Tonellato, Peter J

2017-12-01

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Construct measurement quality improves predictive accuracy in violence risk assessment: an illustration using the personality assessment inventory.

PubMed

Hendry, Melissa C; Douglas, Kevin S; Winter, Elizabeth A; Edens, John F

2013-01-01

Much of the risk assessment literature has focused on the predictive validity of risk assessment tools. However, these tools often comprise a list of risk factors that are themselves complex constructs, and focusing on the quality of measurement of individual risk factors may improve the predictive validity of the tools. The present study illustrates this concern using the Antisocial Features and Aggression scales of the Personality Assessment Inventory (Morey, 1991). In a sample of 1,545 prison inmates and offenders undergoing treatment for substance abuse (85% male), we evaluated (a) the factorial validity of the ANT and AGG scales, (b) the utility of original ANT and AGG scales and newly derived ANT and AGG scales for predicting antisocial outcomes (recidivism and institutional infractions), and (c) whether items with a stronger relationship to the underlying constructs (higher factor loadings) were in turn more strongly related to antisocial outcomes. Confirmatory factor analyses (CFAs) indicated that ANT and AGG items were not structured optimally in these data in terms of correspondence to the subscale structure identified in the PAI manual. Exploratory factor analyses were conducted on a random split-half of the sample to derive optimized alternative factor structures, and cross-validated in the second split-half using CFA. Four-factor models emerged for both the ANT and AGG scales, and, as predicted, the size of item factor loadings was associated with the strength with which items were associated with institutional infractions and community recidivism. This suggests that the quality by which a construct is measured is associated with its predictive strength. Implications for risk assessment are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

PubMed

Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Validity and reliability of clinical prediction rules used to screen for cervical spine injury in alert low-risk patients with blunt trauma to the neck: part 2. A systematic review from the Cervical Assessment and Diagnosis Research Evaluation (CADRE) Collaboration.

PubMed

Moser, N; Lemeunier, N; Southerst, D; Shearer, H; Murnaghan, K; Sutton, D; Côté, P

2018-06-01

To update findings of the 2000-2010 Bone and Joint Decade Task Force on Neck Pain and its Associated Disorders (Neck Pain Task Force) on the validity and reliability of clinical prediction rules used to screen for cervical spine injury in alert low-risk adult patients with blunt trauma to the neck. We searched four databases from 2005 to 2015. Pairs of independent reviewers critically appraised eligible studies using the modified QUADAS-2 and QAREL criteria. We synthesized low risk of bias studies following best evidence synthesis principles. We screened 679 citations; five had a low risk of bias and were included in our synthesis. The sensitivity of the Canadian C-spine rule ranged from 0.90 to 1.00 with negative predictive values ranging from 99 to 100%. Inter-rater reliability of the Canadian C-spine rule varied from k = 0.60 between nurses and physicians to k = 0.93 among paramedics. The inter-rater reliability of the Nexus Low-Risk Criteria was k = 0.53 between resident physicians and faculty physicians. Our review adds new evidence to the Neck Pain Task Force and supports the use of clinical prediction rules in emergency care settings to screen for cervical spine injury in alert low-risk adult patients with blunt trauma to the neck. The Canadian C-spine rule consistently demonstrated excellent sensitivity and negative predictive values. Our review, however, suggests that the reproducibility of the clinical predictions rules varies depending on the examiners level of training and experience.

On the Health Risk of the Lumbar Spine due to Whole-Body VIBRATION—THEORETICAL Approach, Experimental Data and Evaluation of Whole-Body Vibration

NASA Astrophysics Data System (ADS)

Seidel, H.; Blüthner, R.; Hinz, B.; Schust, M.

1998-08-01

The guidance on the effects of vibration on health in standards for whole-body vibration (WBV) does not provide quantitative relationships between WBV and health risk. The paper aims at the elucidation of exposure-response relationships. An analysis of published data on the static and dynamic strength of vertebrae and bone, loaded with various frequencies under different conditions, provided the basis for a theoretical approach to evaluate repetitive loads on the lumbar spine (“internal loads”). The approach enabled the calculation of “equivalent”—with respect to cumulative fatigue failure—combinations of amplitudes and numbers of internal cyclic stress. In order to discover the relation between external peak accelerations at the seat and internal peak loads, biodynamic data of experiments (36 subjects, three somatotypes, two different postures—relaxed and bent forward; random WBV,aw, r.m.s. 1·4 ms-2, containing high transients) were used as input to a biomechanical model. Internal pressure changes were calculated using individual areas of vertebral endplates. The assessment of WBV was based on the quantitative relations between peak accelerations at the seat and pressures predicted for the disk L5/S1. For identical exposures clearly higher rates of pressure rise in the bent forward compared to the relaxed posture were predicted. The risk assessment for internal forces considered the combined internal static and dynamic loads, in relation to the predicted individual strength, and Miner's hypothesis. For exposure durations between 1 min and 8 h, energy equivalent vibration magnitudes (formula B.1, ISO 2631-1, 1997) and equivalent vibration magnitudes according to formula B.2 (time dependence over-energetic) were compared with equivalent combinations of upward peak accelerations and exposure durations according to predicted cumulative fatigue failures of lumbar vertebrae. Formula B.1 seems to underestimate the health risk caused by high magnitudes, formula B.2 is recommended for the evaluation of such conditions.

Comparison of National Operative Mortality in Gastroenterological Surgery Using Web-based Prospective Data Entry Systems.

PubMed

Anazawa, Takayuki; Paruch, Jennifer L; Miyata, Hiroaki; Gotoh, Mitsukazu; Ko, Clifford Y; Cohen, Mark E; Hirahara, Norimichi; Zhou, Lynn; Konno, Hiroyuki; Wakabayashi, Go; Sugihara, Kenichi; Mori, Masaki

2015-12-01

International collaboration is important in healthcare quality evaluation; however, few international comparisons of general surgery outcomes have been accomplished. Furthermore, predictive model application for risk stratification has not been internationally evaluated. The National Clinical Database (NCD) in Japan was developed in collaboration with the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), with a goal of creating a standardized surgery database for quality improvement. The study aimed to compare the consistency and impact of risk factors of 3 major gastroenterological surgical procedures in Japan and the United States (US) using web-based prospective data entry systems: right hemicolectomy (RH), low anterior resection (LAR), and pancreaticoduodenectomy (PD).Data from NCD and ACS-NSQIP, collected over 2 years, were examined. Logistic regression models were used for predicting 30-day mortality for both countries. Models were exchanged and evaluated to determine whether the models built for one population were accurate for the other population.We obtained data for 113,980 patients; 50,501 (Japan: 34,638; US: 15,863), 42,770 (Japan: 35,445; US: 7325), and 20,709 (Japan: 15,527; US: 5182) underwent RH, LAR, and, PD, respectively. Thirty-day mortality rates for RH were 0.76% (Japan) and 1.88% (US); rates for LAR were 0.43% versus 1.08%; and rates for PD were 1.35% versus 2.57%. Patient background, comorbidities, and practice style were different between Japan and the US. In the models, the odds ratio for each variable was similar between NCD and ACS-NSQIP. Local risk models could predict mortality using local data, but could not accurately predict mortality using data from other countries.We demonstrated the feasibility and efficacy of the international collaborative research between Japan and the US, but found that local risk models remain essential for quality improvement.

Student-Teacher Relationships for Young Children with Autism Spectrum Disorder: Risk and Protective Factors

ERIC Educational Resources Information Center

Caplan, Barbara; Feldman, Melanie; Eisenhower, Abbey; Blacher, Jan

2016-01-01

The quality of early student-teacher relationships (STRs) has been shown to predict children's school adjustment, and children with autism spectrum disorder (ASD) are at risk for poor quality STRs. The present study examined 162 children with ASD (ages 4-7) and their teachers to evaluate student, teacher, and classroom characteristics that…

Initial and Over-Time Effects of Fluency Interventions for Students with or at Risk for Disabilities

ERIC Educational Resources Information Center

Morgan, Paul L.; Sideridis, Georgios; Hua, Youjia

2012-01-01

The authors sought to (a) identify interventions that immediately increased the oral reading fluency of students with or at risk for disabilities, (b) estimate to what extent these gains maintained over time, and (c) evaluate whether particular characteristics of students (e.g., gender, disability status) predicted their response to fluency…

Development of an In Silico Metabolic Simulator and Searchable Metabolism Database for Chemical Risk Assessments

EPA Science Inventory

The US EPA is faced with long lists of chemicals that need to be assessed for hazard, and a gap in evaluating chemical risk is accounting for metabolic activation resulting in increased toxicity. The goals of this project are to develop a capability to predict metabolic maps of x...

Identifying Preschool Children at Risk of Later Reading Difficulties: Evaluation of Two Emergent Literacy Screening Tools

ERIC Educational Resources Information Center

Wilson, Shauna B.; Lonigan, Christopher J.

2010-01-01

Emergent literacy skills are predictive of children's early reading success, and literacy achievement in early schooling declines more rapidly for children who are below-average readers. It is therefore important for teachers to identify accurately children at risk for later reading difficulty so children can be exposed to effective emergent…

A Computer-Assisted Instruction Phonological Sensitivity Program for Preschool Children At-Risk for Reading Problems.

ERIC Educational Resources Information Center

Lonigan, Christopher J.; Driscoll, Kimberly; Phillips, Beth M.; Cantor, Brenlee G.; Anthony, Jason L.; Goldstein, Howard

2003-01-01

A study evaluated the use of computer-assisted instruction (CAI) to provide training in phonological sensitivity skills to 45 preschool children at-risk for reading problems. Children exposed to CAI made significantly greater gains on rhyming and elision skills compared to controls. Expressive vocabulary scores were predictive of pre- to posttest…

Perturbed threat monitoring following a traumatic event predicts risk for post-traumatic stress disorder.

PubMed

Naim, R; Wald, I; Lior, A; Pine, D S; Fox, N A; Sheppes, G; Halpern, P; Bar-Haim, Y

2014-07-01

Post-traumatic stress disorder (PTSD) is a chronic and difficult to treat psychiatric disorder. Objective, performance-based diagnostic markers that uniquely index risk for PTSD above and beyond subjective self-report markers could inform attempts to improve prevention and early intervention. We evaluated the predictive value of threat-related attention bias measured immediately after a potentially traumatic event, as a risk marker for PTSD at a 3-month follow-up. We measured the predictive contribution of attentional threat bias above and beyond that of the more established marker of risk for PTSD, self-reported psychological dissociation. Dissociation symptoms and threat-related attention bias were measured in 577 motor vehicle accident (MVA) survivors (mean age = 35.02 years, 356 males) within 24 h of admission to an emergency department (ED) of a large urban hospital. PTSD symptoms were assessed at a 3-month follow-up using the Clinician-Administered PTSD Scale (CAPS). Self-reported dissociation symptoms significantly accounted for 16% of the variance in PTSD at follow-up, and attention bias toward threat significantly accounted for an additional 4% of the variance in PTSD. Threat-related attention bias can be reliably measured in the context of a hospital ED and significantly predicts risk for later PTSD. Possible mechanisms underlying the association between threat bias following a potentially traumatic event and risk for PTSD are discussed. The potential application of an attention bias modification treatment (ABMT) tailored to reduce risk for PTSD is suggested.

