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.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Personal and couple level risk factors: Maternal and paternal parent-child aggression risk.

PubMed

Tucker, Meagan C; Rodriguez, Christina M; Baker, Levi R

2017-07-01

Previous literature examining parent-child aggression (PCA) risk has relied heavily upon mothers, limiting our understanding of paternal risk factors. Moreover, the extent to which factors in the couple relationship work in tandem with personal vulnerabilities to impact PCA risk is unclear. The current study examined whether personal stress and distress predicted PCA risk (child abuse potential, over-reactive discipline style, harsh discipline practices) for fathers as well as mothers and whether couple functioning mediated versus moderated the relation between personal stress and PCA risk in a sample of 81 couples. Additionally, the potential for risk factors in one partner to cross over and affect their partner's PCA risk was considered. Findings indicated higher personal stress predicted elevated maternal and paternal PCA risk. Better couple functioning did not moderate this relationship but partially mediated stress and PCA risk for both mothers and fathers. In addition, maternal stress evidenced a cross-over effect, wherein mothers' personal stress linked to fathers' couple functioning. Findings support the role of stress and couple functioning in maternal and paternal PCA risk, including potential cross-over effects that warrant further inquiry. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing Bleeding Risk in Patients Taking Anticoagulants

PubMed Central

Shoeb, Marwa; Fang, Margaret C.

2013-01-01

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

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.

Unravelling the structure of species extinction risk for predictive conservation science.

PubMed

Lee, Tien Ming; Jetz, Walter

2011-05-07

Extinction risk varies across species and space owing to the combined and interactive effects of ecology/life history and geography. For predictive conservation science to be effective, large datasets and integrative models that quantify the relative importance of potential factors and separate rapidly changing from relatively static threat drivers are urgently required. Here, we integrate and map in space the relative and joint effects of key correlates of The International Union for Conservation of Nature-assessed extinction risk for 8700 living birds. Extinction risk varies significantly with species' broad-scale environmental niche, geographical range size, and life-history and ecological traits such as body size, developmental mode, primary diet and foraging height. Even at this broad scale, simple quantifications of past human encroachment across species' ranges emerge as key in predicting extinction risk, supporting the use of land-cover change projections for estimating future threat in an integrative setting. A final joint model explains much of the interspecific variation in extinction risk and provides a remarkably strong prediction of its observed global geography. Our approach unravels the species-level structure underlying geographical gradients in extinction risk and offers a means of disentangling static from changing components of current and future threat. This reconciliation of intrinsic and extrinsic, and of past and future extinction risk factors may offer a critical step towards a more continuous, forward-looking assessment of species' threat status based on geographically explicit environmental change projections, potentially advancing global predictive conservation science.

Mothers of children with externalizing behavior problems: cognitive risk factors for abuse potential and discipline style and practices.

PubMed

McElroy, Erika M; Rodriguez, Christina M

2008-08-01

Utilizing the conceptual framework of the Social Information Processing (SIP) model (Milner, 1993, 2000), associations between cognitive risk factors and child physical abuse risk and maladaptive discipline style and practices were examined in an at-risk population. Seventy-three mothers of 5-12-year-old children, who were identified by their therapist as having an externalizing behavior problem, responded to self-report measures pertaining to cognitive risk factors (empathic perspective taking, frustration tolerance, developmental expectations, parenting locus of control), abuse risk, and discipline style and practices. The Child Behavior Checklist (CBCL) provided a confirmation of the child's externalizing behaviors independent of the therapist's assessment. The results of this study suggest several cognitive risk factors significantly predict risk of parental aggression toward children. A parent's ability to empathize and take the perspective of their child, parental locus of control, and parental level of frustration tolerance were significant predictors of abuse potential (accounting for 63% of the variance) and inappropriate discipline practices (accounting for 55% of the variance). Findings of the present study provide support for processes theorized in the SIP model. Specifically, results underscore the potential role of parents' frustration tolerance, developmental expectations, locus of control, and empathy as predictive of abuse potential and disciplinary style in an at-risk sample.

Predicting long-term performance of engineered geologic carbon dioxide storage systems to inform decisions amidst uncertainty

NASA Astrophysics Data System (ADS)

Pawar, R.

2016-12-01

Risk assessment and risk management of engineered geologic CO2 storage systems is an area of active investigation. The potential geologic CO2 storage systems currently under consideration are inherently heterogeneous and have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site's characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. A quantitative methodology for predicting a sequestration site's long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. An integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems can be used to predict long-term performance. The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers). CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc. Answers to such questions are critical in making key risk management decisions. A systematic uncertainty quantification approach can been used to understand how uncertain parameters associated with different subsystems (e.g., reservoir permeability, wellbore cement permeability, wellbore density, etc.) impact the overall site performance predictions.

The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat Casualties: A Prospective Observational Cohort Study

DTIC Science & Technology

2015-06-16

are associated with poor outcomes, including death and the need for renal replacement therapy. Methods : We conducted a prospective, observational study...penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 16 JUN 2015...2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE The Potential Utility of Urinary Biomarkers for Risk Prediction in Combat

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

PubMed Central

2011-01-01

Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC. PMID:21797996

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.

Predictive accuracy of combined genetic and environmental risk scores.

PubMed

Dudbridge, Frank; Pashayan, Nora; Yang, Jian

2018-02-01

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

Predictive accuracy of combined genetic and environmental risk scores

PubMed Central

Pashayan, Nora; Yang, Jian

2017-01-01

ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

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.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

PubMed

Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

PubMed Central

Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

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.

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

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).

Coronary heart disease risk stratification: pitfalls and possibilities.

PubMed

Negi, Smita; Nambi, Vijay

Atherosclerosis of the coronary arteries, or coronary heart disease (CHD), is the most common cause of mortality in U.S. adults. The pathobiology of atherosclerosis and its complications is a continuum. At one end of the spectrum are young individuals without atherosclerotic disease who have not yet been exposed to lifestyle or other risk factors, and at the other end are patients with manifest atherosclerosis - myocardial infarction, stroke, and disabling peripheral arterial disease - where risk of recurrent disease and death is driven by the same factors initially responsible for the emergence of disease. However, it is clear that while risk factors are important in the development of CHD, not everyone with risk factors develops the disease and not everyone with CHD has risk factors. Furthermore, even similar degrees of exposure to a risk factor leads to disease in some individuals and not in others. Risk prediction, which is crucial in predicting and hence preventing disease, therefore becomes very challenging. In this article we review the currently available risk stratification tools for predicting CHD risk and discuss potential ways to improve risk prediction.

The joint impact of cognitive performance in adolescence and familial cognitive aptitude on risk for major psychiatric disorders: a delineation of four potential pathways to illness.

PubMed

Kendler, K S; Ohlsson, H; Keefe, R S E; Sundquist, K; Sundquist, J

2018-04-01

How do joint measures of premorbid cognitive ability and familial cognitive aptitude (FCA) reflect risk for a diversity of psychiatric and substance use disorders? To address this question, we examined, using Cox models, the predictive effects of school achievement (SA) measured at age 16 and FCA-assessed from SA in siblings and cousins, and educational attainment in parents-on risk for 12 major psychiatric syndromes in 1 140 608 Swedes born 1972-1990. Four developmental patterns emerged. In the first, risk was predicted jointly by low levels of SA and high levels of FCA-that is a level of SA lower than would be predicted from the FCA. This pattern was strongest in autism spectrum disorders and schizophrenia, and weakest in bipolar illness. In these disorders, a pathologic process seems to have caused cognitive functioning to fall substantially short of familial potential. In the second pattern, seen in the internalizing conditions of major depression and anxiety disorders, risk was associated with low SA but was unrelated to FCA. Externalizing disorders-drug abuse and alcohol use disorders-demonstrated the third pattern, in which risk was predicted jointly by low SA and low FCA. The fourth pattern, seen in eating disorders, was directly opposite of that observed in externalizing disorders with risk associated with high SA and high FCA. When measured together, adolescent cognitive ability and FCA identified four developmental patterns leading to diverse psychiatric disorders. The value of cognitive assessments in psychiatric research can be substantially increased by also evaluating familial cognitive potential.

Risk-rating Saratoga Spittlebug Damage by Abundance of Alternate-host Plants

Treesearch

Louis F. Wilson

1971-01-01

The potential damage of the Saratoga spittlebug to red pine can be predicted by comparing the percentage of ground occupied by sweet-fern with the percentage of ground cover occupied by other nymphal host plants. A risk-rating graph is used to estimate potential damage.

Environmental risk assessment of a genetically-engineered microorganism: Erwinia carotovora

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

Orvos, D.R.

1989-01-01

Environmental use of genetically-engineered microorganisms (GEMs) has raised concerns over potential ecological impact. Development of microcosm systems useful in preliminary testing for risk assessment will provide useful information for predicting potential structural, functional, and genetic effects of GEM release. This study was executed to develop techniques that may be useful in risk assessment and microbial ecology, to ascertain which parameters are useful in determining risk and to predict risk from releasing an engineered strain of Erwinia carotovora. A terrestrial microcosm system for use in GEM risk assessment studies was developed for use in assessing alterations of microbial structure and functionmore » that may be caused by introducing the engineered strain of E. carotovora. This strain is being developed for use as a biological control agent for plant soft rot. Parameters that were monitored included survival and intraspecific competition of E. carotovora, structural effects upon both total bacterial populations and numbers of selected bacterial genera, effects upon activities of dehydrogenase and alkaline phosphatase, effects upon soil nutrients, and potential for gene transfer into or out of the engineered strain.« less

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions.

PubMed

Kaufman, Leyla V; Wright, Mark G

2017-07-07

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions

PubMed Central

Kaufman, Leyla V.; Wright, Mark G.

2017-01-01

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments. PMID:28686180

Predicting spillover risk to non-target plants pre-release: Bikasha collaris a potential biological control agent of Chinese tallowtree (Triadica sebifera)

USDA-ARS?s Scientific Manuscript database

Quarantine host range tests accurately predict direct risk of biological control agents to non-target species. However, a well-known indirect effect of biological control of weeds releases is spillover damage to non-target species. Spillover damage may occur when the population of agents achieves ou...

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.

Assessing the Risk of Invasion by Tephritid Fruit Flies: Intraspecific Divergence Matters

PubMed Central

Godefroid, Martin; Cruaud, Astrid; Rossi, Jean-Pierre; Rasplus, Jean-Yves

2015-01-01

Widely distributed species often show strong phylogeographic structure, with lineages potentially adapted to different biotic and abiotic conditions. The success of an invasion process may thus depend on the intraspecific identity of the introduced propagules. However, pest risk analyses are usually performed without accounting for intraspecific diversity. In this study, we developed bioclimatic models using MaxEnt and boosted regression trees approaches, to predict the potential distribution in Europe of six economically important Tephritid pests (Ceratitis fasciventris (Bezzi), Bactrocera oleae (Rossi), Anastrepha obliqua (Macquart), Anastrepha fraterculus (Wiedemann), Rhagoletis pomonella (Walsh) and Bactrocera cucurbitae (Coquillet)). We considered intraspecific diversity in our risk analyses by independently modeling the distributions of conspecific lineages. The six species displayed different potential distributions in Europe. A strong signal of intraspecific climate envelope divergence was observed in most species. In some cases, conspecific lineages differed strongly in potential distributions suggesting that taxonomic resolution should be accounted for in pest risk analyses. No models (lineage- and species-based approaches) predicted high climatic suitability in the entire invaded range of B. oleae—the only species whose intraspecific identity of invading populations has been elucidated—in California. Host availability appears to play the most important role in shaping the geographic range of this specialist pest. However, climatic suitability values predicted by species-based models are correlated with population densities of B. oleae globally reported in California. Our study highlights how classical taxonomic boundaries may lead to under- or overestimation of the potential pest distributions and encourages accounting for intraspecific diversity when assessing the risk of biological invasion. PMID:26274582

Multimethod Prediction of Physical Parent-Child Aggression Risk in Expectant Mothers and Fathers with Social Information Processing Theory

PubMed Central

Rodriguez, Christina M.; Smith, Tamika L.; Silvia, Paul J.

2015-01-01

The Social Information Processing (SIP) model postulates that parents undergo a series of stages in implementing physical discipline that can escalate into physical child abuse. The current study utilized a multimethod approach to investigate whether SIP factors can predict risk of parent-child aggression (PCA) in a diverse sample of expectant mothers and fathers. SIP factors of PCA attitudes, negative child attributions, reactivity, and empathy were considered as potential predictors of PCA risk; additionally, analyses considered whether personal history of PCA predicted participants’ own PCA risk through its influence on their attitudes and attributions. Findings indicate that, for both mothers and fathers, history influenced attitudes but not attributions in predicting PCA risk, and attitudes and attributions predicted PCA risk; empathy and reactivity predicted negative child attributions for expectant mothers, but only reactivity significantly predicted attributions for expectant fathers. Path models for expectant mothers and fathers were remarkably similar. Overall, the findings provide support for major aspects of the SIP model. Continued work is needed in studying the progression of these factors across time for both mothers and fathers as well as the inclusion of other relevant ecological factors to the SIP model. PMID:26631420

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.

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.

Development of a Melanoma Risk Prediction Model Incorporating MC1R Genotype and Indoor Tanning Exposure: Impact of Mole Phenotype on Model Performance

PubMed Central

Penn, Lauren A.; Qian, Meng; Zhang, Enhan; Ng, Elise; Shao, Yongzhao; Berwick, Marianne; Lazovich, DeAnn; Polsky, David

2014-01-01

Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals. PMID:25003831

A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?

PubMed

Bodford, Jessica E; Kwan, Virginia S Y

2018-02-01

The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

PubMed

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Improving pandemic influenza risk assessment

USDA-ARS?s Scientific Manuscript database

Assessing the pandemic risk posed by specific non-human influenza A viruses remains a complex challenge. As influenza virus genome sequencing becomes cheaper, faster and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk asses...

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

PubMed Central

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

2013-01-01

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

An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

EPAs ToxCast Research Program: Developing Predictive Bioactivity Signatures for Chemicals

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

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

Improving default risk prediction using Bayesian model uncertainty techniques.

PubMed

Kazemi, Reza; Mosleh, Ali

2012-11-01

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation.

PubMed

Pagliaccio, David; Luby, Joan L; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S; Belden, Andrew C; Botteron, Kelly N; Harms, Michael P; Barch, Deanna M

2015-11-01

Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within 4 hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9- to 14-year-olds; N = 120). Whole-brain regression analyses indicated that increasing genetic "risk" predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic "risk" and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. (c) 2015 APA, all rights reserved).

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.

Multimethod prediction of physical parent-child aggression risk in expectant mothers and fathers with Social Information Processing theory.

PubMed

Rodriguez, Christina M; Smith, Tamika L; Silvia, Paul J

2016-01-01

The Social Information Processing (SIP) model postulates that parents undergo a series of stages in implementing physical discipline that can escalate into physical child abuse. The current study utilized a multimethod approach to investigate whether SIP factors can predict risk of parent-child aggression (PCA) in a diverse sample of expectant mothers and fathers. SIP factors of PCA attitudes, negative child attributions, reactivity, and empathy were considered as potential predictors of PCA risk; additionally, analyses considered whether personal history of PCA predicted participants' own PCA risk through its influence on their attitudes and attributions. Findings indicate that, for both mothers and fathers, history influenced attitudes but not attributions in predicting PCA risk, and attitudes and attributions predicted PCA risk; empathy and reactivity predicted negative child attributions for expectant mothers, but only reactivity significantly predicted attributions for expectant fathers. Path models for expectant mothers and fathers were remarkably similar. Overall, the findings provide support for major aspects of the SIP model. Continued work is needed in studying the progression of these factors across time for both mothers and fathers as well as the inclusion of other relevant ecological factors to the SIP model. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling risk for SOD nationwide: what are the effects of model choice on risk prediction?

Treesearch

M. Kelly; D. Shaari; Q. Guo; D. Liu

2006-01-01

Phytophthora ramorum has the potential to infect many forest types found throughout the United States. Efforts to model the potential habitat for P. ramorum and sudden oak death (SOD) are important for disease regulation and management. Yet, spatial models using identical data can have differing results. In this paper we examine...

Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

DTIC Science & Technology

2006-06-01

with benign breast disease ( BBD ) (1967-1991); 2) the application of potential biomarkers of risk to this archival tissue set; and 3) the discovery...of new, potentially relevant biomarkers of risk in fresh and frozen specimens of BBD . The Center includes a multi-institutional team of basic...State). I. Task 1: Establish Retrospective Cohort of BBD and Nested Case-Control Study A. Complete cohort follow-up We provide here an update of

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

PubMed

Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

2017-07-27

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

The Juvenile Addiction Risk Rating: Development and Initial Psychometrics

ERIC Educational Resources Information Center

Powell, Michael; Newgent, Rebecca A.

2016-01-01

This article describes the development and psychometrics of the Juvenile Addiction Risk Rating. The Juvenile Addiction Risk Rating is a brief screening of addiction potential based on 10 risk factors predictive of youth alcohol and drug-related problems that assists examiners in more accurate treatment planning when self-report information is…

Induced changes in island fox (Urocyon littoralis) activity do not mitigate the extinction threat posed by a novel predator.

PubMed

Hudgens, Brian R; Garcelon, David K

2011-03-01

Prey response to novel predators influences the impacts on prey populations of introduced predators, bio-control efforts, and predator range expansion. Predicting the impacts of novel predators on native prey requires an understanding of both predator avoidance strategies and their potential to reduce predation risk. We examine the response of island foxes (Urocyon littoralis) to invasion by golden eagles (Aquila chrysaetos). Foxes reduced daytime activity and increased night time activity relative to eagle-naïve foxes. Individual foxes reverted toward diurnal tendencies following eagle removal efforts. We quantified the potential population impact of reduced diurnality by modeling island fox population dynamics. Our model predicted an annual population decline similar to what was observed following golden eagle invasion and predicted that the observed 11% reduction in daytime activity would not reduce predation risk sufficiently to reduce extinction risk. The limited effect of this behaviorally plastic predator avoidance strategy highlights the importance of linking behavioral change to population dynamics for predicting the impact of novel predators on resident prey populations.

CYR61 and TAZ Upregulation and Focal Epithelial to Mesenchymal Transition May Be Early Predictors of Barrett's Esophagus Malignant Progression.

PubMed

Cardoso, Joana; Mesquita, Marta; Dias Pereira, António; Bettencourt-Dias, Mónica; Chaves, Paula; Pereira-Leal, José B

2016-01-01

Barrett's esophagus is the major risk factor for esophageal adenocarcinoma. It has a low but non-neglectable risk, high surveillance costs and no reliable risk stratification markers. We sought to identify early biomarkers, predictive of Barrett's malignant progression, using a meta-analysis approach on gene expression data. This in silico strategy was followed by experimental validation in a cohort of patients with extended follow up from the Instituto Português de Oncologia de Lisboa de Francisco Gentil EPE (Portugal). Bioinformatics and systems biology approaches singled out two candidate predictive markers for Barrett's progression, CYR61 and TAZ. Although previously implicated in other malignancies and in epithelial-to-mesenchymal transition phenotypes, our experimental validation shows for the first time that CYR61 and TAZ have the potential to be predictive biomarkers for cancer progression. Experimental validation by reverse transcriptase quantitative PCR and immunohistochemistry confirmed the up-regulation of both genes in Barrett's samples associated with high-grade dysplasia/adenocarcinoma. In our cohort CYR61 and TAZ up-regulation ranged from one to ten years prior to progression to adenocarcinoma in Barrett's esophagus index samples. Finally, we found that CYR61 and TAZ over-expression is correlated with early focal signs of epithelial to mesenchymal transition. Our results highlight both CYR61 and TAZ genes as potential predictive biomarkers for stratification of the risk for development of adenocarcinoma and suggest a potential mechanistic route for Barrett's esophagus neoplastic progression.

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale.

PubMed

Jonsson, Jakob; Abbott, Max W; Sjöberg, Anders; Carlbring, Per

2017-01-01

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with 'Over consumption,' 'Gambling fallacies,' and 'Reinforcers' as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed.

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...

A population-based survey in Australia of men's and women's perceptions of genetic risk and predictive genetic testing and implications for primary care.

PubMed

Taylor, S

2011-01-01

Community attitudes research regarding genetic issues is important when contemplating the potential value and utilisation of predictive testing for common diseases in mainstream health services. This article aims to report population-based attitudes and discuss their relevance to integrating genetic services in primary health contexts. Men's and women's attitudes were investigated via population-based omnibus telephone survey in Queensland, Australia. Randomly selected adults (n = 1,230) with a mean age of 48.8 years were interviewed regarding perceptions of genetic determinants of health; benefits of genetic testing that predict 'certain' versus 'probable' future illness; and concern, if any, regarding potential misuse of genetic test information. Most (75%) respondents believed genetic factors significantly influenced health status; 85% regarded genetic testing positively although attitudes varied with age. Risk-based information was less valued than certainty-based information, but women valued risk information significantly more highly than men. Respondents reported 'concern' (44%) and 'no concern' (47%) regarding potential misuse of genetic information. This study contributes important population-based data as most research has involved selected individuals closely impacted by genetic disorders. While community attitudes were positive regarding genetic testing, genetic literacy is important to establish. The nature of gender differences regarding risk perception merits further study and has policy and service implications. Community concern about potential genetic discrimination must be addressed if health benefits of testing are to be maximised. Larger questions remain in scientific, policy, service delivery, and professional practice domains before predictive testing for common disorders is efficacious in mainstream health care. Copyright © 2011 S. Karger AG, Basel.

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation - A Systematic Review.

PubMed

Tomaselli Muensterman, Elena; Tisdale, James E

2018-06-08

Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsades de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors, which include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of ≥ 2 QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this paper are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation, and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduces the risk of QTc interval prolongation in cardiac ICUs and identifies health-system patients at increased risk for mortality. The impact of these QTc interval prolongation predictive analytics on overall patient safety outcomes, such as TdP and sudden cardiac death relative to the cost of development and implementation, requires further study. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

PubMed Central

Janssens, A Cecile JW; Ioannidis, John PA; Bedrosian, Sara; Boffetta, Paolo; Dolan, Siobhan M; Dowling, Nicole; Fortier, Isabel; Freedman, Andrew N; Grimshaw, Jeremy M; Gulcher, Jeffrey; Gwinn, Marta; Hlatky, Mark A; Janes, Holly; Kraft, Peter; Melillo, Stephanie; O'Donnell, Christopher J; Pencina, Michael J; Ransohoff, David; Schully, Sheri D; Seminara, Daniela; Winn, Deborah M; Wright, Caroline F; van Duijn, Cornelia M; Little, Julian; Khoury, Muin J

2011-01-01

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. PMID:21407270

Self-reported clothing size as a proxy measure for body size.

PubMed

Hughes, Laura A E; Schouten, Leo J; Goldbohm, R Alexandra; van den Brandt, Piet A; Weijenberg, Matty P

2009-09-01

Few studies have considered the potential utility of clothing size as a predictor of diseases associated with body weight. We used data on weight-stable men and women from a subcohort of the Netherlands Cohort Study to assess the correlation of clothing size with other anthropometric variables. Cox regression using the case-cohort approach was performed to establish whether clothing size can predict cancer risk after 13.3 years of follow-up, and if additionally considering body mass index (BMI) in the model improves the prediction. Trouser and skirt size correlated well with circumference measurements. Skirt size predicted endometrial cancer risk, and this effect was slightly attenuated when BMI was added to the model. Trouser size predicted risk of renal cell carcinoma, regardless of whether BMI was in the model. Clothing size appears to predict cancer risk independently of BMI, suggesting that clothing size is a useful measure to consider in epidemiologic studies when waist circumference is not available.

Hearing Screening of High-Risk Newborns with Brainstem Auditory Evoked Potentials: A Follow-Up Study.

ERIC Educational Resources Information Center

Shannon, Dorothy A.; And Others

1984-01-01

The brainstem auditory evoked potential (BAEP) was evaluated as a hearing screening test in 168 high-risk newborns. The BAEP was found to be a sensitive procedure for the early identification of hearing-impaired newborns. However, the yield of significant hearing abnormalities was less than predicted in other studies using BAEP. (Author/CL)

Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

ERIC Educational Resources Information Center

Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

2004-01-01

Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Compassion satisfaction, burnout, and secondary traumatic stress in UK therapists who work with adult trauma clients.

PubMed

Sodeke-Gregson, Ekundayo A; Holttum, Sue; Billings, Jo

2013-01-01

Therapists who work with trauma clients are impacted both positively and negatively. However, most studies have tended to focus on the negative impact of the work, the quantitative evidence has been inconsistent, and the research has primarily been conducted outside the United Kingdom. This study aimed to assess the prevalence of, and identify predictor variables for, compassion satisfaction, burnout, and secondary traumatic stress in a group of UK therapists (N=253) working with adult trauma clients. An online questionnaire was developed which used The Professional Quality of Life Scale (Version 5) to assess compassion satisfaction, burnout, and secondary traumatic stress and collect demographics and other pertinent information. Whilst the majority of therapists scored within the average range for compassion satisfaction and burnout, 70% of scores indicated that therapists were at high risk of secondary traumatic stress. Maturity, time spent engaging in research and development activities, a higher perceived supportiveness of management, and supervision predicted higher potential for compassion satisfaction. Youth and a lower perceived supportiveness of management predicted higher risk of burnout. A higher risk of secondary traumatic stress was predicted in therapists engaging in more individual supervision and self-care activities, as well as those who had a personal trauma history. UK therapists working with trauma clients are at high risk of being negatively impacted by their work, obtaining scores which suggest a risk of developing secondary traumatic stress. Of particular note was that exposure to trauma stories did not significantly predict secondary traumatic stress scores as suggested by theory. However, the negative impact of working with trauma clients was balanced by the potential for a positive outcome from trauma work as a majority indicated an average potential for compassion satisfaction.

Modifiable pathways in Alzheimer's disease: Mendelian randomisation analysis.

PubMed

Larsson, Susanna C; Traylor, Matthew; Malik, Rainer; Dichgans, Martin; Burgess, Stephen; Markus, Hugh S

2017-12-06

To determine which potentially modifiable risk factors, including socioeconomic, lifestyle/dietary, cardiometabolic, and inflammatory factors, are associated with Alzheimer's disease. Mendelian randomisation study using genetic variants associated with the modifiable risk factors as instrumental variables. International Genomics of Alzheimer's Project. 17 008 cases of Alzheimer's disease and 37 154 controls. Odds ratio of Alzheimer's per genetically predicted increase in each modifiable risk factor estimated with Mendelian randomisation analysis. This study included analyses of 24 potentially modifiable risk factors. A Bonferroni corrected threshold of P=0.002 was considered to be significant, and P<0.05 was considered suggestive of evidence for a potential association. Genetically predicted educational attainment was significantly associated with Alzheimer's. The odds ratios were 0.89 (95% confidence interval 0.84 to 0.93; P=2.4×10 -6 ) per year of education completed and 0.74 (0.63 to 0.86; P=8.0×10 -5 ) per unit increase in log odds of having completed college/university. The correlated trait intelligence had a suggestive association with Alzheimer's (per genetically predicted 1 SD higher intelligence: 0.73, 0.57 to 0.93; P=0.01). There was suggestive evidence for potential associations between genetically predicted higher quantity of smoking (per 10 cigarettes a day: 0.69, 0.49 to 0.99; P=0.04) and 25-hydroxyvitamin D concentrations (per 20% higher levels: 0.92, 0.85 to 0.98; P=0.01) and lower odds of Alzheimer's and between higher coffee consumption (per one cup a day: 1.26, 1.05 to 1.51; P=0.01) and higher odds of Alzheimer's. Genetically predicted alcohol consumption, serum folate, serum vitamin B 12 , homocysteine, cardiometabolic factors, and C reactive protein were not associated with Alzheimer's disease. These results provide support that higher educational attainment is associated with a reduced risk of Alzheimer's disease. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Compassion satisfaction, burnout, and secondary traumatic stress in UK therapists who work with adult trauma clients

PubMed Central

Sodeke-Gregson, Ekundayo A.; Holttum, Sue; Billings, Jo

2013-01-01

Background Therapists who work with trauma clients are impacted both positively and negatively. However, most studies have tended to focus on the negative impact of the work, the quantitative evidence has been inconsistent, and the research has primarily been conducted outside the United Kingdom. Objectives This study aimed to assess the prevalence of, and identify predictor variables for, compassion satisfaction, burnout, and secondary traumatic stress in a group of UK therapists (N=253) working with adult trauma clients. Method An online questionnaire was developed which used The Professional Quality of Life Scale (Version 5) to assess compassion satisfaction, burnout, and secondary traumatic stress and collect demographics and other pertinent information. Results Whilst the majority of therapists scored within the average range for compassion satisfaction and burnout, 70% of scores indicated that therapists were at high risk of secondary traumatic stress. Maturity, time spent engaging in research and development activities, a higher perceived supportiveness of management, and supervision predicted higher potential for compassion satisfaction. Youth and a lower perceived supportiveness of management predicted higher risk of burnout. A higher risk of secondary traumatic stress was predicted in therapists engaging in more individual supervision and self-care activities, as well as those who had a personal trauma history. Conclusions UK therapists working with trauma clients are at high risk of being negatively impacted by their work, obtaining scores which suggest a risk of developing secondary traumatic stress. Of particular note was that exposure to trauma stories did not significantly predict secondary traumatic stress scores as suggested by theory. However, the negative impact of working with trauma clients was balanced by the potential for a positive outcome from trauma work as a majority indicated an average potential for compassion satisfaction. PMID:24386550

Habitat structure mediates predation risk for sedentary prey: Experimental tests of alternative hypotheses

USGS Publications Warehouse

Chalfoun, A.D.; Martin, T.E.

2009-01-01

Predation is an important and ubiquitous selective force that can shape habitat preferences of prey species, but tests of alternative mechanistic hypotheses of habitat influences on predation risk are lacking. 2. We studied predation risk at nest sites of a passerine bird and tested two hypotheses based on theories of predator foraging behaviour. The total-foliage hypothesis predicts that predation will decline in areas of greater overall vegetation density by impeding cues for detection by predators. The potential-prey-site hypothesis predicts that predation decreases where predators must search more unoccupied potential nest sites. 3. Both observational data and results from a habitat manipulation provided clear support for the potential-prey-site hypothesis and rejection of the total-foliage hypothesis. Birds chose nest patches containing both greater total foliage and potential nest site density (which were correlated in their abundance) than at random sites, yet only potential nest site density significantly influenced nest predation risk. 4. Our results therefore provided a clear and rare example of adaptive nest site selection that would have been missed had structural complexity or total vegetation density been considered alone. 5. Our results also demonstrated that interactions between predator foraging success and habitat structure can be more complex than simple impedance or occlusion by vegetation. ?? 2008 British Ecological Society.

How learning analytics can early predict under-achieving students in a blended medical education course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2017-07-01

Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Neonatal Candidiasis: Epidemiology, Risk Factors, and Clinical Judgment

PubMed Central

Benjamin, Daniel K.; Stoll, Barbara J.; Gantz, Marie G.; Walsh, Michele C.; Sanchez, Pablo J.; Das, Abhik; Shankaran, Seetha; Higgins, Rosemary D.; Auten, Kathy J.; Miller, Nancy A.; Walsh, Thomas J.; Laptook, Abbot R.; Carlo, Waldemar A.; Kennedy, Kathleen A.; Finer, Neil N.; Duara, Shahnaz; Schibler, Kurt; Chapman, Rachel L.; Van Meurs, Krisa P.; Frantz, Ivan D.; Phelps, Dale L.; Poindexter, Brenda B.; Bell, Edward F.; O’Shea, T. Michael; Watterberg, Kristi L.; Goldberg, Ronald N.

2011-01-01

OBJECTIVE Invasive candidiasis is a leading cause of infection-related morbidity and mortality in extremely low-birth-weight (<1000 g) infants. We quantify risk factors predicting infection in high-risk premature infants and compare clinical judgment with a prediction model of invasive candidiasis. METHODS The study involved a prospective observational cohort of infants <1000 g birth weight at 19 centers of the NICHD Neonatal Research Network. At each sepsis evaluation, clinical information was recorded, cultures obtained, and clinicians prospectively recorded their estimate of the probability of invasive candidiasis. Two models were generated with invasive candidiasis as their outcome: 1) potentially modifiable risk factors and 2) a clinical model at time of blood culture to predict candidiasis. RESULTS Invasive candidiasis occurred in 137/1515 (9.0%) infants and was documented by positive culture from ≥ 1 of these sources: blood (n=96), cerebrospinal fluid (n=9), urine obtained by catheterization (n=52), or other sterile body fluid (n=10). Mortality was not different from infants who had positive blood culture compared to those with isolated positive urine culture. Incidence varied from 2–28% at the 13 centers enrolling ≥ 50 infants. Potentially modifiable risk factors (model 1) included central catheter, broad-spectrum antibiotics (e.g., third-generation cephalosporins), intravenous lipid emulsion, endotracheal tube, and antenatal antibiotics. The clinical prediction model (model 2) had an area under the receiver operating characteristic curve of 0.79, and was superior to clinician judgment (0.70) in predicting subsequent invasive candidiasis. Performance of clinical judgment did not vary significantly with level of training. CONCLUSION Prior antibiotics, presence of a central catheter, endotracheal tube, and center were strongly associated with invasive candidiasis. Modeling was more accurate in predicting invasive candidiasis than clinical judgment. PMID:20876174

Potential Population Consequences of Active Sonar Disturbance in Atlantic Herring: Estimating the Maximum Risk.

PubMed

Sivle, Lise Doksæter; Kvadsheim, Petter Helgevold; Ainslie, Michael

2016-01-01

Effects of noise on fish populations may be predicted by the population consequence of acoustic disturbance (PCAD) model. We have predicted the potential risk of population disturbance when the highest sound exposure level (SEL) at which adult herring do not respond to naval sonar (SEL(0)) is exceeded. When the population density is low (feeding), the risk is low even at high sonar source levels and long-duration exercises (>24 h). With densely packed populations (overwintering), a sonar exercise might expose the entire population to levels >SEL(0) within a 24-h exercise period. However, the disturbance will be short and the response threshold used here is highly conservative. It is therefore unlikely that naval sonar will significantly impact the herring population.

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

PubMed

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

2014-03-01

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

The short- to medium-term predictive accuracy of static and dynamic risk assessment measures in a secure forensic hospital.

PubMed

Chu, Chi Meng; Thomas, Stuart D M; Ogloff, James R P; Daffern, Michael

2013-04-01

Although violence risk assessment knowledge and practice has advanced over the past few decades, it remains practically difficult to decide which measures clinicians should use to assess and make decisions about the violence potential of individuals on an ongoing basis, particularly in the short to medium term. Within this context, this study sought to compare the predictive accuracy of dynamic risk assessment measures for violence with static risk assessment measures over the short term (up to 1 month) and medium term (up to 6 months) in a forensic psychiatric inpatient setting. Results showed that dynamic measures were generally more accurate than static measures for short- to medium-term predictions of inpatient aggression. These findings highlight the necessity of using risk assessment measures that are sensitive to important clinical risk state variables to improve the short- to medium-term prediction of aggression within the forensic inpatient setting. Such knowledge can assist with the development of more accurate and efficient risk assessment procedures, including the selection of appropriate risk assessment instruments to manage and prevent the violence of offenders with mental illnesses during inpatient treatment.

Spread of the Tiger: Global Risk of Invasion by the Mosquito Aedes albopictus

PubMed Central

BENEDICT, MARK Q.; LEVINE, REBECCA S.; HAWLEY, WILLIAM A.; LOUNIBOS, L. PHILIP

2008-01-01

Aedes albopictus, commonly known as the Asian tiger mosquito, is currently the most invasive mosquito in the world. It is of medical importance due to its aggressive daytime human-biting behavior and ability to vector many viruses, including dengue, LaCrosse, and West Nile. Invasions into new areas of its potential range are often initiated through the transportation of eggs via the international trade in used tires. We use a genetic algorithm, Genetic Algorithm for Rule Set Production (GARP), to determine the ecological niche of Ae. albopictus and predict a global ecological risk map for the continued spread of the species. We combine this analysis with risk due to importation of tires from infested countries and their proximity to countries that have already been invaded to develop a list of countries most at risk for future introductions and establishments. Methods used here have potential for predicting risks of future invasions of vectors or pathogens. PMID:17417960

Landscape features influence postrelease predation on endangered black-footed ferrets

USGS Publications Warehouse

Poessel, S.A.; Breck, S.W.; Biggins, D.E.; Livieri, T.M.; Crooks, K.R.; Angeloni, L.

2011-01-01

Predation can be a critical factor influencing recovery of endangered species. In most recovery efforts lethal and nonlethal influences of predators are not sufficiently understood to allow prediction of predation risk, despite its importance. We investigated whether landscape features could be used to model predation risk from coyotes (Canis latrans) and great horned owls (Bubo virginianus) on the endangered black-footed ferret (Mustela nigripes). We used location data of reintroduced ferrets from 3 sites in South Dakota to determine whether exposure to landscape features typically associated with predators affected survival of ferrets, and whether ferrets considered predation risk when choosing habitat near perches potentially used by owls or near linear features predicted to be used by coyotes. Exposure to areas near likely owl perches reduced ferret survival, but landscape features potentially associated with coyote movements had no appreciable effect on survival. Ferrets were located within 90 m of perches more than expected in 2 study sites that also had higher ferret mortality due to owl predation. Densities of potential coyote travel routes near ferret locations were no different than expected in all 3 sites. Repatriated ferrets might have selected resources based on factors other than predator avoidance. Considering an easily quantified landscape feature (i.e., owl perches) can enhance success of reintroduction efforts for ferrets. Nonetheless, development of predictive models of predation risk and management strategies to mitigate that risk is not necessarily straightforward for more generalist predators such as coyotes. ?? 2011 American Society of Mammalogists.

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.

PubMed

Janke, Alexander T; Overbeek, Daniel L; Kocher, Keith E; Levy, Phillip D

2016-02-01

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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/

Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.

PubMed

Sutherland, Scott M; Chawla, Lakhmir S; Kane-Gill, Sandra L; Hsu, Raymond K; Kramer, Andrew A; Goldstein, Stuart L; Kellum, John A; Ronco, Claudio; Bagshaw, Sean M

2016-01-01

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

PubMed Central

Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

2016-01-01

Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

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.

Too risky to settle: avian community structure changes in response to perceived predation risk on adults and offspring

USGS Publications Warehouse

Hua, Fangyuan; Fletcher, Robert J.; Sieving, Kathryn E.; Dorazio, Robert M.

2013-01-01

Predation risk is widely hypothesized as an important force structuring communities, but this potential force is rarely tested experimentally, particularly in terrestrial vertebrate communities. How animals respond to predation risk is generally considered predictable from species life-history and natural-history traits, but rigorous tests of these predictions remain scarce. We report on a large-scale playback experiment with a forest bird community that addresses two questions: (i) does perceived predation risk shape the richness and composition of a breeding bird community? And (ii) can species life-history and natural-history traits predict prey community responses to different types of predation risk? On 9 ha plots, we manipulated cues of three avian predators that preferentially prey on either adult birds or offspring, or both, throughout the breeding season. We found that increased perception of predation risk led to generally negative responses in the abundance, occurrence and/or detection probability of most prey species, which in turn reduced the species richness and shifted the composition of the breeding bird community. Species-level responses were largely predicted from the key natural-history trait of body size, but we did not find support for the life-history theory prediction of the relationship between species' slow/fast life-history strategy and their response to predation risk.

Novel Biomarkers for Predicting Cardiovascular Disease in Patients With Diabetes.

PubMed

Retnakaran, Ravi

2018-05-01

It is generally acknowledged that patients with diabetes comprise a high-risk population for the development of cardiovascular disease. However, it is perhaps less well recognized that there actually exists considerable heterogeneity in vascular risk within this patient population, with a sizable subset of individuals seemingly at low risk for major cardiovascular events despite the presence of diabetes. Because traditional clinical risk calculators have shown wide variability in their performance in the setting of diabetes, there exists a need for additional risk predictors in this patient population. In this context, there has been considerable interest in the potential utility of circulating biomarkers as clinical tools that might facilitate risk stratification and thereby guide therapeutic and preventative decision-making. Coupled with the current era of dedicated cardiovascular outcome trials in type 2 diabetes, this interest has spawned a growing literature of recent studies that evaluated potential biomarkers. To date, these studies have identified N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponins, and growth differentiation factor-15 as cardiovascular biomarkers of particular potential in patients with diabetes. Furthermore, recognizing the potential benefit of collective consideration of different biomarkers reflecting distinct pathophysiologic processes that might contribute to the development of cardiovascular disease, there is emerging emphasis on the evaluation of combinations of biomarkers for optimal risk prediction. Although not currently ready for clinical practice, this rapidly-growing topic of biomarker research might ultimately facilitate the goal of individualized risk stratification and thereby enable truly personalized management of diabetes. Copyright © 2017 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

Measuring Gambling Reinforcers, Over Consumption and Fallacies: The Psychometric Properties and Predictive Validity of the Jonsson-Abbott Scale

PubMed Central

Jonsson, Jakob; Abbott, Max W.; Sjöberg, Anders; Carlbring, Per

2017-01-01

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with ‘Over consumption,’ ‘Gambling fallacies,’ and ‘Reinforcers’ as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed. PMID:29085320

CYR61 and TAZ Upregulation and Focal Epithelial to Mesenchymal Transition May Be Early Predictors of Barrett’s Esophagus Malignant Progression

PubMed Central

Mesquita, Marta; Dias Pereira, António; Bettencourt-Dias, Mónica; Chaves, Paula; Pereira-Leal, José B.

2016-01-01

Barrett’s esophagus is the major risk factor for esophageal adenocarcinoma. It has a low but non-neglectable risk, high surveillance costs and no reliable risk stratification markers. We sought to identify early biomarkers, predictive of Barrett’s malignant progression, using a meta-analysis approach on gene expression data. This in silico strategy was followed by experimental validation in a cohort of patients with extended follow up from the Instituto Português de Oncologia de Lisboa de Francisco Gentil EPE (Portugal). Bioinformatics and systems biology approaches singled out two candidate predictive markers for Barrett’s progression, CYR61 and TAZ. Although previously implicated in other malignancies and in epithelial-to-mesenchymal transition phenotypes, our experimental validation shows for the first time that CYR61 and TAZ have the potential to be predictive biomarkers for cancer progression. Experimental validation by reverse transcriptase quantitative PCR and immunohistochemistry confirmed the up-regulation of both genes in Barrett’s samples associated with high-grade dysplasia/adenocarcinoma. In our cohort CYR61 and TAZ up-regulation ranged from one to ten years prior to progression to adenocarcinoma in Barrett’s esophagus index samples. Finally, we found that CYR61 and TAZ over-expression is correlated with early focal signs of epithelial to mesenchymal transition. Our results highlight both CYR61 and TAZ genes as potential predictive biomarkers for stratification of the risk for development of adenocarcinoma and suggest a potential mechanistic route for Barrett’s esophagus neoplastic progression. PMID:27583562

Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2014-03-01

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

Prioritizing human pharmaceuticals for ecological risks in the freshwater environment of Korea.

PubMed

Ji, Kyunghee; Han, Eun Jeong; Back, Sunhyoung; Park, Jeongim; Ryu, Jisung; Choi, Kyungho

2016-04-01

Pharmaceutical residues are potential threats to aquatic ecosystems. Because more than 3000 active pharmaceutical ingredients (APIs) are in use, identifying high-priority pharmaceuticals is important for developing appropriate management options. Priority pharmaceuticals may vary by geographical region, because their occurrence levels can be influenced by demographic, societal, and regional characteristics. In the present study, the authors prioritized human pharmaceuticals of potential ecological risk in the Korean water environment, based on amount of use, biological activity, and regional hydrologic characteristics. For this purpose, the authors estimated the amounts of annual production of 695 human APIs in Korea. Then derived predicted environmental concentrations, using 2 approaches, to develop an initial candidate list of target pharmaceuticals. Major antineoplastic drugs and hormones were added in the initial candidate list regardless of their production amount because of their high biological activity potential. The predicted no effect concentrations were derived for those pharmaceuticals based on ecotoxicity information available in the literature or by model prediction. Priority lists of human pharmaceuticals were developed based on ecological risks and availability of relevant information. Those priority APIs identified include acetaminophen, clarithromycin, ciprofloxacin, ofloxacin, metformin, and norethisterone. Many of these pharmaceuticals have been neither adequately monitored nor assessed for risks in Korea. Further efforts are needed to improve these lists and to develop management decisions for these compounds in Korean water. © 2015 SETAC.

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

PubMed

Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

2016-03-01

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

Risk Adjustment for a Children's Capitation Rate

PubMed Central

Newhouse, Joseph P.; Sloss, Elizabeth M.; Manning, Willard G.; Keeler, Emmett B.

1993-01-01

Few capitation arrangements vary premiums by a child's health characteristics, yielding an incentive to discriminate against children with predictably high expenditures from chronic diseases. In this article, we explore risk adjusters for the 35 percent of the variance in annual outpatient expenditure we find to be potentially predictable. Demographic factors such as age and gender only explain 5 percent of such variance; health status measures explain 25 percent, prior use and health status measures together explain 65 to 70 percent. The profit from risk selection falls less than proportionately with improved ability to adjust for risk. Partial capitation rates may be necessary to mitigate skimming and dumping. PMID:10133708

Risk adjustment for a children's capitation rate.

PubMed

Newhouse, J P; Sloss, E M; Manning, W G; Keeler, E B

1993-01-01

Few capitation arrangements vary premiums by a child's health characteristics, yielding an incentive to discriminate against children with predictably high expenditures from chronic diseases. In this article, we explore risk adjusters for the 35 percent of the variance in annual out-patient expenditure we find to be potentially predictable. Demographic factors such as age and gender only explain 5 percent of such variance; health status measures explain 25 percent, prior use and health status measures together explain 65 to 70 percent. The profit from risk selection falls less than proportionately with improved ability to adjust for risk. Partial capitation rates may be necessary to mitigate skimming and dumping.

Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

EPA Science Inventory

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values and risk screening values. We aim to use computa...

Molecular epidemiology, and possible real-world applications in breast cancer.

PubMed

Ito, Hidemi; Matsuo, Keitaro

2016-01-01

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Designing and operating infrastructure for nonstationary flood risk management

NASA Astrophysics Data System (ADS)

Doss-Gollin, J.; Farnham, D. J.; Lall, U.

2017-12-01

Climate exhibits organized low-frequency and regime-like variability at multiple time scales, causing the risk associated with climate extremes such as floods and droughts to vary in time. Despite broad recognition of this nonstationarity, there has been little theoretical development of ideas for the design and operation of infrastructure considering the regime structure of such changes and their potential predictability. We use paleo streamflow reconstructions to illustrate an approach to the design and operation of infrastructure to address nonstationary flood and drought risk. Specifically, we consider the tradeoff between flood control and conservation storage, and develop design and operation principles for allocating these storage volumes considering both a m-year project planning period and a n-year historical sampling record. As n increases, the potential uncertainty in probabilistic estimates of the return periods associated with the T-year extreme event decreases. As the duration m of the future operation period decreases, the uncertainty associated with the occurrence of the T-year event also increases. Finally, given the quasi-periodic nature of the system it may be possible to offer probabilistic predictions of the conditions in the m-year future period, especially if m is small. In the context of such predictions, one can consider that a m-year prediction may have lower bias, but higher variance, than would be associated with using a stationary estimate from the preceding n years. This bias-variance trade-off, and the potential for considering risk management for multiple values of m, provides an interesting system design challenge. We use wavelet-based simulation models in a Bayesian framework to estimate these biases and uncertainty distributions and devise a risk-optimized decision rule for the allocation of flood and conservation storage. The associated theoretical development also provides a methodology for the sizing of storage for new infrastructure under nonstationarity, and an examination of risk adaptation measures which consider both short term and long term options simultaneously.

Conservative Exposure Predictions for Rapid Risk Assessment of Phase-Separated Additives in Medical Device Polymers.

PubMed

Chandrasekar, Vaishnavi; Janes, Dustin W; Saylor, David M; Hood, Alan; Bajaj, Akhil; Duncan, Timothy V; Zheng, Jiwen; Isayeva, Irada S; Forrey, Christopher; Casey, Brendan J

2018-01-01

A novel approach for rapid risk assessment of targeted leachables in medical device polymers is proposed and validated. Risk evaluation involves understanding the potential of these additives to migrate out of the polymer, and comparing their exposure to a toxicological threshold value. In this study, we propose that a simple diffusive transport model can be used to provide conservative exposure estimates for phase separated color additives in device polymers. This model has been illustrated using a representative phthalocyanine color additive (manganese phthalocyanine, MnPC) and polymer (PEBAX 2533) system. Sorption experiments of MnPC into PEBAX were conducted in order to experimentally determine the diffusion coefficient, D = (1.6 ± 0.5) × 10 -11  cm 2 /s, and matrix solubility limit, C s  = 0.089 wt.%, and model predicted exposure values were validated by extraction experiments. Exposure values for the color additive were compared to a toxicological threshold for a sample risk assessment. Results from this study indicate that a diffusion model-based approach to predict exposure has considerable potential for use as a rapid, screening-level tool to assess the risk of color additives and other small molecule additives in medical device polymers.

Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe.

PubMed

Anderegg, William R L; Klein, Tamir; Bartlett, Megan; Sack, Lawren; Pellegrini, Adam F A; Choat, Brendan; Jansen, Steven

2016-05-03

Drought-induced tree mortality has been observed globally and is expected to increase under climate change scenarios, with large potential consequences for the terrestrial carbon sink. Predicting mortality across species is crucial for assessing the effects of climate extremes on forest community biodiversity, composition, and carbon sequestration. However, the physiological traits associated with elevated risk of mortality in diverse ecosystems remain unknown, although these traits could greatly improve understanding and prediction of tree mortality in forests. We performed a meta-analysis on species' mortality rates across 475 species from 33 studies around the globe to assess which traits determine a species' mortality risk. We found that species-specific mortality anomalies from community mortality rate in a given drought were associated with plant hydraulic traits. Across all species, mortality was best predicted by a low hydraulic safety margin-the difference between typical minimum xylem water potential and that causing xylem dysfunction-and xylem vulnerability to embolism. Angiosperms and gymnosperms experienced roughly equal mortality risks. Our results provide broad support for the hypothesis that hydraulic traits capture key mechanisms determining tree death and highlight that physiological traits can improve vegetation model prediction of tree mortality during climate extremes.

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis.

PubMed

Fazel, Seena; Singh, Jay P; Doll, Helen; Grann, Martin

2012-07-24

To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator-the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Risk assessments were conducted on 73 samples comprising 24,847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management.

ToxCast: EPAs Contribution to the Tox21 Consortium

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

Risk determination and prevention of breast cancer.

PubMed

Howell, Anthony; Anderson, Annie S; Clarke, Robert B; Duffy, Stephen W; Evans, D Gareth; Garcia-Closas, Montserat; Gescher, Andy J; Key, Timothy J; Saxton, John M; Harvie, Michelle N

2014-09-28

Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.

Predicting the transition from juvenile delinquency to adult criminality: Gender-specific influences in two high-risk samples.

PubMed

Rhoades, Kimberly A; Leve, Leslie D; Eddy, J Mark; Chamberlain, Patricia

2016-12-01

Most juvenile offenders desist from offending as they become adults, but many continue and ultimately enter the adult corrections system. There has been little prospective examination of which variables may predict the latter transition, particularly for women. Our aim was to find out, for men and women separately, what variables identifiable in adolescent offenders predict their continuation of offending into adult life. Participants were 61 male and 81 female youths who had been referred from the juvenile justice system for chronic delinquency and recruited into randomised controlled trials comparing Multidimensional Treatment Foster Care with group care ('treatment as usual'). All participants had attained adulthood by the time of our study. We first examined gender differences in childhood risk factors and then used Cox proportional-hazards models to estimate the relationship of potential risk factors to first adult arrest. Results indicated that, for men, juvenile justice referrals alone predicted risk of any first adult arrest as well as arrest for felony arrest specifically. Each additional juvenile referral increased the risk of any adult arrest by 9% and of adult felony arrest by 8%. For women, family violence, parental divorce and cumulative childhood risk factors, but not juvenile justice referrals, were significant predictors of adult arrest. Each additional childhood risk factor increased the risk of adult arrest by 21%. Women who experienced parental divorce were nearly three times more likely to be arrested as an adult, and those who experienced family violence 2.5 times more so than those without such experiences. We found preliminary evidence of gender differences in childhood risk factors for adult offending, and, thus potentially, for the development and use of interventions tailored differently for girls and boys and young men and young women to reduce their risk of becoming adult recidivists. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Patients’ Opinions about Knowing Their Risk for Depression and What to Do about It. The PredictD-Qualitative Study

PubMed Central

Bellón, Juan Á.; Moreno-Peral, Patricia; Moreno-Küstner, Berta; Motrico, Emma; Aiarzagüena, José M.; Fernández, Anna; Fernández-Alonso, Carmen; Montón-Franco, Carmen; Rodríguez-Bayón, Antonina; Ballesta-Rodríguez, María Isabel; Rüntel-Geidel, Ariadne; Payo-Gordón, Janire; Serrano-Blanco, Antoni; Oliván-Blázquez, Bárbara; Araujo, Luz; Muñoz-García, María del Mar; King, Michael; Nazareth, Irwin; Amezcua, Manuel

2014-01-01

Background The predictD study developed and validated a risk algorithm for predicting the onset of major depression in primary care. We aimed to explore the opinion of patients about knowing their risk for depression and the values and criteria upon which these opinions are based. Methods A maximum variation sample of patients was taken, stratified by city, age, gender, immigrant status, socio-economic status and lifetime depression. The study participants were 52 patients belonging to 13 urban health centres in seven different cities around Spain. Seven Focus Groups (FGs) were given held with primary care patients, one for each of the seven participating cities. Results The results showed that patients generally welcomed knowing their risk for depression. Furthermore, in light of available evidence several patients proposed potential changes in their lifestyles to prevent depression. Patients generally preferred to ask their General Practitioners (GPs) for advice, though mental health specialists were also mentioned. They suggested that GPs undertake interventions tailored to each patient, from a “patient-centred” approach, with certain communication skills, and giving advice to help patients cope with the knowledge that they are at risk of becoming depressed. Conclusions Patients are pleased to be informed about their risk for depression. We detected certain beliefs, attitudes, values, expectations and behaviour among the patients that were potentially useful for future primary prevention programmes on depression. PMID:24646951

Patients' opinions about knowing their risk for depression and what to do about it. The predictD-qualitative study.

PubMed

Bellón, Juan Á; Moreno-Peral, Patricia; Moreno-Küstner, Berta; Motrico, Emma; Aiarzagüena, José M; Fernández, Anna; Fernández-Alonso, Carmen; Montón-Franco, Carmen; Rodríguez-Bayón, Antonina; Ballesta-Rodríguez, María Isabel; Runte-Geidel, Ariadne; Rüntel-Geidel, Ariadne; Payo-Gordón, Janire; Serrano-Blanco, Antoni; Oliván-Blázquez, Bárbara; Araujo, Luz; Muñoz-García, María del Mar; King, Michael; Nazareth, Irwin; Amezcua, Manuel

2014-01-01

The predictD study developed and validated a risk algorithm for predicting the onset of major depression in primary care. We aimed to explore the opinion of patients about knowing their risk for depression and the values and criteria upon which these opinions are based. A maximum variation sample of patients was taken, stratified by city, age, gender, immigrant status, socio-economic status and lifetime depression. The study participants were 52 patients belonging to 13 urban health centres in seven different cities around Spain. Seven Focus Groups (FGs) were given held with primary care patients, one for each of the seven participating cities. The results showed that patients generally welcomed knowing their risk for depression. Furthermore, in light of available evidence several patients proposed potential changes in their lifestyles to prevent depression. Patients generally preferred to ask their General Practitioners (GPs) for advice, though mental health specialists were also mentioned. They suggested that GPs undertake interventions tailored to each patient, from a "patient-centred" approach, with certain communication skills, and giving advice to help patients cope with the knowledge that they are at risk of becoming depressed. Patients are pleased to be informed about their risk for depression. We detected certain beliefs, attitudes, values, expectations and behaviour among the patients that were potentially useful for future primary prevention programmes on depression.

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?

Risk Assessment Techniques. A Handbook for Program Management Personnel

DTIC Science & Technology

1983-07-01

tion; not directly usable without further development. 37. Lieber, R.S., "New Approaches for Quantifying Risk and Determining Sharing Arrangements...must be provided. Prediction intervals around cost estimating relationships (CERs) or Monte Carlo simulations will be used as proper in quantifying ... risk ." [emphasis supplied] Para 9.d. "The ISR will address the potential risk in the program office estimate by identifying ’risk’ areas and their

Behavioral and neural correlates of loss aversion and risk avoidance in adolescents and adults.

PubMed

Barkley-Levenson, Emily E; Van Leijenhorst, Linda; Galván, Adriana

2013-01-01

Individuals are frequently faced with risky decisions involving the potential for both gain and loss. Exploring the role of both potential gains and potential losses in predicting risk taking is critical to understanding how adolescents and adults make the choice to engage in or avoid a real-life risk. This study aimed to examine the impact of potential losses as well as gains on adolescent decisions during risky choice in a laboratory task. Adolescent (n=18) and adult (n=16) participants underwent functional magnetic resonance imaging (fMRI) during a mixed gambles task, and completed questionnaires measuring real-world risk-taking behaviors. While potential loss had a significantly greater effect on choice than potential gain in both adolescents and adults and there were no behavioral group differences on the task, adolescents recruited significantly more frontostriatal circuitry than adults when choosing to reject a gamble. During risk-seeking behavior, adolescent activation in medial prefrontal cortex (mPFC) was negatively correlated with self-reported likelihood of risk taking. During risk-avoidant behavior, mPFC activation of in adults was negatively correlated with self-reported benefits of risk-taking. Taken together, these findings reflect different neural patterns during risk-taking and risk-avoidant behaviors in adolescents and adults. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

PubMed

Walters, K; Hardoon, S; Petersen, I; Iliffe, S; Omar, R Z; Nazareth, I; Rait, G

2016-01-21

Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities.

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.

Investigating Married Adults' Communal Coping with Genetic Health Risk and Perceived Discrimination

PubMed Central

Smith, Rachel A.; Sillars, Alan; Chesnut, Ryan P.; Zhu, Xun

2017-01-01

Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples' conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps, and ultimately stress. Dyadic analysis of married adults (N = 266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about AATD-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress. PMID:29731540

Investigating Married Adults' Communal Coping with Genetic Health Risk and Perceived Discrimination.

PubMed

Smith, Rachel A; Sillars, Alan; Chesnut, Ryan P; Zhu, Xun

2018-01-01

Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples' conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps, and ultimately stress. Dyadic analysis of married adults ( N = 266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about AATD-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress.

Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change

PubMed Central

Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng

2009-01-01

Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions. PMID:19558717

Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study.

PubMed

Østergaard, Søren D; Mukherjee, Shubhabrata; Sharp, Stephen J; Proitsi, Petroula; Lotta, Luca A; Day, Felix; Perry, John R B; Boehme, Kevin L; Walter, Stefan; Kauwe, John S; Gibbons, Laura E; Larson, Eric B; Powell, John F; Langenberg, Claudia; Crane, Paul K; Wareham, Nicholas J; Scott, Robert A

2015-06-01

Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk.

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

PubMed

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

2017-07-14

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

Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China

PubMed Central

Si, Yali; Gong, Peng

2017-01-01

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

Early Diagnosis and Intervention Strategies for Post-Traumatic Heterotopic Ossification in Severely Injured Extremities

DTIC Science & Technology

2013-10-01

study will recruit wounded warriors with severe extremity trauma, which places them at high risk for heterotopic ossification (HO); bone formation at...involved in HO; 2) to define accurate and practical methods to predict where HO will develop; and 3) to define potential therapies for prevention or...elicit HO. These tools also need to provide effective methods for early diagnosis or risk assessment (prediction) so that therapies for prevention or

The development and testing of a skin tear risk assessment tool.

PubMed

Newall, Nelly; Lewin, Gill F; Bulsara, Max K; Carville, Keryln J; Leslie, Gavin D; Roberts, Pam A

2017-02-01

The aim of the present study is to develop a reliable and valid skin tear risk assessment tool. The six characteristics identified in a previous case control study as constituting the best risk model for skin tear development were used to construct a risk assessment tool. The ability of the tool to predict skin tear development was then tested in a prospective study. Between August 2012 and September 2013, 1466 tertiary hospital patients were assessed at admission and followed up for 10 days to see if they developed a skin tear. The predictive validity of the tool was assessed using receiver operating characteristic (ROC) analysis. When the tool was found not to have performed as well as hoped, secondary analyses were performed to determine whether a potentially better performing risk model could be identified. The tool was found to have high sensitivity but low specificity and therefore have inadequate predictive validity. Secondary analysis of the combined data from this and the previous case control study identified an alternative better performing risk model. The tool developed and tested in this study was found to have inadequate predictive validity. The predictive validity of an alternative, more parsimonious model now needs to be tested. © 2015 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Can species traits predict the susceptibility of riverine fish to water resource development? An Australian case study.

PubMed

Rolls, Robert J; Sternberg, David

2015-06-01

Water resource developments alter riverine environments by disrupting longitudinal connectivity, transforming lotic habitats, and modifying in-stream hydraulic conditions. Effective management of anthropogenic disturbances therefore requires an understanding of the range of potential ecosystem effects and the inherent traits symptomatic of elevated vulnerability to disturbance. Using 42 riverine fish native to South Eastern Australia as a case study, we quantified six morphological, behavioral, and life-history traits to classify species into groups reflecting potential differences in their response to ecosystem changes as a result of water resource development. Classification analysis identified five strategies based on fish life-history dispersal requirements, climbing potential, and habitat preference. These strategies in turn highlight the potential species at risk from the separate impacts of water resource development and inform management decisions to mitigate those risks. Swimming ability did not contribute to distinguishing species into functional groups, likely due to methodological inconsistencies in quantifying swimming performance that may ultimately hinder the ability of fish passage facilities to function within the physical capabilities of species at risk of habitat fragmentation. This study improves our ability to predict the performance of groups of species at risk from the multiple environmental changes imposed by humans and goes beyond broad-scale dispersal requirements as a predictor of individual species response.

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.

Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: forecasting psychosis risk with mismatch negativity.

PubMed

Perez, Veronica B; Woods, Scott W; Roach, Brian J; Ford, Judith M; McGlashan, Thomas H; Srihari, Vinod H; Mathalon, Daniel H

2014-03-15

Only about one third of patients at high risk for psychosis based on current clinical criteria convert to a psychotic disorder within a 2.5-year follow-up period. Targeting clinical high-risk (CHR) individuals for preventive interventions could expose many to unnecessary treatments, underscoring the need to enhance predictive accuracy with nonclinical measures. Candidate measures include event-related potential components with established sensitivity to schizophrenia. Here, we examined the mismatch negativity (MMN) component of the event-related potential elicited automatically by auditory deviance in CHR and early illness schizophrenia (ESZ) patients. We also examined whether MMN predicted subsequent conversion to psychosis in CHR patients. Mismatch negativity to auditory deviants (duration, frequency, and duration + frequency double deviant) was assessed in 44 healthy control subjects, 19 ESZ, and 38 CHR patients. Within CHR patients, 15 converters to psychosis were compared with 16 nonconverters with at least 12 months of clinical follow-up. Hierarchical Cox regression examined the ability of MMN to predict time to psychosis onset in CHR patients. Irrespective of deviant type, MMN was significantly reduced in ESZ and CHR patients relative to healthy control subjects and in CHR converters relative to nonconverters. Mismatch negativity did not significantly differentiate ESZ and CHR patients. The duration + frequency double deviant MMN, but not the single deviant MMNs, significantly predicted the time to psychosis onset in CHR patients. Neurophysiological mechanisms underlying automatic processing of auditory deviance, as reflected by the duration + frequency double deviant MMN, are compromised before psychosis onset and can enhance the prediction of psychosis risk among CHR patients. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Harsh Parenting and Fearfulness in Toddlerhood Interact to Predict Amplitudes of Preschool Error-Related Negativity

PubMed Central

Brooker, Rebecca J.; Buss, Kristin A.

2014-01-01

Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children’s risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. PMID:24721466

Neurobiological and memory models of risky decision making in adolescents versus young adults.

PubMed

Reyna, Valerie F; Estrada, Steven M; DeMarinis, Jessica A; Myers, Regina M; Stanisz, Janine M; Mills, Britain A

2011-09-01

Predictions of fuzzy-trace theory and neurobiological approaches are examined regarding risk taking in a classic decision-making task--the framing task--as well as in the context of real-life risk taking. We report the 1st study of framing effects in adolescents versus adults, varying risk and reward, and relate choices to individual differences, sexual behavior, and behavioral intentions. As predicted by fuzzy-trace theory, adolescents modulated risk taking according to risk and reward. Adults showed standard framing, reflecting greater emphasis on gist-based (qualitative) reasoning, but adolescents displayed reverse framing when potential gains for risk taking were high, reflecting greater emphasis on verbatim-based (quantitative) reasoning. Reverse framing signals a different way of thinking compared with standard framing (reverse framing also differs from simply choosing the risky option). Measures of verbatim- and gist-based reasoning about risk, sensation seeking, behavioral activation, and inhibition were used to extract dimensions of risk proneness: Sensation seeking increased and then decreased, whereas inhibition increased from early adolescence to young adulthood, predicted by neurobiological theories. Two additional dimensions, verbatim- and gist-based reasoning about risk, loaded separately and predicted unique variance in risk taking. Importantly, framing responses predicted real-life risk taking. Reasoning was the most consistent predictor of real-life risk taking: (a) Intentions to have sex, sexual behavior, and number of partners decreased when gist-based reasoning was triggered by retrieval cues in questions about perceived risk, whereas (b) intentions to have sex and number of partners increased when verbatim-based reasoning was triggered by different retrieval cues in questions about perceived risk. (c) 2011 APA, all rights reserved.

Differentiation, Self-Other Representations, and Rupture-Repair Processes: Predicting Child Maltreatment Risk

ERIC Educational Resources Information Center

Skowron, Elizabeth A.; Kozlowski, JoEllen M.; Pincus, Aaron L.

2010-01-01

This set of studies was designed to examine the relational underpinnings of child abuse potential in a sample of 51 urban families. In Study 1, lower maternal differentiation of self--most notably, greater emotional reactivity and greater emotional cutoff--along with self-attacking introjects distinguished mothers at higher risk (vs. lower risk)…

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.

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

Potential Mechanisms Underlying Inflammation-Enhanced Aminoglycoside-Induced Cochleotoxicity

PubMed Central

Jiang, Meiyan; Taghizadeh, Farshid; Steyger, Peter S.

2017-01-01

Aminoglycoside antibiotics remain widely used for urgent clinical treatment of life-threatening infections, despite the well-recognized risk of permanent hearing loss, i.e., cochleotoxicity. Recent studies show that aminoglycoside-induced cochleotoxicity is exacerbated by bacteriogenic-induced inflammation. This implies that those with severe bacterial infections (that induce systemic inflammation), and are treated with bactericidal aminoglycosides are at greater risk of drug-induced hearing loss than previously recognized. Incorporating this novel comorbid factor into cochleotoxicity risk prediction models will better predict which individuals are more predisposed to drug-induced hearing loss. Here, we review the cellular and/or signaling mechanisms by which host-mediated inflammatory responses to infection could enhance the trafficking of systemically administered aminoglycosides into the cochlea to enhance the degree of cochleotoxicity over that in healthy preclinical models. Once verified, these mechanisms will be potential targets for novel pharmacotherapeutics that reduce the risk of drug-induced hearing loss (and acute kidney damage) without compromising the life-saving bactericidal efficacy of aminoglycosides. PMID:29209174

Cardiovascular risk

PubMed Central

Payne, Rupert A

2012-01-01

Cardiovascular disease is a major, growing, worldwide problem. It is important that individuals at risk of developing cardiovascular disease can be effectively identified and appropriately stratified according to risk. This review examines what we understand by the term risk, traditional and novel risk factors, clinical scoring systems, and the use of risk for informing prescribing decisions. Many different cardiovascular risk factors have been identified. Established, traditional factors such as ageing are powerful predictors of adverse outcome, and in the case of hypertension and dyslipidaemia are the major targets for therapeutic intervention. Numerous novel biomarkers have also been described, such as inflammatory and genetic markers. These have yet to be shown to be of value in improving risk prediction, but may represent potential therapeutic targets and facilitate more targeted use of existing therapies. Risk factors have been incorporated into several cardiovascular disease prediction algorithms, such as the Framingham equation, SCORE and QRISK. These have relatively poor predictive power, and uncertainties remain with regards to aspects such as choice of equation, different risk thresholds and the roles of relative risk, lifetime risk and reversible factors in identifying and treating at-risk individuals. Nonetheless, such scores provide objective and transparent means of quantifying risk and their integration into therapeutic guidelines enables equitable and cost-effective distribution of health service resources and improves the consistency and quality of clinical decision making. PMID:22348281

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

PubMed

Marvin, Hans J P; Bouzembrak, Yamine; Janssen, Esmée M; van der Zande, Meike; Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans

2017-02-01

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Applying a new mammographic imaging marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Developing and validating risk prediction models in an individual participant data meta-analysis

PubMed Central

2014-01-01

Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

Algorithms for the prediction of retinopathy of prematurity based on postnatal weight gain.

PubMed

Binenbaum, Gil

2013-06-01

Current ROP screening guidelines represent a simple risk model with two dichotomized factors, birth weight and gestational age at birth. Pioneering work has shown that tracking postnatal weight gain, a surrogate for low insulin-like growth factor 1, may capture the influence of many other ROP risk factors and improve risk prediction. Models including weight gain, such as WINROP, ROPScore, and CHOP ROP, have demonstrated accurate ROP risk assessment and a potentially large reduction in ROP examinations, compared to current guidelines. However, there is a need for larger studies, and generalizability is limited in countries with developing neonatal care systems. Copyright © 2013 Elsevier Inc. All rights reserved.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

PubMed Central

Eng, Christine L. P.; Tong, Joo Chuan; Tan, Tin Wee

2017-01-01

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. PMID:28587080

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

PubMed

Eng, Christine L P; Tong, Joo Chuan; Tan, Tin Wee

2017-05-25

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

ToxCast: One Step in the NRC Vision of 21st Century Toxicology

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

A Lonely Search?: Risk for Depression When Spirituality Exceeds Religiosity.

PubMed

Vittengl, Jeffrey R

2018-05-01

This study clarified longitudinal relations of spirituality and religiosity with depression. Spirituality's potential emphasis on internal (e.g., intrapsychic search for meaning) versus religiosity's potential emphasis on external (e.g., engagement in socially-sanctioned belief systems) processes may parallel depression-linked cognitive-behavioral phenomena (e.g., rumination and loneliness) conceptually. Thus, this study tested the hypothesis that greater spirituality than religiosity, separate from the overall level of spirituality and religiosity, predicts longitudinal increases in depression. A national sample of midlife adults completed diagnostic interviews and questionnaires of spiritual and religious intensity up to three times over 18 years. In time-lagged multilevel models, overall spirituality plus religiosity did not predict depression. However, in support of the hypothesis, greater spirituality than religiosity significantly predicted subsequent increases in depressive symptoms and risk for major depressive disorder (odds ratio = 1.34). If replicated, the relative balance of spirituality and religiosity may inform depression assessment and prevention efforts.

Visually impaired individuals, safety perceptions and traumatic events: a qualitative study of hazards, reactions and coping.

PubMed

Saur, Randi; Hansen, Marianne Bang; Jansen, Anne; Heir, Trond

2017-04-01

To explore the types of risks and hazards that visually impaired individuals face, how they manage potential threats and how reactions to traumatic events are manifested and coped with. Participants were 17 visually impaired individuals who had experienced some kind of potentially traumatic event. Two focus groups and 13 individual interviews were conducted. The participants experienced a variety of hazards and potential threats in their daily life. Fear of daily accidents was more pronounced than fear of disasters. Some participants reported avoiding help-seeking in unsafe situations due to shame at not being able to cope. The ability to be independent was highlighted. Traumatic events were re-experienced through a variety of sense modalities. Fear of labelling and avoidance of potential risks were recurring topics, and the risks of social withdrawal and isolation were addressed. Visual impairment causes a need for predictability and adequate information to increase and prepare for coping and self-efficacy. The results from this study call for greater emphasis on universal design in order to ensure safety and predictability. Fear of being labelled may inhibit people from using assistive devices and adequate coping strategies and seeking professional help in the aftermath of a trauma. Implications for Rehabilitation Visual impairment entails a greater susceptibility to a variety of hazards and potential threats in daily life. This calls for a greater emphasis on universal design in public spaces to ensure confidence and safety. Visual impairment implies a need for predictability and adequate information to prepare for coping and self-efficacy. Rehabilitation professionals should be aware of the need for independence and self-reliance, the possible fear of labelling, avoidance of help-seeking or reluctance to use assistive devices. In rehabilitation after accidents or potential traumatizing events, professionals' knowledge about the needs for information, training and predictability is crucial. The possibility of social withdrawal or isolation should be considered.

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

PubMed

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

2015-12-01

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

Personal contextual characteristics and cognitions: predicting child abuse potential and disciplinary style.

PubMed

Rodriguez, Christina M

2010-02-01

According to Social Information Processing theory, parents' cognitive processes influence their decisions to engage in physical maltreatment, although cognitions occur in the context of other aspects of the parents' life. The present study investigated whether cognitive processes (external locus of control, inappropriate developmental expectations) predicted child abuse potential and overreactive disciplinary style beyond personal contextual factors characteristic of the parent (hostility, stress, and coping). 363 parents were recruited online. Results highlight the relative importance of the contextual characteristics (particularly stress, avoidant coping, and irritability) relative to cognitive processes in predicting abuse potential and overreactive discipline strategies, although an external locus of control also significantly contributed. Findings do not support that parents' developmental expectations uniquely predict elevated abuse risk. Results indicate stressed parents who utilize avoidance coping strategies are more likely to use overreactive discipline and report increased abuse potential. Findings are discussed with regard to implications for prevention/intervention efforts.

High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors

PubMed Central

Golbamaki-Bakhtyari, Nazanin; Kovarich, Simona; Tebby, Cleo; Gabb, Henry A.; Lemazurier, Emmanuel

2017-01-01

Background: Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. Objectives: We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. Methods: We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast™ aromatase assay provided concentration–inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus–pituitary–ovarian control of ovulation in women. Results: Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. Conclusions: These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment. https://doi.org/10.1289/EHP742 PMID:28886606

Biological risk factors for suicidal behaviors: a meta-analysis

PubMed Central

Chang, B P; Franklin, J C; Ribeiro, J D; Fox, K R; Bentley, K H; Kleiman, E M; Nock, M K

2016-01-01

Prior studies have proposed a wide range of potential biological risk factors for future suicidal behaviors. Although strong evidence exists for biological correlates of suicidal behaviors, it remains unclear if these correlates are also risk factors for suicidal behaviors. We performed a meta-analysis to integrate the existing literature on biological risk factors for suicidal behaviors and to determine their statistical significance. We conducted a systematic search of PubMed, PsycInfo and Google Scholar for studies that used a biological factor to predict either suicide attempt or death by suicide. Inclusion criteria included studies with at least one longitudinal analysis using a biological factor to predict either of these outcomes in any population through 2015. From an initial screen of 2541 studies we identified 94 cases. Random effects models were used for both meta-analyses and meta-regression. The combined effect of biological factors produced statistically significant but relatively weak prediction of suicide attempts (weighted mean odds ratio (wOR)=1.41; CI: 1.09–1.81) and suicide death (wOR=1.28; CI: 1.13–1.45). After accounting for publication bias, prediction was nonsignificant for both suicide attempts and suicide death. Only two factors remained significant after accounting for publication bias—cytokines (wOR=2.87; CI: 1.40–5.93) and low levels of fish oil nutrients (wOR=1.09; CI: 1.01–1.19). Our meta-analysis revealed that currently known biological factors are weak predictors of future suicidal behaviors. This conclusion should be interpreted within the context of the limitations of the existing literature, including long follow-up intervals and a lack of tests of interactions with other risk factors. Future studies addressing these limitations may more effectively test for potential biological risk factors. PMID:27622931

[Prediction of intra-abdominal hypertension risk in patients with acute colonic obstruction under epidural analgesia].