Twenty-Four-Hour Central Pulse Pressure for Cardiovascular Events Prediction in a Low-Cardiovascular-Risk Population: Results From the Bordeaux Cohort.

PubMed

Cremer, Antoine; Boulestreau, Romain; Gaillard, Prune; Lainé, Marion; Papaioannou, Georgios; Gosse, Philippe

2018-02-23

Central blood pressure (BP) is a promising marker to identify subjects with higher cardiovascular risk than expected by traditional risk factors. Significant results have been obtained in populations with high cardiovascular risk, but little is known about low-cardiovascular-risk patients, although the differences between central and peripheral BP (amplification) are usually greater in this population. The study aim was to evaluate central BP over 24 hours for cardiovascular event prediction in hypertensive subjects with low cardiovascular risk. Peripheral and central BPs were recorded during clinical visits and over 24 hours in hypertensive patients with low cardiovascular risk (Systematic Coronary Risk Evaluation ≤5%). Our primary end point is the occurrence of a cardiovascular event during follow-up. To assess the potential interest in central pulse pressure over 24 hours, we performed Cox proportional hazard models analysis and comparison of area under the curves using the contrast test for peripheral and central BP. A cohort of 703 hypertensive subjects from Bordeaux were included. After the first 24 hours of BP measurement, the subjects were then followed up for an average of 112.5±70 months. We recorded 65 cardiovascular events during follow-up. Amplification was found to be significantly associated with cardiovascular events when added to peripheral 24-hour pulse pressure ( P =0.0259). The area under the curve of 24-hour central pulse pressure is significantly more important than area under the curve of office BP ( P =0.0296), and there is a trend of superiority with the area under the curve of peripheral 24-hour pulse pressure. Central pulse pressure over 24 hours improves the prediction of cardiovascular events for hypertensive patients with low cardiovascular risk compared to peripheral pulse pressure. © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Comparative evaluation of Indian Diabetes Risk Score and Finnish Diabetes Risk Score for predicting risk of diabetes mellitus type II: A teaching hospital-based survey in Maharashtra.

PubMed

Pawar, Shivshakti D; Naik, Jayashri D; Prabhu, Priya; Jatti, Gajanan M; Jadhav, Sachin B; Radhe, B K

2017-01-01

India is currently becoming capital for diabetes mellitus. This significantly increasing incidence of diabetes putting an additional burden on health care in India. Unfortunately, half of diabetic individuals are unknown about their diabetic status. Hence, there is an emergent need of effective screening instrument to identify "diabetes risk" individuals. The aim is to evaluate and compare the diagnostic accuracy and clinical utility of Indian Diabetes Risk Score (IDRS) and Finnish Diabetes Risk Score (FINDRISC). This is retrospective, record-based study of diabetes detection camp organized by a teaching hospital. Out of 780 people attended this camp voluntarily only 763 fulfilled inclusion criteria of the study. In this camp, pro forma included the World Health Organization STEP guidelines for surveillance of noncommunicable diseases. Included primary sociodemographic characters, physical measurements, and clinical examination. After that followed the random blood glucose estimation of each individual. Diagnostic accuracy of IDRS and FINDRISC compared by using receiver operative characteristic curve (ROC). Sensitivity, specificity, likelihood ratio, positive predictive and negative predictive values were compared. Clinical utility index (CUI) of each score also compared. SPSS version 22, Stata 13, R3.2.9 used. Out of 763 individuals, 38 were new diabetics. By IDRS 347 and by FINDRISC 96 people were included in high-risk category for diabetes. Odds ratio for high-risk people in FINDRISC for getting affected by diabetes was 10.70. Similarly, it was 4.79 for IDRS. Area under curves of ROCs of both scores were indifferent ( P = 0.98). Sensitivity and specificity of IDRS was 78.95% and 56.14%; whereas for FINDRISC it was 55.26% and 89.66%, respectively. CUI was excellent (0.86) for FINDRISC while IDRS it was "satisfactory" (0.54). Bland-Altman plot and Cohen's Kappa suggested fair agreement between these score in measuring diabetes risk. Diagnostic accuracy and clinical utility of FINDRISC is fairly good than IDRS.

Evaluating predictive modeling’s potential to improve teleretinal screening participation in urban safety net clinics

PubMed Central

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Introduction Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Methods Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. Results The predictive models were modestly predictive with the best model having an AUC of 0.71. Discussion Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics. PMID:23920536

Evaluating predictive modeling's potential to improve teleretinal screening participation in urban safety net clinics.

PubMed

Ogunyemi, Omolola; Teklehaimanot, Senait; Patty, Lauren; Moran, Erin; George, Sheba

2013-01-01

Screening guidelines for diabetic patients recommend yearly eye examinations to detect diabetic retinopathy and other forms of diabetic eye disease. However, annual screening rates for retinopathy in US urban safety net settings remain low. Using data gathered from a study of teleretinal screening in six urban safety net clinics, we assessed whether predictive modeling could be of value in identifying patients at risk of developing retinopathy. We developed and examined the accuracy of two predictive modeling approaches for diabetic retinopathy in a sample of 513 diabetic individuals, using routinely available clinical variables from retrospective medical record reviews. Bayesian networks and radial basis function (neural) networks were learned using ten-fold cross-validation. The predictive models were modestly predictive with the best model having an AUC of 0.71. Using routinely available clinical variables to predict patients at risk of developing retinopathy and to target them for annual eye screenings may be of some usefulness to safety net clinics.

Predicting Low Accrual in the National Cancer Institute’s Cooperative Group Clinical Trials

PubMed Central

Bennette, Caroline S.; Ramsey, Scott D.; McDermott, Cara L.; Carlson, Josh J.; Basu, Anirban; Veenstra, David L.

2016-01-01

Background: The extent to which trial-level factors differentially influence accrual to trials has not been comprehensively studied. Our objective was to evaluate the empirical relationship and predictive properties of putative risk factors for low accrual in the National Cancer Institute’s (NCI’s) Cooperative Group Program, now the National Clinical Trials Network (NCTN). Methods: Data from 787 phase II/III adult NCTN-sponsored trials launched between 2000 and 2011 were used to develop a logistic regression model to predict low accrual, defined as trials that closed with or were accruing at less than 50% of target; 46 trials opened between 2012 and 2013 were used for prospective validation. Candidate predictors were identified from a literature review and expert interviews; final predictors were selected using stepwise regression. Model performance was evaluated by calibration and discrimination via the area under the curve (AUC). All statistical tests were two-sided. Results: Eighteen percent (n = 145) of NCTN-sponsored trials closed with low accrual or were accruing at less than 50% of target three years or more after initiation. A multivariable model of twelve trial-level risk factors had good calibration and discrimination for predicting trials with low accrual (AUC in trials launched 2000–2011 = 0.739, 95% confidence interval [CI] = 0.696 to 0.783]; 2012–2013: AUC = 0.732, 95% CI = 0.547 to 0.917). Results were robust to different definitions of low accrual and predictor selection strategies. Conclusions: We identified multiple characteristics of NCTN-sponsored trials associated with low accrual, several of which have not been previously empirically described, and developed a prediction model that can provide a useful estimate of accrual risk based on these factors. Future work should assess the role of such prediction tools in trial design and prioritization decisions. PMID:26714555

Prediction of individual genetic risk to prostate cancer using a polygenic score.

PubMed

Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind A; Easton, Douglas; Kote-Jarai, Z Sofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Southey, Melissa C; Fitzgerald, Liesel M; Henderson, Brian E; Schumacher, Fredrick; Haiman, Christopher A; Schleutker, Johanna; Wahlfors, Tiina; Tammela, Teuvo L J; Nordestgaard, Børge G; Key, Tim J; Travis, Ruth C; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Pharoah, Paul; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon-Albright, Lisa; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, Jong Y; Sellers, Thomas; Lin, Hui-Yi; Lim, Hui-Yi; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A; Spurdle, Amanda; Teixeira, Manuel R; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Gronberg, Henrik; Wiklund, Fredrik

2015-09-01

Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. © 2015 Wiley Periodicals, Inc.

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.

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.

Utilization of the NSQIP-Pediatric Database in Development and Validation of a New Predictive Model of Pediatric Postoperative Wound Complications.

PubMed

Maizlin, Ilan I; Redden, David T; Beierle, Elizabeth A; Chen, Mike K; Russell, Robert T

2017-04-01

Surgical wound classification, introduced in 1964, stratifies the risk of surgical site infection (SSI) based on a clinical estimate of the inoculum of bacteria encountered during the procedure. Recent literature has questioned the accuracy of predicting SSI risk based on wound classification. We hypothesized that a more specific model founded on specific patient and perioperative factors would more accurately predict the risk of SSI. Using all observations from the 2012 to 2014 pediatric National Surgical Quality Improvement Program-Pediatric (NSQIP-P) Participant Use File, patients were randomized into model creation and model validation datasets. Potential perioperative predictive factors were assessed with univariate analysis for each of 4 outcomes: wound dehiscence, superficial wound infection, deep wound infection, and organ space infection. A multiple logistic regression model with a step-wise backwards elimination was performed. A receiver operating characteristic curve with c-statistic was generated to assess the model discrimination for each outcome. A total of 183,233 patients were included. All perioperative NSQIP factors were evaluated for clinical pertinence. Of the original 43 perioperative predictive factors selected, 6 to 9 predictors for each outcome were significantly associated with postoperative SSI. The predictive accuracy level of our model compared favorably with the traditional wound classification in each outcome of interest. The proposed model from NSQIP-P demonstrated a significantly improved predictive ability for postoperative SSIs than the current wound classification system. This model will allow providers to more effectively counsel families and patients of these risks, and more accurately reflect true risks for individual surgical patients to hospitals and payers. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Pencina, Michael J; Ioannidis, John P A

2017-01-01

Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies. © 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.

Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

PubMed Central

Forman, Jason L.; Kent, Richard W.; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

2012-01-01

This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5–7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992–2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction. PMID:23169122

G6PD as a predictive marker for glioma risk, prognosis and chemosensitivity.

PubMed

Yang, Chin-An; Huang, Hsi-Yuan; Lin, Cheng-Li; Chang, Jan-Gowth

2018-05-29

Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme preventing cells from oxidative damage and has been reported to have tumor-promoting roles. This study aims to comprehensively evaluate the predictive values of G6PD on brain tumor risk, prognosis and chemo-resistance. A retrospective 13-year cohort study analyzing cancer risk using the Taiwan National Health Insurance Research Database (4066 G6PD deficiency patients and 16,264 controls) was conducted. Furthermore, RNAseq and clinical data of grade II-III glioma (LGG, n = 515) and glioblastoma (GBM, n = 155) were downloaded from The Cancer Genome Atlas (TCGA) and analyzed. Bioinformatics methods were applied to build a glioma prognostication model and to predict response to chemotherapy based on tumor G6PD-related gene expressions. The predicted results were validated in another glioma cohort GSE 16011 and in KALS1 cell line. G6PD-dificient patients were found to have an increased risk for cancers, especially for brain tumor (adjusted hazard ratio (HR) 10.5, 95% CI 1.03-7.60). Furthermore, higher tumor G6PD expression was associated with poor patient survival in LGG, but not in GBM. A prognostication model using expression levels of G6PD and 9 related genes (PSMA2, PSMB8, SHFM1, GSS, GSTK1, MGST2, POLD3, MSH2, MSH6) could independently predict LGG patient survival. Boosted decision tree analysis on 213 cancer cell line database revealed predictive values of G6PD expression on response to gemcitabine and bortezomib. Knockdown of G6PD in KALS1 cell line enhanced its sensitivity to both chemotherapeutic agents. Our study suggests that G6PD could be a marker predicting glioma risk, prognosis and chemo-sensitivity.

Momentary changes in craving predict smoking lapse behavior: a laboratory study.

PubMed

Motschman, Courtney A; Germeroth, Lisa J; Tiffany, Stephen T

2018-04-27

Current research on factors that predict smoking lapse behavior is limited in its ability to fully characterize the critical moments leading up to decisions to smoke. We used a validated and widely used experimental analogue for smoking lapse to assess how moment-to-moment dynamics of craving relate to decisions to smoke. Heavy smokers (N = 128, M age = 35.9) participated in a 50-min laboratory delay to smoking task on 2 consecutive days, earning money for each 5 min they remained abstinent or ending the task by choosing to smoke. Participants rated craving and negative affect levels immediately prior to each choice. Participants were randomized to smoking as usual (n = 50) or overnight abstinence (n = 50 successfully abstained, n = 22 failed abstaining) prior to session 2. Discrete-time hazard models were used to examine craving and negative affect as time-varying predictors of smoking. Higher craving levels prior to smoking opportunities predicted increased risk of smoking. When controlling for craving levels, incremental increases in craving predicted increased smoking risk. Increases in negative affect incrementally predicted increased smoking risk at session 2 only. Smokers who failed to abstain were at a higher risk of smoking than those who successfully abstained, whereas abstinent and non-abstinent smokers did not differ in smoking risk. Findings demonstrate an extension of the smoking lapse paradigm that can be utilized to capture momentary changes in craving that predict smoking behavior. Evaluations of nuanced craving experiences may inform clinical and pharmacological research on preventing smoking lapse and relapse.

Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

PubMed Central

Kuchenbaecker, Karoline B.; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Healey, Sue; Dennis, Joe; Lush, Michael; Robson, Mark; Spurdle, Amanda B.; Ramus, Susan J.; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L.; Hamann, Ute; Southey, Melissa; John, Esther M.; Chung, Wendy K.; Daly, Mary B.; Buys, Saundra S.; Goldgar, David E.; Dorfling, Cecilia M.; van Rensburg, Elizabeth J.; Ding, Yuan Chun; Ejlertsen, Bent; Gerdes, Anne-Marie; Hansen, Thomas V. O.; Slager, Susan; Hallberg, Emily; Benitez, Javier; Osorio, Ana; Cohen, Nancy; Lawler, William; Weitzel, Jeffrey N.; Peterlongo, Paolo; Pensotti, Valeria; Dolcetti, Riccardo; Barile, Monica; Bonanni, Bernardo; Azzollini, Jacopo; Manoukian, Siranoush; Peissel, Bernard; Radice, Paolo; Savarese, Antonella; Papi, Laura; Giannini, Giuseppe; Fostira, Florentia; Konstantopoulou, Irene; Adlard, Julian; Brewer, Carole; Cook, Jackie; Davidson, Rosemarie; Eccles, Diana; Eeles, Ros; Ellis, Steve; Frost, Debra; Hodgson, Shirley; Izatt, Louise; Lalloo, Fiona; Ong, Kai-ren; Godwin, Andrew K.; Arnold, Norbert; Dworniczak, Bernd; Engel, Christoph; Gehrig, Andrea; Hahnen, Eric; Hauke, Jan; Kast, Karin; Meindl, Alfons; Niederacher, Dieter; Schmutzler, Rita Katharina; Varon-Mateeva, Raymonda; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Barjhoux, Laure; Collonge-Rame, Marie-Agnès; Elan, Camille; Golmard, Lisa; Barouk-Simonet, Emmanuelle; Lesueur, Fabienne; Mazoyer, Sylvie; Sokolowska, Joanna; Stoppa-Lyonnet, Dominique; Isaacs, Claudine; Claes, Kathleen B. M.; Poppe, Bruce; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Aittomäki, Kristiina; Nevanlinna, Heli; Ausems, Margreet G. E. M.; de Lange, J. L.; Gómez Garcia, Encarna B.; Hogervorst, Frans B. L.; Kets, Carolien M.; Meijers-Heijboer, Hanne E. J.; Oosterwijk, Jan C.; Rookus, Matti A.; van Asperen, Christi J.; van den Ouweland, Ans M. W.; van Doorn, Helena C.; van Os, Theo A. M.; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Lazaro, Conxi; Teulé, Alex; Gronwald, Jacek; Jakubowska, Anna; Kaczmarek, Katarzyna; Lubinski, Jan; Sukiennicki, Grzegorz; Barkardottir, Rosa B.; Chiquette, Jocelyne; Agata, Simona; Montagna, Marco; Teixeira, Manuel R.; Park, Sue Kyung; Olswold, Curtis; Tischkowitz, Marc; Foretova, Lenka; Gaddam, Pragna; Vijai, Joseph; Pfeiler, Georg; Rappaport-Fuerhauser, Christine; Singer, Christian F.; Tea, Muy-Kheng M.; Greene, Mark H.; Loud, Jennifer T.; Rennert, Gad; Imyanitov, Evgeny N.; Hulick, Peter J.; Hays, John L.; Piedmonte, Marion; Rodriguez, Gustavo C.; Martyn, Julie; Glendon, Gord; Mulligan, Anna Marie; Andrulis, Irene L.; Toland, Amanda Ewart; Jensen, Uffe Birk; Kruse, Torben A.; Pedersen, Inge Sokilde; Thomassen, Mads; Caligo, Maria A.; Teo, Soo-Hwang; Berger, Raanan; Friedman, Eitan; Laitman, Yael; Arver, Brita; Borg, Ake; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I.; Ganz, Patricia A.; Nussbaum, Robert L.; Bradbury, Angela R.; Domchek, Susan M.; Nathanson, Katherine L.; Arun, Banu K.; James, Paul; Karlan, Beth Y.; Lester, Jenny; Simard, Jacques; Pharoah, Paul D. P.; Offit, Kenneth; Couch, Fergus J.; Chenevix-Trench, Georgia; Easton, Douglas F.

2017-01-01

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]–positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2×10−53). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2×10−20). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. PMID:28376175

Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers.

PubMed

Kuchenbaecker, Karoline B; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Dennis, Joe; Domchek, Susan M; Robson, Mark; Spurdle, Amanda B; Ramus, Susan J; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L; Schmutzler, Rita Katharina; Simard, Jacques; Pharoah, Paul D P; Offit, Kenneth; Couch, Fergus J; Chenevix-Trench, Georgia; Easton, Douglas F; Antoniou, Antonis C

2017-07-01

Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P =  8.2×10 -53 ). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P =  7.2×10 -20 ). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. © The Author 2017. Published by Oxford University Press.

Predictive value of the APACHE II, SAPS II, SOFA and GCS scoring systems in patients with severe purulent bacterial meningitis.

PubMed

Pietraszek-Grzywaczewska, Iwona; Bernas, Szymon; Łojko, Piotr; Piechota, Anna; Piechota, Mariusz

2016-01-01

Scoring systems in critical care patients are essential for predicting of the patient outcome and evaluating the therapy. In this study, we determined the value of the Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA) and Glasgow Coma Scale (GCS) scoring systems in the prediction of mortality in adult patients admitted to the intensive care unit (ICU) with severe purulent bacterial meningitis. We retrospectively analysed data from 98 adult patients with severe purulent bacterial meningitis who were admitted to the single ICU between March 2006 and September 2015. Univariate logistic regression identified the following risk factors of death in patients with severe purulent bacterial meningitis: APACHE II, SAPS II, SOFA, and GCS scores, and the lengths of ICU stay and hospital stay. The independent risk factors of patient death in multivariate analysis were the SAPS II score, the length of ICU stay and the length of hospital stay. In the prediction of mortality according to the area under the curve, the SAPS II score had the highest accuracy followed by the APACHE II, GCS and SOFA scores. For the prediction of mortality in a patient with severe purulent bacterial meningitis, SAPS II had the highest accuracy.

Predicting Acute and Persistent Neuropathy Associated with Oxaliplatin

PubMed Central

Alejandro, Linh; Behrendt, Carolyn E.; Chen, Kim; Openshaw, Harry; Shibata, Stephen

2014-01-01

Objectives We sought to predict oxaliplatin-associated peripheral neuropathy during modified FOLFOX6 (mFOLFOX6) therapy. Methods In a 50% female sample, patients with previously untreated, primary or recurrent colorectal cancer were followed through a first course of mFOLFOX6 with oxaliplatin 85 mg/m2 every 2 weeks. Accounting for correlation among a subject's cycles, logistic regression estimated per-cycle risk of acute (under 14 days) and persistent (14 days or more) neuropathy. Proportional hazards regression predicted time to persistent neuropathy. Results Among mFOLFOX6 recipients (n=50, age 58.9 +10.1 years), 36% received concomitant bevacizumab. Of total cycles, 94.2% (422/448) were evaluable. Most (84%) subjects reported neuropathy at least once: 74% reported acute and 48% reported persistent symptoms. On multivariate analysis, risk factors shared by acute and persistent neuropathy were body-surface area >2.0, acute neuropathy in a past cycle, and lower body weight. In addition, risk of acute neuropathy decreased with age (adjusted for renal function and winter season), while risk of persistent neuropathy increased with cumulative dose of oxaliplatin and persistent neuropathy in a past cycle. Concomitant bevacizumab was not a risk factor when administered in Stage IV disease but was associated with persistent neuropathy when administered experimentally in Stage III. Females had no increased risk of either form of neuropathy. After 3 cycles, weight, body-surface area, and prior acute neuropathy predicted time to persistent neuropathy. Conclusions Routinely available clinical factors predict acute and persistent neuropathy associated with oxaliplatin. When validated, the proposed prognostic score for persistent neuropathy can help clinicians counsel patients about chemotherapy. PMID:22547012

Clinical validation of an epigenetic assay to predict negative histopathological results in repeat prostate biopsies.