PubMed

Stakanov, A V; Potseluev, E A; Musaeva, T S

2013-01-01

Purpose of the study was to identify prediction possibility of direct current potential level for intra-abdominal hypertension risk in patients with acute colonic obstruction under preoperative epidural analgesia. Prospective analysis of the preoperative period was carried out in 140 patients with acute colonic obstruction caused by colon cancer. Relations between preoperative level of permanent capacity and risk of intra-abdominal hypertension was identified Direct current potential level is an independent predictor of intra-abdominal hypertension. Diagnostic significance increases from first to fifth hour of preoperative period according to AUROC data from 0.821 to 0.905 and calibration 6.9 (p > 0.37) and 4.7 (p > 0.54) by Hosmer-Lemeshou criteria. The use of epidural analgesia in the complex intensive preoperative preparation is pathogenically justified. It reduces intra-abdominal hypertension in patients with acute colonic obstruction.

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

Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation

PubMed Central

Pagliaccio, David; Luby, Joan L.; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S.; Belden, Andrew C.; Botteron, Kelly N.; Harms, Michael P.; Barch, Deanna M.

2015-01-01

Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within four hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9–14 year olds; N=120). Whole-brain regression analyses indicated that increasing genetic ‘risk’ predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic ‘risk’ and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. PMID:26595470

Using existing data to predict and quantify the risks of GM forage to a population of a non-target invertebrate species: a New Zealand case study.

PubMed

O'Callaghan, Maureen; Soboleva, Tanya K; Barratt, Barbara I P

2010-01-01

Determining the effects of genetically modified (GM) crops on non-target organisms is essential as many non-target species provide important ecological functions. However, it is simply not possible to collect field data on more than a few potential non-target species present in the receiving environment of a GM crop. While risk assessment must be rigorous, new approaches are necessary to improve the efficiency of the process. Utilisation of published information and existing data on the phenology and population dynamics of test species in the field can be combined with limited amounts of experimental biosafety data to predict possible outcomes on species persistence. This paper presents an example of an approach where data from laboratory experiments and field studies on phenology are combined using predictive modelling. Using the New Zealand native weevil species Nicaeana cervina as a case study, we could predict that oviposition rates of the weevil feeding on a GM ryegrass could be reduced by up to 30% without threat to populations of the weevil in pastoral ecosystems. In addition, an experimentally established correlation between feeding level and oviposition led to the prediction that a consistent reduction in feeding of 50% or higher indicated a significant risk to the species and could potentially lead to local extinctions. This approach to biosafety risk assessment, maximising the use of pre-existing field and laboratory data on non-target species, can make an important contribution to informed decision-making by regulatory authorities and developers of new technologies. © ISBR, EDP Sciences, 2011.

Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system.

PubMed

Aiba née Kaneko, Maki; Hirota, Morihiko; Kouzuki, Hirokazu; Mori, Masaaki

2015-02-01

Genotoxicity is the most commonly used endpoint to predict the carcinogenicity of chemicals. The International Conference on Harmonization (ICH) M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk offers guidance on (quantitative) structure-activity relationship ((Q)SAR) methodologies that predict the outcome of bacterial mutagenicity assay for actual and potential impurities. We examined the effectiveness of the (Q)SAR approach with the combination of DEREK NEXUS as an expert rule-based system and ADMEWorks as a statistics-based system for the prediction of not only mutagenic potential in the Ames test, but also genotoxic potential in mutagenicity and clastogenicity tests, using a data set of 342 chemicals extracted from the literature. The prediction of mutagenic potential or genotoxic potential by DEREK NEXUS or ADMEWorks showed high values of sensitivity and concordance, while prediction by the combination of DEREK NEXUS and ADMEWorks (battery system) showed the highest values of sensitivity and concordance among the three methods, but the lowest value of specificity. The number of false negatives was reduced with the battery system. We also separately predicted the mutagenic potential and genotoxic potential of 41 cosmetic ingredients listed in the International Nomenclature of Cosmetic Ingredients (INCI) among the 342 chemicals. Although specificity was low with the battery system, sensitivity and concordance were high. These results suggest that the battery system consisting of DEREK NEXUS and ADMEWorks is useful for prediction of genotoxic potential of chemicals, including cosmetic ingredients.

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

PubMed Central

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

2013-01-01

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

DEVELOPMENT OF AQUATIC MODELS FOR TESTING THE RELATIONSHIP BETWEEN GENETIC DIVERSITY AND POPULATION EXTINCTION RISK

EPA Science Inventory

The relationship between population adaptive potential and extinction risk in a changing environment is not well understood. Although the expectation is that genetic diversity is directly related to the capacity of populations to adapt, the statistical and predictive aspects of ...

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals

EPA Science Inventory

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxic...

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

PubMed Central

Lucas, Joseph E.; Bazemore, Taylor C.; Alo, Celan; Monahan, Patrick B.

2017-01-01

HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits. PMID:29155848

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.

Separate and interactive contributions of weak inhibitory control and threat sensitivity to prediction of suicide risk.

PubMed

Venables, Noah C; Sellbom, Martin; Sourander, Andre; Kendler, Kenneth S; Joiner, Thomas E; Drislane, Laura E; Sillanmäki, Lauri; Elonheimo, Henrik; Parkkola, Kai; Multimaki, Petteri; Patrick, Christopher J

2015-04-30

Biobehavioral dispositions can serve as valuable referents for biologically oriented research on core processes with relevance to many psychiatric conditions. The present study examined two such dispositional variables-weak response inhibition (or disinhibition; INH-) and threat sensitivity (or fearfulness; THT+)-as predictors of the serious transdiagnostic problem of suicide risk in two samples: male and female outpatients from a U.S. clinic (N=1078), and a population-based male military cohort from Finland (N=3855). INH- and THT+ were operationalized through scores on scale measures of disinhibition and fear/fearlessness, known to be related to DSM-defined clinical conditions and brain biomarkers. Suicide risk was assessed by clinician ratings (clinic sample) and questionnaires (both samples). Across samples and alternative suicide indices, INH- and THT+ each contributed uniquely to prediction of suicide risk-beyond internalizing and externalizing problems in the case of the clinic sample where diagnostic data were available. Further, in both samples, INH- and THT+ interactively predicted suicide risk, with individuals scoring concurrently high on both dispositions exhibiting markedly augmented risk. Findings demonstrate that dispositional constructs of INH- and THT+ are predictive of suicide risk, and hold potential as referents for biological research on suicidal behavior. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

ToxCast: One Step in the NRC Vision of 21st Century Toxicology (T)

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

ToxCast: One Step in the NRC Vision of 21st Century Toxicology (S)

EPA Science Inventory

The international community needs better predictive tools for assessing the hazards and risks of chemicals. It is technically feasible to collect bioactivity data on virtually all chemicals of potential concern ToxCast is providing a proof of concept for obtaining predictive, b...

Longitudinal Monitoring of Patients With Chronic Low Back Pain During Physical Therapy Treatment Using the STarT Back Screening Tool.

PubMed

Medeiros, Flávia Cordeiro; Costa, Leonardo Oliveira Pena; Added, Marco Aurélio Nemitalla; Salomão, Evelyn Cassia; Costa, Lucíola da Cunha Menezes

2017-05-01

Study Design Preplanned secondary analysis of a randomized clinical trial. Background The STarT Back Screening Tool (SBST) was developed to screen and to classify patients with low back pain into subgroups for the risk of having a poor prognosis. However, this classification at baseline does not take into account variables that can influence the prognosis during treatment or over time. Objectives (1) To investigate the changes in risk subgroup measured by the SBST over a period of 6 months, and (2) to assess the long-term predictive ability of the SBST when administered at different time points. Methods Patients with chronic nonspecific low back pain (n = 148) receiving physical therapy care as part of a randomized trial were analyzed. Pain intensity, disability, global perceived effect, and the SBST were collected at baseline, 5 weeks, 3 months, and 6 months. Changes in SBST risk classification were calculated. Hierarchical linear regression models adjusted for potential confounders were built to analyze the predictive capabilities of the SBST when administered at different time points. Results A large proportion of patients (60.8%) changed their risk subgroup after receiving physical therapy care. The SBST improved the prediction for all 6-month outcomes when using the 5-week risk subgroup and the difference between baseline and 5-week subgroup, after controlling for potential confounders. The SBST at baseline did not improve the predictive ability of the models after adjusting for confounders. Conclusion This study shows that many patients change SBST risk subgroup after receiving physical therapy care, and that the predictive ability of the SBST in patients with chronic low back pain increases when administered at different time points. Level of Evidence Prognosis, 2b. J Orthop Sports Phys Ther 2017;47(5):314-323. Epub 29 Mar 2017. doi:10.2519/jospt.2017.7199.

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

Forest fuels and landscape-level fire risk assessment of the ozark highlands, Missouri

Treesearch

Michael C. Stambaugh; Richard P. Guyette; Daniel C. Dey

2007-01-01

In this paper we describe a fire risk assessment of the Ozark Highlands. Fire risk is rated using information on ignition potential and fuel hazard. Fuel loading, a component of the fire hazard module, is weakly predicted (r2 = 0.19) by site- and landscape-level attributes. Fuel loading does not significantly differ between Ozark ecological...

The number of discharge medications predicts thirty-day hospital readmission: a cohort study.

PubMed

Picker, David; Heard, Kevin; Bailey, Thomas C; Martin, Nathan R; LaRossa, Gina N; Kollef, Marin H

2015-07-23

Hospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined. To determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications. The final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17-1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643-0.679]). The number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

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

PubMed

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

2015-10-01

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

Possession attachment predicts cell phone use while driving.

PubMed

Weller, Joshua A; Shackleford, Crystal; Dieckmann, Nathan; Slovic, Paul

2013-04-01

Distracted driving has become an important public health concern. However, little is known about the predictors of this health-risking behavior. One overlooked risk factor for distracted driving is the perceived attachment that one feels toward his or her phone. Prior research has suggested that individuals develop bonds toward objects, and qualitative research suggests that the bond between young drivers and their phones can be strong. It follows that individuals who perceive a strong attachment to their phone would be more likely to use it, even when driving. In a nationally representative sample of young drivers (17-28 years), participants (n = 1,006) completed a survey about driving behaviors and phone use. Risk perception surrounding cell phone use while driving and perceived attachment to one's phone were assessed by administering factor-analytically derived scales that were created as part of a larger project. Attachment toward one's phone predicted the proportion of trips in which a participant reported using their cell phone while driving, beyond that accounted for by risk perception and overall phone use. Further, attachment predicted self-reported distracted driving behaviors, such as the use of social media while driving. Attachment to one's phone may be an important but overlooked risk factor for the engagement of potentially health-risking driving behaviors. Understanding that phone attachment may adversely affect driving behaviors has the potential to inform prevention and intervention efforts designed to reduce distracted driving behaviors, especially in young drivers. 2013 APA, all rights reserved

Are the Sendai and Fukuoka consensus guidelines for cystic mucinous neoplasms of the pancreas useful in the initial triage of all suspected pancreatic cystic neoplasms? A single-institution experience with 317 surgically-treated patients.

PubMed

Goh, Brian K P; Tan, Damien M Y; Thng, Choon-Hua; Lee, Ser-Yee; Low, Albert S C; Chan, Chung-Yip; Wong, Jen-San; Lee, Victor T W; Cheow, Peng-Chung; Chow, Pierce K H; Chung, Alexander Y F; Wong, Wai-Keong; Ooi, London L P J

2014-06-01

The Sendai Consensus Guidelines (SCG) were formulated in 2006 and updated in Fukuoka in 2012 (FCG) to guide management of cystic mucinous neoplasms of the pancreas. This study aims to evaluate the clinical utility of the SCG and FCG in the initial triage of all suspected pancreatic cystic neoplasms. Overall, 317 surgically-treated patients with a suspected pancreatic cystic neoplasm were classified according to the SCG as high risk (HR(SCG)) and low risk (LR(SCG)), and according to the FCG as high risk (HR(FCG)), worrisome (W(FCG)), and low risk (LR(FCG)). Cystic lesions of the pancreas (CLP) were classified as potentially malignant/malignant or benign according to the final pathology. The presence of symptoms, proximal lesions with obstructive jaundice, elevated serum carcinoembryonic antigen/carbohydrate antigen 19-9 (CEA/CA 19-9), size ≥3 cm, presence of solid component, main pancreatic duct dilatation, thickened enhancing walls, and change in ductal caliber with distal atrophy were predictive of a potentially malignant/malignant CLP on univariate analyses. The positive predictive value (PPV) and negative predictive value (NPV) of HR(SCG) and HR(ICG2012) for a potentially malignant/malignant lesion was 67 and 88 %, and 88 and 92.5 %, respectively. There were no malignant lesions in both LR groups but some potentially malignant lesions such as cystic pancreatic neuroendocrine neoplasms with uncertain behavior were classified as LR. The updated FCG was superior to the SCG for the initial triage of all suspected pancreatic cystic neoplasms. CLP in the LR(FCG) group can be safely managed conservatively, and those in the HR(FCG) group should undergo resection.

A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment

PubMed Central

Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection. PMID:28686651

A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment.

PubMed

Nicol, Sam; Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.

Inability to predict postpartum hemorrhage: insights from Egyptian intervention data

PubMed Central

2011-01-01

Background Knowledge on how well we can predict primary postpartum hemorrhage (PPH) can help policy makers and health providers design current delivery protocols and PPH case management. The purpose of this paper is to identify risk factors and determine predictive probabilities of those risk factors for primary PPH among women expecting singleton vaginal deliveries in Egypt. Methods From a prospective cohort study, 2510 pregnant women were recruited over a six-month period in Egypt in 2004. PPH was defined as blood loss ≥ 500 ml. Measures of blood loss were made every 20 minutes for the first 4 hours after delivery using a calibrated under the buttocks drape. Using all variables available in the patients' charts, we divided them in ante-partum and intra-partum factors. We employed logistic regression to analyze socio-demographic, medical and past obstetric history, and labor and delivery outcomes as potential PPH risk factors. Post-model predicted probabilities were estimated using the identified risk factors. Results We found a total of 93 cases of primary PPH. In multivariate models, ante-partum hemoglobin, history of previous PPH, labor augmentation and prolonged labor were significantly associated with PPH. Post model probability estimates showed that even among women with three or more risk factors, PPH could only be predicted in 10% of the cases. Conclusions The predictive probability of ante-partum and intra-partum risk factors for PPH is very low. Prevention of PPH to all women is highly recommended. PMID:22123123

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.

The accumulation of heavy metals in agricultural land and the associated potential ecological risks in Shenzhen, China.

PubMed

Wu, Jiansheng; Song, Jing; Li, Weifeng; Zheng, Maokun

2016-01-01

Accumulation of heavy metals in agricultural land and their ecological risks are key issues in soil security studies. This study investigated the concentrations of six heavy metals--copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), and chromium (Cr) in Shenzhen's agricultural lands and examined the potential hazards and possible sources of these metals. Eighty-two samples from agricultural topsoil were collected. Potential ecological risk index was used to calculate the potential risk of heavy metals. Principal component analysis (PCA) was applied to explore pollution sources of the metals. Finally, Kriging was used to predict the spatial distribution of the metals' potential ecological risks. The concentrations of the heavy metals were higher than their background values. Most of them presented little potential ecological risk, except for the heavy metal cadmium (Cd). Four districts (Longgang, Longhua, Pingshan, and Dapeng) exhibited some degree of potential risk, which tended to have more industries and road networks. Three major sources of heavy metals included geochemical processes, industrial pollutants, and traffic pollution. The heavy metal Cd was the main contributor to the pollution in agricultural land during the study period. It also poses the potential hazard for the future. High potential risk is closely related to industrial pollution and transportation. Since the 1980s, the sources of heavy metals have evolved from parent rock weathering, erosion, degradation of organics, and mineralization to human disturbances resulting in chemical changes in the soil.

Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

PubMed

Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

2015-05-01

Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Significant SNPs have limited prediction ability for thyroid cancer

PubMed Central

Guo, Shicheng; Wang, Yu-Long; Li, Yi; Jin, Li; Xiong, Momiao; Ji, Qing-Hai; Wang, Jiucun

2014-01-01

Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case–control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associated with thyroid cancer in Han Chinese population, while no polymorphism was observed for rs116909374. Small familial relative risks (1.02–1.05) and limited power to predict thyroid cancer (AUCs: 0.54–0.60) indicate limited clinical potential. Four significant SNPs have limited prediction ability for thyroid cancer. PMID:24591304

Risk Perception, Rape, and Sexual Revictimization: A Prospective Study of College Women

ERIC Educational Resources Information Center

Messman-Moore, Terri L.; Brown, Amy L.

2006-01-01

Risk perception was examined in relation to sexual victimization among 262 college women. Participants were presented with written vignettes that described hypothetical situations with a stranger and with an acquaintance. Participants' hypothetical decision to leave a potentially risky situation with an acquaintance predicted rape and…

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…

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...

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).

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

Risk attitudes and personality traits predict perceptions of benefits and risks for medicinal products: a field study of European medical assessors.

PubMed

Beyer, Andrea R; Fasolo, Barbara; de Graeff, P A; Hillege, H L

2015-01-01

Risk attitudes and personality traits are known predictors of decision making among laypersons, but very little is known of their influence among experts participating in organizational decision making. Seventy-five European medical assessors were assessed in a field study using the Domain Specific Risk Taking scale and the Big Five Inventory scale. Assessors rated the risks and benefits for a mock "clinical dossier" specific to their area of expertise, and ordinal regression models were used to assess the odds of risk attitude or personality traits in predicting either the benefit or the risk ratings. An increase in the "conscientiousness" score predicted an increase in the perception of the drug's benefit, and male assessors gave higher scores for the drug's benefit ratings than did female assessors. Extraverted assessors saw fewer risks, and assessors with a perceived neutral-averse or averse risk profile saw greater risks. Medical assessors perceive the benefits and risks of medicines via a complex interplay of the medical situation, their personality traits and even their gender. Further research in this area is needed to determine how these potential biases are managed within the regulatory setting. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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.

Meta-analysis of the predictive value of DNA aneuploidy in malignant transformation of oral potentially malignant disorders.

PubMed

Alaizari, Nader A; Sperandio, Marcelo; Odell, Edward W; Peruzzo, Daiane; Al-Maweri, Sadeq A

2018-02-01

DNA aneuploidy is an imbalance of chromosomal DNA content that has been highlighted as a predictor of biological behavior and risk of malignant transformation. To date, DNA aneuploidy in oral potentially malignant diseases (OPMD) has been shown to correlate strongly with severe dysplasia and high-risk lesions that appeared non-dysplastic can be identified by ploidy analysis. Nevertheless, the prognostic value of DNA aneuploidy in predicting malignant transformation of OPMD remains to be validated. The aim of this meta-analysis was to assess the role of DNA aneuploidy in predicting malignant transformation in OPMD. The questions addressed were (i) Is DNA aneuploidy a useful marker to predict malignant transformation in OPMD? (ii) Is DNA diploidy a useful negative marker of malignant transformation in OPMD? These questions were addressed using the PECO method. Five studies assessing aneuploidy as a risk marker of malignant change were pooled into the meta-analysis. Aneuploidy was found to be associated with a 3.12-fold increased risk to progress into cancer (RR=3.12, 95% CI 1.86-5.24). Based on the five studies meta-analyzed, "no malignant progression" was more likely to occur in DNA diploid OPMD by 82% when compared to aneuploidy (RR=0.18, 95% CI 0.08-0.41). In conclusion, aneuploidy is a useful marker of malignant transformation in OPMD, although a diploid result should be interpreted with caution. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Predicting Parent-Child Aggression Risk: Cognitive Factors and Their Interaction With Anger.

PubMed

Rodriguez, Christina M

2018-02-01

Several cognitive elements have previously been proposed to elevate risk for physical child abuse. To predict parent-child aggression risk, the current study evaluated the role of approval of parent-child aggression, perceptions of children as poorly behaved, and discipline attributions. Several dimensions of attributions specifically tied to parents' discipline practices were targeted. In addition, anger experienced during discipline episodes was considered a potential moderator of these cognitive processes. Using a largely multiple-indicator approach, a sample of 110 mothers reported on these cognitive and affective aspects that may occur when disciplining their children as well as responding to measures of parent-child aggression risk. Findings suggest that greater approval of parent-child aggression, negative perceptions of their child's behavior, and discipline attributions independently predicted parent-child aggression risk, with anger significantly interacting with mothers' perception of their child as more poorly behaved to exacerbate their parent-child aggression risk. Of the discipline attribution dimensions evaluated, mothers' sense of external locus of control and believing their child deserved their discipline were related to increase parent-child aggression risk. Future work is encouraged to comprehensively evaluate how cognitive and affective components contribute and interact to increase risk for parent-child aggression.

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

PubMed

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

2014-11-04

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

Separating sensitivity from exposure in assessing extinction risk from climate change

PubMed Central

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

2014-01-01

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

Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis

PubMed Central

Singh, Jay P; Doll, Helen; Grann, Martin

2012-01-01

Objective To investigate the predictive validity of tools commonly used to assess the risk of violence, sexual, and criminal behaviour. Design Systematic review and tabular meta-analysis of replication studies following PRISMA guidelines. Data sources PsycINFO, Embase, Medline, and United States Criminal Justice Reference Service Abstracts. Review methods We included replication studies from 1 January 1995 to 1 January 2011 if they provided contingency data for the offending outcome that the tools were designed to predict. We calculated the diagnostic odds ratio, sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, the number needed to detain to prevent one offence, as well as a novel performance indicator—the number safely discharged. We investigated potential sources of heterogeneity using metaregression and subgroup analyses. Results Risk assessments were conducted on 73 samples comprising 24 847 participants from 13 countries, of whom 5879 (23.7%) offended over an average of 49.6 months. When used to predict violent offending, risk assessment tools produced low to moderate positive predictive values (median 41%, interquartile range 27-60%) and higher negative predictive values (91%, 81-95%), and a corresponding median number needed to detain of 2 (2-4) and number safely discharged of 10 (4-18). Instruments designed to predict violent offending performed better than those aimed at predicting sexual or general crime. Conclusions Although risk assessment tools are widely used in clinical and criminal justice settings, their predictive accuracy varies depending on how they are used. They seem to identify low risk individuals with high levels of accuracy, but their use as sole determinants of detention, sentencing, and release is not supported by the current evidence. Further research is needed to examine their contribution to treatment and management. PMID:22833604

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

PubMed Central

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

2013-01-01

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

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.

The impacts of uncertainty and variability in groundwater-driven health risk assessment. (Invited)

NASA Astrophysics Data System (ADS)

Maxwell, R. M.

2010-12-01

Potential human health risk from contaminated groundwater is becoming an important, quantitative measure used in management decisions in a range of applications from Superfund to CO2 sequestration. Quantitatively assessing the potential human health risks from contaminated groundwater is challenging due to the many coupled processes, uncertainty in transport parameters and the variability in individual physiology and behavior. Perspective on human health risk assessment techniques will be presented and a framework used to predict potential, increased human health risk from contaminated groundwater will be discussed. This framework incorporates transport of contaminants through the subsurface from source to receptor and health risks to individuals via household exposure pathways. The subsurface is shown subject to both physical and chemical heterogeneity which affects downstream concentrations at receptors. Cases are presented where hydraulic conductivity can exhibit both uncertainty and spatial variability in addition to situations where hydraulic conductivity is the dominant source of uncertainty in risk assessment. Management implications, such as characterization and remediation will also be discussed.

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Assessing patient risk of central line-associated bacteremia via machine learning.

PubMed

Beeler, Cole; Dbeibo, Lana; Kelley, Kristen; Thatcher, Levi; Webb, Douglas; Bah, Amadou; Monahan, Patrick; Fowler, Nicole R; Nicol, Spencer; Judy-Malcolm, Alisa; Azar, Jose

2018-04-13

Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Assessment of environmental risks from toxic and nontoxic stressors; a proposed concept for a risk-based management tool for offshore drilling discharges.

PubMed

Smit, Mathijs G D; Jak, Robbert G; Rye, Henrik; Frost, Tone Karin; Singsaas, Ivar; Karman, Chris C

2008-04-01

In order to improve the ecological status of aquatic systems, both toxic (e.g., chemical) and nontoxic stressors (e.g., suspended particles) should be evaluated. This paper describes an approach to environmental risk assessment of drilling discharges to the sea. These discharges might lead to concentrations of toxic compounds and suspended clay particles in the water compartment and concentrations of toxic compounds, burial of biota, change in sediment structure, and oxygen depletion in marine sediments. The main challenges were to apply existing protocols for environmental risk assessment to nontoxic stressors and to combine risks arising from exposure to these stressors with risk from chemical exposure. The defined approach is based on species sensitivity distributions (SSDs). In addition, precautionary principles from the EU-Technical Guidance Document were incorporated to assure that the method is acceptable in a regulatory context. For all stressors a protocol was defined to construct an SSD for no observed effect concentrations (or levels; NOEC(L)-SSD) to allow for the calculation of the potentially affected fraction of species from predicted exposures. Depending on the availability of data, a NOEC-SSD for toxicants can either be directly based on available NOECs or constructed from the predicted no effect concentration and the variation in sensitivity among species. For nontoxic stressors a NOEL-SSD can be extrapolated from an SSD based on effect or field data. Potentially affected fractions of species at predicted exposures are combined into an overall risk estimate. The developed approach facilitates environmental management of drilling discharges and can be applied to define risk-mitigating measures for both toxic and nontoxic stress.

Comparison of RISK-PCI, GRACE, TIMI risk scores for prediction of major adverse cardiac events in patients with acute coronary syndrome.

PubMed

Jakimov, Tamara; Mrdović, Igor; Filipović, Branka; Zdravković, Marija; Djoković, Aleksandra; Hinić, Saša; Milić, Nataša; Filipović, Branislav

2017-12-31

To compare the prognostic performance of three major risk scoring systems including global registry for acute coronary events (GRACE), thrombolysis in myocardial infarction (TIMI), and prediction of 30-day major adverse cardiovascular events after primary percutaneous coronary intervention (RISK-PCI). This single-center retrospective study involved 200 patients with acute coronary syndrome (ACS) who underwent invasive diagnostic approach, ie, coronary angiography and myocardial revascularization if appropriate, in the period from January 2014 to July 2014. The GRACE, TIMI, and RISK-PCI risk scores were compared for their predictive ability. The primary endpoint was a composite 30-day major adverse cardiovascular event (MACE), which included death, urgent target-vessel revascularization (TVR), stroke, and non-fatal recurrent myocardial infarction (REMI). The c-statistics of the tested scores for 30-day MACE or area under the receiver operating characteristic curve (AUC) with confidence intervals (CI) were as follows: RISK-PCI (AUC=0.94; 95% CI 1.790-4.353), the GRACE score on admission (AUC=0.73; 95% CI 1.013-1.045), the GRACE score on discharge (AUC=0.65; 95% CI 0.999-1.033). The RISK-PCI score was the only score that could predict TVR (AUC=0.91; 95% CI 1.392-2.882). The RISK-PCI scoring system showed an excellent discriminative potential for 30-day death (AUC=0.96; 95% CI 1.339-3.548) in comparison with the GRACE scores on admission (AUC=0.88; 95% CI 1.018-1.072) and on discharge (AUC=0.78; 95% CI 1.000-1.058). In comparison with the GRACE and TIMI scores, RISK-PCI score showed a non-inferior ability to predict 30-day MACE and death in ACS patients. Moreover, RISK-PCI was the only scoring system that could predict recurrent ischemia requiring TVR.

Comparison of RISK-PCI, GRACE, TIMI risk scores for prediction of major adverse cardiac events in patients with acute coronary syndrome

PubMed Central

Jakimov, Tamara; Mrdović, Igor; Filipović, Branka; Zdravković, Marija; Djoković, Aleksandra; Hinić, Saša; Milić, Nataša; Filipović, Branislav

2017-01-01

Aim To compare the prognostic performance of three major risk scoring systems including global registry for acute coronary events (GRACE), thrombolysis in myocardial infarction (TIMI), and prediction of 30-day major adverse cardiovascular events after primary percutaneous coronary intervention (RISK-PCI). Methods This single-center retrospective study involved 200 patients with acute coronary syndrome (ACS) who underwent invasive diagnostic approach, ie, coronary angiography and myocardial revascularization if appropriate, in the period from January 2014 to July 2014. The GRACE, TIMI, and RISK-PCI risk scores were compared for their predictive ability. The primary endpoint was a composite 30-day major adverse cardiovascular event (MACE), which included death, urgent target-vessel revascularization (TVR), stroke, and non-fatal recurrent myocardial infarction (REMI). Results The c-statistics of the tested scores for 30-day MACE or area under the receiver operating characteristic curve (AUC) with confidence intervals (CI) were as follows: RISK-PCI (AUC = 0.94; 95% CI 1.790-4.353), the GRACE score on admission (AUC = 0.73; 95% CI 1.013-1.045), the GRACE score on discharge (AUC = 0.65; 95% CI 0.999-1.033). The RISK-PCI score was the only score that could predict TVR (AUC = 0.91; 95% CI 1.392-2.882). The RISK-PCI scoring system showed an excellent discriminative potential for 30-day death (AUC = 0.96; 95% CI 1.339-3.548) in comparison with the GRACE scores on admission (AUC = 0.88; 95% CI 1.018-1.072) and on discharge (AUC = 0.78; 95% CI 1.000-1.058). Conclusions In comparison with the GRACE and TIMI scores, RISK-PCI score showed a non-inferior ability to predict 30-day MACE and death in ACS patients. Moreover, RISK-PCI was the only scoring system that could predict recurrent ischemia requiring TVR. PMID:29308832

High-definition endoscopy with digital chromoendoscopy for histologic prediction of distal colorectal polyps.

PubMed

Rath, Timo; Tontini, Gian E; Nägel, Andreas; Vieth, Michael; Zopf, Steffen; Günther, Claudia; Hoffman, Arthur; Neurath, Markus F; Neumann, Helmut

2015-10-22

Distal diminutive colorectal polyps are common and accurate endoscopic prediction of hyperplastic or adenomatous polyp histology could reduce procedural time, costs and potential risks associated with the resection. Within this study we assessed whether digital chromoendoscopy can accurately predict the histology of distal diminutive colorectal polyps according to the ASGE PIVI statement. In this prospective cohort study, 224 consecutive patients undergoing screening or surveillance colonoscopy were included. Real time histology of 121 diminutive distal colorectal polyps was evaluated using high-definition endoscopy with digital chromoendoscopy and the accuracy of predicting histology with digital chromoendoscopy was assessed. The overall accuracy of digital chromoendoscopy for prediction of adenomatous polyp histology was 90.1 %. Sensitivity, specificity, positive and negative predictive values were 93.3, 88.7, 88.7, and 93.2 %, respectively. In high-confidence predictions, the accuracy increased to 96.3 % while sensitivity, specificity, positive and negative predictive values were calculated as 98.1, 94.4, 94.5, and 98.1 %, respectively. Surveillance intervals with digital chromoendoscopy were correctly predicted with >90 % accuracy. High-definition endoscopy in combination with digital chromoendoscopy allowed real-time in vivo prediction of distal colorectal polyp histology and is accurate enough to leave distal colorectal polyps in place without resection or to resect and discard them without pathologic assessment. This approach has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps. ClinicalTrials NCT02217449.

Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants.

PubMed

Romanos, Jihane; Rosén, Anna; Kumar, Vinod; Trynka, Gosia; Franke, Lude; Szperl, Agata; Gutierrez-Achury, Javier; van Diemen, Cleo C; Kanninga, Roan; Jankipersadsing, Soesma A; Steck, Andrea; Eisenbarth, Georges; van Heel, David A; Cukrowska, Bozena; Bruno, Valentina; Mazzilli, Maria Cristina; Núñez, Concepcion; Bilbao, Jose Ramon; Mearin, M Luisa; Barisani, Donatella; Rewers, Marian; Norris, Jill M; Ivarsson, Anneli; Boezen, H Marieke; Liu, Edwin; Wijmenga, Cisca

2014-03-01

The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD. We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing. We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case-control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals. Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations. Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD.

Electronic Health Record-Enabled Big-Data Approaches to Nephrotoxin-Associated Acute Kidney Injury Risk Prediction.

PubMed

Sutherland, Scott M

2018-06-09

Nephrotoxin-associated acute kidney injury (NTx-AKI) has become one of the most common causes of AKI among hospitalized adults and children; across acute and intensive care populations, exposure to nephrotoxins accounts for 15-25% of AKI. Although some interventions have shown promise in observational studies, no treatments currently exist for NTx-AKI once it occurs. Thus, nearly all effective strategies are aimed at prevention. The primary obstacle to prevention is risk prediction and the determination of which patients are more likely to develop NTx-AKI when exposed to medications with nephrotoxic potential. Historically, traditional statistical modeling has been applied to previously recognized clinical risk factors to identify predictors of NTx-AKI. However, increased electronic health record adoption and the evolution of "big-data" approaches to predictive analytics may offer a unique opportunity to prevent NTx-AKI events. This article describes prior and current approaches to NTx-AKI prediction and offers three novel use cases for electronic health record-enabled NTx-AKI forecasting and risk profiling. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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

Environmental toxicology and risk assessment of pharmaceuticals from hospital wastewater.

PubMed

Escher, Beate I; Baumgartner, Rebekka; Koller, Mirjam; Treyer, Karin; Lienert, Judit; McArdell, Christa S

2011-01-01

In this paper, we evaluated the ecotoxicological potential of the 100 pharmaceuticals expected to occur in highest quantities in the wastewater of a general hospital and a psychiatric center in Switzerland. We related the toxicity data to predicted concentrations in different wastewater streams to assess the overall risk potential for different scenarios, including conventional biological pretreatment in the hospital and urine source separation. The concentrations in wastewater were estimated with pharmaceutical usage information provided by the hospitals and literature data on human excretion into feces and urine. Environmental concentrations in the effluents of the exposure scenarios were predicted by estimating dilution in sewers and with literature data on elimination during wastewater treatment. Effect assessment was performed using quantitative structure-activity relationships because experimental ecotoxicity data were only available for less than 20% of the 100 pharmaceuticals with expected highest loads. As many pharmaceuticals are acids or bases, a correction for the speciation was implemented in the toxicity prediction model. The lists of Top-100 pharmaceuticals were distinctly different between the two hospital types with only 37 pharmaceuticals overlapping in both datasets. 31 Pharmaceuticals in the general hospital and 42 pharmaceuticals in the psychiatric center had a risk quotient above 0.01 and thus contributed to the mixture risk quotient. However, together they constituted only 14% (hospital) and 30% (psychiatry) of the load of pharmaceuticals. Hence, medical consumption data alone are insufficient predictors of environmental risk. The risk quotients were dominated by amiodarone, ritonavir, clotrimazole, and diclofenac. Only diclofenac is well researched in ecotoxicology, while amiodarone, ritonavir, and clotrimazole have no or very limited experimental fate or toxicity data available. The presented computational analysis thus helps setting priorities for further testing. Separate treatment of hospital wastewater would reduce the pharmaceutical load of wastewater treatment plants, and the risk from the newly identified priority pharmaceuticals. However, because high-risk pharmaceuticals are excreted mainly with feces, urine source separation is not a viable option for reducing the risk potential from hospital wastewater, while a sorption step could be beneficial. Copyright © 2010 Elsevier Ltd. All rights reserved.

Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study

PubMed Central

Østergaard, Søren D.; Mukherjee, Shubhabrata; Sharp, Stephen J.; Proitsi, Petroula; Lotta, Luca A.; Day, Felix; Perry, John R. B.; Boehme, Kevin L.; Walter, Stefan; Kauwe, John S.; Gibbons, Laura E.; Larson, Eric B.; Powell, John F.; Langenberg, Claudia; Crane, Paul K.; Wareham, Nicholas J.; Scott, Robert A.

2015-01-01

Background Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). Methods and Findings We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, N SNPs = 49), fasting glucose (N SNPs = 36), insulin resistance (N SNPs = 10), body mass index (BMI, N SNPs = 32), total cholesterol (N SNPs = 73), HDL-cholesterol (N SNPs = 71), LDL-cholesterol (N SNPs = 57), triglycerides (N SNPs = 39), systolic blood pressure (SBP, N SNPs = 24), smoking initiation (N SNPs = 1), smoking quantity (N SNPs = 3), university completion (N SNPs = 2), and years of education (N SNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP–AD associations from the International Genomics of Alzheimer’s Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62–0.91]; p = 3.4 × 10−3). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10−8). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51–0.89]; p = 6.5 × 10−3), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. Conclusions Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure—or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications—may reduce AD risk. PMID:26079503

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.

Modelling the seasonality of Lyme disease risk and the potential impacts of a warming climate within the heterogeneous landscapes of Scotland.

PubMed

Li, Sen; Gilbert, Lucy; Harrison, Paula A; Rounsevell, Mark D A

2016-03-01

Lyme disease is the most prevalent vector-borne disease in the temperate Northern Hemisphere. The abundance of infected nymphal ticks is commonly used as a Lyme disease risk indicator. Temperature can influence the dynamics of disease by shaping the activity and development of ticks and, hence, altering the contact pattern and pathogen transmission between ticks and their host animals. A mechanistic, agent-based model was developed to study the temperature-driven seasonality of Ixodes ricinus ticks and transmission of Borrelia burgdorferi sensu lato across mainland Scotland. Based on 12-year averaged temperature surfaces, our model predicted that Lyme disease risk currently peaks in autumn, approximately six weeks after the temperature peak. The risk was predicted to decrease with increasing altitude. Increases in temperature were predicted to prolong the duration of the tick questing season and expand the risk area to higher altitudinal and latitudinal regions. These predicted impacts on tick population ecology may be expected to lead to greater tick-host contacts under climate warming and, hence, greater risks of pathogen transmission. The model is useful in improving understanding of the spatial determinants and system mechanisms of Lyme disease pathogen transmission and its sensitivity to temperature changes. © 2016 The Author(s).

Modelling the seasonality of Lyme disease risk and the potential impacts of a warming climate within the heterogeneous landscapes of Scotland

PubMed Central

Gilbert, Lucy; Harrison, Paula A.; Rounsevell, Mark D. A.

2016-01-01

Lyme disease is the most prevalent vector-borne disease in the temperate Northern Hemisphere. The abundance of infected nymphal ticks is commonly used as a Lyme disease risk indicator. Temperature can influence the dynamics of disease by shaping the activity and development of ticks and, hence, altering the contact pattern and pathogen transmission between ticks and their host animals. A mechanistic, agent-based model was developed to study the temperature-driven seasonality of Ixodes ricinus ticks and transmission of Borrelia burgdorferi sensu lato across mainland Scotland. Based on 12-year averaged temperature surfaces, our model predicted that Lyme disease risk currently peaks in autumn, approximately six weeks after the temperature peak. The risk was predicted to decrease with increasing altitude. Increases in temperature were predicted to prolong the duration of the tick questing season and expand the risk area to higher altitudinal and latitudinal regions. These predicted impacts on tick population ecology may be expected to lead to greater tick–host contacts under climate warming and, hence, greater risks of pathogen transmission. The model is useful in improving understanding of the spatial determinants and system mechanisms of Lyme disease pathogen transmission and its sensitivity to temperature changes. PMID:27030039

Work expectations, cultural sensitivity, schizophrenia, and suicide risk in male patients.