PubMed

Partin, Alan W; Van Neste, Leander; Klein, Eric A; Marks, Leonard S; Gee, Jason R; Troyer, Dean A; Rieger-Christ, Kimberly; Jones, J Stephen; Magi-Galluzzi, Cristina; Mangold, Leslie A; Trock, Bruce J; Lance, Raymond S; Bigley, Joseph W; Van Criekinge, Wim; Epstein, Jonathan I

2014-10-01

The DOCUMENT multicenter trial in the United States validated the performance of an epigenetic test as an independent predictor of prostate cancer risk to guide decision making for repeat biopsy. Confirming an increased negative predictive value could help avoid unnecessary repeat biopsies. We evaluated the archived, cancer negative prostate biopsy core tissue samples of 350 subjects from a total of 5 urological centers in the United States. All subjects underwent repeat biopsy within 24 months with a negative (controls) or positive (cases) histopathological result. Centralized blinded pathology evaluation of the 2 biopsy series was performed in all available subjects from each site. Biopsies were epigenetically profiled for GSTP1, APC and RASSF1 relative to the ACTB reference gene using quantitative methylation specific polymerase chain reaction. Predetermined analytical marker cutoffs were used to determine assay performance. Multivariate logistic regression was used to evaluate all risk factors. The epigenetic assay resulted in a negative predictive value of 88% (95% CI 85-91). In multivariate models correcting for age, prostate specific antigen, digital rectal examination, first biopsy histopathological characteristics and race the test proved to be the most significant independent predictor of patient outcome (OR 2.69, 95% CI 1.60-4.51). The DOCUMENT study validated that the epigenetic assay was a significant, independent predictor of prostate cancer detection in a repeat biopsy collected an average of 13 months after an initial negative result. Due to its 88% negative predictive value adding this epigenetic assay to other known risk factors may help decrease unnecessary repeat prostate biopsies. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Stepped approach for prediction of syndrome Z in patients attending sleep clinic: a north Indian hospital-based study.

PubMed

Agrawal, Swastik; Sharma, Surendra Kumar; Sreenivas, Vishnubhatla; Lakshmy, Ramakrishnan; Mishra, Hemant K

2012-09-01

Syndrome Z is the occurrence of metabolic syndrome (MS) with obstructive sleep apnea. Knowledge of its risk factors is useful to screen patients requiring further evaluation for syndrome Z. Consecutive patients referred from sleep clinic undergoing polysomnography in the Sleep Laboratory of AIIMS Hospital, New Delhi were screened between June 2008 and May 2010, and 227 patients were recruited. Anthropometry, body composition analysis, blood pressure, fasting blood sugar, and lipid profile were measured. MS was defined using the National Cholesterol Education Program (adult treatment panel III) criteria, with Asian cutoff values for abdominal obesity. Prevalence of MS and syndrome Z was 74% and 65%, respectively. Age, percent body fat, excessive daytime sleepiness (EDS), and ΔSaO(2) (defined as difference between baseline and minimum SaO(2) during polysomnography) were independently associated with syndrome Z. Using a cutoff of 15% for level of desaturation, the stepped predictive score using these risk factors had sensitivity, specificity, positive predictive value, and negative predictive value of 75%, 73%, 84%, and 61%, respectively for the diagnosis of syndrome Z. It correctly characterized presence of syndrome Z 75% of the time and obviated need for detailed evaluation in 42% of the screened subjects. A large proportion of patients presenting to sleep clinics have MS and syndrome Z. Age, percent body fat, EDS, and ΔSaO(2) are independent risk factors for syndrome Z. A stepped predictive score using these parameters is cost-effective and useful in diagnosing syndrome Z in resource-limited settings.

Present and Potential Future Distribution of Common Vampire Bats in the Americas and the Associated Risk to Cattle

PubMed Central

Lee, Dana N.; Papeş, Monica; Van Den Bussche, Ronald A.

2012-01-01

Success of the cattle industry in Latin America is impeded by the common vampire bat, Desmodus rotundus, through decreases in milk production and mass gain and increased risk of secondary infection and rabies. We used ecological niche modeling to predict the current potential distribution of D. rotundus and the future distribution of the species for the years 2030, 2050, and 2080 based on the A2, A1B, and B1 climate scenarios from the Intergovernmental Panel on Climate Change. We then combined the present day potential distribution with cattle density estimates to identify areas where cattle are at higher risk for the negative impacts due to D. rotundus. We evaluated our risk prediction by plotting 17 documented outbreaks of cattle rabies. Our results indicated highly suitable habitat for D. rotundus occurs throughout most of Mexico and Central America as well as portions of Venezuela, Guyana, the Brazilian highlands, western Ecuador, northern Argentina, and east of the Andes in Peru, Bolivia, and Paraguay. With future climate projections suitable habitat for D. rotundus is predicted in these same areas and additional areas in French Guyana, Suriname, Venezuela and Columbia; however D. rotundus are not likely to expand into the U.S. because of inadequate ‘temperature seasonality.’ Areas with large portions of cattle at risk include Mexico, Central America, Paraguay, and Brazil. Twelve of 17 documented cattle rabies outbreaks were represented in regions predicted at risk. Our present day and future predictions can help authorities focus rabies prevention efforts and inform cattle ranchers which areas are at an increased risk of cattle rabies because it has suitable habitat for D. rotundus. PMID:22900023

Present and potential future distribution of common vampire bats in the Americas and the associated risk to cattle.

PubMed

Lee, Dana N; Papeş, Monica; Van den Bussche, Ronald A

2012-01-01

Success of the cattle industry in Latin America is impeded by the common vampire bat, Desmodus rotundus, through decreases in milk production and mass gain and increased risk of secondary infection and rabies. We used ecological niche modeling to predict the current potential distribution of D. rotundus and the future distribution of the species for the years 2030, 2050, and 2080 based on the A2, A1B, and B1 climate scenarios from the Intergovernmental Panel on Climate Change. We then combined the present day potential distribution with cattle density estimates to identify areas where cattle are at higher risk for the negative impacts due to D. rotundus. We evaluated our risk prediction by plotting 17 documented outbreaks of cattle rabies. Our results indicated highly suitable habitat for D. rotundus occurs throughout most of Mexico and Central America as well as portions of Venezuela, Guyana, the Brazilian highlands, western Ecuador, northern Argentina, and east of the Andes in Peru, Bolivia, and Paraguay. With future climate projections suitable habitat for D. rotundus is predicted in these same areas and additional areas in French Guyana, Suriname, Venezuela and Columbia; however D. rotundus are not likely to expand into the U.S. because of inadequate 'temperature seasonality.' Areas with large portions of cattle at risk include Mexico, Central America, Paraguay, and Brazil. Twelve of 17 documented cattle rabies outbreaks were represented in regions predicted at risk. Our present day and future predictions can help authorities focus rabies prevention efforts and inform cattle ranchers which areas are at an increased risk of cattle rabies because it has suitable habitat for D. rotundus.

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

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

Progression of coronary artery calcification seems to be inevitable, but predictable - results of the Heinz Nixdorf Recall (HNR) study.

PubMed

Erbel, Raimund; Lehmann, Nils; Churzidse, Sofia; Rauwolf, Michael; Mahabadi, Amir A; Möhlenkamp, Stefan; Moebus, Susanne; Bauer, Marcus; Kälsch, Hagen; Budde, Thomas; Montag, Michael; Schmermund, Axel; Stang, Andreas; Führer-Sakel, Dagmar; Weimar, Christian; Roggenbuck, Ulla; Dragano, Nico; Jöckel, Karl-Heinz

2014-11-07

Coronary artery calcification (CAC), as a sign of atherosclerosis, can be detected and progression quantified using computed tomography (CT). We develop a tool for predicting CAC progression. In 3481 participants (45-74 years, 53.1% women) CAC percentiles at baseline (CACb) and after five years (CAC₅y) were evaluated, demonstrating progression along gender-specific percentiles, which showed exponentially shaped age-dependence. Using quantile regression on the log-scale (log(CACb+1)) we developed a tool to individually predict CAC₅y, and compared to observed CAC₅y. The difference between observed and predicted CAC₅y (log-scale, mean±SD) was 0.08±1.11 and 0.06±1.29 in men and women. Agreement reached a kappa-value of 0.746 (95% confidence interval: 0.732-0.760) and concordance correlation (log-scale) of 0.886 (0.879-0.893). Explained variance of observed by predicted log(CAC₅y+1) was 80.1% and 72.0% in men and women, and 81.0 and 73.6% including baseline risk factors. Evaluating the tool in 1940 individuals with CACb>0 and CACb<400 at baseline, of whom 242 (12.5%) developed CAC₅y>400, yielded a sensitivity of 59.5%, specificity 96.1%, (+) and (-) predictive values of 68.3% and 94.3%. A pre-defined acceptance range around predicted CAC₅y contained 68.1% of observed CAC₅y; only 20% were expected by chance. Age, blood pressure, lipid-lowering medication, diabetes, and smoking contributed to progression above the acceptance range in men and, excepting age, in women. CAC nearly inevitably progresses with limited influence of cardiovascular risk factors. This allowed the development of a mathematical tool for prediction of individual CAC progression, enabling anticipation of the age when CAC thresholds of high risk are reached. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Cardiology.

Home, automated office, and conventional office blood pressure as predictors of cardiovascular risk.