PubMed

Lewine, Richard; Shriner, Brooke

2009-04-01

This study examines the relationship between "vocational lost potential" and suicide risk in a mixed sample of severely and persistently mentally ill psychiatric patients. We hypothesized that increased lost potential would be associated with increased suicide risk indicator ratings and that this relationship would be moderated by patients' social class of origin. One hundred sixty-seven psychiatric patients rated a range of clinical symptoms and vocational expectations, as well as providing sociodemographic information including their parents' years of education (used as a proxy for social class of origin). Contrary to our prediction, the results suggest that individuals from higher social class who experience minimal lost potential may be at a higher risk for suicide than their counterparts with maximal lost potential; this is especially true when based on fathers' educational level. In discussing the clinical implications of our findings, we suggest that a subgroup of individuals' vocational success may depend on first addressing the cognitive conflict inherent in the phenomenon of lost potential.

The Analysis of Genomic Dose-Response Data in the EPA ToxCast™ Program

EPA Science Inventory

The U.S. EPA must assess the potential adverse effects of thousands of chemicals, often with limited toxicity information. Accurate toxicity predictions will help prioritize chemicals for further testing, focusing resources on the greater potential hazards or risks. In vitro geno...

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

Predicting invasion risk using measures of introduction effort and environmental niche models.

PubMed

Herborg, Leif-Matthias; Jerde, Christopher L; Lodge, David M; Ruiz, Gregory M; MacIsaac, Hugh J

2007-04-01

The Chinese mitten crab (Eriocheir sinensis) is native to east Asia, is established throughout Europe, and is introduced but geographically restricted in North America. We developed and compared two separate environmental niche models using genetic algorithm for rule set prediction (GARP) and mitten crab occurrences in Asia and Europe to predict the species' potential distribution in North America. Since mitten crabs must reproduce in water with >15% per hundred salinity, we limited the potential North American range to freshwater habitats within the highest documented dispersal distance (1260 km) and a more restricted dispersal limit (354 km) from the sea. Applying the higher dispersal distance, both models predicted the lower Great Lakes, most of the eastern seaboard, the Gulf of Mexico and southern extent of the Mississippi River watershed, and the Pacific northwest as suitable environment for mitten crabs, but environmental match for southern states (below 35 degrees N) was much lower for the European model. Use of the lower range with both models reduced the expected range, especially in the Great Lakes, Mississippi drainage, and inland areas of the Pacific Northwest. To estimate the risk of introduction of mitten crabs, the amount of reported ballast water discharge into major United States ports from regions in Asia and Europe with established mitten crab populations was used as an index of introduction effort. Relative risk of invasion was estimated based on a combination of environmental match and volume of unexchanged ballast water received (July 1999-December 2003) for major ports. The ports of Norfolk and Baltimore were most vulnerable to invasion and establishment, making Chesapeake Bay the most likely location to be invaded by mitten crabs in the United States. The next highest risk was predicted for Portland, Oregon. Interestingly, the port of Los Angeles/Long Beach, which has a large shipping volume, had a low risk of invasion. Ports such as Jacksonville, Florida, had a medium risk owing to small shipping volume but high environmental match. This study illustrates that the combination of environmental niche- and vector-based models can provide managers with more precise estimates of invasion risk than can either of these approaches alone.

Intracranial pressure-induced optic nerve sheath response as a predictive biomarker for optic disc edema in astronauts.

PubMed

Wostyn, Peter; De Deyn, Peter Paul

2017-11-01

A significant proportion of the astronauts who spend extended periods in microgravity develop ophthalmic abnormalities. Understanding this syndrome, called visual impairment and intracranial pressure (VIIP), has become a high priority for National Aeronautics and Space Administration, especially in view of future long-duration missions (e.g., Mars missions). Moreover, to ensure selection of astronaut candidates who will be able to complete long-duration missions with low risk of the VIIP syndrome, it is imperative to identify biomarkers for VIIP risk prediction. Here, we hypothesize that the optic nerve sheath response to alterations in intracranial pressure may be a potential predictive biomarker for optic disc edema in astronauts. If confirmed, this biomarker could be used for preflight identification of astronauts at risk for developing VIIP-associated optic disc edema.

Familial risk moderates the association between sleep and zBMI in children.

PubMed

Bagley, Erika J; El-Sheikh, Mona

2013-08-01

A cumulative risk approach was used to examine the moderating effect of familial risk factors on relations between actigraphy-based sleep quantity (minutes) and quality (efficiency) and sex- and age-standardized body mass index (zBMI). The sample included 124 boys and 104 girls with a mean age of 10.41 years (SD = 0.67). Children wore actigraphs for 1 week, and their height and weight were assessed in the lab. After controlling for potential confounds, multiple regression analyses indicated that sleep minutes predicted children's zBMI and that both sleep minutes and efficiency interacted with family risk in the prediction of zBMI. The association between poor sleep and zBMI was especially evident for children exposed to higher levels of family risk. Findings suggest that not all children who exhibit poor sleep are at equal risk for higher zBMI and that familial and contextual conditions need to be considered in this link.

Understanding social anxiety as a risk for alcohol use disorders: Fear of scrutiny, not social interaction fears, prospectively predicts alcohol use disorders

PubMed Central

Buckner, Julia D.; Schmidt, Norman B.

2009-01-01

Increasing evidence indicates that social anxiety may be a premorbid risk factor for alcohol use disorders (AUD). The aim of this study was to replicate and extend previous work examining whether social anxiety is a risk factor for AUD by evaluating both the temporal antecedence and non-spuriousness of this relationship. We also examined whether social anxiety first-order factors (social interaction anxiety, observation anxieties) served as specific predictors of AUD. A non-referred sample of 404 psychologically healthy young adults (i.e. free from current or past Axis I psychopathology) was prospectively followed over approximately two years. Social anxiety (but not depression or trait anxiety) at baseline significantly predicted subsequent AUD onset. The relationship between social anxiety and AUD remained even after controlling for relevant variables (gender, depression, trait anxiety). Further, social anxiety first-order factors differentially predicted AUD onset, such that observation anxieties (but not social interaction anxiety) were prospectively linked to AUD onset. This study provides further support that social anxiety (and fear of scrutiny specifically) appears to serve as an important and potentially specific AUD-related variable that deserves serious attention as a potential vulnerability factor. PMID:18547587

The Association between Sexual Assault and Suicidal Activity in a National Sample

ERIC Educational Resources Information Center

Tomasula, Jessica L.; Anderson, Laura M.; Littleton, Heather L.; Riley-Tillman, T. Chris

2012-01-01

Sexual violence is a potential key risk factor for adolescent suicidal behavior but has not been studied extensively. Thus, the current study examined the extent to which sexual assault predicted suicide attempts among adolescent students in the national Youth Risk Behavior Surveillance System survey (2007 data). Gender differences in suicidal…

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.

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

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

PubMed Central

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

2012-01-01

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

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.

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

PubMed

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

2016-09-01

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

Association of Coronary Artery Calcification with Estimated Coronary Heart Disease Risk from Prediction Models in a Community-Based Sample of Japanese Men: The Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA).

PubMed

Fujiyoshi, Akira; Arima, Hisatomi; Tanaka-Mizuno, Sachiko; Hisamatsu, Takahashi; Kadowaki, Sayaka; Kadota, Aya; Zaid, Maryam; Sekikawa, Akira; Yamamoto, Takashi; Horie, Minoru; Miura, Katsuyuki; Ueshima, Hirotsugu

2017-12-05

The clinical significance of coronary artery calcification (CAC) is not fully determined in general East Asian populations where background coronary heart disease (CHD) is less common than in USA/Western countries. We cross-sectionally assessed the association between CAC and estimated CHD risk as well as each major risk factor in general Japanese men. Participants were 996 randomly selected Japanese men aged 40-79 y, free of stroke, myocardial infarction, or revascularization. We examined an independent relationship between each risk factor used in prediction models and CAC score ≥100 by logistic regression. We then divided the participants into quintiles of estimated CHD risk per prediction model to calculate odds ratio of having CAC score ≥100. Receiver operating characteristic curve and c-index were used to examine discriminative ability of prevalent CAC for each prediction model. Age, smoking status, and systolic blood pressure were significantly associated with CAC score ≥100 in the multivariable analysis. The odds of having CAC score ≥100 were higher for those in higher quintiles in all prediction models (p-values for trend across quintiles ＜0.0001 for all models). All prediction models showed fair and similar discriminative abilities to detect CAC score ≥100, with similar c-statistics (around 0.70). In a community-based sample of Japanese men free of CHD and stroke, CAC score ≥100 was significantly associated with higher estimated CHD risk by prediction models. This finding supports the potential utility of CAC as a biomarker for CHD in a general Japanese male population.

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

PubMed

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

2016-10-01

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

Population-based absolute risk estimation with survey data

PubMed Central

Kovalchik, Stephanie A.; Pfeiffer, Ruth M.

2013-01-01

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level. PMID:23686614

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.

Systematic literature review of the risk factors, comorbidities, and consequences of hypogonadism in men.

PubMed

Zarotsky, V; Huang, M-Y; Carman, W; Morgentaler, A; Singhal, P K; Coffin, D; Jones, T H

2014-11-01

The objective of this review was to summarize the literature on the risk factors, comorbidities, and consequences of male hypogonadism, which is defined as a syndrome complex that includes biochemical confirmation of low testosterone (T) and the consistent symptoms and signs associated with low T. A systematic literature search was performed in PubMed/MEDLINE, EMBASE, Cochrane Library for articles published in the last 10 years on risk factors, comorbidities, and consequences of male hypogonadism. Of the 53 relevant studies identified, nine examined potential risk factors, 14 examined potential comorbidities, and 30 examined potential consequences of male hypogonadism. Based on studies conducted in Asia, Australia, Europe, and North & South America, the important factors that predicted and correlated with hypogonadism were advanced age, obesity, a diagnosis of metabolic syndrome (MetS), and a poor general health status. Diabetes mellitus was correlated with hypogonadism in most studies, but was not established as a risk factor. Although diseases, such as coronary heart disease, hypertension, stroke, and peripheral arterial disease did not predict hypogonadism, they did correlate with incident low T. The data reviewed on potential consequences suggest that low T levels may be linked to earlier all-cause and cardiovascular related mortality among men. This literature review suggests that men with certain factors, such as advanced age, obesity, MetS, and poor general health, are more likely to have and develop hypogonadism. Low levels of T may have important long-term negative health consequences. © 2014 American Society of Andrology and European Academy of Andrology.

Heterogeneity in the Relationship of Substance Use to Risky Sexual Behavior Among Justice-Involved Youth: A Regression Mixture Modeling Approach.

PubMed

Schmiege, Sarah J; Bryan, Angela D

2016-04-01

Justice-involved adolescents engage in high levels of risky sexual behavior and substance use, and understanding potential relationships among these constructs is important for effective HIV/STI prevention. A regression mixture modeling approach was used to determine whether subgroups could be identified based on the regression of two indicators of sexual risk (condom use and frequency of intercourse) on three measures of substance use (alcohol, marijuana and hard drugs). Three classes were observed among n = 596 adolescents on probation: none of the substances predicted outcomes for approximately 18 % of the sample; alcohol and marijuana use were predictive for approximately 59 % of the sample, and marijuana use and hard drug use were predictive in approximately 23 % of the sample. Demographic, individual difference, and additional sexual and substance use risk variables were examined in relation to class membership. Findings are discussed in terms of understanding profiles of risk behavior among at-risk youth.

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

The Priority Heuristic: Making Choices Without Trade-Offs

PubMed Central

Brandstätter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2010-01-01

Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, we generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (i) Allais' paradox, (ii) risk aversion for gains if probabilities are high, (iii) risk seeking for gains if probabilities are low (lottery tickets), (iv) risk aversion for losses if probabilities are low (buying insurance), (v) risk seeking for losses if probabilities are high, (vi) certainty effect, (vii) possibility effect, and (viii) intransitivities. We test how accurately the heuristic predicts people's choices, compared to previously proposed heuristics and three modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory. PMID:16637767

A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China.

PubMed

Yang, Guo-Jing; Vounatsou, Penelope; Zhou, Xiao-Nong; Utzinger, Jürg; Tanner, Marcel

2005-01-01

Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere.

Epidemiology of Plasmodium falciparum gametocytemia in India: prevalence, age structure, risk factors and the role of a predictive score for detection.

PubMed

Shah, Naman K; Poole, Charles; MacDonald, Pia D M; Srivastava, Bina; Schapira, Allan; Juliano, Jonathan J; Anvikar, Anup; Meshnick, Steven R; Valecha, Neena; Mishra, Neelima

2013-07-01

To characterise the epidemiology of Plasmodium falciparum gametocytemia and determine the prevalence, age structure and the viability of a predictive model for detection. We collected data from 21 therapeutic efficacy trials conducted in India during 2009-2010 and estimated the contribution of each age group to the reservoir of transmission. We built a predictive model for gametocytemia and calculated the diagnostic utility of different score cut-offs from our risk score. Gametocytemia was present in 18% (248/1 335) of patients and decreased with age. Adults constituted 43%, school-age children 45% and under fives 12% of the reservoir for potential transmission. Our model retained age, sex, region and previous antimalarial drug intake as predictors of gametocytemia. The area under the receiver operator characteristic curve was 0.76 (95%CI:0.73,0.78), and a cut-off of 14 or more on a risk score ranging from 0 to 46 provided 91% (95%CI:88,95) sensitivity and 33% (95%CI:31,36) specificity for detecting gametocytemia. Gametocytemia was common in India and varied by region. Notably, adults contributed substantially to the reservoir for potential transmission. Predictive modelling to generate a clinical algorithm for detecting gametocytemia did not provide sufficient discrimination for targeting interventions. © 2013 Blackwell Publishing Ltd.

A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients.

PubMed

Sun, Jie; Chen, Xihai; Wang, Zhenzhen; Guo, Maoni; Shi, Hongbo; Wang, Xiaojun; Cheng, Liang; Zhou, Meng

2015-11-09

Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.

New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort.

PubMed

Robinson, Tom; Elley, C Raina; Wells, Sue; Robinson, Elizabeth; Kenealy, Tim; Pylypchuk, Romana; Bramley, Dale; Arroll, Bruce; Crengle, Sue; Riddell, Tania; Ameratunga, Shanthi; Metcalf, Patricia; Drury, Paul L

2012-09-01

New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data. To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme. People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications. Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model. The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided.

Validation of a multifactorial risk factor model used for predicting future caries risk with Nevada adolescents.

PubMed

Ditmyer, Marcia M; Dounis, Georgia; Howard, Katherine M; Mobley, Connie; Cappelli, David

2011-05-20

The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

Examination of Substance Use, Risk Factors, and Protective Factors on Student Academic Test Score Performance.

PubMed

Arthur, Michael W; Brown, Eric C; Briney, John S; Hawkins, J David; Abbott, Robert D; Catalano, Richard F; Becker, Linda; Langer, Michael; Mueller, Martin T

2015-08-01

School administrators and teachers face difficult decisions about how best to use school resources to meet academic achievement goals. Many are hesitant to adopt prevention curricula that are not focused directly on academic achievement. Yet, some have hypothesized that prevention curricula can remove barriers to learning and, thus, promote achievement. We examined relationships among school levels of student substance use and risk and protective factors that predict adolescent problem behaviors and achievement test performance. Hierarchical generalized linear models were used to predict associations involving school-averaged levels of substance use and risk and protective factors and students' likelihood of meeting achievement test standards on the Washington Assessment of Student Learning, statistically controlling for demographic and economic factors known to be associated with achievement. Levels of substance use and risk/protective factors predicted the academic test score performance of students. Many of these effects remained significant even after controlling for model covariates. Implementing prevention programs that target empirically identified risk and protective factors has the potential to have a favorable effect on students' academic achievement. © 2015, American School Health Association.

A Brief Actuarial Assessment for the Prediction of Wife Assault Recidivism: The Ontario Domestic Assault Risk Assessment

ERIC Educational Resources Information Center

Hilton, N. Zoe; Harris, Grant T.; Rice, Marnie E.; Lang, Carol; Cormier, Catherine A.; Lines, Kathryn J.

2004-01-01

An actuarial assessment to predict male-to-female marital violence was constructed from a pool of potential predictors in a sample of 589 offenders identified in police records and followed up for an average of almost 5 years. Archival information in several domains (offender characteristics, domestic violence history, nondomestic criminal…

Neuroanatomy Predicts Individual Risk Attitudes

PubMed Central

Gilaie-Dotan, Sharon; Tymula, Agnieszka; Cooper, Nicole; Kable, Joseph W.; Glimcher, Paul W.

2014-01-01

Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers. PMID:25209279

Ethical principles and pitfalls of genetic testing for dementia.

PubMed

Hedera, P

2001-01-01

Progress in the genetics of dementing disorders and the availability of clinical tests for practicing physicians increase the need for a better understanding of multifaceted issues associated with genetic testing. The genetics of dementia is complex, and genetic testing is fraught with many ethical concerns. Genetic testing can be considered for patients with a family history suggestive of a single gene disorder as a cause of dementia. Testing of affected patients should be accompanied by competent genetic counseling that focuses on probabilistic implications for at-risk first-degree relatives. Predictive testing of at-risk asymptomatic patients should be modeled after presymptomatic testing for Huntington's disease. Testing using susceptibility genes has only a limited diagnostic value at present because potential improvement in diagnostic accuracy does not justify potentially negative consequences for first-degree relatives. Predictive testing of unaffected subjects using susceptibility genes is currently not recommended because individual risk cannot be quantified and there are no therapeutic interventions for dementia in presymptomatic patients.

Predictive genetic testing for neurodegenerative conditions: how should conflicting interests within families be managed?

PubMed

Stark, Zornitza; Wallace, Jane; Gillam, Lynn; Burgess, Matthew; Delatycki, Martin B

2016-10-01

Predictive genetic testing for a neurodegenerative condition in one individual in a family may have implications for other family members, in that it can reveal their genetic status. Herein a complex clinical case is explored where the testing wish of one family member was in direct conflict to that of another. The son of a person at 50% risk of an autosomal dominant neurodegenerative condition requested testing to reveal his genetic status. The main reason for the request was if he had the familial mutation, he and his partner planned to utilise preimplantation genetic diagnosis to prevent his offspring having the condition. His at-risk parent was clear that if they found out they had the mutation, they would commit suicide. We assess the potential benefits and harms from acceding to or denying such a request and present an approach to balancing competing rights of individuals within families at risk of late-onset genetic conditions, where family members have irreconcilable differences with respect to predictive testing. We argue that while it may not be possible to completely avoid harm in these situations, it is important to consider the magnitude of risks, and make every effort to limit the potential for adverse outcomes. 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/

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

PubMed

Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

2016-01-01

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?

PubMed

Padalia, Hitendra; Srivastava, Vivek; Kushwaha, S P S

2015-04-01

Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals

PubMed Central

Dunlop, Malcolm G.; Tenesa, Albert; Farrington, Susan M.; Ballereau, Stephane; Brewster, David H.; Pharoah, Paul DP.; Schafmayer, Clemens; Hampe, Jochen; Völzke, Henry; Chang-Claude, Jenny; Hoffmeister, Michael; Brenner, Hermann; von Holst, Susanna; Picelli, Simone; Lindblom, Annika; Jenkins, Mark A.; Hopper, John L.; Casey, Graham; Duggan, David; Newcomb, Polly; Abulí, Anna; Bessa, Xavier; Ruiz-Ponte, Clara; Castellví-Bel, Sergi; Niittymäki, Iina; Tuupanen, Sari; Karhu, Auli; Aaltonen, Lauri; Zanke, Brent W.; Hudson, Thomas J.; Gallinger, Steven; Barclay, Ella; Martin, Lynn; Gorman, Maggie; Carvajal-Carmona, Luis; Walther, Axel; Kerr, David; Lubbe, Steven; Broderick, Peter; Chandler, Ian; Pittman, Alan; Penegar, Steven; Campbell, Harry; Tomlinson, Ian; Houlston, Richard S.

2016-01-01

Objective Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. In a large, multi-population study, we set out to assess the feasibility of CRC risk prediction using common genetic variant data, combined with other risk factors. We built a risk prediction model and applied it to the Scottish population using available data. Design Nine populations of European descent were studied to develop and validate colorectal cancer risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence colorectal cancer risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266), and in combination with gender, age and family history (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4,187 independent samples. 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Results Median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). Mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05–1.13). Discriminative performance was poor across the risk spectrum (area under curve (AUC) for genotypes alone - 0.57; AUC for genotype/age/gender/FH - 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. Conclusion We show that genotype data provides additional information that complements age, gender and FH as risk factors. However, individualized genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential, since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance. PMID:22490517

A decision-analytic approach to predict state regulation of hydraulic fracturing.

PubMed

Linkov, Igor; Trump, Benjamin; Jin, David; Mazurczak, Marcin; Schreurs, Miranda

2014-01-01

The development of horizontal drilling and hydraulic fracturing methods has dramatically increased the potential for the extraction of previously unrecoverable natural gas. Nonetheless, the potential risks and hazards associated with such technologies are not without controversy and are compounded by frequently changing information and an uncertain landscape of international politics and laws. Where each nation has its own energy policies and laws, predicting how a state with natural gas reserves that require hydraulic fracturing will regulate the industry is of paramount importance for potential developers and extractors. We present a method for predicting hydraulic fracturing decisions using multiple-criteria decision analysis. The case study evaluates the decisions of five hypothetical countries with differing political, social, environmental, and economic priorities, choosing among four policy alternatives: open hydraulic fracturing, limited hydraulic fracturing, completely banned hydraulic fracturing, and a cap and trade program. The result is a model that identifies the preferred policy alternative for each archetypal country and demonstrates the sensitivity the decision to particular metrics. Armed with such information, observers can predict each country's likely decisions related to natural gas exploration as more data become available or political situations change. Decision analysis provides a method to manage uncertainty and address forecasting concerns where rich and objective data may be lacking. For the case of hydraulic fracturing, the various political pressures and extreme uncertainty regarding the technology's risks and benefits serve as a prime platform to demonstrate how decision analysis can be used to predict future behaviors.

Poverty, AIDS and child health: identifying highest-risk children in South Africa.

PubMed

Cluver, Lucie; Boyes, Mark; Orkin, Mark; Sherr, Lorraine

2013-10-11

Identifying children at the highest risk of negative health effects is a prerequisite to effective public health policies in Southern Africa. A central ongoing debate is whether poverty, orphanhood or parental AIDS most reliably indicates child health risks. Attempts to address this key question have been constrained by a lack of data allowing distinction of AIDS-specific parental death or morbidity from other causes of orphanhood and chronic illness. To examine whether household poverty, orphanhood and parental illness (by AIDS or other causes) independently or interactively predict child health, developmental and HIV-infection risks. We interviewed 6 002 children aged 10 - 17 years in 2009 - 2011, using stratified random sampling in six urban and rural sites across three South African provinces. Outcomes were child mental health risks, educational risks and HIV-infection risks. Regression models that controlled for socio-demographic co-factors tested potential impacts and interactions of poverty, AIDS-specific and other orphanhood and parental illness status. Household poverty independently predicted child mental health and educational risks, AIDS orphanhood independently predicted mental health risks and parental AIDS illness independently predicted mental health, educational and HIV-infection risks. Interaction effects of poverty with AIDS orphanhood and parental AIDS illness were found across all outcomes. No effects, or interactions with poverty, were shown by AIDS-unrelated orphanhood or parental illness. The identification of children at highest risk requires recognition and measurement of both poverty and parental AIDS. This study shows negative impacts of poverty and AIDS-specific vulnerabilities distinct from orphanhood and adult illness more generally. Additionally, effects of interaction between family AIDS and poverty suggest that, where these co-exist, children are at highest risk of all.

Predictive validity of the HCR-20 for inpatient aggression: the effect of intellectual disability on accuracy.

PubMed

O'Shea, L E; Picchioni, M M; McCarthy, J; Mason, F L; Dickens, G L

2015-11-01

People with intellectual disability (ID) account for a large proportion of aggressive incidents in secure and forensic psychiatric services. Although the Historical, Clinical, Risk Management 20 (HCR-20) has good predictive validity in inpatient settings, it does not perform equally in all groups and there is little evidence for its efficacy in those with ID. A pseudo-prospective cohort study of the predictive efficacy of the HCR-20 for those with ID (n = 109) was conducted in a UK secure mental health setting using routinely collected risk data. Performance of the HCR-20 in the ID group was compared with a comparison group of adult inpatients without an ID (n = 504). Analysis controlled for potential covariates including security level, length of stay, gender and diagnosis. The HCR-20 total score was a significant predictor of any aggression and of physical aggression for both groups, although the area under the curve values did not reach the threshold for a large effect size. The clinical subscale performed significantly better in those without an ID compared with those with. The ID group had a greater number of relevant historical and risk management items. The clinicians' summary judgment significantly predicted both types of aggressive outcomes in the ID group, but did not predict either in those without an ID. This study demonstrates that, after controlling for a range of potential covariates, the HCR-20 is a significant predictor of inpatient aggression in people with an ID and performs as well as for a comparison group of mentally disordered individuals without ID. The potency of HCR-20 subscales and items varied between the ID and comparison groups suggesting important target areas for improved prediction and risk management interventions in those with ID. © 2015 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Could the Recent Zika Epidemic Have Been Predicted?

NASA Astrophysics Data System (ADS)

Vecchi, G. A.; Munoz, A. G.; Thomson, M. C.; Stewart-Ibarra, A. M.; Chourio, X.; Nájera, P.; Moran, Z.; Yang, X.

2017-12-01

Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

Could the Recent Zika Epidemic Have Been Predicted?

PubMed

Muñoz, Ángel G; Thomson, Madeleine C; Stewart-Ibarra, Anna M; Vecchi, Gabriel A; Chourio, Xandre; Nájera, Patricia; Moran, Zelda; Yang, Xiaosong

2017-01-01

Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes -borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus . We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower-but still of potential use to decision-makers-for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

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.

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

Men's and Women's Childhood Sexual Abuse and Victimization in Adult Partner Relationships: A Study of Risk Factors

ERIC Educational Resources Information Center

Daigneault, Isabelle; Hebert, Martine; McDuff, Pierre

2009-01-01

Objectives: (1) Document the prevalence of childhood sexual abuse (CSA), childhood physical assault, psychological, physical and sexual intimate partner violence (IPV) in a nationally representative sample. (2) Assess the predictive value of CSA and other characteristics of the respondents and their current partners as potential risk factors for…

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals

Treesearch

Valery E. Forbes; Chris J. Salice; Bjorn Birnir; Randy J.F. Bruins; Peter Calow; Virginie Ducrot; Nika Galic; Kristina Garber; Bret C. Harvey; Henriette Jager; Andrew Kanarek; Robert Pastorok; Steve F. Railsback; Richard Rebarber; Pernille Thorbek

2017-01-01

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem...

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

PubMed

Heidari, Morteza; Khuzani, Abolfazl Zargari; Hollingsworth, Alan B; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-01-30

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-02-01

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Opioid Attentional Bias and Cue-Elicited Craving Predict Future Risk of Prescription Opioid Misuse Among Chronic Pain Patients*

PubMed Central

Garland, Eric L.; Howard, Matthew O.

2014-01-01

Background Some chronic pain patients receiving long-term opioid analgesic pharmacotherapy are at risk for misusing opioids. Like other addictive behaviors, risk of opioid misuse may be signaled by an attentional bias (AB) towards drug-related cues. The purpose of this study was to examine opioid AB as a potential predictor of opioid misuse among chronic pain patients following behavioral treatment. Methods Chronic pain patients taking long-term opioid analgesics (N = 47) completed a dot probe task designed to assess opioid AB, as well as self-report measures of opioid misuse and pain severity, and then participated in behavioral treatment. Regression analyses examined opioid AB and cue-elicited craving as predictors of opioid misuse at 3-months posttreatment follow-up. Results Patients who scored high on a measure of opioid misuse risk following treatment exhibited significantly greater opioid AB scores than patients at low risk for opioid misuse. Opioid AB for 200 ms cues and cue-elicited craving significantly predicted opioid misuse risk 20 weeks later, even after controlling for pre-treatment opioid dependence diagnosis, opioid misuse, and pain severity (Model R2 = .50). Conclusion Biased initial attentional orienting to prescription opioid cues and cue-elicited craving may reliably signal future opioid misuse risk following treatment. These measures may therefore provide potential prognostic indicators of treatment outcome. PMID:25282309

Avoiding Drought Risks and Social Conflict Under Climate Change

NASA Astrophysics Data System (ADS)

Towler, E.; Lazrus, H.; Paimazumder, D.

2014-12-01

Traditional drought research has mainly focused on physical drought risks and less on the cultural processes that also contribute to how drought risks are perceived and managed. However, as society becomes more vulnerable to drought and climate change threatens to increase water scarcity, it is clear that drought research would benefit from a more interdisciplinary approach. To assess avoided drought impacts from reduced climate change, drought risks need to be assessed in the context of both climate prediction as well as improved understanding of socio-cultural processes. To this end, this study explores a risk-based framework to combine physical drought likelihoods with perceived risks from stakeholder interviews. Results are presented from a case study on how stakeholders in south-central Oklahoma perceive drought risks given diverse cultural beliefs, water uses, and uncertainties in future drought prediction. Stakeholder interviews (n=38) were conducted in 2012 to understand drought risks to various uses of water, as well as to measure worldviews from the cultural theory of risk - a theory that explains why people perceive risks differently, potentially leading to conflict over management decisions. For physical drought risk, drought projections are derived from a large ensemble of future climates generated from two RCPs that represent higher and lower emissions trajectories (i.e., RCP8.5 and RCP4.5). These are used to develop a Combined Drought Risk Matrix (CDRM) that characterizes drought risks for different water uses as the products of both physical likelihood (from the climate ensemble) and risk perception (from the interviews). We use the CRDM to explore the avoided drought risks posed to various water uses, as well as to investigate the potential for reduction of conflict over water management.

Identification and testing of early indicators for N leaching from urine patches.

PubMed

Vogeler, Iris; Cichota, Rogerio; Snow, Val

2013-11-30

Nitrogen leaching from urine patches has been identified as a major source of nitrogen loss under intensive grazing dairy farming. Leaching is notoriously variable, influenced by management, soil type, year-to-year variation in climate and timing and rate of urine depositions. To identify early indicators for the risk of N leaching from urine patches for potential usage in a precision management system, we used the simulation model APSIM (Agricultural Production Systems SIMulator) to produce an extensive N leaching dataset for the Waikato region of New Zealand. In total, nearly forty thousand simulation runs with different combinations of soil type and urine deposition times, in 33 different years, were done. The risk forecasting indicators were chosen based on their practicality: being readily measured on farm (soil water content, temperature and pasture growth) or that could be centrally supplied to farms (such as actual and forecast weather data). The thresholds of the early indicators that are used to forecast a period for high risk of N leaching were determined via classification and regression tree analysis. The most informative factors were soil temperature, pasture dry matter production, and average soil water content in the top soil over the two weeks prior to the urine N application event. Rainfall and air temperature for the two weeks following urine deposition were also important to fine-tune the predictions. The identified early indicators were then tested for their potential to predict the risk of N leaching in two typical soils from the Waikato region in New Zealand. The accuracy of the predictions varied with the number of indicators, the soil type and the risk level, and the number of correct predictions ranged from about 45 to over 90%. Further expansion and fine-tuning of the indicators and the development of a practical N risk tool based on these indicators is needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Prospective Cohort Study of Absconsion Incidents in Forensic Psychiatric Settings: Can We Identify Those at High-Risk?

PubMed Central

Cullen, Alexis E.; Jewell, Amelia; Tully, John; Coghlan, Suzanne; Dean, Kimberlie; Fahy, Tom

2015-01-01

Background Incidents of absconsion in forensic psychiatric units can have potentially serious consequences, yet surprisingly little is known about the characteristics of patients who abscond from these settings. The few previous studies conducted to date have employed retrospective designs, and no attempt has been made to develop an empirically-derived risk assessment scale. In this prospective study, we aimed to identify predictors of absconsion over a two-year period and investigate the feasibility of developing a brief risk assessment scale. Methods The study examined a representative sample of 135 patients treated in forensic medium- and low-secure wards. At baseline, demographic, clinical, treatment-related, and offending/behavioural factors were ascertained from electronic medical records and the treating teams. Incidents of absconsion (i.e., failure to return from leave, incidents of escape, and absconding whilst on escorted leave) were assessed at a two-year follow-up. Logistic regression analyses were used to determine the strongest predictors of absconsion which were then weighted according to their ability to discriminate absconders and non-absconders. The predictive utility of a brief risk assessment scale based on these weighted items was evaluated using receiver operator characteristics (ROC). Results During the two-year follow-up period, 27 patients (20%) absconded, accounting for 56 separate incidents. In multivariate analyses, four factors relating to offending and behaviour emerged as the strongest predictors of absconsion: history of sexual offending, previous absconsion, recent inpatient verbal aggression, and recent inpatient substance use. The weighted risk scale derived from these factors had moderate-to-good predictive accuracy (ROC area under the curve: 0.80; sensitivity: 067; specificity: 0.71), a high negative predictive value (0.91), but a low positive predictive value (0.34). Conclusion Potentially-targetable recent behaviours, such as inpatient verbal aggression and substance use, are strong predictors of absconsion in forensic settings; the absence of these factors may enable clinical teams to identify unnecessarily restricted low-risk individuals. PMID:26401653

Predictions in the face of clinical reality: HistoCheck versus high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease.

PubMed

Askar, Medhat; Sobecks, Ronald; Morishima, Yasuo; Kawase, Takakazu; Nowacki, Amy; Makishima, Hideki; Maciejewski, Jaroslaw

2011-09-01

HLA polymorphism remains a major hurdle for hematopoietic stem cell transplantation (HSCT). In 2004, Elsner et al. proposed the HistoCheck Web-based tool to estimate the allogeneic potential between HLA-mismatched stem cell donor/recipient pairs expressed as a sequence similarity matching (SSM). SSM is based on the structure of HLA molecules and the functional similarity of amino acids. According to this algorithm, a high SSM score represents high dissimilarity between MHC molecules, resulting in a potentially more deleterious impact on stem cell transplant outcomes. We investigated the potential of SSM to predict high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease (aGVHD grades III and IV) published by Kawase et al., by comparing SSM in low- and high-risk combinations. SSM was calculated for allele mismatch combinations using the HistoCheck tool available on the Web (www.histocheck.org). We compared ranges and means of SSM among high-risk (15 combinations observed in 722 donor/recipient pairs) versus low-risk allele combinations (94 combinations in 3490 pairs). Simulation scenarios were created where the recipient's HLA allele was involved in multiple allele mismatch combinations with at least 1 high-risk and 1 low-risk mismatch combination. SSM values were then compared. The mean SSM for high- versus low-risk combinations were 2.39 and 2.90 at A, 1.06 and 2.53 at B, 16.60 and 14.99 at C, 4.02 and 3.81 at DRB1, and 7.47 and 6.94 at DPB1 loci, respectively. In simulation scenarios, no predictable SSM association with high- or low-risk combinations could be distinguished. No DQB1 combinations met the statistical criteria for our study. In conclusion, our analysis demonstrates that mean SSM scores were not significantly different, and SSM distributions were overlapping among high- and low-risk allele combinations within loci HLA-A, B, C, DRB1, and DPB1. This analysis does not support selecting donors for HSCT recipients based on low HistoCheck SSM scores. Copyright © 2011 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Space Weather Impacts to Conjunction Assessment: A NASA Robotic Orbital Safety Perspective

NASA Technical Reports Server (NTRS)

Ghrist, Richard; Ghrist, Richard; DeHart, Russel; Newman, Lauri

2013-01-01

National Aeronautics and Space Administration (NASA) recognizes the risk of on-orbit collisions from other satellites and debris objects and has instituted a process to identify and react to close approaches. The charter of the NASA Robotic Conjunction Assessment Risk Analysis (CARA) task is to protect NASA robotic (unmanned) assets from threats posed by other space objects. Monitoring for potential collisions requires formulating close-approach predictions a week or more in the future to determine analyze, and respond to orbital conjunction events of interest. These predictions require propagation of the latest state vector and covariance assuming a predicted atmospheric density and ballistic coefficient. Any differences between the predicted drag used for propagation and the actual drag experienced by the space objects can potentially affect the conjunction event. Therefore, the space environment itself, in particular how space weather impacts atmospheric drag, is an essential element to understand in order effectively to assess the risk of conjunction events. The focus of this research is to develop a better understanding of the impact of space weather on conjunction assessment activities: both accurately determining the current risk and assessing how that risk may change under dynamic space weather conditions. We are engaged in a data-- ]mining exercise to corroborate whether or not observed changes in a conjunction event's dynamics appear consistent with space weather changes and are interested in developing a framework to respond appropriately to uncertainty in predicted space weather. In particular, we use historical conjunction event data products to search for dynamical effects on satellite orbits from changing atmospheric drag. Increased drag is expected to lower the satellite specific energy and will result in the satellite's being 'later' than expected, which can affect satellite conjunctions in a number of ways depending on the two satellites' orbits and the geometry of the conjunction. These satellite time offsets can form the basis of a new technique under development to determine whether space weather perturbations, such as coronal mass ejections, are likely to increase, decrease, or have a neutral effect on the collision risk due to a particular close approach.