PubMed

Andreadis, Emmanuel A; Papademetriou, Vasilios; Geladari, Charalampia V; Kolyvas, George N; Angelopoulos, Epameinondas T; Aronis, Konstantinos N

2017-03-01

Automated office blood pressure (AOBP) has recently been shown to closely predict cardiovascular (CV) events in the elderly. Home blood pressure (HBP) has also been accepted as a valuable method in the prediction of CV disease. This study aimed to compare conventional office BP (OBP), HBP, and AOBP in order to evaluate their value in predicting CV events and deaths in hypertensives. We assessed 236 initially treatment naïve hypertensives, examined between 2009 and 2013. The end points were any CV and non-CV event including mortality, myocardial infarction, coronary heart disease, hospitalization for heart failure, severe arrhythmia, stroke, and intermittent claudication. We fitted proportional hazards models using the different modalities as predictors and evaluated their predictive performance using three metrics: time-dependent receiver operating characteristics curves, the Akaike's Information Criterion, and Harrell's C-index. After a mean follow-up of 7 years, 23 participants (39% women) had experienced ≥1 CV event. Conventional office systolic (hazard ratio [HR] per 1 mm Hg increase in BP, 1.028; 95% confidence interval [CI], 1.009-1.048), automated office systolic (HR per 1 mm Hg increase in BP, 1.031; 95% CI, 1.008-1.054), and home systolic (HR, 1.025; 95% CI, 1.003-1.047) were predictive of CV events. All systolic BP measurements were predictive after adjustment for other CV risk factors (P < .05). The predictive performance of the different modalities was similar. Conventional OBP was significantly higher than AOBP and average HBP. AOBP predicts equally well to OBP and HBP CV events. It appears to be comparable to HBP in the assessment of CV risk, and therefore, its introduction into guidelines and clinical practice as the reference method for assessing BP in the office seems reasonable after verification of these findings by randomized trials. Copyright © 2017 American Society of Hypertension. All rights reserved.

Some factors influencing the nonexpert's perception and evaluation of environmental risks

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

Vaughan, E.

Policy makers and decision analysts have been limited somewhat in their ability to predict public reactions to regulatory decisions about hazardous substances or technologies. Most studies of the nonexpert's evaluation of environmental risks have relied on survey data and correlational analyses which preclude the determination of interactive effects, effects that could explain apparent inconsistencies. Three experimental studies were designed to test empirically the effect of six dimensions of environmental risk on judgments of (1) perceived risk, (2) acceptability of risk, (3) subjective probability of negative outcomes due to exposure, and (4) perceived severity of consequences. Factors examined included: (a) familiaritymore » with the terms used to describe a hazard, (b) environmental persistence of a chemical, (c) personal relevance of data used to evaluate cancer-causing potential, (d) personal relevance of possible adverse consequences, (e) perceived control over exposure, and (f) vividness of the exposure pathway. The findings were discussed in terms of their implications for the nonexpert's formulation of risk perceptions, and public policy in the domain of environmental risks.« less

Evaluation of Radiation Belt Space Weather Forecasts for Internal Charging Analyses

NASA Technical Reports Server (NTRS)

Minow, Joseph I.; Coffey, Victoria N.; Jun, Insoo; Garrett, Henry B.

2007-01-01

A variety of static electron radiation belt models, space weather prediction tools, and energetic electron datasets are used by spacecraft designers and operations support personnel as internal charging code inputs to evaluate electrostatic discharge risks in space systems due to exposure to relativistic electron environments. Evaluating the environment inputs is often accomplished by comparing whether the data set or forecast tool reliability predicts measured electron flux (or fluence over a given period) for some chosen period. While this technique is useful as a model metric, it does not provide the information necessary to evaluate whether short term deviances of the predicted flux is important in the charging evaluations. In this paper, we use a 1-D internal charging model to compute electric fields generated in insulating materials as a function of time when exposed to relativistic electrons in the Earth's magnetosphere. The resulting fields are assumed to represent the "true" electric fields and are compared with electric field values computed from relativistic electron environments derived from a variety of space environment and forecast tools. Deviances in predicted fields compared to the "true" fields which depend on insulator charging time constants will be evaluated as a potential metric for determining the importance of predicted and measured relativistic electron flux deviations over a range of time scales.

Problems With Risk Reclassification Methods for Evaluating Prediction Models

PubMed Central

Pepe, Margaret S.

2011-01-01

For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714

Evaluation of the Prostate Cancer Prevention Trial Risk Calculator in a High-Risk Screening Population

PubMed Central

Kaplan, David J.; Boorjian, Stephen A.; Ruth, Karen; Egleston, Brian L.; Chen, David Y.T.; Viterbo, Rosalia; Uzzo, Robert G.; Buyyounouski, Mark K.; Raysor, Susan; Giri, Veda N.

2009-01-01

Introduction Clinical factors in addition to PSA have been evaluated to improve risk assessment for prostate cancer. The Prostate Cancer Prevention Trial (PCPT) risk calculator provides an assessment of prostate cancer risk based on age, PSA, race, prior biopsy, and family history. This study evaluated the risk calculator in a screening cohort of young, racially diverse, high-risk men with a low baseline PSA enrolled in the Prostate Cancer Risk Assessment Program. Patients and Methods Eligibility for PRAP include men ages 35-69 who are African-American, have a family history of prostate cancer, or have a known BRCA1/2 mutation. PCPT risk scores were determined for PRAP participants, and were compared to observed prostate cancer rates. Results 624 participants were evaluated, including 382 (61.2%) African-American men and 375 (60%) men with a family history of prostate cancer. Median age was 49.0 years (range 34.0-69.0), and median PSA was 0.9 (range 0.1-27.2). PCPT risk score correlated with prostate cancer diagnosis, as the median baseline risk score in patients diagnosed with prostate cancer was 31.3%, versus 14.2% in patients not diagnosed with prostate cancer (p<0.0001). The PCPT calculator similarly stratified the risk of diagnosis of Gleason score ≥7 disease, as the median risk score was 36.2% in patients diagnosed with Gleason ≥7 prostate cancer versus 15.2% in all other participants (p<0.0001). Conclusion PCPT risk calculator score was found to stratify prostate cancer risk in a cohort of young, primarily African-American men with a low baseline PSA. These results support further evaluation of this predictive tool for prostate cancer risk assessment in high-risk men. PMID:19709072

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

PubMed

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

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.

Attachment style dimensions can affect prolonged grief risk in caregivers of terminally ill patients with cancer.

PubMed

Lai, Carlo; Luciani, Massimiliano; Galli, Federico; Morelli, Emanuela; Cappelluti, Roberta; Penco, Italo; Aceto, Paola; Lombardo, Luigi

2015-12-01

The aim of the present study was to evaluate the predictive role of attachment dimensions on the risk of prolonged grief. Sixty caregivers of 51 terminally ill patients with cancer who had been admitted in a hospice were selected. Caregivers were interviewed using Attachment Scale Questionnaire, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Prolonged Grief Disorder 12 (PG-12). The consort caregivers showed higher PG-12 level compared to the sibling caregivers. Anxiety, depression, need for approval, and preoccupation with relationships levels were significantly correlated with PG-12 scores. Female gender, high levels of depression, and preoccupation with relationships significantly predicted higher levels of prolonged grief risk. © The Author(s) 2014.

Evaluation of the effects of implant materials and designs on thermal necrosis of bone in cemented hip arthroplasty.

PubMed

Li, Chaodi; Kotha, Shiva; Mason, James

2003-01-01

The exothermic polymerization of bone cement may induce thermal necrosis of bone in cemented hip arthroplasty. A finite element formulation was developed to predict the evolution of the temperature with time in the cemented hip replacement system. The developed method is capable of taking into account both the chemical reaction that generates heat during bone cement polymerization (through a kinetic model) and the physical process of heat conduction (with an energy balance equation). The possibility of thermal necrosis of bone was then evaluated based on the temperature history in the bone and an appropriate damage criterion. Specifically, we evaluate the role of implant materials and designs on the thermal response of the system. Results indicated that the peak temperature at the bone/cement interface with a metal prosthesis was lower than that with a polymer or a composite prosthesis in hip replacement systems. Necrosis of bone was predicted to occur with a polymer or a composite prosthesis while no necrosis was predicted with a metal prosthesis in the simulated conditions. When reinforcing osteoporotic hips with injected bone cement in the cancellous core of the femur, the volume of bone cement implanted is increased which may increase the risk of thermal necrosis of bone. We evaluate whether this risk can be decreased through the use of an insulator to contain the bone cement. No thermal necrosis of bone was predicted with a 3 mm thick polyurethane insulator while more damage is predicted for the use of bone cement without the insulator. This method provides a numerical tool for the quantitative simulation of the thermal behavior of bone-cement-prosthesis designs and for examining and refining new designs computationally.

A re-evaluation of PETROTOX for predicting acute and chronic toxicity of petroleum substances.

PubMed

Redman, Aaron D; Parkerton, Thomas F; Leon Paumen, Miriam; Butler, Josh D; Letinski, Daniel J; den Haan, Klass

2017-08-01

The PETROTOX model was developed to perform aquatic hazard assessment of petroleum substances based on substance composition. The model relies on the hydrocarbon block method, which is widely used for conducting petroleum substance risk assessments providing further justification for evaluating model performance. Previous work described this model and provided a preliminary calibration and validation using acute toxicity data for limited petroleum substance. The objective of the present study was to re-evaluate PETROTOX using expanded data covering both acute and chronic toxicity endpoints on invertebrates, algae, and fish for a wider range of petroleum substances. The results indicated that recalibration of 2 model parameters was required, namely, the algal critical target lipid body burden and the log octanol-water partition coefficient (K OW ) limit, used to account for reduced bioavailability of hydrophobic constituents. Acute predictions from the updated model were compared with observed toxicity data and found to generally be within a factor of 3 for algae and invertebrates but overestimated fish toxicity. Chronic predictions were generally within a factor of 5 of empirical data. Furthermore, PETROTOX predicted acute and chronic hazard classifications that were consistent or conservative in 93 and 84% of comparisons, respectively. The PETROTOX model is considered suitable for the purpose of characterizing petroleum substance hazard in substance classification and risk assessments. Environ Toxicol Chem 2017;36:2245-2252. © 2017 SETAC. © 2017 SETAC.

The Moderating Role of Mood and Personal Relevance on Persuasive Effects of Gain- and Loss-Framed Health Messages.