The Pediatric Risk of Mortality Score: Update 2015

PubMed Central

Pollack, Murray M.; Holubkov, Richard; Funai, Tomohiko; Dean, J. Michael; Berger, John T.; Wessel, David L.; Meert, Kathleen; Berg, Robert A.; Newth, Christopher J. L.; Harrison, Rick E.; Carcillo, Joseph; Dalton, Heidi; Shanley, Thomas; Jenkins, Tammara L.; Tamburro, Robert

2016-01-01

Objectives Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for “optimizing” care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain. Design Prospective cohort study from December 4, 2011, to April 7, 2013. Measurements and Main Results Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3–5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets. Conclusions The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance. PMID:26492059

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

PubMed

Fusar-Poli, P; Schultze-Lutter, F

2016-02-01

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Blackout Drinking Predicts Sexual Revictimization in a College Sample of Binge-Drinking Women

PubMed Central

Valenstein-Mah, Helen; Larimer, Mary; Zoellner, Lori; Kaysen, Debra

2016-01-01

Sexual victimization is prevalent on U.S. college campuses. Some women experience multiple sexual victimizations with heightened risk among those with prior victimization histories. One risk factor for sexual revictimization is alcohol use. Most research has focused on associations between alcohol consumption and revictimization. The current study’s objective was to understand potential mechanisms by which drinking confers risk for revictimization. We hypothesized that specific drinking consequences would predict risk for revictimization above and beyond the quantity of alcohol consumed. There were 162 binge-drinking female students (mean age = 20.21 years, 71.3% White, 36.9% juniors) from the University of Washington who were assessed for baseline victimization (categorized as childhood vs. adolescent victimization), quantity of alcohol consumed, and drinking consequences experienced, then assessed 30 days later for revictimization. There were 40 (24.6%) women who were revictimized in the following 30 days. Results showed that blackout drinking at baseline predicted incapacitated sexual revictimization among women previously victimized as adolescents, after accounting for quantity of alcohol consumed (OR = 1.79, 95% CI [1.07, 3.01]). Other drinking consequences were not strongly predictive of revictimization. Adolescent sexual victimization was an important predictor of sexual revictimization in college women; blackout drinking may confer unique risk for revictimization. PMID:26401899

Radiation Dose-Volume Effects in the Stomach and Small Bowel

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

Kavanagh, Brian D., E-mail: Brian.Kavanagh@ucdenver.ed; Pan, Charlie C.; Dawson, Laura A.

2010-03-01

Published data suggest that the risk of moderately severe (>=Grade 3) radiation-induced acute small-bowel toxicity can be predicted with a threshold model whereby for a given dose level, D, if the volume receiving that dose or greater (VD) exceeds a threshold quantity, the risk of toxicity escalates. Estimates of VD depend on the means of structure segmenting (e.g., V15 = 120 cc if individual bowel loops are outlined or V45 = 195 cc if entire peritoneal potential space of bowel is outlined). A similar predictive model of acute toxicity is not available for stomach. Late small-bowel/stomach toxicity is likely relatedmore » to maximum dose and/or volume threshold parameters qualitatively similar to those related to acute toxicity risk. Concurrent chemotherapy has been associated with a higher risk of acute toxicity, and a history of abdominal surgery has been associated with a higher risk of late toxicity.« less

Subjective Sleep Quality in Women With Divorce Histories: The Role of Intimate Partner Victimization.

PubMed

Newton, Tamara L; Burns, Vicki Ellison; Miller, James J; Fernandez-Botran, G Rafael

2016-05-01

A marital status of divorced or separated, as opposed to married, predicts increased risk of health problems, but not for all persons. Focusing on one established health risk that has been linked with divorce--poor subjective sleep quality--the present cross-sectional study examined whether a history of physical intimate partner victimization (IPV) helps identify divorced women at potentially greater risk of health problems. Community midlife women with divorce histories, all of whom were free of current IPV, reported on their past month sleep quality and lifetime IPV. The predicted odds of poor sleep quality were significantly greater for women with, versus without, IPV histories. This held after adjusting for socioemotional, medical, or sociodemographic risks. A dose-response relationship between IPV chronicity and poor quality sleep was observed. IPV history may help identify divorced women at increased risk of poor quality sleep and, more broadly, poor health. © The Author(s) 2015.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

Characterizing crown fuel distribution for conifers in the interior western United States

Treesearch

Seth Ex; Frederick W. Smith; Tara Keyser

2015-01-01

Canopy fire hazard evaluation is essential for prioritizing fuel treatments and for assessing potential risk to firefighters during suppression activities. Fire hazard is usually expressed as predicted potential fire behavior, which is sensitive to the methodology used to quantitatively describe fuel profiles: methodologies that assume that fuel is distributed...

Using additional information on working hours to predict coronary heart disease: a cohort study

PubMed Central

Kivimäki, Mika; Batty, G. David; Hamer, Mark; Ferrie, Jane E.; Vahtera, Jussi; Virtanen, Marianna; Marmot, Michael G.; Singh-Manoux, Archana; Shipley, Martin J.

2011-01-01

Background Long hours are associated with increased risk of coronary heart disease. Adding information on long hours to traditional risk factors could potentially help improve risk prediction. Objective To examine whether information on long working hours improves the ability of the Framingham risk model to predict coronary heart disease in a low-risk employed population. Design Prospective cohort study; baseline medical examination (1991-1993) and coronary heart disease follow-up to 2004. Settings Civil service departments in London (the Whitehall II study). Participants 7095 adults (2109 women) aged 39 to 62, working full time, and free of coronary heart disease at baseline. Measurements Working hours and the Framingham risk score were measured at baseline. Coronary death and non-fatal myocardial infarction were ascertained from three sources: medical screenings every 5 years, hospital data and register linkage. Results 192 persons had incident coronary heart disease during a median 12.3 year follow-up. After adjustment for the Framingham score, participants working ≥11 hours per day had a 1.67-fold (95% CI: 1.10-2.55) increased risk of coronary heart disease relative to those working 7-8 hours. The addition of working hours to the Framingham score led to a net reclassification improvement of 4.7% (p=0.034), resulting from a better identification of individuals who later developed coronary heart disease (sensitivity gain). Limitations The findings may not be generalizable to populations with a larger proportion of high-risk individuals. Furthermore, the predictive utility of working hours was not validated in an independent cohort. Conclusion Information on working hours may improve prediction of coronary heart disease risk based on the Framingham risk score in low-risk working populations. Primary Funding Source Medical Research Council, British Heart Foundation, BUPA Foundation, UK; National Heart, Lung and Blood Institute and National Institute on Aging, NIH, US. PMID:21464347

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

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.

Scoring Systems for Estimating the Risk of Anticoagulant-Associated Bleeding.

PubMed

Parks, Anna L; Fang, Margaret C

2017-07-01

Anticoagulant medications are frequently used to prevent and treat thromboembolic disease. However, the benefits of anticoagulants must be balanced with a careful assessment of the risk of bleeding complications that can ensue from their use. Several bleeding risk scores are available, including the Outpatient Bleeding Risk Index, HAS-BLED, ATRIA, and HEMORR 2 HAGES risk assessment tools, and can be used to help estimate patients' risk for bleeding on anticoagulants. These tools vary by their individual risk components and in how they define and weigh clinical factors. However, it is not yet clear how best to integrate bleeding risk tools into clinical practice. Current bleeding risk scores generally have modest predictive ability and limited ability to predict the most devastating complication of anticoagulation, intracranial hemorrhage. In clinical practice, bleeding risk tools should be paired with a formal determination of thrombosis risk, as their results may be most influential for patients at the lower end of thrombosis risk, as well as for highlighting potentially modifiable risk factors for bleeding. Use of bleeding risk scores may assist clinicians and patients in making informed and individualized anticoagulation decisions. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

[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.

Can the Seattle heart failure model be used to risk-stratify heart failure patients for potential left ventricular assist device therapy?

PubMed

Levy, Wayne C; Mozaffarian, Dariush; Linker, David T; Farrar, David J; Miller, Leslie W

2009-03-01

According to results of the REMATCH trial, left ventricular assist device therapy in patients with severe heart failure has resulted in a 48% reduction in mortality. A decision tool will be necessary to aid in the selection of patients for destination left ventricular assist devices (LVADs) as the technology progresses for implantation in ambulatory Stage D heart failure patients. The purpose of this analysis was to determine whether the Seattle Heart Failure Model (SHFM) can be used to risk-stratify heart failure patients for potential LVAD therapy. The SHFM was applied to REMATCH patients with the prospective addition of inotropic agents and intra-aortic balloon pump (IABP) +/- ventilator. The SHFM was highly predictive of survival (p = 0.0004). One-year SHFM-predicted survival was similar to actual survival for both the REMATCH medical (30% vs 28%) and LVAD (49% vs 52%) groups. The estimated 1-year survival with medical therapy for patients in REMATCH was 30 +/- 21%, but with a range of 0% to 74%. The 1- and 2-year estimated survival was

The Use of Simulation to Reduce the Domain of "Black Swans" with Application to Hurricane Impacts to Power Systems.

PubMed

Berner, Christine L; Staid, Andrea; Flage, Roger; Guikema, Seth D

2017-10-01

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts. © 2016 Society for Risk Analysis.

Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

NASA Astrophysics Data System (ADS)

Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

2017-12-01

The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

Climate-Induced Range Shifts and Possible Hybridisation Consequences in Insects

PubMed Central

Sánchez-Guillén, Rosa Ana; Muñoz, Jesús; Rodríguez-Tapia, Gerardo; Feria Arroyo, T. Patricia; Córdoba-Aguilar, Alex

2013-01-01

Many ectotherms have altered their geographic ranges in response to rising global temperatures. Current range shifts will likely increase the sympatry and hybridisation between recently diverged species. Here we predict future sympatric distributions and risk of hybridisation in seven Mediterranean ischnurid damselfly species (I. elegans, I. fountaineae, I. genei, I. graellsii, I. pumilio, I. saharensis and I. senegalensis). We used a maximum entropy modelling technique to predict future potential distribution under four different Global Circulation Models and a realistic emissions scenario of climate change. We carried out a comprehensive data compilation of reproductive isolation (habitat, temporal, sexual, mechanical and gametic) between the seven studied species. Combining the potential distribution and data of reproductive isolation at different instances (habitat, temporal, sexual, mechanical and gametic), we infer the risk of hybridisation in these insects. Our findings showed that all but I. graellsii will decrease in distributional extent and all species except I. senegalensis are predicted to have northern range shifts. Models of potential distribution predicted an increase of the likely overlapping ranges for 12 species combinations, out of a total of 42 combinations, 10 of which currently overlap. Moreover, the lack of complete reproductive isolation and the patterns of hybridisation detected between closely related ischnurids, could lead to local extinctions of native species if the hybrids or the introgressed colonising species become more successful. PMID:24260411

Spatial response surface modelling in the presence of data paucity for the evaluation of potential human health risk due to the contamination of potable water resources.

PubMed

Liu, Shen; McGree, James; Hayes, John F; Goonetilleke, Ashantha

2016-10-01

Potential human health risk from waterborne diseases arising from unsatisfactory performance of on-site wastewater treatment systems is driven by landscape factors such as topography, soil characteristics, depth to water table, drainage characteristics and the presence of surface water bodies. These factors are present as random variables which are spatially distributed across a region. A methodological framework is presented that can be applied to model and evaluate the influence of various factors on waterborne disease potential. This framework is informed by spatial data and expert knowledge. For prediction at unsampled sites, interpolation methods were used to derive a spatially smoothed surface of disease potential which takes into account the uncertainty due to spatial variation at any pre-determined level of significance. This surface was constructed by accounting for the influence of multiple variables which appear to contribute to disease potential. The framework developed in this work strengthens the understanding of the characteristics of disease potential and provides predictions of this potential across a region. The study outcomes presented constitutes an innovative approach to environmental monitoring and management in the face of data paucity. Copyright © 2016 Elsevier B.V. All rights reserved.

Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling

PubMed Central

Sinnott, Jennifer A.; Cai, Tianxi

2013-01-01

Summary Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai et al., 2011). In this paper, we derive testing and prediction methods for KM regression under the accelerated failure time model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. PMID:24328713

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.

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk.

PubMed

Jieyi Li; Arandjelovic, Ognjen

2017-07-01

Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories, or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

Concepts and challenges in cancer risk prediction for the space radiation environment

NASA Astrophysics Data System (ADS)

Barcellos-Hoff, Mary Helen; Blakely, Eleanor A.; Burma, Sandeep; Fornace, Albert J.; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G.; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M.

2015-07-01

Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program.

Site specific risk assessment of an energy-from-waste/thermal treatment facility in Durham Region, Ontario, Canada. Part B: Ecological risk assessment.

PubMed

Ollson, Christopher A; Whitfield Aslund, Melissa L; Knopper, Loren D; Dan, Tereza

2014-01-01

The regions of Durham and York in Ontario, Canada have partnered to construct an energy-from-waste (EFW) thermal treatment facility as part of a long term strategy for the management of their municipal solid waste. In this paper we present the results of a comprehensive ecological risk assessment (ERA) for this planned facility, based on baseline sampling and site specific modeling to predict facility-related emissions, which was subsequently accepted by regulatory authorities. Emissions were estimated for both the approved initial operating design capacity of the facility (140,000 tonnes per year) and the maximum design capacity (400,000 tonnes per year). In general, calculated ecological hazard quotients (EHQs) and screening ratios (SRs) for receptors did not exceed the benchmark value (1.0). The only exceedances noted were generally due to existing baseline media concentrations, which did not differ from those expected for similar unimpacted sites in Ontario. This suggests that these exceedances reflect conservative assumptions applied in the risk assessment rather than actual potential risk. However, under predicted upset conditions at 400,000 tonnes per year (i.e., facility start-up, shutdown, and loss of air pollution control), a potential unacceptable risk was estimated for freshwater receptors with respect to benzo(g,h,i)perylene (SR=1.1), which could not be attributed to baseline conditions. Although this slight exceedance reflects a conservative worst-case scenario (upset conditions coinciding with worst-case meteorological conditions), further investigation of potential ecological risk should be performed if this facility is expanded to the maximum operating capacity in the future. © 2013.

Climate-based species distribution models for Armillaria solidipes in Wyoming: A preliminary assessment

Treesearch

John W. Hanna; James T. Blodgett; Eric W. I. Pitman; Sarah M. Ashiglar; John E. Lundquist; Mee-Sook Kim; Amy L. Ross-Davis; Ned B. Klopfenstein

2014-01-01

As part of an ongoing project to predict Armillaria root disease in the Rocky Mountain zone, this project predicts suitable climate space (potential distribution) for A. solidipes in Wyoming and associated forest areas at risk to disease caused by this pathogen. Two bioclimatic models are being developed. One model is based solely on verified locations of A. solidipes...

Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk.

PubMed

Martin, Gerardo; Yanez-Arenas, Carlos; Chen, Carla; Plowright, Raina K; Webb, Rebecca J; Skerratt, Lee F

2018-03-19

Disease risk mapping is important for predicting and mitigating impacts of bat-borne viruses, including Hendra virus (Paramyxoviridae:Henipavirus), that can spillover to domestic animals and thence to humans. We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. conspicillatus. We included additional climatic variables that might affect spillover risk through other biological processes (such as bat or horse behaviour, plant phenology and bat foraging habitat). Models were fit with a Poisson point process model and a log-Gaussian Cox process. In response to climate change, risk expanded southwards due to an expansion of P. alecto suitable habitat, which increased the number of horses at risk by 175-260% (110,000-165,000). In the northern limits of the current distribution, spillover risk was highly uncertain because of model extrapolation to novel climatic conditions. The extent of areas at risk of spillover from P. conspicillatus was predicted shrink. Due to a likely expansion of P. alecto into these areas, it could replace P. conspicillatus as the main HeV reservoir. We recommend: (1) HeV monitoring in bats, (2) enhancing HeV prevention in horses in areas predicted to be at risk, (3) investigate and develop mitigation strategies for areas that could experience reservoir host replacements.

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.

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

PubMed

Dolan, Mairead; Blattner, Regine

2010-09-29

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

Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk

PubMed Central

Zhu, Weifei; Gregory, Jill C.; Org, Elin; Buffa, Jennifer A.; Gupta, Nilaksh; Wang, Zeneng; Li, Lin; Fu, Xiaoming; Wu, Yuping; Mehrabian, Margarete; Sartor, R. Balfour; McIntyre, Thomas M.; Silverstein, Roy L.; Tang, W.H. Wilson; DiDonato, Joseph A.; Brown, J. Mark; Lusis, Aldons J.; Hazen, Stanley L.

2016-01-01

SUMMARY Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (N>4000) independently predicted incident (3 yr) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced submaximal stimulus-dependent platelet activation from multiple agonists through augmented Ca2+ release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation, collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential, and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk. PMID:26972052

Predicting spatial distribution of postfire debris flows and potential consequences for native trout in headwater streams

USGS Publications Warehouse

Sedell, Edwin R; Gresswell, Bob; McMahon, Thomas E.

2015-01-01

Habitat fragmentation and degradation and invasion of nonnative species have restricted the distribution of native trout. Many trout populations are limited to headwater streams where negative effects of predicted climate change, including reduced stream flow and increased risk of catastrophic fires, may further jeopardize their persistence. Headwater streams in steep terrain are especially susceptible to disturbance associated with postfire debris flows, which have led to local extirpation of trout populations in some systems. We conducted a reach-scale spatial analysis of debris-flow risk among 11 high-elevation watersheds of the Colorado Rocky Mountains occupied by isolated populations of Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus). Stream reaches at high risk of disturbance by postfire debris flow were identified with the aid of a qualitative model based on 4 primary initiating and transport factors (hillslope gradient, flow accumulation pathways, channel gradient, and valley confinement). This model was coupled with a spatially continuous survey of trout distributions in these stream networks to assess the predicted extent of trout population disturbances related to debris flows. In the study systems, debris-flow potential was highest in the lower and middle reaches of most watersheds. Colorado River Cutthroat Trout occurred in areas of high postfire debris-flow risk, but they were never restricted to those areas. Postfire debris flows could extirpate trout from local reaches in these watersheds, but trout populations occupy refugia that should allow recolonization of interconnected, downstream reaches. Specific results of our study may not be universally applicable, but our risk assessment approach can be applied to assess postfire debris-flow risk for stream reaches in other watersheds.

The impact of sport related stressors on immunity and illness risk in team-sport athletes.

PubMed

Keaney, Lauren C; Kilding, Andrew E; Merien, Fabrice; Dulson, Deborah K

2018-06-19

Elite team-sport athletes are frequently exposed to stressors that have the potential to depress immunity and increase infection risk. Therefore, the purpose of this review is to describe how team-sport stressors impact upon immune responses, along with exploring whether alterations in these markers have the potential to predict upper respiratory tract illness symptoms. Narrative review. Salivary secretory immunoglobulin A (SIgA) and T-cell markers have been shown to predict infection risk in individual endurance athletes. Papers discussing the impact of team-sport stressors on SIgA and T-cells were discussed in the review, studies discussing other aspects of immunity were excluded. Journal articles were sourced from PubMed, Web of science and Scopus. Key search terms included team-sport athletes, stressors, immunity, T-cells, cytokines, SIgA and upper respiratory illness. Most team-sport stressors appear to increase risk for illness. An association between reduced SIgA and increased illness incidence has been demonstrated. Intensive training and competition periods have been shown to reduce SIgA, however, it is less clear how additional stressors including extreme environmental conditions, travel, psychological stress, sleep disturbance and poor nutrition affect immune responses. Monitoring SIgA may provide an assessment of a team-sport athletes risk status for developing upper respiratory tract symptoms, however there is currently not enough evidence to suggest SIgA alone can predict illness. Team-sport stressors challenge immunity and it is possible that the combination of stressors could have a compounding effect on immunodepression and infection risk. Given that illness can disrupt training and performance, further research is required to better elucidate how stressors individually and collectively influence immunity and illness. Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Water contamination risks associated with a combination of planned and unplanned fire in south eastern Australia

NASA Astrophysics Data System (ADS)

Sheridan, G. J.; Nyman, P.; Langhans, C.; Noske, P. J.; Lane, P. N. J.

2014-12-01

Planned burning reduces fuel loads in forests, potentially reducing the severity of subsequent wildfires. However planned burning also increases the risk of a significant water quality impact by maintaining a proportion of the catchment in a burnt condition conducive to generating high magnitude erosion events (eg. debris flows). Differences in the frequency and magnitude of planned and unplanned fire, combined with poorly understood relationships between fire severity and hydrologic impacts, means that predictions of the net water contamination risks associated with any particular fire regime are difficult to predict. This presentation synthesises results from 10 years of point, plot and catchment-scale post-fire hydrology and erosion studies in SE Australia to estimate the likely benifits and risks of planned burning scenarios from a drinking water supply perspective

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

PubMed

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-09-05

Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda

PubMed Central

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-01-01

Introduction: Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. Methods: The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus – environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. Results: We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Conclusions: Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area. PMID:29034123

Pre-Kidney Transplant Lower Extremity Impairment and Post-Kidney Transplant Mortality.

PubMed

Nastasi, A J; McAdams-DeMarco, M A; Schrack, J; Ying, H; Olorundare, I; Warsame, F; Mountford, A; Haugen, C E; González Fernández, M; Norman, S P; Segev, D L

2018-01-01

Prediction models for post-kidney transplantation mortality have had limited success (C-statistics ≤0.70). Adding objective measures of potentially modifiable factors may improve prediction and, consequently, kidney transplant (KT) survival through intervention. The Short Physical Performance Battery (SPPB) is an easily administered objective test of lower extremity function consisting of three parts (balance, walking speed, chair stands), each with scores of 0-4, for a composite score of 0-12, with higher scores indicating better function. SPPB performance and frailty (Fried frailty phenotype) were assessed at admission for KT in a prospective cohort of 719 KT recipients at Johns Hopkins Hospital (8/2009 to 6/2016) and University of Michigan (2/2013 to 12/2016). The independent associations between SPPB impairment (SPPB composite score ≤10) and composite score with post-KT mortality were tested using adjusted competing risks models treating graft failure as a competing risk. The 5-year posttransplantation mortality for impaired recipients was 20.6% compared to 4.5% for unimpaired recipients (p < 0.001). Impaired recipients had a 2.30-fold (adjusted hazard ratio [aHR] 2.30, 95% confidence interval [CI] 1.12-4.74, p = 0.02) increased risk of postkidney transplantation mortality compared to unimpaired recipients. Each one-point decrease in SPPB score was independently associated with a 1.19-fold (95% CI 1.09-1.30, p < 0.001) higher risk of post-KT mortality. SPPB-derived lower extremity function is a potentially highly useful and modifiable objective measure for pre-KT risk prediction. © 2017 The American Society of Transplantation and the American Society of Transplant Surgeons.

Derivation and validation of a discharge disposition predicting model after acute stroke.

PubMed

Tseng, Hung-Pin; Lin, Feng-Jenq; Chen, Pi-Tzu; Mou, Chih-Hsin; Lee, Siu-Pak; Chang, Chun-Yuan; Chen, An-Chih; Liu, Chung-Hsiang; Yeh, Chung-Hsin; Tsai, Song-Yen; Hsiao, Yu-Jen; Lin, Ching-Huang; Hsu, Shih-Pin; Yu, Shih-Chieh; Hsu, Chung-Y; Sung, Fung-Chang

2015-06-01

Discharge disposition planning is vital for poststroke patients. We investigated clinical factors associated with discharging patients to nursing homes, using the Taiwan Stroke Registry data collected from 39 major hospitals. We randomly assigned 21,575 stroke inpatients registered from 2006 to 2008 into derivation and validation groups at a 3-to-1 ratio. We used the derivation group to develop a prediction model by measuring cumulative risk scores associated with potential predictors: age, sex, hypertension, diabetes mellitus, heart diseases, stroke history, snoring, main caregivers, stroke types, and National Institutes of Health Stroke Scale (NIHSS). Probability of nursing home care and odds ratio (OR) of nursing home care relative to home care by cumulative risk scores were measured for the prediction. The area under the receiver operating characteristic curve (AUROC) was used to assess the model discrimination against the validation group. Except for hypertension, all remaining potential predictors were significant independent predictors associated with stroke patient disposition to nursing home care after discharge from hospitals. The risk sharply increased with age and NIHSS. Patients with a cumulative risk score of 15 or more had an OR of 86.4 for the nursing home disposition. The AUROC plots showed similar areas under curves for the derivation group (.86, 95% confidence interval [CI], .85-.87) and for the validation group (.84, 95% CI, .83-.86). The cumulative risk score is an easy-to-estimate tool for preparing stroke patients and their family for disposition on discharge. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Psychosocial predictors of coronary artery calcification progression in postmenopausal women.

PubMed

Low, Carissa A; Matthews, Karen A; Kuller, Lewis H; Edmundowicz, Daniel

2011-01-01

Coronary artery calcification (CAC) has been associated with psychosocial factors in some but not all cross-sectional analyses. The goal of this study was to determine whether positive and negative psychosocial factors prospectively predict CAC progression in postmenopausal women. Participants from the Healthy Women Study who also participated in the Pittsburgh Mind-Body Center protocol (n = 149) completed self-report psychosocial measures before two electron beam computed tomographic scans of CAC separated by an average of 3.3 years. Results of exploratory factor analysis were used to create aggregate psychosocial indices: psychological risk (depressive symptoms, perceived stress, cynicism, and anger-in) and psychosocial resources (optimism, purpose in life, mastery, self-esteem, and social support). The psychological risk index predicted significantly greater CAC progression over 3 years (β = 0.16, p = .035, ΔR(2) = 0.03), whereas the psychosocial resources index was not predictive of CAC progression (β = -0.08, p = .30, ΔR(2) = 0.01). On individual scales, higher scores on cynicism emerged as a significant predictor of CAC progression, along with a trend linking anger-in to atherosclerosis progression. A post hoc analysis showed a significant interaction between cynicism and anger-in (β = 0.20, p = .01, ΔR(2) = 0.03), such that women reporting high levels of both cynicism and anger suppression exhibited the most CAC progression. These findings highlight psychosocial risk factors that may accelerate the progression of subclinical atherosclerosis in older women, suggest the potential importance of examining combinations of psychosocial risk factors, and identify potential targets for psychological interventions to reduce cardiovascular risk.

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.

Mapping human health risks from exposure to trace metal contamination of drinking water sources in Pakistan.

PubMed

Bhowmik, Avit Kumar; Alamdar, Ambreen; Katsoyiannis, Ioannis; Shen, Heqing; Ali, Nadeem; Ali, Syeda Maria; Bokhari, Habib; Schäfer, Ralf B; Eqani, Syed Ali Musstjab Akber Shah

2015-12-15

The consumption of contaminated drinking water is one of the major causes of mortality and many severe diseases in developing countries. The principal drinking water sources in Pakistan, i.e. ground and surface water, are subject to geogenic and anthropogenic trace metal contamination. However, water quality monitoring activities have been limited to a few administrative areas and a nationwide human health risk assessment from trace metal exposure is lacking. Using geographically weighted regression (GWR) and eight relevant spatial predictors, we calculated nationwide human health risk maps by predicting the concentration of 10 trace metals in the drinking water sources of Pakistan and comparing them to guideline values. GWR incorporated local variations of trace metal concentrations into prediction models and hence mitigated effects of large distances between sampled districts due to data scarcity. Predicted concentrations mostly exhibited high accuracy and low uncertainty, and were in good agreement with observed concentrations. Concentrations for Central Pakistan were predicted with higher accuracy than for the North and South. A maximum 150-200 fold exceedance of guideline values was observed for predicted cadmium concentrations in ground water and arsenic concentrations in surface water. In more than 53% (4 and 100% for the lower and upper boundaries of 95% confidence interval (CI)) of the total area of Pakistan, the drinking water was predicted to be at risk of contamination from arsenic, chromium, iron, nickel and lead. The area with elevated risks is inhabited by more than 74 million (8 and 172 million for the lower and upper boundaries of 95% CI) people. Although these predictions require further validation by field monitoring, the results can inform disease mitigation and water resources management regarding potential hot spots. Copyright © 2015 Elsevier B.V. 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.

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

Development and validation of a clinical risk score for predicting drug-resistant bacterial pneumonia in older Chinese patients.

PubMed

Ma, Hon Ming; Ip, Margaret; Woo, Jean; Hui, David S C

2014-05-01

Health care-associated pneumonia (HCAP) and drug-resistant bacterial pneumonia may not share identical risk factors. We have shown that bronchiectasis, recent hospitalization and severe pneumonia (confusion, blood urea level, respiratory rate, low blood pressure and 65 year old (CURB-65) score ≥ 3) were independent predictors of pneumonia caused by potentially drug-resistant (PDR) pathogens. This study aimed to develop and validate a clinical risk score for predicting drug-resistant bacterial pneumonia in older patients. We derived a risk score by assigning a weighting to each of these risk factors as follows: 14, bronchiectasis; 5, recent hospitalization; 2, severe pneumonia. A 0.5 point was defined for the presence of other risk factors for HCAP. We compared the areas under the receiver-operating characteristics curve (AUROC) of our risk score and the HCAP definition in predicting PDR pathogens in two cohorts of older patients hospitalized with non-nosocomial pneumonia. The derivation and validation cohorts consisted of 354 and 96 patients with bacterial pneumonia, respectively. PDR pathogens were isolated in 48 and 21 patients in the derivation and validation cohorts, respectively. The AUROCs of our risk score and the HCAP definition were 0.751 and 0.650, respectively, in the derivation cohort, and were 0.782 and 0.671, respectively, in the validation cohort. The differences between our risk score and the HCAP definition reached statistical significance. A score ≥ 2.5 had the best balance between sensitivity and specificity. Our risk score outperformed the HCAP definition to predict pneumonia caused by PDR pathogens. A history of bronchiectasis or recent hospitalization is the major indication of starting empirical broad-spectrum antibiotics. © 2014 Asian Pacific Society of Respirology.

Real time forest fire warning and forest fire risk zoning: a Vietnamese case study

NASA Astrophysics Data System (ADS)

Chu, T.; Pham, D.; Phung, T.; Ha, A.; Paschke, M.

2016-12-01

Forest fire occurs seriously in Vietnam and has been considered as one of the major causes of forest lost and degradation. Several studies of forest fire risk warning were conducted using Modified Nesterov Index (MNI) but remaining shortcomings and inaccurate predictions that needs to be urgently improved. In our study, several important topographic and social factors such as aspect, slope, elevation, distance to residential areas and road system were considered as "permanent" factors while meteorological data were updated hourly using near-real-time (NRT) remotely sensed data (i.e. MODIS Terra/Aqua and TRMM) for the prediction and warning of fire. Due to the limited number of weather stations in Vietnam, data from all active stations (i.e. 178) were used with the satellite data to calibrate and upscale meteorological variables. These data with finer resolution were then used to generate MNI. The only significant "permanent" factors were selected as input variables based on the correlation coefficients that computed from multi-variable regression among true fire-burning (collected from 1/2007) and its spatial characteristics. These coefficients also used to suggest appropriate weight for computing forest fire risk (FR) model. Forest fire risk model was calculated from the MNI and the selected factors using fuzzy regression models (FRMs) and GIS based multi-criteria analysis. By this approach, the FR was slightly modified from MNI by the integrated use of various factors in our fire warning and prediction model. Multifactor-based maps of forest fire risk zone were generated from classifying FR into three potential danger levels. Fire risk maps were displayed using webgis technology that is easy for managing data and extracting reports. Reported fire-burnings thereafter have been used as true values for validating the forest fire risk. Fire probability has strong relationship with potential danger levels (varied from 5.3% to 53.8%) indicating that the higher potential risk, the more chance of fire happen. By adding spatial factors to continuous daily updated remote sensing based meteo-data, results are valuable for both mapping forest fire risk zones in short and long-term and real time fire warning in Vietnam. Key words: Near-real-time, forest fire warning, fuzzy regression model, remote sensing.

Tryptophan Predicts the Risk for Future Type 2 Diabetes

PubMed Central

Chen, Tianlu; Zheng, Xiaojiao; Ma, Xiaojing; Bao, Yuqian; Ni, Yan; Hu, Cheng; Rajani, Cynthia; Huang, Fengjie; Zhao, Aihua; Jia, Weiping; Jia, Wei

2016-01-01

Recently, 5 amino acids were identified and verified as important metabolites highly associated with type 2 diabetes (T2D) development. This report aims to assess the association of tryptophan with the development of T2D and to evaluate its performance with existing amino acid markers. A total of 213 participants selected from a ten-year longitudinal Shanghai Diabetes Study (SHDS) were examined in two ways: 1) 51 subjects who developed diabetes and 162 individuals who remained metabolically healthy in 10 years; 2) the same 51 future diabetes and 23 strictly matched ones selected from the 162 healthy individuals. Baseline fasting serum tryptophan concentrations were quantitatively measured using ultra-performance liquid chromatography triple quadruple mass spectrometry. First, serum tryptophan level was found significantly higher in future T2D and was positively and independently associated with diabetes onset risk. Patients with higher tryptophan level tended to present higher degree of insulin resistance and secretion, triglyceride and blood pressure. Second, the prediction potential of tryptophan is non-inferior to the 5 existing amino acids. The predictive performance of the combined score improved after taking tryptophan into account. Our findings unveiled the potential of tryptophan as a new marker associated with diabetes risk in Chinese populations. The addition of tryptophan provided complementary value to the existing amino acid predictors. PMID:27598004

Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

PubMed

Mangoni, Arduino A

2012-05-01

Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

The Short- to Medium-Term Predictive Accuracy of Static and Dynamic Risk Assessment Measures in a Secure Forensic Hospital

ERIC Educational Resources Information Center

Chu, Chi Meng; Thomas, Stuart D. M.; Ogloff, James R. P.; Daffern, Michael

2013-01-01

Although violence risk assessment knowledge and practice has advanced over the past few decades, it remains practically difficult to decide which measures clinicians should use to assess and make decisions about the violence potential of individuals on an ongoing basis, particularly in the short to medium term. Within this context, this study…

The Relations among Narcissism, Self-Esteem, and Delinquency in a Sample of At-Risk Adolescents

ERIC Educational Resources Information Center

Barry, Christopher T.; Grafeman, Sarah J.; Adler, Kristy K.; Pickard, Jessica D.

2007-01-01

The present study explores the relation between narcissism and delinquency among 372 at-risk 16-18-year-olds. The study also considered the relation between narcissism and self-esteem, as well as the potential interaction between narcissism and self-esteem for predicting delinquency in this age group. Narcissism and self-esteem were positively…

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

PubMed

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

2016-06-01

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

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.

Recognition of Atypical Symptoms of Acute Myocardial Infarction: Development and Validation of a Risk Scoring System.

PubMed

Li, Polly W C; Yu, Doris S F

Atypical symptom presentation in patients with acute myocardial infarction (AMI) is associated with longer delay in care seeking and poorer prognosis. Symptom recognition in these patients is a challenging task. Our purpose in this risk prediction model development study was to develop and validate a risk scoring system for estimating cumulative risk for atypical AMI presentation. A consecutive sample was recruited for the developmental (n = 300) and validation (n = 97) cohorts. Symptom experience was measured with the validated Chinese version of the Symptoms of Acute Coronary Syndromes Inventory. Potential predictors were identified from the literature. Multivariable logistic regression was performed to identify significant predictors. A risk scoring system was then constructed by assigning weights to each significant predictor according to their b coefficients. Five independent predictors for atypical symptom presentation were older age (≥75 years), female gender, diabetes mellitus, history of AMI, and absence of hyperlipidemia. The Hosmer and Lemeshow test (χ6 = 4.47, P = .62) indicated that this predictive model was adequate to predict the outcome. Acceptable discrimination was demonstrated, with area under the receiver operating characteristic curve as 0.74 (95% confidence interval, 0.67-0.82) (P < .001). The predictive power of this risk scoring system was confirmed in the validation cohort. Atypical AMI presentation is common. A simple risk scoring system developed on the basis of the 5 identified predictors can raise awareness of atypical AMI presentation and promote symptom recognition by estimating the cumulative risk for an individual to present with atypical AMI symptoms.

Familial Risk Moderates the Association Between Sleep and zBMI in Children

PubMed Central

El-Sheikh, Mona

2013-01-01

Objective A cumulative risk approach was used to examine the moderating effect of familial risk factors on relations between actigraphy-based sleep quantity (minutes) and quality (efficiency) and sex- and age-standardized body mass index (zBMI). Methods The sample included 124 boys and 104 girls with a mean age of 10.41 years (SD = 0.67). Children wore actigraphs for 1 week, and their height and weight were assessed in the lab. Results After controlling for potential confounds, multiple regression analyses indicated that sleep minutes predicted children’s zBMI and that both sleep minutes and efficiency interacted with family risk in the prediction of zBMI. The association between poor sleep and zBMI was especially evident for children exposed to higher levels of family risk. Conclusions Findings suggest that not all children who exhibit poor sleep are at equal risk for higher zBMI and that familial and contextual conditions need to be considered in this link. PMID:23699749

Ventral striatum activation to prosocial rewards predicts longitudinal declines in adolescent risk taking.

PubMed

Telzer, Eva H; Fuligni, Andrew J; Lieberman, Matthew D; Galván, Adriana

2013-01-01

Adolescence is a period of intensified emotions and an increase in motivated behaviors and passions. Evidence from developmental neuroscience suggests that this heightened emotionality occurs, in part, due to a peak in functional reactivity to rewarding stimuli, which renders adolescents more oriented toward reward-seeking behaviors. Most prior work has focused on how reward sensitivity may create vulnerabilities, leading to increases in risk taking. Here, we test whether heightened reward sensitivity may potentially be an asset for adolescents when engaged in prosocial activities. Thirty-two adolescents were followed over a one-year period to examine whether ventral striatum activation to prosocial rewards predicts decreases in risk taking over a year. Results show that heightened ventral striatum activation to prosocial stimuli relates to longitudinal declines in risk taking. Therefore, the very same neural region that has conferred vulnerability for adolescent risk taking may also be protective against risk taking. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predictive Models and Computational Toxicology

EPA Science Inventory

Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The ToxCast computational toxicology research program was l...

Which dogs with appendicular osteosarcoma benefit most from chemotherapy after surgery? Results from an individual patient data meta-analysis.

PubMed

Schmidt, A F; Groenwold, R H H; Amsellem, P; Bacon, N; Klungel, O H; Hoes, A W; de Boer, A; Kow, K; Maritato, K; Kirpensteijn, J; Nielen, M

2016-03-01

Osteosarcoma (OS) is a malignant tumor of mesenchymal origin that produces osteoid. Given that the prognosis can vary considerably between dogs, we aimed to explore whether treatment could be tailored towards patient subgroups, characterized by their predicted risk of mortality. For the current study, a subset of five nonrandomized studies (400 subjects of whom 88 were dead at 5 months follow-up) was used from a previously published 20 study individual patient data meta-analysis. Missing data was dependent on observed variables and was imputed to correct for this dependency. Based on a previously published multivariable prognostic model, the 5-month mortality risk was predicted. Subsequently, in surgically treated dogs, using a logistic regression model with a random intercept for a study indicator, we explored whether chemotherapy effectiveness depended on predicted 5-month mortality risk. After adjustment for potential confounders the main effect of any chemotherapy was 0.48 (odds ratio) (95%CI 0.30; 0.78). Testing for chemotherapy by predicted 5-month mortality risk interaction revealed that the effects of any chemotherapy decreased with increasing predicted risk; interaction OR 3.41 (1.07; 10.84). Results from individually comparing carboplatin, cisplatin, doxorubicin and doxorubicin combination therapy to no chemotherapy, were similar in magnitude and direction. These results indicate that the main treatment effects of chemotherapy do not necessarily apply to all patients. Copyright © 2015 Elsevier B.V. All rights reserved.

Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS.

PubMed

Lu, Yonghai; Wang, Yeli; Ong, Choon-Nam; Subramaniam, Tavintharan; Choi, Hyung Won; Yuan, Jian-Min; Koh, Woon-Puay; Pan, An

2016-11-01

Metabolomics has provided new insight into diabetes risk assessment. In this study we characterised the human serum metabolic profiles of participants in the Singapore Chinese Health Study cohort to identify metabolic signatures associated with an increased risk of type 2 diabetes. In this nested case-control study, baseline serum metabolite profiles were measured using LC-MS and GC-MS during a 6-year follow-up of 197 individuals with type 2 diabetes but without a history of cardiovascular disease or cancer before diabetes diagnosis, and 197 healthy controls matched by age, sex and date of blood collection. A total of 51 differential metabolites were identified between cases and controls. Of these, 35 were significantly associated with diabetes risk in the multivariate analysis after false discovery rate adjustment, such as increased branched-chain amino acids (leucine, isoleucine and valine), non-esterified fatty acids (palmitic acid, stearic acid, oleic acid and linoleic acid) and lysophosphatidylinositol (LPI) species (16:1, 18:1, 18:2, 20:3, 20:4 and 22:6). A combination of six metabolites including proline, glycerol, aminomalonic acid, LPI (16:1), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid and urea showed the potential to predict type 2 diabetes in at-risk individuals with high baseline HbA1c levels (≥6.5% [47.5 mmol/mol]) with an AUC of 0.935. Combined lysophosphatidylglycerol (LPG) (12:0) and LPI (16:1) also showed the potential to predict type 2 diabetes in individuals with normal baseline HbA1c levels (<6.5% [47.5 mmol/mol]; AUC = 0.781). Our findings show that branched-chain amino acids and NEFA are potent predictors of diabetes development in Chinese adults. Our results also indicate the potential of lysophospholipids for predicting diabetes.

DYT1 dystonia increases risk taking in humans.

PubMed

Arkadir, David; Radulescu, Angela; Raymond, Deborah; Lubarr, Naomi; Bressman, Susan B; Mazzoni, Pietro; Niv, Yael

2016-06-01

It has been difficult to link synaptic modification to overt behavioral changes. Rodent models of DYT1 dystonia, a motor disorder caused by a single gene mutation, demonstrate increased long-term potentiation and decreased long-term depression in corticostriatal synapses. Computationally, such asymmetric learning predicts risk taking in probabilistic tasks. Here we demonstrate abnormal risk taking in DYT1 dystonia patients, which is correlated with disease severity, thereby supporting striatal plasticity in shaping choice behavior in humans.

Multi-dimensional perspectives of flood risk - using a participatory framework to develop new approaches to flood risk communication

NASA Astrophysics Data System (ADS)

Rollason, Edward; Bracken, Louise; Hardy, Richard; Large, Andy

2017-04-01

Flooding is a major hazard across Europe which, since, 1998 has caused over €52 million in damages and displaced over half a million people. Climate change is predicted to increase the risks posed by flooding in the future. The 2007 EU Flood Directive cemented the use of flood risk maps as a central tool in understanding and communicating flood risk. Following recent flooding in England, an urgent need to integrate people living at risk from flooding into flood management approaches, encouraging flood resilience and the up-take of resilient activities has been acknowledged. The effective communication of flood risk information plays a major role in allowing those at risk to make effective decisions about flood risk and increase their resilience, however, there are emerging concerns over the effectiveness of current approaches. The research presented explores current approaches to flood risk communication in England and the effectiveness of these methods in encouraging resilient actions before and during flooding events. The research also investigates how flood risk communications could be undertaken more effectively, using a novel participatory framework to integrate the perspectives of those living at risk. The research uses co-production between local communities and researchers in the environmental sciences, using a participatory framework to bring together local knowledge of flood risk and flood communications. Using a local competency group, the research explores what those living at risk from flooding want from flood communications in order to develop new approaches to help those at risk understand and respond to floods. Suggestions for practice are refined by the communities to co-produce recommendations. The research finds that current approaches to real-time flood risk communication fail to forecast the significance of predicted floods, whilst flood maps lack detailed information about how floods occur, or use scientific terminology which people at risk find confusing or lacking in realistic grounding. This means users do not have information they find useful to make informed decisions about how to prepare for and respond to floods. Working together with at-risk participants, the research has developed new approaches for communicating flood risk. These approaches focus on understanding flood mechanisms and dynamics, to help participants imagine their flood risk and link potential scenarios to reality, and provide forecasts of predicted flooding at a variety of scales, allowing participants to assess the significance of predicted flooding and make more informed judgments on what action to take in response. The findings presented have significant implications for the way in which flood risk is communicated, changing the focus of mapping from probabilistic future scenarios to understanding flood dynamics and mechanisms. Such ways of communicating flood risk embrace how people would like to see risk communicated, and help those at risk grow their resilience. Communicating in such a way has wider implications for flood modelling and data collection. However, these represent potential opportunities to build more effective local partnerships for assessing and managing flood risks.

Predicting evolutionary rescue via evolving plasticity in stochastic environments

PubMed Central

Baskett, Marissa L.

2016-01-01

Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables the evolution of increased plasticity. However, the environmental stochasticity ubiquitous in natural systems may alter these predictions, because high plasticity may amplify phenotype–environment mismatches. Although previous studies have highlighted this potential detrimental effect of plasticity in stochastic environments, they have not investigated how it affects extinction risk in the context of evolutionary rescue and with evolving plasticity. We investigate this question here by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. We develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. We find that reduced predictability increases extinction risk and reduces persistence because it increases stochastic load during rescue. This understanding of how stochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests. PMID:27655762

Decreased Plasma Histidine Level Predicts Risk of Relapse in Patients with Ulcerative Colitis in Remission

PubMed Central

Hisamatsu, Tadakazu; Ono, Nobukazu; Imaizumi, Akira; Mori, Maiko; Suzuki, Hiroaki; Uo, Michihide; Hashimoto, Masaki; Naganuma, Makoto; Matsuoka, Katsuyoshi; Mizuno, Shinta; Kitazume, Mina T.; Yajima, Tomoharu; Ogata, Haruhiko; Iwao, Yasushi; Hibi, Toshifumi; Kanai, Takanori

2015-01-01

Ulcerative colitis (UC) is characterized by chronic intestinal inflammation. Patients with UC have repeated remission and relapse. Clinical biomarkers that can predict relapse in UC patients in remission have not been identified. To facilitate the prediction of relapse of UC, we investigated the potential of novel multivariate indexes using statistical modeling of plasma free amino acid (PFAA) concentrations. We measured fasting PFAA concentrations in 369 UC patients in clinical remission, and 355 were observed prospectively for up to 1 year. Relapse rate within 1 year was 23% (82 of 355 patients). The age- and gender-adjusted hazard ratio for the lowest quartile compared with the highest quartile of plasma histidine concentration was 2.55 (95% confidence interval: 1.41–4.62; p = 0.0020 (log-rank), p for trend = 0.0005). We demonstrated that plasma amino acid profiles in UC patients in clinical remission can predict the risk of relapse within 1 year. Decreased histidine level in PFAAs was associated with increased risk of relapse. Metabolomics could be promising for the establishment of a non-invasive predictive marker in inflammatory bowel disease. PMID:26474176

Plant distributions in the southwestern United States; a scenario assessment of the modern-day and future distribution ranges of 166 Species

USGS Publications Warehouse

Thomas, Kathryn A.; Guertin, Patricia P.; Gass, Leila

2012-01-01

The authors developed spatial models of the predicted modern-day suitable habitat (SH) of 166 dominant and indicator plant species of the southwestern United States (herein referred to as the Southwest) and then conducted a coarse assessment of potential future changes in the distribution of their suitable habitat under three climate-change scenarios for two time periods. We used Maxent-based spatial modeling to predict the modern-day and future scenarios of SH for each species in an over 342-million-acre area encompassing all or parts of six states in the Southwest--Arizona, California, Colorado, Nevada, New Mexico, and Utah. Modern-day SH models were predicted by our using 26 annual and monthly average temperature and precipitation variables, averaged for the years 1971-2000. Future SH models were predicted for each species by our using six climate models based on application of the average of 16 General Circulation Models to Intergovernmental Panel on Climate Change emission scenarios B1, A1B, and A2 for two time periods, 2040 to 2069 and 2070 and 2100, referred to respectively as the 2050 and 2100 time periods. The assessment examined each species' vulnerability to loss of modern-day SH under future climate scenarios, potential to gain SH under future climate scenarios, and each species' estimated risk as a function of both vulnerability and potential gains. All 166 species were predicted to lose modern-day SH in the future climate change scenarios. In the 2050 time period, nearly 30 percent of the species lost 75 percent or more of their modern-day suitable habitat, 21 species gained more new SH than their modern-day SH, and 30 species gained less new SH than 25 percent of their modern-day SH. In the 2100 time period, nearly half of the species lost 75 percent or more of their modern-day SH, 28 species gained more new SH than their modern-day SH, and 34 gained less new SH than 25 percent of their modern-day SH. Using nine risk categories we found only two species were in the least risk category, while 20 species were in the highest risk category. The assessment showed that species respond independently to predicted climate change, suggesting that current plant assemblages may disassemble under predicted climate change scenarios. This report presents the results for each species in tables (Appendix A) and maps (14 for each species) in Appendix B.

Uncertainties in radiation effect predictions for the natural radiation environments of space.

PubMed

McNulty, P J; Stassinopoulos, E G

1994-10-01

Future manned missions beyond low earth orbit require accurate predictions of the risk to astronauts and to critical systems from exposure to ionizing radiation. For low-level exposures, the hazards are dominated by rare single-event phenomena where individual cosmic-ray particles or spallation reactions result in potentially catastrophic changes in critical components. Examples might be a biological lesion leading to cancer in an astronaut or a memory upset leading to an undesired rocket firing. The risks of such events appears to depend on the amount of energy deposited within critical sensitive volumes of biological cells and microelectronic components. The critical environmental information needed to estimate the risks posed by the natural space environments, including solar flares, is the number of times more than a threshold amount of energy for an event will be deposited in the critical microvolumes. These predictions are complicated by uncertainties in the natural environments, particularly the composition of flares, and by the effects of shielding. Microdosimetric data for large numbers of orbits are needed to improve the environmental models and to test the transport codes used to predict event rates.

Uncertainties in radiation effect predictions for the natural radiation environments of space

NASA Technical Reports Server (NTRS)

Mcnulty, P. J.; Stassinopoulos, E. G.

1994-01-01

Future manned missions beyond low earth orbit require accurate predictions of the risk to astronauts and to critical systems from exposure to ionizing radiation. For low-level exposures, the hazards are dominated by rare single-event phenomena where individual cosmic-ray particles or spallation reactions result in potentially catastrophic changes in critical components. Examples might be a biological lesion leading to cancer in an astronaut or a memory upset leading to an undesired rocket firing. The risks of such events appears to depend on the amount of energy deposited within critical sensitive volumes of biological cells and microelectronic components. The critical environmental information needed to estimate the risks posed by the natural space environments, including solar flares, is the number of times more than a threshold amount of energy for an event will be deposited in the critical microvolumes. These predictions are complicated by uncertainties in the natural environments, particularly the composition of flares, and by the effects of shielding. Microdosimetric data for large numbers of orbits are needed to improve the environmental models and to test the transport codes used to predict event rates.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

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

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Diagnostic Accuracy of Somatosensory Evoked Potential Monitoring in Evaluating Neurological Complications During Endovascular Aneurysm Treatment.

PubMed

Ares, William J; Grandhi, Ramesh M; Panczykowski, David M; Weiner, Gregory M; Thirumala, Parthasarathy; Habeych, Miguel E; Crammond, Donald J; Horowitz, Michael B; Jankowitz, Brian T; Jadhav, Ashutosh; Jovin, Tudor G; Ducruet, Andrew F; Balzer, Jeffrey

2018-02-01

Somatosensory evoked potential (SSEP) monitoring is used extensively for early detection and prevention of neurological complications in patients undergoing many different neurosurgical procedures. However, the predictive ability of SSEP monitoring during endovascular treatment of cerebral aneurysms is not well detailed. To evaluate the performance of intraoperative SSEP in the prediction postprocedural neurological deficits (PPNDs) after coil embolization of intracranial aneurysms. This population-based cohort study included patients ≥18 years of age undergoing intracranial aneurysm embolization with concurrent SSEP monitoring between January 2006 and August 2012. The ability of SSEP to predict PPNDs was analyzed by multiple regression analyses and assessed by the area under the receiver operating characteristic curve. In a population of 888 patients, SSEP changes occurred in 8.6% (n = 77). Twenty-eight patients (3.1%) suffered PPNDs. A 50% to 99% loss in SSEP waveform was associated with a 20-fold increase in risk of PPND; a total loss of SSEP waveform, regardless of permanence, was associated with a greater than 200-fold risk of PPND. SSEPs displayed very good predictive ability for PPND, with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.76-0.92). This study supports the predictive ability of SSEPs for the detection of PPNDs. The magnitude and persistence of SSEP changes is clearly associated with the development of PPNDs. The utility of SSEP monitoring in detecting ischemia may provide an opportunity for neurointerventionalists to respond to changes intraoperatively to mitigate the potential for PPNDs. Copyright © 2017 by the Congress of Neurological Surgeons

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.

Coronary Artery Calcium Volume and Density: Potential Interactions and Overall Predictive Value: The Multi-Ethnic Study of Atherosclerosis.

PubMed

Criqui, Michael H; Knox, Jessica B; Denenberg, Julie O; Forbang, Nketi I; McClelland, Robyn L; Novotny, Thomas E; Sandfort, Veit; Waalen, Jill; Blaha, Michael J; Allison, Matthew A

2017-08-01

This study sought to determine the possibility of interactions between coronary artery calcium (CAC) volume or CAC density with each other, and with age, sex, ethnicity, the new atherosclerotic cardiovascular disease (ASCVD) risk score, diabetes status, and renal function by estimated glomerular filtration rate, and, using differing CAC scores, to determine the improvement over the ASCVD risk score in risk prediction and reclassification. In MESA (Multi-Ethnic Study of Atherosclerosis), CAC volume was positively and CAC density inversely associated with cardiovascular disease (CVD) events. A total of 3,398 MESA participants free of clinical CVD but with prevalent CAC at baseline were followed for incident CVD events. During a median 11.0 years of follow-up, there were 390 CVD events, 264 of which were coronary heart disease (CHD). With each SD increase of ln CAC volume (1.62), risk of CHD increased 73% (p < 0.001) and risk of CVD increased 61% (p < 0.001). Conversely, each SD increase of CAC density (0.69) was associated with 28% lower risk of CHD (p < 0.001) and 25% lower risk of CVD (p < 0.001). CAC density was inversely associated with risk at all levels of CAC volume (i.e., no interaction was present). In multivariable Cox models, significant interactions were present for CAC volume with age and ASCVD risk score for both CHD and CVD, and CAC density with ASCVD risk score for CVD. Hazard ratios were generally stronger in the lower risk groups. Receiver-operating characteristic area under the curve and Net Reclassification Index analyses showed better prediction by CAC volume than by Agatston, and the addition of CAC density to CAC volume further significantly improved prediction. The inverse association between CAC density and incident CHD and CVD events is robust across strata of other CVD risk factors. Added to the ASCVD risk score, CAC volume and density provided the strongest prediction for CHD and CVD events, and the highest correct reclassification. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Examination of Substance Use, Risk Factors, and Protective Factors on Student Academic Test Score Performance

PubMed Central

Arthur, Michael W.; Brown, Eric C.; Briney, John S.; Hawkins, J. David; Abbott, Robert D.; Catalano, Richard F.; Becker, Linda; Langer, Michael; Mueller, Martin T.

2016-01-01

BACKGROUND School administrators and teachers face difficult decisions about how best to use school resources in order to meet academic achievement goals. Many are hesitant to adopt prevention curricula that are not focused directly on academic achievement. Yet, some have hypothesized that prevention curricula can remove barriers to learning and, thus, promote achievement. This study examined relationships between school levels of student substance use and risk and protective factors that predict adolescent problem behaviors and achievement test performance in Washington State. METHODS Hierarchical Generalized Linear Models were used to examine predictive associations between school-averaged levels of substance use and risk and protective factors and Washington State students’ likelihood of meeting achievement test standards on the Washington Assessment of Student Learning, statistically controlling for demographic and economic factors known to be associated with achievement. RESULTS Results indicate that levels of substance use and risk/protective factors predicted the academic test score performance of students. Many of these effects remained significant even after controlling for model covariates. CONCLUSIONS The findings suggest that implementing prevention programs that target empirically identified risk and protective factors have the potential to positively affect students’ academic achievement. PMID:26149305

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.

Don't panic: interpretation bias is predictive of new onsets of panic disorder.

PubMed

Woud, Marcella L; Zhang, Xiao Chi; Becker, Eni S; McNally, Richard J; Margraf, Jürgen

2014-01-01

Psychological models of panic disorder postulate that interpretation of ambiguous material as threatening is an important maintaining factor for the disorder. However, demonstrations of whether such a bias predicts onset of panic disorder are missing. In the present study, we used data from the Dresden Prediction Study, in which a epidemiologic sample of young German women was tested at two time points approximately 17 months apart, allowing the study of biased interpretation as a potential risk factor. At time point one, participants completed an Interpretation Questionnaire including two types of ambiguous scenarios: panic-related and general threat-related. Analyses revealed that a panic-related interpretation bias predicted onset of panic disorder, even after controlling for two established risk factors: anxiety sensitivity and fear of bodily sensations. This is the first prospective study demonstrating the incremental validity of interpretation bias as a predictor of panic disorder onset. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer.

PubMed

Chow, Eric J; Chen, Yan; Hudson, Melissa M; Feijen, Elizabeth A M; Kremer, Leontien C; Border, William L; Green, Daniel M; Meacham, Lillian R; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Ronckers, Cécile M; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; van Dijk, Irma W E M; van Leeuwen, Flora E; Weathers, Rita E; Robison, Leslie L; Armstrong, Gregory T; Yasui, Yutaka

2018-01-01

Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children's Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

Predicting post-fire hillslope erosion in forest lands of the western United States

Treesearch

Mary Ellen Miller; Lee H. MacDonald; Peter R. Robichaud; William J. Elliot

2011-01-01

Many forests and their associated water resources are at increasing risk from large and severe wildfires due to high fuel accumulations and climate change. Extensive fuel treatments are being proposed, but it is not clear where such treatments should be focussed. The goals of this project were to: (1) predict potential post-fire erosion rates for forests and shrublands...

Intelligent instrumentation applied in environment management

NASA Astrophysics Data System (ADS)

Magheti, Mihnea I.; Walsh, Patrick; Delassus, Patrick

2005-06-01

The use of information and communications technology in environment management and research has witnessed a renaissance in recent years. From optical sensor technology, expert systems, GIS and communications technologies to computer aided harvesting and yield prediction, these systems are increasable used for applications developing in the management sector of natural resources and biodiversity. This paper presents an environmental decision support system, used to monitor biodiversity and present a risk rating for the invasion of pests into the particular systems being examined. This system will utilise expert mobile technology coupled with artificial intelligence and predictive modelling, and will emphasize the potential for expansion into many areas of intelligent remote sensing and computer aided decision-making for environment management or certification. Monitoring and prediction in natural systems, harnessing the potential of computing and communication technologies is an emerging technology within the area of environmental management. This research will lead to the initiation of a hardware and software multi tier decision support system for environment management allowing an evaluation of areas for biodiversity or areas at risk from invasive species, based upon environmental factors/systems.

Predicting risk in patients with acetaminophen overdose

PubMed Central

James, Laura P.; Gill, Prit; Simpson, Pippa

2014-01-01

Acetaminophen (APAP) overdose is a very common cause of drug overdose and acute liver failure in the US and Europe. Mechanism-based biomarkers of APAP toxicity have the potential to improve the clinical management of patients with large dose ingestions of APAP. The current approach to the management of APAP toxicity is limited by imprecise and time-constrained risk assessments and late-stage markers of liver injury. A recent study of “low-risk” APAP overdose patients who all received treatment with N-acetylcysteine, found that cell-death biomarkers were more sensitive than alanine aminotransferase (ALT) and APAP concentrations in predicting the development of acute liver injury. The data suggest a potential role for new biomarkers to identify “low risk” patients following APAP overdose. However, a practical and ethical consideration that complicates predictive biomarker research in this area is the clinical need to deliver antidote treatment within 10 hours of APAP overdose. The treatment effect and time-dependent nature of N-acetylcysteine treatment must be considered in future “predictive” toxicology studies of APAP-induced liver injury. PMID:23984999

Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case

PubMed Central

Li, Guowei; Thabane, Lehana; Delate, Thomas; Witt, Daniel M.; Levine, Mitchell A. H.; Cheng, Ji; Holbrook, Anne

2016-01-01

Objectives To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both) in patients with atrial fibrillation (AF) with and without warfarin therapy. Methods Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR) modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling. Results We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632), there were 136 strokes (2.94%), 280 major bleedings (6.04%) and 1194 deaths (25.78%) occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances. Conclusions In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making PMID:27513986

Hemisphere Differences in Speech-Sound Event-Related Potentials in Intensive Care Neonates: Associations and Predictive Value for Development in Infancy

PubMed Central

Maitre, Nathalie L.; Slaughter, James C.; Aschner, Judy L.; Key, Alexandra P.

2014-01-01

Neurodevelopmental delays in intensive care neonates are common but difficult to predict. In children, hemisphere differences in cortical processing of speech are predictive of cognitive performance. We hypothesized that hemisphere differences in auditory event-related potentials in intensive care neonates are predictive of neurodevelopment in infancy, even in those born preterm. Event-related potentials to speech sounds were prospectively recorded in 57 infants (gestational age 24–40 weeks) prior to discharge. The Developmental Assessment of Young Children was performed at 6 and 12 months. Hemisphere differences in mean amplitudes increased with postnatal age (P < .01) but not with gestational age. Greater hemisphere differences were associated with improved communication and cognitive scores at 6 and 12 months, but decreased in significance at 12 months after adjusting for socioeconomic and clinical factors. Auditory cortical responses can be used in intensive care neonates to help identify infants at higher risk for delays in infancy. PMID:23864588

Subjective Life Expectancy Among College Students.

PubMed

Rodemann, Alyssa E; Arigo, Danielle

2017-09-14

Establishing healthy habits in college is important for long-term health. Despite existing health promotion efforts, many college students fail to meet recommendations for behaviors such as healthy eating and exercise, which may be due to low perceived risk for health problems. The goals of this study were to examine: (1) the accuracy of life expectancy predictions, (2) potential individual differences in accuracy (i.e., gender and conscientiousness), and (3) potential change in accuracy after inducing awareness of current health behaviors. College students from a small northeastern university completed an electronic survey, including demographics, initial predictions of their life expectancy, and their recent health behaviors. At the end of the survey, participants were asked to predict their life expectancy a second time. Their health data were then submitted to a validated online algorithm to generate calculated life expectancy. Participants significantly overestimated their initial life expectancy, and neither gender nor conscientiousness was related to the accuracy of these predictions. Further, subjective life expectancy decreased from initial to final predictions. These findings suggest that life expectancy perceptions present a unique-and potentially modifiable-psychological process that could influence college students' self-care.

EARLY BIOMARKERS OF ACUTE RESPIRATORY ALLERGEN EXPOSURE

EPA Science Inventory

Rationale: Allergic asthma prevalence has been increasing in Western societies for several decades. Identification of potential allergens facilitates reduction in exposure and may reduce the risk of asthma development. Predictive models for recognition of sensitizers require th...

Predictive Modeling of Developmental Toxicity

EPA Science Inventory

The use of alternative methods in conjunction with traditional in vivo developmental toxicity testing has the potential to (1) reduce cost and increase throughput of testing the chemical universe, (2) prioritize chemicals for further targeted toxicity testing and risk assessment,...

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.

Predicting invasiveness of species in trade: Climate match, trophic guild and fecundity influence establishment and impact of non-native freshwater fishes

USGS Publications Warehouse

Howeth, Jennifer G.; Gantz, Crysta A.; Angermeier, Paul; Frimpong, Emmanuel A.; Hoff, Michael H.; Keller, Reuben P.; Mandrak, Nicholas E.; Marchetti, Michael P.; Olden, Julian D.; Romagosa, Christina M.; Lodge, David M.

2016-01-01

AimImpacts of non-native species have motivated development of risk assessment tools for identifying introduced species likely to become invasive. Here, we develop trait-based models for the establishment and impact stages of freshwater fish invasion, and use them to screen non-native species common in international trade. We also determine which species in the aquarium, biological supply, live bait, live food and water garden trades are likely to become invasive. Results are compared to historical patterns of non-native fish establishment to assess the relative importance over time of pathways in causing invasions.LocationLaurentian Great Lakes region.MethodsTrait-based classification trees for the establishment and impact stages of invasion were developed from data on freshwater fish species that established or failed to establish in the Great Lakes. Fishes in trade were determined from import data from Canadian and United States regulatory agencies, assigned to specific trades and screened through the developed models.ResultsClimate match between a species’ native range and the Great Lakes region predicted establishment success with 75–81% accuracy. Trophic guild and fecundity predicted potential harmful impacts of established non-native fishes with 75–83% accuracy. Screening outcomes suggest the water garden trade poses the greatest risk of introducing new invasive species, followed by the live food and aquarium trades. Analysis of historical patterns of introduction pathways demonstrates the increasing importance of these trades relative to other pathways. Comparisons among trades reveal that model predictions parallel historical patterns; all fishes previously introduced from the water garden trade have established. The live bait, biological supply, aquarium and live food trades have also contributed established non-native fishes.Main conclusionsOur models predict invasion risk of potential fish invaders to the Great Lakes region and could help managers prioritize efforts among species and pathways to minimize such risk. Similar approaches could be applied to other taxonomic groups and geographic regions.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

PubMed

Balicer, Ran D; Cohen, Chandra J; Leibowitz, Morton; Feldman, Becca S; Brufman, Ilan; Roberts, Craig; Hoshen, Moshe

2014-02-12

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting. Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting. In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia. We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international guidelines. Health care managers and policy-makers may consider the strategic potential of utilizing clinical and administrative databases for creating population-specific risk prediction models to inform vaccination campaigns. Copyright © 2013 Elsevier Ltd. All rights reserved.

Readmission prediction via deep contextual embedding of clinical concepts.

PubMed

Xiao, Cao; Ma, Tengfei; Dieng, Adji B; Blei, David M; Wang, Fei

2018-01-01

Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers.

PubMed

Klimov, Sergey; Rida, Padmashree Cg; Aleskandarany, Mohammed A; Green, Andrew R; Ellis, Ian O; Janssen, Emiel Am; Rakha, Emad A; Aneja, Ritu

2017-09-05

Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC. A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses. Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status. Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism.

Existing and potential infection risk zones of yellow fever worldwide: a modelling analysis.

PubMed

Shearer, Freya M; Longbottom, Joshua; Browne, Annie J; Pigott, David M; Brady, Oliver J; Kraemer, Moritz U G; Marinho, Fatima; Yactayo, Sergio; de Araújo, Valdelaine E M; da Nóbrega, Aglaêr A; Fullman, Nancy; Ray, Sarah E; Mosser, Jonathan F; Stanaway, Jeffrey D; Lim, Stephen S; Reiner, Robert C; Moyes, Catherine L; Hay, Simon I; Golding, Nick

2018-03-01

Yellow fever cases are under-reported and the exact distribution of the disease is unknown. An effective vaccine is available but more information is needed about which populations within risk zones should be targeted to implement interventions. Substantial outbreaks of yellow fever in Angola, Democratic Republic of the Congo, and Brazil, coupled with the global expansion of the range of its main urban vector, Aedes aegypti, suggest that yellow fever has the propensity to spread further internationally. The aim of this study was to estimate the disease's contemporary distribution and potential for spread into new areas to help inform optimal control and prevention strategies. We assembled 1155 geographical records of yellow fever virus infection in people from 1970 to 2016. We used a Poisson point process boosted regression tree model that explicitly incorporated environmental and biological explanatory covariates, vaccination coverage, and spatial variability in disease reporting rates to predict the relative risk of apparent yellow fever virus infection at a 5 × 5 km resolution across all risk zones (47 countries across the Americas and Africa). We also used the fitted model to predict the receptivity of areas outside at-risk zones to the introduction or reintroduction of yellow fever transmission. By use of previously published estimates of annual national case numbers, we used the model to map subnational variation in incidence of yellow fever across at-risk countries and to estimate the number of cases averted by vaccination worldwide. Substantial international and subnational spatial variation exists in relative risk and incidence of yellow fever as well as varied success of vaccination in reducing incidence in several high-risk regions, including Brazil, Cameroon, and Togo. Areas with the highest predicted average annual case numbers include large parts of Nigeria, the Democratic Republic of the Congo, and South Sudan, where vaccination coverage in 2016 was estimated to be substantially less than the recommended threshold to prevent outbreaks. Overall, we estimated that vaccination coverage levels achieved by 2016 avert between 94 336 and 118 500 cases of yellow fever annually within risk zones, on the basis of conservative and optimistic vaccination scenarios. The areas outside at-risk regions with predicted high receptivity to yellow fever transmission (eg, parts of Malaysia, Indonesia, and Thailand) were less extensive than the distribution of the main urban vector, A aegypti, with low receptivity to yellow fever transmission in southern China, where A aegypti is known to occur. Our results provide the evidence base for targeting vaccination campaigns within risk zones, as well as emphasising their high effectiveness. Our study highlights areas where public health authorities should be most vigilant for potential spread or importation events. Bill & Melinda Gates Foundation. Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Modeling risk for child abuse and harsh parenting in families with depressed and substance-abusing parents.

PubMed

Kelley, Michelle L; Lawrence, Hannah R; Milletich, Robert J; Hollis, Brittany F; Henson, James M

2015-05-01

Children with substance abusing parents are at considerable risk for child maltreatment. The current study applied an actor-partner interdependence model to examine how father only (n=52) and dual couple (n=33) substance use disorder, as well as their depressive symptomology influenced parents' own (actor effects) and the partner's (partner effects) overreactivity in disciplinary interactions with their children, as well as their risk for child maltreatment. Parents completed the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977), the overreactivity subscale from the Parenting Scale (Arnold, O'Leary, Wolff, & Acker, 1993), and the Brief Child Abuse Potential Inventory (Ondersma, Chaffin, Mullins, & LeBreton, 2005). Results of multigroup structural equation models revealed that a parent's own report of depressive symptoms predicted their risk for child maltreatment in both father SUD and dual SUD couples. Similarly, a parent's report of their own depressive symptoms predicted their overreactivity in disciplinary encounters both in father SUD and dual SUD couples. In all models, partners' depressive symptoms did not predict their partner's risk for child maltreatment or overreactivity. Findings underscore the importance of a parent's own level of depressive symptoms in their risk for child maltreatment and for engaging in overreactivity during disciplinary episodes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling Risk for Child Abuse and Harsh Parenting in Families with Depressed and Substance-abusing Parents

PubMed Central

Kelley, Michelle L.; Lawrence, Hannah R.; Milletich, Robert R.; Hollis, Brittany F.; Henson, James M.

2015-01-01

Children with substance abusing parents are at considerable risk for child maltreatment. The current study applied an actor-partner interdependence model to examine how father only (n = 52) and dual couple (n = 33) substance use disorder, as well as their depressive symptomology influenced parents’ own (actor effects) and the partner's (partner effects) overreactivity in disciplinary interactions with their children, as well as their risk for child maltreatment. Parents completed the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977), the overreactivity subscale from the Parenting Scale (Arnold, O'Leary, Wolff, & Acker, 1993), and the Brief Child Abuse Potential Inventory (Ondersma, Chaffin, Mullins, & LeBreton, 2005). Results of multigroup structural equation models revealed that a parent's own report of depressive symptoms predicted their risk for child maltreatment in both father SUD and dual SUD couples. Similarly, a parent's report of their own depressive symptoms predicted their overreactivity in disciplinary encounters both in father SUD and dual SUD couples. In all models, partners’ depressive symptoms did not predict their partner's risk for child maltreatment or overreactivity. Findings underscore the importance of a parent's own levels of depressive symptoms in their risk for child maltreatment and for engaging in overreactivity during disciplinary episodes. PMID:25724658

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia.

PubMed

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G R William; Robinson, Timothy P; Tatem, Andrew J; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P; Martin, Vincent; Bhatt, Samir; Gething, Peter W; Farrar, Jeremy J; Hay, Simon I; Yu, Hongjie

2014-06-17

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

Forecasting disease risk for increased epidemic preparedness in public health

NASA Technical Reports Server (NTRS)

Myers, M. F.; Rogers, D. J.; Cox, J.; Flahault, A.; Hay, S. I.

2000-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

PubMed Central

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G. R. William; Robinson, Timothy P.; Tatem, Andrew J.; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P.; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P.; Martin, Vincent; Bhatt, Samir; Gething, Peter W.; Farrar, Jeremy J.; Hay, Simon I.; Yu, Hongjie

2014-01-01

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease. PMID:24937647

Forecasting Disease Risk for Increased Epidemic Preparedness in Public Health

PubMed Central

Myers, M.F.; Rogers, D.J.; Cox, J.; Flahault, A.; Hay, S.I.

2011-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world’s epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector–environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development. PMID:10997211

Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits.

PubMed

Patterson, Brian W; Repplinger, Michael D; Pulia, Michael S; Batt, Robert J; Svenson, James E; Trinh, Alex; Mendonça, Eneida A; Smith, Maureen A; Hamedani, Azita G; Shah, Manish N

2018-04-01

To evaluate the utility of routinely collected Hendrich II fall scores in predicting returns to the emergency department (ED) for falls within 6 months. Retrospective electronic record review. Academic medical center ED. Individuals aged 65 and older seen in the ED from January 1, 2013, through September 30, 2015. We evaluated the utility of routinely collected Hendrich II fall risk scores in predicting ED visits for a fall within 6 months of an all-cause index ED visit. For in-network patient visits resulting in discharge with a completed Hendrich II score (N = 4,366), the return rate for a fall within 6 months was 8.3%. When applying the score alone to predict revisit for falls among the study population the resultant receiver operating characteristic (ROC) plot had an area under the curve (AUC) of 0.64. In a univariate model, the odds of returning to the ED for a fall in 6 months were 1.23 times as high for every 1-point increase in Hendrich II score (odds ratio (OR)=1.23 (95% confidence interval (CI)=1.19-1.28). When included in a model with other potential confounders or predictors of falls, the Hendrich II score is a significant predictor of a return ED visit for fall (adjusted OR=1.15, 95% CI=1.10-1.20, AUC=0.75). Routinely collected Hendrich II scores were correlated with outpatient falls, but it is likely that they would have little utility as a stand-alone fall risk screen. When combined with easily extractable covariates, the screen performs much better. These results highlight the potential for secondary use of electronic health record data for risk stratification of individuals in the ED. Using data already routinely collected, individuals at high risk of falls after discharge could be identified for referral without requiring additional screening resources. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Combined early and adult life risk factor associations for mid-life obesity in a prospective birth cohort: assessing potential public health impact.

PubMed

Pinto Pereira, Snehal M; van Veldhoven, Karin; Li, Leah; Power, Chris

2016-04-12

The combined effect of life-course influences on obesity development and thus their potential public health impact is unclear. We evaluated combined associations and predicted probabilities for early and adult life risk factors with central and general obesity in mid-adulthood. 1958 British birth cohort. 4629 males and 4670 females with data on waist circumference. 45 year obesity measured via waist circumference, waist-hip ratio (WHR) and BMI. At 45 years, approximately a third of the population were centrally obese and a quarter were generally obese. Three factors (parental overweight, maternal smoking during pregnancy and adult inactivity) were consistently associated with central and general obesity. Predicted probabilities for waist obesity increased from those with none to all three risk factors (0.15-0.33 in men; 0.19-0.39 in women (ptrend<0.001)), with a similar trend for general obesity. Additional factors (adult smoking, low fibre and heavy alcohol consumption) were associated with WHR obesity, although varying by gender. Prevalence of risk factors was higher in manual than non-manual groups: for example, in men 38% versus 25%, respectively, had ≥2 risk factors for waist and general obesity. Early-life and adult factors that are amenable to change are highly prevalent and accumulate in association with central and general obesity in mid-adulthood. The increase in probabilities for mid-adult obesity associated with cumulative levels of risk factors suggests the potential for public health impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Application of predictive modelling techniques in industry: from food design up to risk assessment.

PubMed

Membré, Jeanne-Marie; Lambert, Ronald J W

2008-11-30

In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

Understanding children's injury-risk behaviors: the independent contributions of cognitions and emotions.

PubMed

Morrongiello, Barbara A; Lasenby-Lessard, Jennifer; Matheis, Shawn

2007-09-01

Unintentional injuries are a leading threat to the health of elementary-school children, with many injuries happening when children are left to make their own decisions about risk taking during play. The present study sought to identify determinants of children's physical taking. An ecologically valid task that posed some threat of injury was used (i.e., highest height of a balance beam they would walk across). Ratings of cognitions (extent of danger, perceived vulnerability for personal injury, potential severity of injury) and emotional reactions (fear, excitement) were taken when on the beam, just before the children walked across. Regression analysis, controlling for age and sex, revealed that risk taking was predicted from ratings of danger, fear, and excitement. Both cognitive and emotional factors independently contribute to predict children's physical risk taking. Theoretical and practical implications of these findings are discussed.

Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

PubMed

Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

2013-01-01

Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

The priority heuristic: making choices without trade-offs.

PubMed

Brandstätter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2006-04-01

Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, the authors generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (a) the Allais paradox, (b) risk aversion for gains if probabilities are high, (c) risk seeking for gains if probabilities are low (e.g., lottery tickets), (d) risk aversion for losses if probabilities are low (e.g., buying insurance), (e) risk seeking for losses if probabilities are high, (f) the certainty effect, (g) the possibility effect, and (h) intransitivities. The authors test how accurately the heuristic predicts people's choices, compared with previously proposed heuristics and 3 modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory. ((c) 2006 APA, all rights reserved).