PubMed

Wirtz, John G; Sar, Sela; Ghuge, Shreyas

2015-01-01

We predicted that mood would moderate the relation between message framing and two outcome variables, message evaluation and behavioral intention, when the message was personally relevant to the target audience. Participants (N = 242) were randomly assigned to an experimental condition in which a positive or negative mood was induced. Participants then read and evaluated a health message that emphasized potential benefits or risks associated with a vaccine. As predicted, participants who received a loss-framed message reported higher message evaluation and intention scores but only when the message was personally relevant and they were in a positive mood.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Psychosocial risks and stress as predictors of burnout in junior doctors performing emergency guards

PubMed

Fernández-Prada, María; González-Cabrera, Joaquín; Iribar-Ibabe, Concepción; Peinado, José María

2017-01-01

To study the stress, the psychosocial risks associated to the job and the burnout, in a group of junior doctors working at the emergency ward; and to analyze what of those variables could predict and are better related with burnout. Cross-sectional study, with a sample of 42 junior doctors which are on duty in the emergency ward of the University Hospital San Cecilio, Granada (Spain). The Spanish adapted version of the Perceived Stress Scale was used to evaluate stress, the Maslach Burnout Inventory (MBI) to evaluate the professional burnout and the adapted and scaled questionnaire for the self-evaluation of psychosocial risks at work (CopSoQ-ISTAS21). 78% of the junior doctors are in the unfavorable or intermediate range for all CopSoQ-ISTAS21 dimensions, being particularly relevant that 90% of them display unfavorable score in psychological demands. In addition, MBI results show that 45% of our population presents high emotional exhaustion simultaneously to high depersonalization. ISTAS21 psychological demands dimensions (ß = 0.393; p < 0.003) and stress scores (ß = 0.451; p < 0.001) significantly predict emotional exhaustion (r 2 = 0.443). Finally, 38% of junior doctors experienced a threat/aggression during their work in the emergency ward urgencies. Junior doctors develop its professional activity under adverse circumstances probably due to the high psychosocial risk associated to the job. Psychological demands are suggested as the main predicting factor of burnout. These results indicate the need of psychological and structural interventions in order to improve the professional performance of junior doctors at the emergency ward. Copyright: © 2017 SecretarÍa de Salud

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

Development and Sensitivity Analysis of a Frost Risk model based primarily on freely distributed Earth Observation data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis

2015-04-01

The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece

Controls on the Environmental Fate of Compounds Controlled by Coupled Hydrologic and Reactive Processes

NASA Astrophysics Data System (ADS)

Hixson, J.; Ward, A. S.; McConville, M.; Remucal, C.

2017-12-01

Current understanding of how compounds interact with hydrologic processes or reactive processes have been well established. However, the environmental fate for compounds that interact with hydrologic AND reactive processes is not well known, yet critical in evaluating environmental risk. Evaluations of risk are often simplified to homogenize processes in space and time and to assess processes independently of one another. However, we know spatial heterogeneity and time-variable reactivities complicate predictions of environmental transport and fate, and is further complicated by the interaction of these processes, limiting our ability to accurately predict risk. Compounds that interact with both systems, such as photolytic compounds, require that both components are fully understood in order to predict transport and fate. Release of photolytic compounds occurs through both unintentional releases and intentional loadings. Evaluating risks associated with unintentional releases and implementing best management practices for intentional releases requires an in-depth understanding of the sensitivity of photolytic compounds to external controls. Lampricides, such as 3-trifluoromethyl-4-nitrophenol (TFM), are broadly applied in the Great Lakes system to control the population of invasive sea lamprey. Over-dosing can yield fish kills and other detrimental impacts. Still, planning accounts for time of passage and dilution, but not the interaction of the physical and chemical systems (i.e., storage in the hyporheic zone and time-variable decay rates). In this study, we model a series of TFM applications to test the efficacy of dosing as a function of system characteristics. Overall, our results demonstrate the complexity associated with photo-sensitive compounds through stream-hyporheic systems, and highlight the need to better understand how physical and chemical systems interact to control transport and fate in the environment.

Validity of the current risk assessment scale for pressure ulcers in intensive care (EVARUCI) and the Norton-MI scale in critically ill patients.

PubMed

Lospitao-Gómez, Sara; Sebastián-Viana, Tomás; González-Ruíz, José M; Álvarez-Rodríguez, Joaquín

2017-12-01

The objective of this study was to evaluate the validity of risk detection scales EVARUCI and Norton-MI (modified by INSALUD) to detect critical adult patients with the risk of developing pressure ulcers (PU) in an intensive care unit (ICU). The authors have conducted a descriptive, prospective study at the ICU in their hospital from 2008 to 2014. The evaluations of both scales were registered daily by nurses from the unit. Adult patients admitted into the ICU. The study measured the sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) of each of the scores for both scales and areas under curve (AUC) of receiver operating characteristics (ROC). The authors have evaluated a total of 2534 patients. For the cut-off point recommended by the authors in the scale Norton-MI (PC 14), a sensitivity of 94,05% (93,28-94,82) was obtained, specificity of 40,47% (39,72-41,22), VPP 26,22% and VPN 96,80%. For EVARUCI (CP 10) a sensitivity of 80,43% (79,15-81,72), specificity 64,41 (63,68-65,14), VPP 33,71% and VPN of 93,60%. The ABC-COR was 0,774 with a 95% CI of 0,766 to 0,781 for the scale of Norton-MI and 0.756 with a 95% CI of 0,749 to 0,764 for EVARUCI. Both scales are valid to help predict the risk of developing PU in critical patients. The sensitivity and ABC-COR are very similar for EVARUCI and Norton-Mi. The authors state they do not have any financial interests linked to this article. Copyright © 2017 Elsevier Inc. All rights reserved.

Validation of the Beck Hopelessness Scale in patients with suicide risk.

PubMed

Rueda-Jaimes, German Eduardo; Castro-Rueda, Vanessa Alexandra; Rangel-Martínez-Villalba, Andrés Mauricio; Moreno-Quijano, Catalina; Martinez-Salazar, Gustavo Adolfo; Camacho, Paul Anthony

Only a few scales have been validated in Spanish for the assessment of suicide risk, and none of them have achieved predictive validity. To determine the validity and reliability of the Beck Hopelessness Scale in patients with suicide risk attending the specialist clinic. The Beck Hopelessness Scale, reasons for living inventory, and the suicide behaviour questionnaire were applied in patients with suicide risk attending the psychiatric clinic and the emergency department. A new assessment was made 30 days later to determine the predictive validity of suicide or suicide attempt. The evaluation included a total of 244 patients, with a mean age of 30.7±13.2 years, and the majority were women. The internal consistency was .9 (Kuder-Richardson formula 20). Four dimensions were found which accounted for 50% of the variance. It was positively correlated with the suicidal behaviour questionnaire (Spearman .48, P<.001), number of suicide attempts (Spearman .25, P<.001), severity of suicide risk (Spearman .23, P<.001). The correlation with the reasons for living inventory was negative (Spearman -.52, P<.001). With a cut-off ≥12, the negative predictive value was 98.4% (95% CI: 94.2-99.8), and the positive predictive value was 14.8% (95% CI: 6.6-27.1). The Beck Hopelessness Scale in Colombian patients with suicidality shows results similar to the original version, with adequate reliability and moderate concurrent and predictive validity. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

Shear wave elastography predicts hepatocellular carcinoma risk in hepatitis C patients after sustained virological response.

PubMed

Hamada, Koichi; Saitoh, Satoshi; Nishino, Noriyuki; Fukushima, Daizo; Horikawa, Yoshinori; Nishida, Shinya; Honda, Michitaka

2018-01-01

To evaluate the relationship between fibrosis and HCC after sustained virological response (SVR) to treatment for chronic hepatitis C (HCV). This single-center study retrospectively evaluated 196 patients who achieved SVR after HCV infection. The associations of risk factors with HCC development after HCV eradication were evaluated using univariate and multivariate Cox proportional hazards regression models. Among the 196 patients, 8 patients (4.1%) developed HCC after SVR during a median follow-up of 26 months. Multivariate analyses revealed that HCC development was independently associated with age of ≥75 years (risk ratio [RR] = 35.16), α- fetoprotein levels of ≥6 ng/mL (RR = 40.30), and SWE results of ≥11 kPa (RR = 28.71). Our findings indicate that SWE may facilitate HCC surveillance after SVR and the identification of patients who have an increased risk of HCC after HCV clearance.

Prediction of new brain metastases after radiosurgery: validation and analysis of performance of a multi-institutional nomogram.

PubMed

Ayala-Peacock, Diandra N; Attia, Albert; Braunstein, Steve E; Ahluwalia, Manmeet S; Hepel, Jaroslaw; Chung, Caroline; Contessa, Joseph; McTyre, Emory; Peiffer, Ann M; Lucas, John T; Isom, Scott; Pajewski, Nicholas M; Kotecha, Rupesh; Stavas, Mark J; Page, Brandi R; Kleinberg, Lawrence; Shen, Colette; Taylor, Robert B; Onyeuku, Nasarachi E; Hyde, Andrew T; Gorovets, Daniel; Chao, Samuel T; Corso, Christopher; Ruiz, Jimmy; Watabe, Kounosuke; Tatter, Stephen B; Zadeh, Gelareh; Chiang, Veronica L S; Fiveash, John B; Chan, Michael D

2017-11-01

Stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) for brain metastases can avoid WBRT toxicities, but with risk of subsequent distant brain failure (DBF). Sole use of number of metastases to triage patients may be an unrefined method. Data on 1354 patients treated with SRS monotherapy from 2000 to 2013 for new brain metastases was collected across eight academic centers. The cohort was divided into training and validation datasets and a prognostic model was developed for time to DBF. We then evaluated the discrimination and calibration of the model within the validation dataset, and confirmed its performance with an independent contemporary cohort. Number of metastases (≥8, HR 3.53 p = 0.0001), minimum margin dose (HR 1.07 p = 0.0033), and melanoma histology (HR 1.45, p = 0.0187) were associated with DBF. A prognostic index derived from the training dataset exhibited ability to discriminate patients' DBF risk within the validation dataset (c-index = 0.631) and Heller's explained relative risk (HERR) = 0.173 (SE = 0.048). Absolute number of metastases was evaluated for its ability to predict DBF in the derivation and validation datasets, and was inferior to the nomogram. A nomogram high-risk threshold yielding a 2.1-fold increased need for early WBRT was identified. Nomogram values also correlated to number of brain metastases at time of failure (r = 0.38, p < 0.0001). We present a multi-institutionally validated prognostic model and nomogram to predict risk of DBF and guide risk-stratification of patients who are appropriate candidates for radiosurgery versus upfront WBRT.

Assessing Susceptibility to Age-Related Macular Degeneration With Genetic Markers and Environmental Factors

PubMed Central

Chen, Yuhong; Zeng, Jiexi; Zhao, Chao; Wang, Kevin; Trood, Elizabeth; Buehler, Jeanette; Weed, Matthew; Kasuga, Daniel; Bernstein, Paul S.; Hughes, Guy; Fu, Victoria; Chin, Jessica; Lee, Clara; Crocker, Maureen; Bedell, Matthew; Salasar, Francesca; Yang, Zhenglin; Goldbaum, Michael; Ferreyra, Henry; Freeman, William R.; Kozak, Igor; Zhang, Kang

2014-01-01

Objectives To evaluate the independent and joint effects of genetic factors and environmental variables on advanced forms of age-related macular degeneration (AMD), including geographic atrophy and choroidal neovascularization, and to develop a predictive model with genetic and environmental factors included. Methods Demographic information, including age at onset, smoking status, and body mass index, was collected for 1844 participants. Genotypes were evaluated for 8 variants in 5 genes related to AMD. Unconditional logistic regression analyses were performed to generate a risk predictive model. Results All genetic variants showed a strong association with AMD. Multivariate odds ratios were 3.52 (95% confidence interval, 2.08-5.94) for complement factor H, CFH rs1061170 CC, 4.21 (2.30-7.70) for CFH rs2274700 CC, 0.46 (0.27-0.80) for C2 rs9332739 CC/CG, 0.44 (0.30-0.66) for CFB rs641153 TT/CT, 10.99 (6.04-19.97) for HTRA1/LOC387715 rs10490924 TT, and 2.66 (1.43-4.96) for C3 rs2230199 GG. Smoking was independently associated with advanced AMD after controlling for age, sex, body mass index, and all genetic variants. Conclusion CFH confers more risk to the bilaterality of geographic atrophy, whereas HTRA1/LOC387715 contributes more to the bilaterality of choroidal neovascularization. C3 confers more risk for geographic atrophy than choroidal neovascularization. Risk models with combined genetic and environmental factors have notable discrimination power. Clinical Relevance Early detection and risk prediction of AMD could help to improve the prognosis of AMD and to reduce the outcome of blindness. Targeting high-risk individuals for surveillance and clinical interventions may help reduce disease burden. PMID:21402993

Predictors of violent behavior among acute psychiatric patients: clinical study.

PubMed

Amore, Mario; Menchetti, Marco; Tonti, Cristina; Scarlatti, Fabiano; Lundgren, Eva; Esposito, William; Berardi, Domenico

2008-06-01

Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. The aim of the present study was to determine violence risk factors in acute psychiatric inpatients. The study was conducted in a locked, short-term psychiatric inpatient unit and involved 374 patients consecutively admitted in a 1-year period. Sociodemographic and clinical data were obtained through a review of the medical records and patient interviews. Psychiatric symptoms at admission were assessed using the Brief Psychiatric Rating Scale (BPRS). Psychiatric diagnosis was formulated using the Structured Clinical Interview for DSM-IV. Past aggressive behavior was evaluated by interviewing patients, caregivers or other collateral informants. Aggressive behaviors in the ward were assessed using the Overt Aggression Scale. Patients who perpetrated verbal and against-object aggression or physical aggression in the month before admission were compared to non-aggressive patients, moreover, aggressive behavior during hospitalization and persistence of physical violence after admission were evaluated. Violent behavior in the month before admission was associated with male sex, substance abuse and positive symptoms. The most significant risk factor for physical violence was a past history of physically aggressive behavior. The persistent physical assaultiveness before and during hospitalization was related to higher BPRS total scores and to more severe thought disturbances. Higher levels of hostility-suspiciousness BPRS scores predicted a change for the worse in violent behavior, from verbal to physical. A comprehensive evaluation of the history of past aggressive behavior and psychopathological variables has important implications for the prediction of violence in psychiatric settings.

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

Research on Bereavement: Implications for Social Policy Development.

ERIC Educational Resources Information Center

Kiely, Margaret C.

This paper describes the results of an evaluation of the Palliative Care Service, one of the first hospices in North America (Montreal), and the implications of that research for social policy development. The objectives of the research were to evaluate the reliability of predictive assessments of bereavement risk and the effectiveness of…

The relationships of the level of response to alcohol and additional characteristics to alcohol use disorders across adulthood: a discrete-time survival analysis.

PubMed

Trim, Ryan S; Schuckit, Marc A; Smith, Tom L

2009-09-01

A low level of response (LR) to alcohol has been shown to relate to a higher risk for alcohol use disorders (AUDs). However, no previous research has examined the association between LR and the development of AUDs in the context of additional robust risk factors for AUDs. This study evaluated whether LR and other related characteristics predicted the occurrence of AUDs across adulthood using discrete-time survival analysis (DTSA). A total of 297 probands from the San Diego Prospective Study reported on the LR to alcohol, a family history (FH) of AUDs, the typical drinking quantity, the age of drinking onset, the body mass index and the age at the baseline (T1) assessment. Alcohol use disorders (AUDs) were evaluated at the 10-year (T10), T15, T20, and T25 follow-ups. A low LR to alcohol predicted AUD occurrence over the course of adulthood even after controlling for the effects of other robust risk factors. Interaction effects revealed that the impact of FH on AUDs was only observed for subjects with high T1 drinking levels, and probands with high T1 drinking were at high risk for AUDs regardless of their age of onset. The findings illustrate that LR is a unique risk factor for AUDs across adulthood, and not simply a reflection of a broader range of risk factors. The continued investigation of how LR is related to AUD onset later in life will help inform treatment providers about this high-risk population, and future longitudinal evaluations will utilize DTSA to assess rates of AUD remission as well as the onset of drinking outcomes in adolescent samples.

The Relationships of the Level of Response to Alcohol and Additional Characteristics to Alcohol Use Disorders across Adulthood: A Discrete-Time Survival Analysis

PubMed Central

Trim, Ryan S.; Schuckit, Marc A.; Smith, Tom L.

2010-01-01

Background A low level of response (LR) to alcohol has been shown to relate to a higher risk for alcohol use disorders (AUDs). However, no previous research has examined the association between LR and the development of AUDs in the context of additional robust risk factors for AUDs. This study evaluated whether LR and other related characteristics predicted the occurrence of AUDs across adulthood using discrete-time survival analysis (DTSA). Methods 297 probands from the San Diego Prospective Study reported on the LR to alcohol, a family history (FH) of AUDs, the typical drinking quantity, the age of drinking onset, the body mass index and the age at the baseline (T1) assessment. Alcohol use disorders (AUDs) were evaluated at the 10-year (T10), T15, T20, and T25 follow-ups. Results A low LR to alcohol predicted AUD occurrence over the course of adulthood even after controlling for the effects of other robust risk factors. Interaction effects revealed that the impact of FH on AUDs was only observed for subjects with high T1 drinking levels, and probands with high T1 drinking were at high risk for AUDs regardless of their age of onset. Conclusion The findings illustrate that LR is a unique risk factor for AUDs across adulthood, and not simply a reflection of a broader range of risk factors. The continued investigation of how LR is related to AUD onset later in life will help inform treatment providers about this high-risk population, and future longitudinal evaluations will utilize DTSA to assess rates of AUD remission as well as the onset of drinking outcomes in adolescent samples. PMID:19485971

Using alternative or direct anthropometric measurements to assess risk for malnutrition in nursing homes.

PubMed

Lorini, Chiara; Collini, Francesca; Castagnoli, Mariangela; Di Bari, Mauro; Cavallini, Maria Chiara; Zaffarana, Nicoletta; Pepe, Pasquale; Lucenteforte, Ersilia; Vannacci, Alfredo; Bonaccorsi, Guglielmo

2014-10-01

The aim of this study was to use the Malnutrition Universal Screening Tool (MUST) to assess the applicability of alternative versus direct anthropometric measurements for evaluating the risk for malnutrition in older individuals living in nursing homes (NHs). We conducted a cross-sectional survey in 67 NHs in Tuscany, Italy. We measured the weight, standing height (SH), knee height (KH), ulna length (UL), and middle-upper-arm circumference of 641 NH residents. Correlations between the different methods for calculating body mass index (BMI; using direct or alternative measurements) were evaluated by the intraclass correlation coefficient and the Bland-Altman method; agreement in the allocation of participants to the same risk category was assessed by squared weighted kappa statistic and indicators of internal relative validity. The intraclass correlation coefficient for BMI calculated using KH was 0.839 (0.815-0.861), whereas those calculated by UL were 0.890 (0.872-0.905). The limits of agreement were ±6.13 kg/m(2) using KH and ±4.66 kg/m(2) using UL. For BMI calculated using SH, 79.9% of the patients were at low risk, 8.1% at medium risk, and 12.2% at high risk for malnutrition. The agreement between this classification and that obtained using BMI calculated by alternative measurements was "fair-good." When it is not possible to determine risk category by using SH, we suggest using the alternative measurements (primarily UL, due to its highest sensitivity) to predict the height and to compare these evaluations with those obtained by using middle-upper-arm-circumference to predict the BMI. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

U.S. Civil Air Show Crashes, 1993 to 2013: Burden, Fatal Risk Factors, and Evaluation of a Risk Index for Aviation Crashes.

PubMed

Ballard, Sarah-Blythe; Osorio, Victor B

2015-01-01

This study provides new public health data about U.S. civil air shows. Risk factors for fatalities in civil air show crashes were analyzed. The value of the FIA score in predicting fatal outcomes was evaluated. With the use of the FAA's General Aviation and Air Taxi Survey and the National Transportation Safety Board's data, the incidence of civil air show crashes from 1993 to 2013 was calculated. Fatality risk factors for crashes were analyzed by means of regression methods. The FIA index was validated to predict fatal outcomes by using the factors of fire, instrument conditions, and away-from-airport location, and was evaluated through receiver operating characteristic (ROC) curves. The civil air show crash rate was 31 crashes per 1,000 civil air events. Of the 174 civil air show crashes that occurred during the study period, 91 (52%) involved at least one fatality; on average, 1.1 people died per fatal crash. Fatalities were associated with four major risk factors: fire [adjusted odds ratio (AOR) = 7.1, 95% confidence interval (CI) = 2.4 to 20.6, P < .001], pilot error (AOR = 5.2, 95% CI = 1.8 to 14.5, P = .002), aerobatic flight (AOR = 3.6, 95% CI = 1.6 to 8.2, P = .002), and off-airport location (AOR = 3.4, 95% CI = 1.5 to 7.5, P = .003). The area under the FIA score's ROC curve was 0.71 (95% CI = 0.64 to 0.78). Civil air show crashes were marked by a high risk of fatal outcomes to pilots in aerobatic performances but rare mass casualties. The FIA score was not a valid measurement of fatal risk in civil air show crashes.

Predicting Long-Term Outcomes in Pleural Infections. RAPID Score for Risk Stratification.