Functional Movement ScreenTM and history of injury in assessment of potential risk of injury among team handball players.

PubMed

Slodownik, Robert; Ogonowska-Slodownik, Anna; Morgulec-Adamowicz, Natalia

2017-09-29

Handball is known to be one of the team sports representing the highest risk of injury. Several investigators have tried to identify injury risk factors in team sports including handball and suggested the need to develop an optimal tool to capture and quantify the potential risk of injury. The aim of the study was to evaluate potential risk of injury among handball players. It was a mixed design study. Handball players from 1st and 2nd division were evaluated (n = 30) using the Functional Movement ScreenTM (FMSTM). Additionally, self-reported history of injury was collected during FMSTM evaluation and after 6 months. Competitive level, training experience, playing position, anthropometric features, symmetry of movement patterns and history of previous injury were analysed while assessing the potential risk of injury. Significant difference between the right and left side (upper limb) was revealed for Shoulder Mobility Test (U = 308.5, p = 0.014). Odds Ratio analysis revealed that having previous injury in the last 12 months is the only statistically significant injury risk factor (OR = 13.71, p = 0.02). Based on this study we can assume that previous injury history reports are crucial in predicting injuries. FMSTM can help in identifying a typical adaptation in throwing shoulder among handball players, but should not be used alone to assess injury risk.

Potential impact of global climate change on malaria risk

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

Martens, W.J.M.; Rotmans, J.; Niessen, L.W.

The biological activity and geographic distribution of the malarial parasite and its vector are sensitive to climatic influences, especially temperature and precipitation. We have incorporated General Circulation Model-based scenarios of anthropogenic global climate change in an integrated linked-system model for predicting changes in malaria epidemic potential in the next century. The concept of the disability-adjusted life years is included to arrive at a single measure of the effect of anthropogenic climate change on the health impact of malaria. Assessment of the potential impact of global climate change on the incidence of malaria suggests a widespread increase of risk due tomore » expansion of the areas suitable for malaria transmission. This predicted increase is most pronounced at the borders of endemic malaria areas and at higher altitudes within malarial areas. The incidence of infection is sensitive to climate changes in areas of Southeast Asia, South America, and parts of Africa where the disease is less endemic; in these regions the numbers of years of healthy life lost may increase significantly. However, the simulated changes in malaria risk must be interpreted on the basis of local environmental conditions, the effects of socioeconomic developments, and malaria control programs or capabilities. 33 refs., 5 figs., 1 tab.« less

The role of narcissism in health-risk and health-protective behaviors.

PubMed

Hill, Erin M

2016-09-01

This study examined the role of narcissism in health-risk and health-protective behaviors in a sample of 365 undergraduate students. Regression analyses were used to test the influence of narcissism on health behaviors. Narcissism was positively predictive of alcohol use, marijuana use, and risky driving behaviors, and it was associated with an increased likelihood of consistently having a healthy eating pattern. Narcissism was also positively predictive of physical activity. Results are discussed with reference to the potential short-term and long-term health implications and the need for future research on the factors involved in the relationship between narcissism and health behaviors. © The Author(s) 2015.

Development of a Prediction Model for Stress Fracture During an Intensive Physical Training Program: The Royal Marines Commandos

PubMed Central

Sanchez-Santos, Maria T.; Davey, Trish; Leyland, Kirsten M.; Allsopp, Adrian J.; Lanham-New, Susan A.; Judge, Andrew; Arden, Nigel K.; Fallowfield, Joanne L.

2017-01-01

Background: Stress fractures (SFs) are one of the more severe overuse injuries in military training, and therefore, knowledge of potential risk factors is needed to assist in developing mitigating strategies. Purpose: To develop a prediction model for risk of SF in Royal Marines (RM) recruits during an arduous military training program. Study Design: Case-control study; Level of evidence, 3. Methods: RM recruits (N = 1082; age range, 16-33 years) who enrolled between September 2009 and July 2010 were prospectively followed through the 32-week RM training program. SF diagnosis was confirmed from a positive radiograph or magnetic resonance imaging scan. Potential risk factors assessed at week 1 included recruit characteristics, anthropometric assessment, dietary supplement use, lifestyle habits, fitness assessment, blood samples, 25(OH)D, bone strength as measured by heel broadband ultrasound attention, history of physical activity, and previous and current food intake. A logistic least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select potential predictors among 47 candidate variables. Model performance was assessed using measures of discrimination (c-index) and calibration. Bootstrapping was used for internal validation of the developed model and to quantify optimism. Results: A total of 86 (8%) volunteer recruits presented at least 1 SF during training. Twelve variables were identified as the most important risk factors of SF. Variables strongly associated with SF were age, body weight, pretraining weightbearing exercise, pretraining cycling, and childhood intake of milk and milk products. The c-index for the prediction model, which represents the model performance in future volunteers, was 0.73 (optimism-corrected c-index, 0.68). Although 25(OH)D and VO2max had only a borderline statistically significant association with SF, the inclusion of these factors improved the performance of the model. Conclusion: These findings will assist in identifying recruits at greater risk of SF during training and will support interventions to mitigate this injury risk. However, external validation of the model is still required. PMID:28804727

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

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

Greitzer, Frank L.; Frincke, Deborah A.

2010-09-01

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data tomore » yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.« less

Wildfire risk assessment in a typical Mediterranean wildland-urban interface of Greece.

PubMed

Mitsopoulos, Ioannis; Mallinis, Giorgos; Arianoutsou, Margarita

2015-04-01

The purpose of this study was to assess spatial wildfire risk in a typical Mediterranean wildland-urban interface (WUI) in Greece and the potential effect of three different burning condition scenarios on the following four major wildfire risk components: burn probability, conditional flame length, fire size, and source-sink ratio. We applied the Minimum Travel Time fire simulation algorithm using the FlamMap and ArcFuels tools to characterize the potential response of the wildfire risk to a range of different burning scenarios. We created site-specific fuel models of the study area by measuring the field fuel parameters in representative natural fuel complexes, and we determined the spatial extent of the different fuel types and residential structures in the study area using photointerpretation procedures of large scale natural color orthophotographs. The results included simulated spatially explicit fire risk components along with wildfire risk exposure analysis and the expected net value change. Statistical significance differences in simulation outputs between the scenarios were obtained using Tukey's significance test. The results of this study provide valuable information for decision support systems for short-term predictions of wildfire risk potential and inform wildland fire management of typical WUI areas in Greece.

Wildfire Risk Assessment in a Typical Mediterranean Wildland-Urban Interface of Greece

NASA Astrophysics Data System (ADS)

Mitsopoulos, Ioannis; Mallinis, Giorgos; Arianoutsou, Margarita

2015-04-01

The purpose of this study was to assess spatial wildfire risk in a typical Mediterranean wildland-urban interface (WUI) in Greece and the potential effect of three different burning condition scenarios on the following four major wildfire risk components: burn probability, conditional flame length, fire size, and source-sink ratio. We applied the Minimum Travel Time fire simulation algorithm using the FlamMap and ArcFuels tools to characterize the potential response of the wildfire risk to a range of different burning scenarios. We created site-specific fuel models of the study area by measuring the field fuel parameters in representative natural fuel complexes, and we determined the spatial extent of the different fuel types and residential structures in the study area using photointerpretation procedures of large scale natural color orthophotographs. The results included simulated spatially explicit fire risk components along with wildfire risk exposure analysis and the expected net value change. Statistical significance differences in simulation outputs between the scenarios were obtained using Tukey's significance test. The results of this study provide valuable information for decision support systems for short-term predictions of wildfire risk potential and inform wildland fire management of typical WUI areas in Greece.

SELF-RATED EXPECTATIONS OF SUICIDAL BEHAVIOR PREDICT FUTURE SUICIDE ATTEMPTS AMONG ADOLESCENT AND YOUNG ADULT PSYCHIATRIC EMERGENCY PATIENTS.

PubMed

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

2016-06-01

This study's purpose was to examine the predictive validity and clinical utility of a brief measure assessing youths' own expectations of their future risk of suicidal behavior, administered in a psychiatric emergency (PE) department; and determine if youths' ratings improve upon a clinician-administered assessment of suicidal ideation severity. The outcome was suicide attempts up to 18 months later. In this medical record review study, 340 consecutively presenting youths (ages 13-24) seeking PE services over a 7-month period were included. Subsequent PE visits and suicide attempts were retrospectively tracked for up to 18 months. The 3-item scale assessing patients' perception of their own suicidal behavior risk and the clinician-administered ideation severity scale were used routinely at the study site. Cox regression results showed that youths' expectations of suicidal behavior were independently associated with increased risk of suicide attempts, even after adjusting for key covariates. Results were not moderated by sex, suicide attempt history, or age. Receiver-operating characteristic (ROC) analyses indicated that self-assessed expectations of risk improved the predictive accuracy of the clinician-administered suicidal ideation measure. Youths' ratings indicative of lower confidence in maintaining safety uniquely predicted follow-up attempts and provided incremental validity over and above the clinician-administered assessment and improved its accuracy, suggesting their potential for augmenting suicide risk formulation. Assessing youths' own perceptions of suicide risk appears to be clinically useful, feasible to implement in PE settings, and, if replicated, promising for improving identification of youth at risk for suicidal behavior. © 2016 Wiley Periodicals, Inc.

Risk assessment and management to prevent preterm birth.

PubMed

Koullali, B; Oudijk, M A; Nijman, T A J; Mol, B W J; Pajkrt, E

2016-04-01

Preterm birth is the most important cause of neonatal mortality and morbidity worldwide. In this review, we review potential risk factors associated with preterm birth and the subsequent management to prevent preterm birth in low and high risk women with a singleton or multiple pregnancy. A history of preterm birth is considered the most important risk factor for preterm birth in subsequent pregnancy. General risk factors with a much lower impact include ethnicity, low socio-economic status, maternal weight, smoking, and periodontal status. Pregnancy-related characteristics, including bacterial vaginosis and asymptomatic bacteriuria, appear to be of limited value in the prediction of preterm birth. By contrast, a mid-pregnancy cervical length measurement is independently associated with preterm birth and could be used to identify women at risk of a premature delivery. A fetal fibronectin test may be of additional value in the prediction of preterm birth. The most effective methods to prevent preterm birth depend on the obstetric history, which makes the identification of women at risk of preterm birth an important task for clinical care providers. Copyright © 2016 Elsevier Ltd. All rights reserved.

The intergenerational transfer of psychosocial risk: mediators of vulnerability and resilience.

PubMed

Serbin, Lisa A; Karp, Jennifer

2004-01-01

The recurrence of social, behavioral, and health problems in successive generations of families is a prevalent theme in both the scientific and popular literatures. This review discusses recent conceptual models and findings from longitudinal studies concerning the intergenerational transfer of psychosocial risk, including intergenerational continuity, and the processes whereby a generation of parents may place their offspring at elevated risk for social, behavioral, and health problems. Key findings include the mediational effects of parenting and environmental factors in the transfer of risk. In both girls and boys, childhood aggression and antisocial behavior appear to predict long-term trajectories that place offspring at risk. Sequelae of childhood aggression that may threaten the well-being of offspring include school failure, adolescent risk-taking behavior, early and single parenthood, and family poverty. These childhood and adolescent behavioral styles also predict harsh, aggressive, neglectful, and unstimulating parenting behavior toward offspring. Buffering factors within at-risk families include maternal educational attainment and constructive parenting practices (e.g., emotional warmth, consistent disciplinary practices, and cognitive scaffolding). These findings highlight the potential application and relevance of intergenerational studies for social, educational, and health policy.

Predicting high-risk preterm birth using artificial neural networks.

PubMed

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

2006-07-01

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

The Link Between ADHD and the Risk of Sexual Victimization Among College Women: Expanding the Lifestyles/Routine Activities Framework.

PubMed

Snyder, Jamie A

2015-11-01

Using data from a nationally representative sample of college women, the current study examines attention deficit hyperactivity disorder (ADHD) as a potential risk factor in the prediction of sexual victimization among college women and as an extension of the lifestyles/routine activities framework. The findings indicate that college women with ADHD experienced sexual victimization at significantly higher rates than college women without ADHD. Furthermore, ADHD emerged as a significant predictor of sexual victimization across models. The lifestyles/routine activities theory also received general support, particularly for the concepts of exposure, proximity, and guardianship. This research suggests that other risk factors outside the lifestyles/routine activities framework are important in the prediction of sexual victimization in college women. © The Author(s) 2015.

Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.

PubMed

Yang, Fei; Xu, Zhencheng; Zhu, Yunqiang; He, Chansheng; Wu, Genyi; Qiu, Jin Rong; Fu, Qiang; Liu, Qingsong

2013-01-01

Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.

That's What Friends Are For: Bystander Responses to Friends or Strangers at Risk for Party Rape Victimization.

PubMed

Katz, Jennifer; Pazienza, Rena; Olin, Rachel; Rich, Hillary

2015-10-01

The present research examined bystander responses to potential party rape scenarios involving either a friend or a stranger at risk. Undergraduate students (N = 151) imagined attending a party and seeing a man lead an intoxicated woman (friend or stranger) into a bedroom. After random assignment to conditions, participants reported on intentions to help, barriers to helping, victim blame, and empathic concern. As expected, based on their shared social group membership, bystanders intended to offer more help to friends than to strangers. Bystanders also reported more personal responsibility to help and more empathic concern when the potential victim was a friend rather than stranger. Observing a friend versus stranger at risk did not affect audience inhibition or perceived victim blame. Compared with women, men reported more blame and less empathic concern for potential victims. However, there were no gender differences in bystander intent to help or barriers to helping. In multivariate analyses, both responsibility to help and empathic concern for the potential victim uniquely predicted bystanders' intent to help a woman at risk for party rape. Results suggest that promoting social identification with peers at risk could increase bystander intervention. © The Author(s) 2014.

Dysfunctional attitudes and poor problem solving skills predict hopelessness in major depression.

PubMed

Cannon, B; Mulroy, R; Otto, M W; Rosenbaum, J F; Fava, M; Nierenberg, A A

1999-09-01

Hopelessness is a significant predictor of suicidality, but not all depressed patients feel hopeless. If clinicians can predict hopelessness, they may be able to identify those patients at risk of suicide and focus interventions on factors associated with hopelessness. In this study, we examined potential predictors of hopelessness in a sample of depressed outpatients. In this study, we examined potential demographic, diagnostic, and symptom predictors of hopelessness in a sample of 138 medication-free outpatients (73 women and 65 men) with a primary diagnosis of major depression. The significance of predictors was evaluated in both simple and multiple regression analyses. Consistent with previous studies, we found no significant associations between demographic and diagnostic variables and greater hopelessness. Hopelessness was significantly associated with greater depression severity, poor problem solving abilities as assessed by the Problem Solving Inventory, and each of two measures of dysfunctional cognitions (the Dysfunctional Attitudes Scale and the Cognitions Questionnaire). In a stepwise multiple regression equation, however, only dysfunctional cognitions and poor problem solving offered non-redundant prediction of hopelessness scores, and accounted for 20% of the variance in these scores. This study is based on depressed patients entering into an outpatient treatment protocol. All analyses were correlational in nature, and no causal links can be concluded. Our findings, identifying clinical correlates of hopelessness, provide clinicians with potential additional targets for assessment and treatment of suicidal risk. In particular, clinical attention to dysfunctional attitudes and problem solving skills may be important for further reduction of hopelessness and perhaps suicidal risk.

Omnibus risk assessment via accelerated failure time kernel machine modeling.

PubMed

Sinnott, Jennifer A; Cai, Tianxi

2013-12-01

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

Winter birth, urbanicity and immigrant status predict psychometric schizotypy dimensions in adolescents.

PubMed

Mimarakis, D; Roumeliotaki, T; Roussos, P; Giakoumaki, S G; Bitsios, P

2018-01-01

Urbanicity, immigration and winter-birth are stable epidemiological risk factors for schizophrenia, but their relationship to schizotypy is unknown. This is a first examination of the association of these epidemiological risk factors with positive schizotypy, in nonclinical adolescents, controlling for a range of potential and known confounders. We collected socio-demographics, life-style, family and school circumstances, positive schizotypy dimensions and other personality traits from 445 high school pupils (192 males, 158 immigrants) from 9 municipalities in Athens and Heraklion, Greece, which covered a range of host population and migrant densities. Using multivariate hierarchical linear regressions models, we estimated the association of schizotypy dimensions with: (1) demographics of a priori interest (winter-birth, immigrant status, urban characteristics), including family financial and mental health status; (2) factors resulting from principal component analysis (PCA) of the demographic and personal data; (3) factors resulting from PCA of the personality questionnaires. Adolescent women scored higher on schizotypy than men. High anxiety/neuroticism was the most consistent and significant predictor of all schizotypy dimensions in both sexes. In the fully adjusted models, urbanicity predicted magical thinking and unusual experiences in women, while winter-birth and immigration predicted paranoid ideation and unusual experiences respectively in men. These results support the continuum hypothesis and offer potential insights in the nature of risk conferred by winter-birth, urbanicity and immigration and the nature of important sex differences. Controlling for a wide range of potential confounding factors increases the robustness of these results and confidence that these were not spurious associations. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Ecological risk assessment of depleted uranium in the environment at Aberdeen Proving Ground

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

Clements, W.H.; Kennedy, P.L.; Myers, O.B.

1993-01-01

A preliminary ecological risk assessment was conducted to evaluate the effects of depleted uranium (DU) in the Aberdeen Proving Ground (APG) ecosystem and its potential for human health effects. An ecological risk assessment of DU should include the processes of hazard identification, dose-response assessment, exposure assessment, and risk characterization. Ecological risk assessments also should explicitly examine risks incurred by nonhuman as well as human populations, because risk assessments based only on human health do not always protect other species. To begin to assess the potential ecological risk of DU release to the environment we modeled DU transport through the principalmore » components of the aquatic ecosystem at APG. We focused on the APG aquatic system because of the close proximity of the Chesapeake Bay and concerns about potential impacts on this ecosystem. Our objective in using a model to estimate environmental fate of DU is to ultimately reduce the uncertainty about predicted ecological risks due to DU from APG. The model functions to summarize information on the structure and functional properties of the APG aquatic system, to provide an exposure assessment by estimating the fate of DU in the environment, and to evaluate the sources of uncertainty about DU transport.« less

Ecological risk assessment of depleted uranium in the environment at Aberdeen Proving Ground. Annual report, 1991

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

Clements, W.H.; Kennedy, P.L.; Myers, O.B.

1993-03-01

A preliminary ecological risk assessment was conducted to evaluate the effects of depleted uranium (DU) in the Aberdeen Proving Ground (APG) ecosystem and its potential for human health effects. An ecological risk assessment of DU should include the processes of hazard identification, dose-response assessment, exposure assessment, and risk characterization. Ecological risk assessments also should explicitly examine risks incurred by nonhuman as well as human populations, because risk assessments based only on human health do not always protect other species. To begin to assess the potential ecological risk of DU release to the environment we modeled DU transport through the principalmore » components of the aquatic ecosystem at APG. We focused on the APG aquatic system because of the close proximity of the Chesapeake Bay and concerns about potential impacts on this ecosystem. Our objective in using a model to estimate environmental fate of DU is to ultimately reduce the uncertainty about predicted ecological risks due to DU from APG. The model functions to summarize information on the structure and functional properties of the APG aquatic system, to provide an exposure assessment by estimating the fate of DU in the environment, and to evaluate the sources of uncertainty about DU transport.« less

Coupling the Biophysical and Social Dimensions of Wildfire Risk to Improve Wildfire Mitigation Planning.

PubMed

Ager, Alan A; Kline, Jeffrey D; Fischer, A Paige

2015-08-01

We describe recent advances in biophysical and social aspects of risk and their potential combined contribution to improve mitigation planning on fire-prone landscapes. The methods and tools provide an improved method for defining the spatial extent of wildfire risk to communities compared to current planning processes. They also propose an expanded role for social science to improve understanding of community-wide risk perceptions and to predict property owners' capacities and willingness to mitigate risk by treating hazardous fuels and reducing the susceptibility of dwellings. In particular, we identify spatial scale mismatches in wildfire mitigation planning and their potential adverse impact on risk mitigation goals. Studies in other fire-prone regions suggest that these scale mismatches are widespread and contribute to continued wildfire dwelling losses. We discuss how risk perceptions and behavior contribute to scale mismatches and how they can be minimized through integrated analyses of landscape wildfire transmission and social factors that describe the potential for collaboration among landowners and land management agencies. These concepts are then used to outline an integrated socioecological planning framework to identify optimal strategies for local community risk mitigation and improve landscape-scale prioritization of fuel management investments by government entities. © 2015 Society for Risk Analysis.

Neural responses to exclusion predict susceptibility to social influence.

PubMed

Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

2014-05-01

Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

Neural responses to exclusion predict susceptibility to social influence

PubMed Central

Falk, Emily B.; Cascio, Christopher N.; O’Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J.; Bingham, C. Raymond; Shope, Jean T.; Ouimet, Marie Claude; Pradhan, Anuj K.; Simons-Morton, Bruce G.

2014-01-01

Purpose Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence, and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American teens, traffic-related crashes are leading causes of non-fatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents’ vulnerability to peer influence. Methods We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently-licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately one week after the neuroimaging session. Results Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside of the neuroimaging lab one week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. Conclusions These results speak to the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging lab. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. PMID:24759437

Turn up the heat: thermal tolerances of lizards at La Selva, Costa Rica.

PubMed

Brusch, George A; Taylor, Emily N; Whitfield, Steven M

2016-02-01

Global temperature increases over the next century are predicted to contribute to the extinction of a number of taxa, including up to 40% of all lizard species. Lizards adapted to living in lowland tropical areas are especially vulnerable because of their dependence on specific microhabitats, low vagility, and a reduced capacity to physiologically adjust to environmental change. To assess the potential effects of climate change on lizards in the lowland tropics, we measured the critical thermal maximum (CTmax) of ten species from La Selva, Costa Rica. We also examined how well body size, microhabitat type, and species predicted the CTmax. We used current temperature data along with projected temperature increases for 2080 to predict which species may be at the greatest risk at La Selva. Of the ten species sampled, four are at serious risk of lowland extirpation and three others might also be at risk under the highest predicted temperature-increase models. Forest floor lizards at La Selva have already experienced significant population declines over the past 40 years, and we found that each of the forest floor species we studied is at serious risk of local extirpation. We also found that microhabitat type is the strongest predictor of CTmax, demonstrating the profound impact habitat specialization has on the thermal limits of tropical lizards.

Identifying Childhood Characteristics that Underlie Pre-Morbid Risk for Substance Use Disorders: Socialization and Boldness

PubMed Central

Hicks, Brian M.; Iacono, William G.; McGue, Matt

2013-01-01

Utilizing a longitudinal twin study (N = 2510), we identified the child characteristics present prior to initiation of substance use that best predicted later substance use disorders. Two independent traits accounted for the majority of pre-morbid risk: socialization (conformity to rules and conventional values) and boldness (sociability and social assurance, stress resilience, and thrill seeking). Low socialization was associated with disruptive behavior disorders, parental externalizing disorders, and environmental adversity, and exhibited moderate genetic (.45) and shared environmental influences (.30). Boldness was highly heritable (.71) and associated with less internalizing distress and environmental adversity. Together, these traits exhibited robust associations with adolescent and young adult substance use disorders (R = .48 and .50, respectively), and incremental prediction over disruptive behavior disorders, parental externalizing disorders, and environmental adversity. Results were replicated in an independent sample. Socialization and boldness offer a novel conceptualization of underlying risk for substance use disorders that has the potential to improve prediction and theory with implications for basic research, prevention, and intervention. PMID:24280373

Identifying childhood characteristics that underlie premorbid risk for substance use disorders: socialization and boldness.

PubMed

Hicks, Brian M; Iacono, William G; McGue, Matt

2014-02-01

We utilized a longitudinal twin study (N = 2,510) to identify the child characteristics present prior to initiation of substance use that best predicted later substance use disorders. Two independent traits accounted for the majority of premorbid risk: socialization (conformity to rules and conventional values) and boldness (sociability and social assurance, stress resilience, and thrill seeking). Low socialization was associated with disruptive behavior disorders, parental externalizing disorders, and environmental adversity and exhibited moderate genetic (0.45) and shared environmental influences (0.30). Boldness was highly heritable (0.71) and associated with less internalizing distress and environmental adversity. In combination, these traits exhibited robust associations with adolescent and young adult substance use disorders (R = .48 and .50, respectively) and incremental prediction over disruptive behavior disorders, parental externalizing disorders, and environmental adversity. The results were replicated in an independent sample. Socialization and boldness offer a novel conceptualization of underlying risk for substance use disorders that has the potential to improve prediction and theory with implications for basic research, prevention, and intervention.

Increasing Potential Risk of a Global Aquatic Invader in Europe in Contrast to Other Continents under Future Climate Change

PubMed Central

Liu, Xuan; Guo, Zhongwei; Ke, Zunwei; Wang, Supen; Li, Yiming

2011-01-01

Background Anthropogenically-induced climate change can alter the current climatic habitat of non-native species and can have complex effects on potentially invasive species. Predictions of the potential distributions of invasive species under climate change will provide critical information for future conservation and management strategies. Aquatic ecosystems are particularly vulnerable to invasive species and climate change, but the effect of climate change on invasive species distributions has been rather neglected, especially for notorious global invaders. Methodology/Principal Findings We used ecological niche models (ENMs) to assess the risks and opportunities that climate change presents for the red swamp crayfish (Procambarus clarkii), which is a worldwide aquatic invasive species. Linking the factors of climate, topography, habitat and human influence, we developed predictive models incorporating both native and non-native distribution data of the crayfish to identify present areas of potential distribution and project the effects of future climate change based on a consensus-forecast approach combining the CCCMA and HADCM3 climate models under two emission scenarios (A2a and B2a) by 2050. The minimum temperature from the coldest month, the human footprint and precipitation of the driest quarter contributed most to the species distribution models. Under both the A2a and B2a scenarios, P. clarkii shifted to higher latitudes in continents of both the northern and southern hemispheres. However, the effect of climate change varied considerately among continents with an expanding potential in Europe and contracting changes in others. Conclusions/Significance Our findings are the first to predict the impact of climate change on the future distribution of a globally invasive aquatic species. We confirmed the complexities of the likely effects of climate change on the potential distribution of globally invasive species, and it is extremely important to develop wide-ranging and effective control measures according to predicted geographical shifts and changes. PMID:21479188

Disgust as a Mechanism for Decision Making Under Risk: Illuminating Sex Differences and Individual Risk-Taking Correlates of Disgust Propensity.

PubMed

Sparks, Adam Maxwell; Fessler, Daniel M T; Chan, Kai Qin; Ashokkumar, Ashwini; Holbrook, Colin

2018-02-01

The emotion disgust motivates costly behavioral strategies that mitigate against potentially larger costs associated with pathogens, sexual behavior, and moral transgressions. Because disgust thereby regulates exposure to harm, it is by definition a mechanism for calibrating decision making under risk. Understanding this illuminates two features of the demographic distribution of this emotion. First, this approach predicts and explains sex differences in disgust. Greater female disgust propensity is often reported and discussed in the literature, but, to date, conclusions have been based on informal comparisons across a small number of studies, while existing functionalist explanations are at best incomplete. We report the results of an extensive meta-analysis documenting this sex difference, arguing that key features of this pattern are best explained as one manifestation of a broad principle of the evolutionary biology of risk-taking: for a given potential benefit, males in an effectively polygynous mating system accept the risk of harm more willingly than do females. Second, viewing disgust as a mechanism for decision making under risk likewise predicts that individual differences in disgust propensity should correlate with individual differences in various forms of risky behavior, because situational and dispositional factors that influence valuation of opportunity and hazard are often correlated across multiple decision contexts. In two large-sample online studies, we find consistent associations between disgust and risk avoidance. We conclude that disgust and related emotions can be usefully examined through the theoretical lens of decision making under risk in light of human evolution. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

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

PubMed Central

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

2016-01-01

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

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system.

PubMed

Moon, Kyoung-Ja; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

2018-01-01

A key component of the delirium management is prevention and early detection. To develop an automated delirium risk assessment system (Auto-DelRAS) that automatically alerts health care providers of an intensive care unit (ICU) patient's delirium risk based only on data collected in an electronic health record (EHR) system, and to evaluate the clinical validity of this system. Cohort and system development designs were used. Medical and surgical ICUs in two university hospitals in Seoul, Korea. A total of 3284 patients for the development of Auto-DelRAS, 325 for external validation, 694 for validation after clinical applications. The 4211 data items were extracted from the EHR system and delirium was measured using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). The potential predictors were selected and a logistic regression model was established to create a delirium risk scoring algorithm to construct the Auto-DelRAS. The Auto-DelRAS was evaluated at three months and one year after its application to clinical practice to establish the predictive validity of the system. Eleven predictors were finally included in the logistic regression model. The results of the Auto-DelRAS risk assessment were shown as high/moderate/low risk on a Kardex screen. The predictive validity, analyzed after the clinical application of Auto-DelRAS after one year, showed a sensitivity of 0.88, specificity of 0.72, positive predictive value of 0.53, negative predictive value of 0.94, and a Youden index of 0.59. A relatively high level of predictive validity was maintained with the Auto-DelRAS system, even one year after it was applied to clinical practice. Copyright © 2017. Published by Elsevier Ltd.

Intraindividual Cognitive Variability in Middle Age Predicts Cognitive Impairment 8-10 Years Later: Results from the Wisconsin Registry for Alzheimer's Prevention.

PubMed

Koscik, Rebecca L; Berman, Sara E; Clark, Lindsay R; Mueller, Kimberly D; Okonkwo, Ozioma C; Gleason, Carey E; Hermann, Bruce P; Sager, Mark A; Johnson, Sterling C

2016-11-01

Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer's Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer's disease). The primary outcome was Wave 4 cognitive status ("cognitively normal" vs. "impaired") determined by consensus conference; "impaired" included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: "6 Factor IICV" and "4 Test IICV". Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016-1025).

Adaptation of a Biomarker-Based Sepsis Mortality Risk Stratification Tool for Pediatric Acute Respiratory Distress Syndrome.

PubMed

Yehya, Nadir; Wong, Hector R

2018-01-01

The original Pediatric Sepsis Biomarker Risk Model and revised (Pediatric Sepsis Biomarker Risk Model-II) biomarker-based risk prediction models have demonstrated utility for estimating baseline 28-day mortality risk in pediatric sepsis. Given the paucity of prediction tools in pediatric acute respiratory distress syndrome, and given the overlapping pathophysiology between sepsis and acute respiratory distress syndrome, we tested the utility of Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II for mortality prediction in a cohort of pediatric acute respiratory distress syndrome, with an a priori plan to revise the model if these existing models performed poorly. Prospective observational cohort study. University affiliated PICU. Mechanically ventilated children with acute respiratory distress syndrome. Blood collection within 24 hours of acute respiratory distress syndrome onset and biomarker measurements. In 152 children with acute respiratory distress syndrome, Pediatric Sepsis Biomarker Risk Model performed poorly and Pediatric Sepsis Biomarker Risk Model-II performed modestly (areas under receiver operating characteristic curve of 0.61 and 0.76, respectively). Therefore, we randomly selected 80% of the cohort (n = 122) to rederive a risk prediction model for pediatric acute respiratory distress syndrome. We used classification and regression tree methodology, considering the Pediatric Sepsis Biomarker Risk Model biomarkers in addition to variables relevant to acute respiratory distress syndrome. The final model was comprised of three biomarkers and age, and more accurately estimated baseline mortality risk (area under receiver operating characteristic curve 0.85, p < 0.001 and p = 0.053 compared with Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II, respectively). The model was tested in the remaining 20% of subjects (n = 30) and demonstrated similar test characteristics. A validated, biomarker-based risk stratification tool designed for pediatric sepsis was adapted for use in pediatric acute respiratory distress syndrome. The newly derived Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model demonstrates good test characteristics internally and requires external validation in a larger cohort. Tools such as Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model have the potential to provide improved risk stratification and prognostic enrichment for future trials in pediatric acute respiratory distress syndrome.

Weather Regulates Location, Timing, and Intensity of Dengue Virus Transmission between Humans and Mosquitoes.

PubMed

Campbell, Karen M; Haldeman, Kristin; Lehnig, Chris; Munayco, Cesar V; Halsey, Eric S; Laguna-Torres, V Alberto; Yagui, Martín; Morrison, Amy C; Lin, Chii-Dean; Scott, Thomas W

2015-01-01

Dengue is one of the most aggressively expanding mosquito-transmitted viruses. The human burden approaches 400 million infections annually. Complex transmission dynamics pose challenges for predicting location, timing, and magnitude of risk; thus, models are needed to guide prevention strategies and policy development locally and globally. Weather regulates transmission-potential via its effects on vector dynamics. An important gap in understanding risk and roadblock in model development is an empirical perspective clarifying how weather impacts transmission in diverse ecological settings. We sought to determine if location, timing, and potential-intensity of transmission are systematically defined by weather. We developed a high-resolution empirical profile of the local weather-disease connection across Peru, a country with considerable ecological diversity. Applying 2-dimensional weather-space that pairs temperature versus humidity, we mapped local transmission-potential in weather-space by week during 1994-2012. A binary classification-tree was developed to test whether weather data could classify 1828 Peruvian districts as positive/negative for transmission and into ranks of transmission-potential with respect to observed disease. We show that transmission-potential is regulated by temperature-humidity coupling, enabling epidemics in a limited area of weather-space. Duration within a specific temperature range defines transmission-potential that is amplified exponentially in higher humidity. Dengue-positive districts were identified by mean temperature >22°C for 7+ weeks and minimum temperature >14°C for 33+ weeks annually with 95% sensitivity and specificity. In elevated-risk locations, seasonal peak-incidence occurred when mean temperature was 26-29°C, coincident with humidity at its local maximum; highest incidence when humidity >80%. We profile transmission-potential in weather-space for temperature-humidity ranging 0-38°C and 5-100% at 1°C x 2% resolution. Local duration in limited areas of temperature-humidity weather-space identifies potential locations, timing, and magnitude of transmission. The weather-space profile of transmission-potential provides needed data that define a systematic and highly-sensitive weather-disease connection, demonstrating separate but coupled roles of temperature and humidity. New insights regarding natural regulation of human-mosquito transmission across diverse ecological settings advance our understanding of risk locally and globally for dengue and other mosquito-borne diseases and support advances in public health policy/operations, providing an evidence-base for modeling, predicting risk, and surveillance-prevention planning.

Developing predictive systems models to address complexity and relevance for ecological risk assessment.

PubMed

Forbes, Valery E; Calow, Peter

2013-07-01

Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have. Copyright © 2013 SETAC.

Concepts and challenges in cancer risk prediction for the space radiation environment.

PubMed

Barcellos-Hoff, Mary Helen; Blakely, Eleanor A; Burma, Sandeep; Fornace, Albert J; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M

2015-07-01

Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers.

PubMed

Karstoft, Karen-Inge; Statnikov, Alexander; Andersen, Søren B; Madsen, Trine; Galatzer-Levy, Isaac R

2015-09-15

Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC = 0.84 (95% CI = 0.81-0.87), post-deployment: AUC = 0.88 (95% CI = 0.85-0.91)). This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war. Copyright © 2015 Elsevier B.V. All rights reserved.

Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis.

PubMed

Crowson, Cynthia S; Rollefstad, Silvia; Kitas, George D; van Riel, Piet L C M; Gabriel, Sherine E; Semb, Anne Grete

2017-01-01

Cardiovascular disease (CVD) risk calculators designed for use in the general population do not accurately predict the risk of CVD among patients with rheumatoid arthritis (RA), who are at increased risk of CVD. The process of developing risk prediction models involves numerous issues. Our goal was to develop a CVD risk calculator for patients with RA. Thirteen cohorts of patients with RA originating from 10 different countries (UK, Norway, Netherlands, USA, Sweden, Greece, South Africa, Spain, Canada and Mexico) were combined. CVD risk factors and RA characteristics at baseline, in addition to information on CVD outcomes were collected. Cox models were used to develop a CVD risk calculator, considering traditional CVD risk factors and RA characteristics. Model performance was assessed using measures of discrimination and calibration with 10-fold cross-validation. A total of 5638 RA patients without prior CVD were included (mean age: 55 [SD: 14] years, 76% female). During a mean follow-up of 5.8 years (30139 person years), 389 patients developed a CVD event. Event rates varied between cohorts, necessitating inclusion of high and low risk strata in the models. The multivariable analyses revealed 2 risk prediction models including either a disease activity score including a 28 joint count and erythrocyte sedimentation rate (DAS28ESR) or a health assessment questionnaire (HAQ) along with age, sex, presence of hypertension, current smoking and ratio of total cholesterol to high-density lipoprotein cholesterol. Unfortunately, performance of these models was similar to general population CVD risk calculators. Efforts to develop a specific CVD risk calculator for patients with RA yielded 2 potential models including RA disease characteristics, but neither demonstrated improved performance compared to risk calculators designed for use in the general population. Challenges encountered and lessons learned are discussed in detail.

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

PubMed

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

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

PubMed

Bachur, R G; Harper, M B

2001-08-01

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

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine.

PubMed

Vistisen, Dorte; Andersen, Gregers Stig; Hansen, Christian Stevns; Hulman, Adam; Henriksen, Jan Erik; Bech-Nielsen, Henning; Jørgensen, Marit Eika

2016-03-15

Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts. This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting. © 2016 American Heart Association, Inc.

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

PubMed

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

2017-06-23

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

Prediction of muscle performance during dynamic repetitive movement

NASA Technical Reports Server (NTRS)

Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

2003-01-01

BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.

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.

10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study).