PubMed

White, Heath D; Henry, Christopher; Stock, Eileen M; Arroliga, Alejandro C; Ghamande, Shekhar

2015-09-01

Pleural infections are associated with significant morbidity and mortality. The recently developed RAPID (renal, age, purulence, infection source, and dietary factors) score consists of five clinical factors that can identify patients at risk for increased mortality. The objective of this study was to further validate the RAPID score in a diverse cohort, identify factors associated with mortality, and provide long-term outcomes. We evaluated a single-center retrospective cohort of 187 patients with culture-positive pleural infections. Patients were classified by RAPID scores into low-risk (0-2), medium-risk (3-4), and high-risk (5-7) groups. The Social Security Death Index was used to determine date of death. All-cause mortality was assessed at 3 months, 1 year, 3 years, and 5 years. Clinical factors and comorbid conditions were evaluated for association. Three-month mortality for low-, medium-, and high-risk groups was 1.5, 17.8, and 47.8%, respectively. Increased odds were observed among medium-risk (odds ratio, 14.3; 95% confidence interval, 1.8-112.6; P = 0.01) and high-risk groups (odds ratio, 53.3; 95% confidence interval, 6.8-416.8; P < 0.01). This trend continued at 1, 3, and 5 years. Factors associated with high-risk scores include gram-negative rod infections, heart disease, diabetes, cancer, lung disease, and increased length of stay. When applied to a diverse patient cohort, the RAPID score predicts outcomes in patients up to 5 years and may aid in long-term risk stratification on presentation.

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/.

Use of clinical risk factors to identify postmenopausal women with vertebral fractures.

PubMed

Tobias, J H; Hutchinson, A P; Hunt, L P; McCloskey, E V; Stone, M D; Martin, J C; Thompson, P W; Palferman, T G; Bhalla, A K

2007-01-01

Previous studies have been unable to identify risk factors for prevalent vertebral fractures (VF), which are suitable for use in selection strategies intended to target high-risk sub-groups for diagnostic assessment. However, these studies generally consisted of large epidemiology surveys based on questionnaires and were only able to evaluate a limited number of risk factors. Here, we investigated whether a stronger relationship exists with prevalent VF when conventional risk factors are combined with additional information obtained from detailed one-to-one assessment. Women aged 65-75 registered at four geographically distinct GP practices were invited to participate (n=1,518), of whom 540 attended for assessment as follows: a questionnaire asking about risk factors for osteoporosis such as height loss compared to age 25 and history of non-vertebral fracture (NVF), the get-up-and-go test, Margolis back pain score, measurement of wall-tragus and rib-pelvis distances, and BMD as measured by the distal forearm BMD. A lateral thoraco-lumbar spine X-ray was obtained, which was subsequently scored for the presence of significant vertebral deformities. Of the 509 subjects who underwent spinal radiographs, 37 (7.3%) were found to have one or more VF. Following logistic regression analysis, the four most predictive clinical risk factors for prevalent VF were: height loss (P=0.006), past NVF (P=0.004), history of back pain (P=0.075) and age (P=0.05). BMD was also significantly associated with prevalent VF (P=0.002), but its inclusion did not affect associations with other variables. Factors elicited from detailed one-to-one assessment were not related to the risk of one or more prevalent VFs. The area under ROC curves derived from these regressions, which suggested that models for prevalent VF had modest predictive accuracy, were as follows: 0.68 (BMD), 0.74 (four clinical risk factors above) and 0.78 (clinical risk factors + BMD). Analyses were repeated in relation to the subgroup of 13 patients with two or more VFs, which revealed that in this instance, the Margolis back pain score and rib-pelvis distance were associated with the presence of multiple VFs (P=0.022 and 0.026, respectively). Moreover, the predictive value as reflected by the ROC curve area was improved: 0.80 (BMD), 0.88 (the four most predictive clinical risk factors consisting of the height loss, past NVF, Margolis back pain score and rib-pelvis distance) and 0.91 (clinical risk factors + BMD). Evaluation of additional risk factors from detailed one-to-one assessment does not improve the predictive value of risk factors for one or more prevalent vertebral deformities in postmenopausal women. However, the use of factors such as the Margolis back pain score and rib-pelvis distance may be helpful in identifying postmenopausal women at high risk of multiple prevalent VFs.

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

PubMed

Hahn, Lance W; Moore, Jason H

2004-01-01

Multifactor Dimensionality Reduction (MDR) is a method for the classification and prediction of discrete clinical endpoints using attributes constructed from multilocus genotype data. Empirical studies with both real and simulated data suggest that MDR has good power for detecting gene-gene interactions in the absence of independent main effects. The purpose of this study is to develop an objective, theory-driven approach to evaluate the strengths and limitations of MDR. To accomplish this goal, we borrow concepts from ideal observer analysis used in visual perception to evaluate the theoretical limits of classifying and predicting discrete clinical endpoints using multilocus genotype data. We conclude that MDR ideally discriminates between low risk and high risk subjects using attributes constructed from multilocus genotype data. We also how that the classification approach used once a multilocus attribute is constructed is similar to that of a naive Bayes classifier. This study provides a theoretical foundation for the continued development, evaluation, and application of the MDR as a data mining tool in the domain of statistical genetics and genetic epidemiology.

Relationship between amputation and risk factors in individuals with diabetes mellitus: A study with Brazilian patients.

PubMed

Mantovani, Alessandra M; Fregonesi, Cristina E P T; Palma, Mariana R; Ribeiro, Fernanda E; Fernandes, Rômulo A; Christofaro, Diego G D

Individuals with diabetes develop lower extremity amputation for several reasons. Investigations into pathways to the development of complications are important both for treatment and prevention. To evaluate the relationship between amputation and risk factors in people with diabetes mellitus. All participants included in this study (n=165) were recruited from the Diabetic Foot Program, developed in a Brazilian University, over seven years (2007-2014) and all information for this study was extracted from their clinical records. The prevalence of amputation in patients with diabetes with four risk factors was up to 20% higher when compared to those with only one risk factor. The main predictive risk factors for amputation in this population were the presence of an ulcer and smoking. The risk factors for amputation can be predicted for people with diabetes mellitus and, in the present study, the main factors were the presence of an ulcer and the smoking habit. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.

Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.

PubMed

Galatzer-Levy, Isaac R; Karstoft, Karen-Inge; Statnikov, Alexander; Shalev, Arieh Y

2014-12-01

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information. Copyright © 2014 Elsevier Ltd. All rights reserved.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Developing an objective evaluation method to estimate diabetes risk in community-based settings.

PubMed

Kenya, Sonjia; He, Qing; Fullilove, Robert; Kotler, Donald P

2011-05-01

Exercise interventions often aim to affect abdominal obesity and glucose tolerance, two significant risk factors for type 2 diabetes. Because of limited financial and clinical resources in community and university-based environments, intervention effects are often measured with interviews or questionnaires and correlated with weight loss or body fat indicated by body bioimpedence analysis (BIA). However, self-reported assessments are subject to high levels of bias and low levels of reliability. Because obesity and body fat are correlated with diabetes at different levels in various ethnic groups, data reflecting changes in weight or fat do not necessarily indicate changes in diabetes risk. To determine how exercise interventions affect diabetes risk in community and university-based settings, improved evaluation methods are warranted. We compared a noninvasive, objective measurement technique--regional BIA--with whole-body BIA for its ability to assess abdominal obesity and predict glucose tolerance in 39 women. To determine regional BIA's utility in predicting glucose, we tested the association between the regional BIA method and blood glucose levels. Regional BIA estimates of abdominal fat area were significantly correlated (r = 0.554, P < 0.003) with fasting glucose. When waist circumference and family history of diabetes were added to abdominal fat in multiple regression models, the association with glucose increased further (r = 0.701, P < 0.001). Regional BIA estimates of abdominal fat may predict fasting glucose better than whole-body BIA as well as provide an objective assessment of changes in diabetes risk achieved through physical activity interventions in community settings.

Toxicities and risk assessment of heavy metals in sediments of Taihu Lake, China, based on sediment quality guidelines.

PubMed

Zhang, Yanfeng; Han, Yuwei; Yang, Jinxi; Zhu, Lingyan; Zhong, Wenjue

2017-12-01

The occurrence, toxicities, and ecological risks of five heavy metals (Pb, Cu, Cd, Zn and Ni) in the sediment of Taihu Lake were investigated in this study. To evaluate the toxicities caused by the heavy metals, the toxicities induced by organic contaminants and ammonia in the sediments were screened out with activated carbon and zeolite. The toxicities of heavy metals in sediments were tested with benthic invertebrates (tubificid and chironomid). The correlations between toxicity of sediment and the sediment quality guidelines (SQGs) derived previously were evaluated. There were significant correlations (p<0.0001) between the observed toxicities and the total risk quotients of the heavy metals based on SQGs, indicating that threshold effect level (TEL) and probable effect level (PEL) were reliable to predict the toxicities of heavy metals in the sediments of Taihu Lake. By contrast, the method based on acid volatile sulfides (AVS) and simultaneously extracted metals (SEM), such as ∑SEM/AVS and ∑SEM-AVS, did not show correlations with the toxicities. Moreover, the predictive ability of SQGs was confirmed by a total predicting accuracy of 77%. Ecological risk assessment based on TELs and PELs showed that the contaminations of Pb, Cu, Cd and Zn in the sediments of Taihu Lake were at relatively low or medium levels. The risks caused by heavy metals in the sediments of northern bay of the lake, which received more wastewater discharge from upper stream, were higher than other area of the lake. Copyright © 2017. Published by Elsevier B.V.

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

PubMed Central

Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

2014-01-01

Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

Risk adjusted surgical audit in gynaecological oncology: P-POSSUM does not predict outcome.

PubMed

Das, N; Talaat, A S; Naik, R; Lopes, A D; Godfrey, K A; Hatem, M H; Edmondson, R J

2006-12-01

To assess the Physiological and Operative Severity Score for the enumeration of mortality and morbidity (POSSUM) and its validity for use in gynaecological oncology surgery. All patients undergoing gynaecological oncology surgery at the Northern Gynaecological Oncology Centre (NGOC) Gateshead, UK over a period of 12months (2002-2003) were assessed prospectively. Mortality and morbidity predictions using the Portsmouth modification of the POSSUM algorithm (P-POSSUM) were compared to the actual outcomes. Performance of the model was also evaluated using the Hosmer and Lemeshow Chi square statistic (testing the goodness of fit). During this period 468 patients were assessed. The P-POSSUM appeared to over predict mortality rates for our patients. It predicted a 7% mortality rate for our patients compared to an observed rate of 2% (35 predicted deaths in comparison to 10 observed deaths), a difference that was statistically significant (H&L chi(2)=542.9, d.f. 8, p<0.05). The P-POSSUM algorithm overestimates the risk of mortality for gynaecological oncology patients undergoing surgery. The P-POSSUM algorithm will require further adjustments prior to adoption for gynaecological cancer surgery as a risk adjusted surgical audit tool.