PubMed

McClelland, Robyn L; Jorgensen, Neal W; Budoff, Matthew; Blaha, Michael J; Post, Wendy S; Kronmal, Richard A; Bild, Diane E; Shea, Steven; Liu, Kiang; Watson, Karol E; Folsom, Aaron R; Khera, Amit; Ayers, Colby; Mahabadi, Amir-Abbas; Lehmann, Nils; Jöckel, Karl-Heinz; Moebus, Susanne; Carr, J Jeffrey; Erbel, Raimund; Burke, Gregory L

2015-10-13

Several studies have demonstrated the tremendous potential of using coronary artery calcium (CAC) in addition to traditional risk factors for coronary heart disease (CHD) risk prediction. However, to date, no risk score incorporating CAC has been developed. The goal of this study was to derive and validate a novel risk score to estimate 10-year CHD risk using CAC and traditional risk factors. Algorithm development was conducted in the MESA (Multi-Ethnic Study of Atherosclerosis), a prospective community-based cohort study of 6,814 participants age 45 to 84 years, who were free of clinical heart disease at baseline and followed for 10 years. MESA is sex balanced and included 39% non-Hispanic whites, 12% Chinese Americans, 28% African Americans, and 22% Hispanic Americans. External validation was conducted in the HNR (Heinz Nixdorf Recall Study) and the DHS (Dallas Heart Study). Inclusion of CAC in the MESA risk score offered significant improvements in risk prediction (C-statistic 0.80 vs. 0.75; p < 0.0001). External validation in both the HNR and DHS studies provided evidence of very good discrimination and calibration. Harrell's C-statistic was 0.779 in HNR and 0.816 in DHS. Additionally, the difference in estimated 10-year risk between events and nonevents was approximately 8% to 9%, indicating excellent discrimination. Mean calibration, or calibration-in-the-large, was excellent for both studies, with average predicted 10-year risk within one-half of a percent of the observed event rate. An accurate estimate of 10-year CHD risk can be obtained using traditional risk factors and CAC. The MESA risk score, which is available online on the MESA web site for easy use, can be used to aid clinicians when communicating risk to patients and when determining risk-based treatment strategies. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Vibrio bacteria in raw oysters: managing risks to human health.

PubMed

Froelich, Brett A; Noble, Rachel T

2016-03-05

The human-pathogenic marine bacteria Vibrio vulnificus and V. parahaemolyticus are strongly correlated with water temperature, with concentrations increasing as waters warm seasonally. Both of these bacteria can be concentrated in filter-feeding shellfish, especially oysters. Because oysters are often consumed raw, this exposes people to large doses of potentially harmful bacteria. Various models are used to predict the abundance of these bacteria in oysters, which guide shellfish harvest policy meant to reduce human health risk. Vibrio abundance and behaviour varies from site to site, suggesting that location-specific studies are needed to establish targeted risk reduction strategies. Moreover, virulence potential, rather than simple abundance, should be also be included in future modeling efforts. © 2016 The Author(s).

The ACS NSQIP Risk Calculator Is a Fair Predictor of Acute Periprosthetic Joint Infection.

PubMed

Wingert, Nathaniel C; Gotoff, James; Parrilla, Edgardo; Gotoff, Robert; Hou, Laura; Ghanem, Elie

2016-07-01

Periprosthetic joint infection (PJI) is a severe complication from the patient's perspective and an expensive one in a value-driven healthcare model. Risk stratification can help identify those patients who may have risk factors for complications that can be mitigated in advance of elective surgery. Although numerous surgical risk calculators have been created, their accuracy in predicting outcomes, specifically PJI, has not been tested. (1) How accurate is the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Site Infection Calculator in predicting 30-day postoperative infection? (2) How accurate is the calculator in predicting 90-day postoperative infection? We isolated 1536 patients who underwent 1620 primary THAs and TKAs at our institution during 2011 to 2013. Minimum followup was 90 days. The ACS NSQIP Surgical Risk Calculator was assessed in its ability to predict acute PJI within 30 and 90 days postoperatively. Patients who underwent a repeat surgical procedure within 90 days of the index arthroplasty and in whom at least one positive intraoperative culture was obtained at time of reoperation were considered to have PJI. A total of 19 cases of PJI were identified, including 11 at 30 days and an additional eight instances by 90 days postoperatively. Patient-specific risk probabilities for PJI based on demographics and comorbidities were recorded from the ACS NSQIP Surgical Risk Calculator website. The area under the curve (AUC) for receiver operating characteristic (ROC) curves was calculated to determine the predictability of the risk probability for PJI. The AUC is an effective method for quantifying the discriminatory capacity of a diagnostic test to correctly classify patients with and without infection in which it is defined as excellent (AUC 0.9-1), good (AUC 0.8-0.89), fair (AUC 0.7-0.79), poor (AUC 0.6-0.69), or fail/no discriminatory capacity (AUC 0.5-0.59). A p value of < 0.05 was considered to be statistically significant. The ACS NSQIP Surgical Risk Calculator showed only fair accuracy in predicting 30-day PJI (AUC: 74.3% [confidence interval {CI}, 59.6%-89.0%]. For 90-day PJI, the risk calculator was also only fair in accuracy (AUC: 71.3% [CI, 59.9%-82.6%]). Conclusions The ACS NSQIP Surgical Risk Calculator is a fair predictor of acute PJI at the 30- and 90-day intervals after primary THA and TKA. Practitioners should exercise caution in using this tool as a predictive aid for PJI, because it demonstrates only fair value in this application. Existing predictive tools for PJI could potentially be made more robust by incorporating preoperative risk factors and including operative and early postoperative variables. Level III, diagnostic study.

Faecal Pathogen Flows and Their Public Health Risks in Urban Environments: A Proposed Approach to Inform Sanitation Planning

PubMed Central

Mills, Freya; Petterson, Susan; Norman, Guy

2018-01-01

Public health benefits are often a key political driver of urban sanitation investment in developing countries, however, pathogen flows are rarely taken systematically into account in sanitation investment choices. While several tools and approaches on sanitation and health risks have recently been developed, this research identified gaps in their ability to predict faecal pathogen flows, to relate exposure risks to the existing sanitation services, and to compare expected impacts of improvements. This paper outlines a conceptual approach that links faecal waste discharge patterns with potential pathogen exposure pathways to quantitatively compare urban sanitation improvement options. An illustrative application of the approach is presented, using a spreadsheet-based model to compare the relative effect on disability-adjusted life years of six sanitation improvement options for a hypothetical urban situation. The approach includes consideration of the persistence or removal of different pathogen classes in different environments; recognition of multiple interconnected sludge and effluent pathways, and of multiple potential sites for exposure; and use of quantitative microbial risk assessment to support prediction of relative health risks for each option. This research provides a step forward in applying current knowledge to better consider public health, alongside environmental and other objectives, in urban sanitation decision making. Further empirical research in specific locations is now required to refine the approach and address data gaps. PMID:29360775

Bulk tank milk prevalence and production losses, spatial analysis, and predictive risk mapping of Ostertagia ostertagi infections in Mexican cattle herds.

PubMed

Villa-Mancera, Abel; Pastelín-Rojas, César; Olivares-Pérez, Jaime; Córdova-Izquierdo, Alejandro; Reynoso-Palomar, Alejandro

2018-05-01

This study investigated the prevalence, production losses, spatial clustering, and predictive risk mapping in different climate zones in five states of Mexico. The bulk tank milk samples obtained between January and April 2015 were analyzed for antibodies against Ostertagia ostertagi using the Svanovir ELISA. A total of 1204 farm owners or managers answered the questionnaire. The overall herd prevalence and mean optical density ratio (ODR) of parasite were 61.96% and 0.55, respectively. Overall, the production loss was approximately 0.542 kg of milk per parasited cow per day (mean ODR = 0.92, 142 farms, 11.79%). The spatial disease cluster analysis using SatScan software indicated that two high-risk clusters were observed. In the multivariable analysis, three models were tested for potential association with the ELISA results supported by climatic, environmental, and management factors. The final logistic regression model based on both climatic/environmental and management variables included the factors rainfall, elevation, land surface temperature (LST) day, and parasite control program that were significantly associated with an increased risk of infection. Geostatistical kriging was applied to generate a risk map for the presence of parasite in dairy cattle herds in Mexico. The results indicate that climatic and meteorological factors had a higher potential impact on the spatial distribution of O. ostertagi than the management factors.

The Cassava Mealybug (Phenacoccus manihoti) in Asia: First Records, Potential Distribution, and an Identification Key

PubMed Central

Parsa, Soroush; Kondo, Takumasa; Winotai, Amporn

2012-01-01

Phenacoccus manihoti Matile-Ferrero (Hemiptera: Pseudococcidae), one of the most serious pests of cassava worldwide, has recently reached Asia, raising significant concern over its potential spread throughout the region. To support management decisions, this article reports recent distribution records, and estimates the climatic suitability for its regional spread using a CLIMEX distribution model. The article also presents a taxonomic key that separates P. manihoti from all other mealybug species associated with the genus Manihot. Model predictions suggest P. manihoti imposes an important, yet differential, threat to cassava production in Asia. Predicted risk is most acute in the southern end of Karnataka in India, the eastern end of the Ninh Thuan province in Vietnam, and in most of West Timor in Indonesia. The model also suggests P. manihoti is likely to be limited by cold stress across Vietnam's northern regions and in the entire Guangxi province in China, and by high rainfall across the wet tropics in Indonesia and the Philippines. Predictions should be particularly important to guide management decisions for high risk areas where P. manihoti is absent (e.g., India), or where it has established but populations remain small and localized (e.g., South Vietnam). Results from this article should help decision-makers assess site-specific risk of invasion, and develop proportional prevention and surveillance programs for early detection and rapid response. PMID:23077659

Contrasting olfaction, vision, and audition as predictors of cognitive change and impairment in non-demented older adults.

PubMed

MacDonald, Stuart W S; Keller, Connor J C; Brewster, Paul W H; Dixon, Roger A

2018-05-01

This study examines the relative utility of a particular class of noninvasive functional biomarkers-sensory functions-for detecting those at risk of cognitive decline and impairment. Three central research objectives were examined including whether (a) olfactory function, vision, and audition exhibited significant longitudinal declines in nondemented older adults; (b) multiwave change for these sensory function indicators predicted risk of mild cognitive impairment (MCI); and (c) change within persons for each sensory measure shared dynamic time-varying associations with within-person change in cognitive functioning. A longitudinal sample (n = 408) from the Victoria Longitudinal Study was assembled. Three cognitive status subgroups were identified: not impaired cognitively, single-assessment MCI, and multiple-assessment MCI. We tested independent predictive associations, contrasting change in sensory function as predictors of cognitive decline and impairment, utilizing both linear mixed models and logistic regression analysis. Olfaction and, to a lesser extent, vision were identified as the most robust predictors of cognitive status and decline; audition showed little predictive influence. These findings underscore the potential utility of deficits in olfactory function, in particular, as an early marker of age- and pathology-related cognitive decline. Functional biomarkers may represent potential candidates for use in the early stages of a multistep screening approach for detecting those at risk of cognitive impairment, as well as for targeted intervention. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Electronic health record-based cardiac risk assessment and identification of unmet preventive needs.

PubMed

Persell, Stephen D; Dunne, Alexis P; Lloyd-Jones, Donald M; Baker, David W

2009-04-01

Cardiac risk assessment may not be routinely performed. Electronic health records (EHRs) offer the potential to automate risk estimation. We compared EHR-based assessment with manual chart review to determine the accuracy of automated cardiac risk estimation and determination of candidates for antiplatelet or lipid-lowering interventions. We performed an observational retrospective study of 23,111 adults aged 20 to 79 years, seen in a large urban primary care group practice. Automated assessments classified patients into 4 cardiac risk groups or as unclassifiable and determined candidates for antiplatelet or lipid-lowering interventions based on current guidelines. A blinded physician manually reviewed 100 patients from each risk group and the unclassifiable group. We determined the agreement between full review and automated assessments for cardiac risk estimation and identification of which patients were candidates for interventions. By automated methods, 9.2% of the population were candidates for lipid-lowering interventions, and 8.0% were candidates for antiplatelet medication. Agreement between automated risk classification and manual review was high (kappa = 0.91; 95% confidence interval [CI], 0.88-0.93). Automated methods accurately identified candidates for antiplatelet therapy [sensitivity, 0.81 (95% CI, 0.73-0.89); specificity, 0.98 (95% CI, 0.96-0.99); positive predictive value, 0.86 (95% CI, 0.78-0.94); and negative predictive value, 0.98 (95% CI, 0.97-0.99)] and lipid lowering [sensitivity, 0.92 (95% CI, 0.87-0.96); specificity, 0.98 (95% CI, 0.97-0.99); positive predictive value, 0.94 (95% CI, 0.89-0.99); and negative predictive value, 0.99 (95% CI, 0.98-> or =0.99)]. EHR data can be used to automatically perform cardiovascular risk stratification and identify patients in need of risk-lowering interventions. This could improve detection of high-risk patients whom physicians would otherwise be unaware.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

PubMed Central

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development. PMID:26262876

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

USGS Publications Warehouse

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales.

PubMed

Tack, Jason D; Fedy, Bradley C

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Breast Cancer Risk Prediction and Mammography Biopsy Decisions

PubMed Central

Armstrong, Katrina; Handorf, Elizabeth A.; Chen, Jinbo; Demeter, Mirar N. Bristol

2012-01-01

Background Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. Purpose To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. Methods The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. Results Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BIRADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. Conclusions The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors. PMID:23253645

Schistosomiasis: Geospatial Surveillance and Response Systems in Southeast Asia

NASA Astrophysics Data System (ADS)

Malone, John; Bergquist, Robert; Rinaldi, Laura; Xiao-nong, Zhou

2016-10-01

Geographic information system (GIS) and remote sensing (RS) from Earth-observing satellites offer opportunities for rapid assessment of areas endemic for vector-borne diseases including estimates of populations at risk and guidance to intervention strategies. This presentation deals with GIS and RS applications for the control of schistosomiasis in China and the Philippines. It includes large-scale risk mapping including identification of suitable habitats for Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Predictions of infection risk are discussed with reference to ecological transformations and the potential impact of climate change and the potential for long-term temperature increases in the North as well as the impact on rivers, lakes and water resource developments. Potential integration of geospatial mapping and modeling in schistosomiasis surveillance and response systems in Asia within Global Earth Observation System of Systems (GEOSS) guidelines in the health societal benefit area is discussed.

Development of Organ-Specific Donor Risk Indices

PubMed Central

Akkina, Sanjeev K.; Asrani, Sumeet K.; Peng, Yi; Stock, Peter; Kim, Ray; Israni, Ajay K.

2012-01-01

Due to the shortage of deceased donor organs, transplant centers accept organs from marginal deceased donors, including older donors. Organ-specific donor risk indices have been developed to predict graft survival using various combinations of donor and recipient characteristics. We will review the kidney donor risk index (KDRI) and liver donor risk index (LDRI) and compare and contrast their strengths, limitations, and potential uses. The Kidney Donor Risk Index has a potential role in developing new kidney allocation algorithms. The Liver Donor Risk Index allows for greater appreciation of the importance of donor factors, particularly for hepatitis C-positive recipients; as the donor risk index increases, rates of allograft and patient survival among these recipients decrease disproportionately. Use of livers with high donor risk index is associated with increased hospital costs independent of recipient risk factors, and transplanting livers with high donor risk index into patients with Model for End-Stage Liver Disease scores < 15 is associated with lower allograft survival; use of the Liver Donor Risk Index has limited this practice. Significant regional variation in donor quality, as measured by the Liver Donor Risk Index, remains in the United States. We also review other potential indices for liver transplant, including donor-recipient matching and the retransplant donor risk index. While substantial progress has been made in developing donor risk indices to objectively assess donor variables that affect transplant outcomes, continued efforts are warranted to improve these indices to enhance organ allocation policies and optimize allograft survival. PMID:22287036

Potential clinical value of PET/CT in predicting occult nodal metastasis in T1-T2N0M0 lung cancer patients staged by PET/CT

PubMed Central

Zhou, Xiang; Chen, Ruohua; Huang, Gang; Liu, Jianjun

2017-01-01

We assessed the clinical value of 2-fluoro-2-deoxyglucose (18F-FDG) PET/CT imaging for predicting occult nodal metastasis in non-small cell lung cancer (NSCLC) patients. This retrospective study included 54 patients with T1-2N0M0 NSCLC who had undergone 18F-FDG PET/CT before surgery. Occult nodal metastasis was detected in 25.9% (14/54) of the patients. Immunohistochemical analysis revealed that increased glucose transporter 1 expression was associated with occult nodal metastasis, but hexokinase 2 expression was not. Compared to the negative nodal metastasis group, the positive nodal metastasis group was associated with increased maximum standardized uptake value (SUVmax) and tumor size. Multivariate analysis indicated that SUVmax and tumor size were associated with nodal metastasis. Nodal metastasis could be predicted with a sensitivity of 92.9% and a specificity of 55.0% when the SUVmax cutoff was 4.35. When patients were divided into low-risk (tumor size ≤ 2.5 cm and SUVmax ≤ 4.35), moderate-risk (tumor size ≤ 2.5 cm and SUVmax > 4.35 or tumor size > 2.5 cm and SUVmax ≤ 4.35) and high-risk (tumor size > 2.5 cm and SUVmax > 4.35) groups, the lymph node metastasis rates were 4.3%, 22.7%, and 88.9%, respectively. These results indicate that the combination of SUVmax and tumor size has potential clinical value for predicting occult nodal metastasis in NSCLC patients. PMID:29137276

Recent advances in understanding idiopathic pulmonary fibrosis

PubMed Central

Daccord, Cécile; Maher, Toby M.

2016-01-01

Despite major research efforts leading to the recent approval of pirfenidone and nintedanib, the dismal prognosis of idiopathic pulmonary fibrosis (IPF) remains unchanged. The elaboration of international diagnostic criteria and disease stratification models based on clinical, physiological, radiological, and histopathological features has improved the accuracy of IPF diagnosis and prediction of mortality risk. Nevertheless, given the marked heterogeneity in clinical phenotype and the considerable overlap of IPF with other fibrotic interstitial lung diseases (ILDs), about 10% of cases of pulmonary fibrosis remain unclassifiable. Moreover, currently available tools fail to detect early IPF, predict the highly variable course of the disease, and assess response to antifibrotic drugs. Recent advances in understanding the multiple interrelated pathogenic pathways underlying IPF have identified various molecular phenotypes resulting from complex interactions among genetic, epigenetic, transcriptional, post-transcriptional, metabolic, and environmental factors. These different disease endotypes appear to confer variable susceptibility to the condition, differing risks of rapid progression, and, possibly, altered responses to therapy. The development and validation of diagnostic and prognostic biomarkers are necessary to enable a more precise and earlier diagnosis of IPF and to improve prediction of future disease behaviour. The availability of approved antifibrotic therapies together with potential new drugs currently under evaluation also highlights the need for biomarkers able to predict and assess treatment responsiveness, thereby allowing individualised treatment based on risk of progression and drug response. This approach of disease stratification and personalised medicine is already used in the routine management of many cancers and provides a potential road map for guiding clinical care in IPF. PMID:27303645

NOAA predicts moderate flood potential in Midwest, elevated risk of ice

Science.gov Websites

individuals to become weather-ready by ensuring you have real-time access to flood warnings via mobile devices and marine resources. Join us on Facebook, Twitter and our other social media channels. NOAA Mobile

In Vitro Pulmonary Toxicity of Metal Oxide Nanoparticles

EPA Science Inventory

Nanomaterials (NMs) encompass a diversity of materials with unique physicochemical characteristics which raise concerns about their potential risk to human health. Rapid predictive testing methods are needed to characterize NMs health effects as well as to screen and prioritize N...

Potential impacts of radon, terrestrial gamma and cosmic rays on childhood leukemia in France: a quantitative risk assessment.

PubMed

Laurent, Olivier; Ancelet, Sophie; Richardson, David B; Hémon, Denis; Ielsch, Géraldine; Demoury, Claire; Clavel, Jacqueline; Laurier, Dominique

2013-05-01

Previous epidemiological studies and quantitative risk assessments (QRA) have suggested that natural background radiation may be a cause of childhood leukemia. The present work uses a QRA approach to predict the excess risk of childhood leukemia in France related to three components of natural radiation: radon, cosmic rays and terrestrial gamma rays, using excess relative and absolute risk models proposed by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Both models were developed from the Life Span Study (LSS) of Japanese A-bomb survivors. Previous risk assessments were extended by considering uncertainties in radiation-related leukemia risk model parameters as part of this process, within a Bayesian framework. Estimated red bone marrow doses cumulated during childhood by the average French child due to radon, terrestrial gamma and cosmic rays are 4.4, 7.5 and 4.3 mSv, respectively. The excess fractions of cases (expressed as percentages) associated with these sources of natural radiation are 20 % [95 % credible interval (CI) 0-68 %] and 4 % (95 % CI 0-11 %) under the excess relative and excess absolute risk models, respectively. The large CIs, as well as the different point estimates obtained under these two models, highlight the uncertainties in predictions of radiation-related childhood leukemia risks. These results are only valid provided that models developed from the LSS can be transferred to the population of French children and to chronic natural radiation exposures, and must be considered in view of the currently limited knowledge concerning other potential risk factors for childhood leukemia. Last, they emphasize the need for further epidemiological investigations of the effects of natural radiation on childhood leukemia to reduce uncertainties and help refine radiation protection standards.

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

PubMed Central

Korolev, Igor O.; Symonds, Laura L.; Bozoki, Andrea C.

2016-01-01

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. PMID:26901338

Systems Toxicology: From Basic Research to Risk Assessment

PubMed Central

2014-01-01

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment. PMID:24446777

Systems toxicology: from basic research to risk assessment.

PubMed

Sturla, Shana J; Boobis, Alan R; FitzGerald, Rex E; Hoeng, Julia; Kavlock, Robert J; Schirmer, Kristin; Whelan, Maurice; Wilks, Martin F; Peitsch, Manuel C

2014-03-17

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.

Evaluation of biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes mellitus: A systematic review.

PubMed

Wotherspoon, Amy C; Young, Ian S; McCance, David R; Holmes, Valerie A

2016-07-01

Pre-eclampsia is a leading cause of maternal and perinatal morbidity and mortality. Women with type 1 diabetes are considered a high-risk group for developing pre-eclampsia. Much research has focused on biomarkers as a means of screening for pre-eclampsia in the general maternal population; however, there is a lack of evidence for women with type 1 diabetes. To undertake a systematic review to identify potential biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes. We searched Medline, EMBASE, Maternity and Infant Care, Scopus, Web of Science and CINAHL SELECTION CRITERIA: Studies were included if they measured biomarkers in blood or urine of women who developed pre-eclampsia and had pre-gestational type 1 diabetes mellitus Data collection and analysis A narrative synthesis was adopted as a meta-analysis could not be performed, due to high study heterogeneity. A total of 72 records were screened, with 21 eligible studies being included in the review. A wide range of biomarkers was investigated and study size varied from 34 to 1258 participants. No single biomarker appeared to be effective in predicting pre-eclampsia; however, glycaemic control was associated with an increased risk while a combination of angiogenic and anti-angiogenic factors seemed to be potentially useful. Limited evidence suggests that combinations of biomarkers may be more effective in predicting pre-eclampsia than single biomarkers. Further research is needed to verify the predictive potential of biomarkers that have been measured in the general maternal population, as many studies exclude women with diabetes preceding pregnancy. Copyright © 2016 Elsevier Inc. All rights reserved.

Postpartum Care and Contraception in Obese Women.

PubMed

Maclean, Courtney C; Thompson, Ivana S

2016-03-01

Postpartum obese women have an increased risk of breastfeeding difficulties and depression. Retaining the pregnancy weight at 6 months postpartum predicts long-term obesity. Risks for weight retention include excessive gestational weight gain, ethnicity, socioeconomic status, diet, exercise, depression, and duration of breastfeeding. Exercise and reducing total caloric intake promote postpartum weight loss. Intrauterine devices and contraceptive implants are the most effective contraceptives for obese women. Contraceptive pills, patches, and vaginal rings are effective options; however, obese women should be made aware of a potential increased risk of venous thromboembolism. Vasectomy and hysteroscopic sterilization carry the least surgical risk for obese women.

Behind the cycle of violence, beyond abuse history: a brief report on the association of parental attachment to physical child abuse potential.

PubMed

Rodriguez, Christina M; Tucker, Meagan C

2011-01-01

Although the concept of a cycle of violence presumes that the transmission of violence is expressed directly across generations, the role of the overall quality of the parent-child relationship may ultimately be more influential in later parenting behavior. This study investigated whether mothers' poorer attachment to their parents was associated with their current increased child abuse potential and dysfunctional disciplinary style independent of a personal history of child abuse. A sample of 73 at-risk mothers raising children with behavior problems reported on their parental attachment, abuse potential, dysfunctional parenting style, and personal abuse history. An at-risk sample, rather than a sample of identified abuse victims or perpetrators, was studied to better examine the potential continuity or discontinuity from history of abuse to current abuse risk, allowing consideration of those who may break the cycle versus those who potentially initiate abuse in the absence of a personal history. Findings indicate that poor attachment significantly predicted both dysfunctional parenting practices and elevated child abuse potential, controlling for personal child abuse history. Such results highlight the importance of the overall quality of the relationship between the parent and child in potentially shaping future abuse risk. Findings are discussed in terms of continuity or discontinuity in the cycle of violence and future directions for research on attachment in relation to the development of later child abuse risk.

A Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

PubMed Central

Paulus, Jessica K.; Wessler, Benjamin S.; Lundquist, Christine; Lai, Lana L.Y.; Raman, Gowri; Lutz, Jennifer S.; Kent, David M.

2017-01-01

Background Several widely-used risk scores for cardiovascular disease (CVD) incorporate sex effects, yet there has been no systematic summary of the role of sex in clinical prediction models (CPMs). To better understand the potential of these models to support sex-specific care, we conducted a field synopsis of sex effects in CPMs for CVD. Methods and Results We identified CPMs in the Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry, a comprehensive database of CVD CPMs published from 1/1990–5/2012. We report the proportion of models including sex effects on CVD incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 592 CVD-related CPMs, 193 (33%) included sex as a predictor or presented sex-stratified models. Sex effects were included in 78% (53/68) of models predicting incidence of CVD in a general population, versus only 35% (59/171), 21% (12/58) and 17% (12/72) of models predicting outcomes in patients with coronary artery disease (CAD), stroke, and heart failure, respectively. Among sex-including CPMs, women with heart failure were at lower mortality risk in 8/8 models; women undergoing revascularization for CAD were at higher mortality risk in 10/12 models. Factors associated with the inclusion of sex effects included the number of outcome events and using cohorts at-risk for CVD (rather than with established CVD). Conclusions While CPMs hold promise for supporting sex-specific decision making in CVD clinical care, sex effects are included in only one third of published CPMs. PMID:26908865

Sleep characteristics, body mass index, and risk for hypertension in young adolescents.

PubMed

Peach, Hannah; Gaultney, Jane F; Reeve, Charlie L

2015-02-01

Inadequate sleep has been identified as a risk factor for a variety of health consequences. For example, short sleep durations and daytime sleepiness, an indicator of insufficient sleep and/or poor sleep quality, have been identified as risk factors for hypertension in the adult population. However, less evidence exists regarding whether these relationships hold within child and early adolescent samples and what factors mediate the relationship between sleep and risk for hypertension. Using data from the Study of Early Child Care and Youth Development, the present study examined body mass index (BMI) as a possible mediator for the effects of school-night sleep duration, weekend night sleep duration, and daytime sleepiness on risk for hypertension in a sample of sixth graders. The results demonstrated gender-specific patterns. Among boys, all three sleep characteristics predicted BMI and yielded significant indirect effects on risk for hypertension. Oppositely, only daytime sleepiness predicted BMI among girls and yielded a significant indirect effect on risk for hypertension. The findings provide clarification for the influence of sleep on the risk for hypertension during early adolescence and suggest a potential need for gender-specific designs in future research and application endeavors.

Longitudinal Pathways from Cumulative Contextual Risk at Birth to School Functioning in Adolescence: Analysis of Mediation Effects and Gender Moderation.

PubMed

January, Stacy-Ann A; Mason, W Alex; Savolainen, Jukka; Solomon, Starr; Chmelka, Mary B; Miettunen, Jouko; Veijola, Juha; Moilanen, Irma; Taanila, Anja; Järvelin, Marjo-Riitta

2017-01-01

Children and adolescents exposed to multiple contextual risks are more likely to have academic difficulties and externalizing behavior problems than those who experience fewer risks. This study used data from the Northern Finland Birth Cohort 1986 (a population-based study; N = 6961; 51 % female) to investigate (a) the impact of cumulative contextual risk at birth on adolescents' academic performance and misbehavior in school, (b) learning difficulties and/or externalizing behavior problems in childhood as intervening mechanisms in the association of cumulative contextual risk with functioning in adolescence, and (c) potential gender differences in the predictive associations of cumulative contextual risk at birth with functioning in childhood or adolescence. The results of the structural equation modeling analysis suggested that exposure to cumulative contextual risk at birth had negative associations with functioning 16 years later, and academic difficulties and externalizing behavior problems in childhood mediated some of the predictive relations. Gender, however, did not moderate any of the associations. Therefore, the findings of this study have implications for the prevention of learning and conduct problems in youth and future research on the impact of cumulative risk exposure.

Longitudinal Pathways from Cumulative Contextual Risk at Birth to School Functioning in Adolescence: Analysis of Mediation Effects and Gender Moderation

PubMed Central

January, Stacy-Ann A.; Mason, W. Alex; Savolainen, Jukka; Solomon, Starr; Chmelka, Mary B.; Miettunen, Jouko; Veijola, Juha; Moilanen, Irma; Taanila, Anja; Järvelin, Marjo-Riitta

2016-01-01

Children and adolescents exposed to multiple contextual risks are more likely to have academic difficulties and externalizing behavior problems than those who experience fewer risks. This study used data from the Northern Finland Birth Cohort 1986 (a population-based study; N = 6,961; 51% female) to investigate (a) the impact of cumulative contextual risk at birth on adolescents’ academic performance and misbehavior in school, (b) learning difficulties and/or externalizing behavior problems in childhood as intervening mechanisms in the association of cumulative contextual risk with functioning in adolescence, and (c) potential gender differences in the predictive associations of cumulative contextual risk at birth with functioning in childhood or adolescence. The results of the structural equation modeling analysis suggested that exposure to cumulative contextual risk at birth had negative associations with functioning 16 years later, and academic difficulties and externalizing behavior problems in childhood mediated some of the predictive relations. Gender, however, did not moderate any of the associations. Therefore, the findings of this study have implications for the prevention of learning and conduct problems in youth and future research on the impact of cumulative risk exposure. PMID:27665276

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

A brief actuarial assessment for the prediction of wife assault recidivism: the Ontario domestic assault risk assessment.

PubMed

Hilton, N Zoe; Harris, Grant T; Rice, Marnie E; Lang, Carol; Cormier, Catherine A; Lines, Kathryn J

2004-09-01

An actuarial assessment to predict male-to-female marital violence was constructed from a pool of potential predictors in a sample of 589 offenders identified in police records and followed up for an average of almost 5 years. Archival information in several domains (offender characteristics, domestic violence history, nondomestic criminal history, relationship characteristics, victim characteristics, index offense) and recidivism were subjected to setwise and stepwise logistic regression. The resulting 13-item scale, the Ontario Domestic Assault Risk Assessment (ODARA), showed a large effect size in predicting new assaults against legal or common-law wives or ex-wives (Cohen's d = 1.1, relative operating characteristic area =.77) and was associated with number and severity of new assaults and time until recidivism. Cross-validation and comparisons with other instruments are also reported.

Assessing the fate and effects of an insecticidal formulation.

PubMed

de Perre, Chloé; Williard, Karl W J; Schoonover, Jon E; Young, Bryan G; Murphy, Tracye M; Lydy, Michael J

2015-01-01

A 3-yr study was conducted on a corn field in central Illinois, USA, to understand the fate and effects of an insecticidal formulation containing the active ingredients phostebupirim and cyfluthrin. The objectives were to determine the best tillage practice (conventional vs conservation tillage) in terms of grain yields and potential environmental risk, to assess insecticidal exposure using concentrations measured in soil and runoff water and sediments, to compare measured insecticidal concentrations with predicted concentrations from selected risk assessment exposure models, and to calculate toxicity benchmarks from laboratory bioassays performed on reference aquatic and terrestrial nontarget organisms, using individual active ingredients and the formulation. Corn grain yields were not significantly different based on tillage treatment. Similarly, field concentrations of insecticides were not significantly (p > 0.05) different in strip tillage versus conventional tillage, suggesting that neither of the tillage systems would enable greater environmental risk from the insecticidal formulation. Risk quotients were calculated from field concentrations and toxicity data to determine potential risk to nontarget species. The insecticidal formulation used at the recommended rate resulted in soil, sediment, and water concentrations that were potentially harmful to aquatic and terrestrial invertebrates, if exposure occurred, with risk quotients up to 34. © 2014 SETAC.

Psychosis prediction in secondary mental health services. A broad, comprehensive approach to the "at risk mental state" syndrome.

PubMed

Francesconi, M; Minichino, A; Carrión, R E; Delle Chiaie, R; Bevilacqua, A; Parisi, M; Rullo, S; Bersani, F Saverio; Biondi, M; Cadenhead, K

2017-02-01

Accuracy of risk algorithms for psychosis prediction in "at risk mental state" (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status. 138 non-psychotic outpatients (aged 17-31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD=0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index. 48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS-). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (-6.2%), but increased the sensitivity (+9.5%). These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Drought: A comprehensive R package for drought monitoring, prediction and analysis

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang

2015-04-01

Drought may impose serious challenges to human societies and ecosystems. Due to complicated causing effects and wide impacts, a universally accepted definition of drought does not exist. The drought indicator is commonly used to characterize drought properties such as duration or severity. Various drought indicators have been developed in the past few decades for the monitoring of a certain aspect of drought condition along with the development of multivariate drought indices for drought characterizations from multiple sources or hydro-climatic variables. Reliable drought prediction with suitable drought indicators is critical to the drought preparedness plan to reduce potential drought impacts. In addition, drought analysis to quantify the risk of drought properties would provide useful information for operation drought managements. The drought monitoring, prediction and risk analysis are important components in drought modeling and assessments. In this study, a comprehensive R package "drought" is developed to aid the drought monitoring, prediction and risk analysis (available from R-Forge and CRAN soon). The computation of a suite of univariate and multivariate drought indices that integrate drought information from various sources such as precipitation, temperature, soil moisture, and runoff is available in the drought monitoring component in the package. The drought prediction/forecasting component consists of statistical drought predictions to enhance the drought early warning for decision makings. Analysis of drought properties such as duration and severity is also provided in this package for drought risk assessments. Based on this package, a drought monitoring and prediction/forecasting system is under development as a decision supporting tool. The package will be provided freely to the public to aid the drought modeling and assessment for researchers and practitioners.

Screening for potential child maltreatment in parents of a newborn baby: The predictive validity of an Instrument for early identification of Parents At Risk for child Abuse and Neglect (IPARAN).

PubMed

van der Put, Claudia E; Bouwmeester-Landweer, Merian B R; Landsmeer-Beker, Eleonore A; Wit, Jan M; Dekker, Friedo W; Kousemaker, N Pieter J; Baartman, Herman E M

2017-08-01

For preventive purposes it is important to be able to identify families with a high risk of child maltreatment at an early stage. Therefore we developed an actuarial instrument for screening families with a newborn baby, the Instrument for identification of Parents At Risk for child Abuse and Neglect (IPARAN). The aim of this study was to assess the predictive validity of the IPARAN and to examine whether combining actuarial and clinical methods leads to an improvement of the predictive validity. We examined the predictive validity by calculating several performance indicators (i.e., sensitivity, specificity and the Area Under the receiver operating characteristic Curve [AUC]) in a sample of 4692 Dutch families with newborns. The outcome measure was a report of child maltreatment at Child Protection Services during a follow-up of 3 years. For 17 children (.4%) a report of maltreatment was registered. The predictive validity of the IPARAN was significantly better than chance (AUC=.700, 95% CI [.567-.832]), in contrast to a low value for clinical judgement of nurses of the Youth Health Care Centers (AUC=.591, 95% CI [.422-.759]). The combination of the IPARAN and clinical judgement resulted in the highest predictive validity (AUC=.720, 95% CI [.593-.847]), however, the difference between the methods did not reach statistical significance. The good predictive validity of the IPARAN in combination with clinical judgment of the nurse enables professionals to assess risks at an early stage and to make referrals to early intervention programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

The potential of intensity-modulated proton radiotherapy to reduce swallowing dysfunction in the treatment of head and neck cancer: A planning comparative study.

PubMed

van der Laan, Hans Paul; van de Water, Tara A; van Herpt, Heleen E; Christianen, Miranda E M C; Bijl, Hendrik P; Korevaar, Erik W; Rasch, Coen R; van 't Veld, Aart A; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A

2013-04-01

Predictive models for swallowing dysfunction were developed previously and showed the potential of improved intensity-modulated radiotherapy to reduce the risk of swallowing dysfunction. Still the risk is high. The aim of this study was to determine the potential of swallowing-sparing (SW) intensity-modulated proton therapy (IMPT) in head and neck cancer (HNC) for reducing the risk of swallowing dysfunction relative to currently used photon therapy. Twenty-five patients with oropharyngeal (n = 21) and hypopharyngeal (n = 4) cancer received primary radiotherapy, including bilateral neck irradiation, using standard (ST) intensity-modulated photon therapy (IMRT). Prophylactic (54 Gy) and therapeutic (70 Gy) target volumes were defined. The dose to the parotid and submandibular glands was reduced as much as possible. Four additional radiotherapy plans were created for each patient: SW-IMRT, ST-IMPT, 3-beam SW-IMPT (3B-SW-IMPT) and 7-beam SW-IMPT (7B-SW-IMPT). All plans were optimized similarly, with additional attempts to spare the swallowing organs at risk (SWOARs) in the SW plans. Probabilities of swallowing dysfunction were calculated with recently developed predictive models. All plans complied with standard HNC radiotherapy objectives. The mean parotid gland doses were similar for the ST and SW photon plans, but clearly lower in all IMPT plans (ipsilateral parotid gland ST-IMRT: 46 Gy, 7B-SW-IMPT: 29 Gy). The mean dose in the SWOARs was lowest with SW-IMPT, in particular with 7B-SW-IMPT (supraglottic larynx ST-IMRT: 60 Gy, 7B-SW-IMPT: 40 Gy). The observed dose reductions to the SWOARs translated into substantial overall reductions in normal tissue complication risks for different swallowing dysfunction endpoints. Compared with ST-IMRT, the risk of physician-rated grade 2-4 swallowing dysfunction was reduced on average by 8.8% (95% CI 6.5-11.1%) with SW-IMRT, and by 17.2% (95% CI: 12.7-21.7%) with 7B-SW-IMPT. SWOAR-sparing with proton therapy has the potential to substantially reduce the risk of swallowing dysfunction compared to similar treatment with photons.

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.

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

Lifetime risks of kidney donation: a medical decision analysis.

PubMed

Kiberd, Bryce A; Tennankore, Karthik K

2017-09-01

This study estimated the potential loss of life and the lifetime cumulative risk of end-stage renal disease (ESRD) from live kidney donation. Markov medical decision analysis. USA. 40-year-old live kidney donors of both sexes and black/white race. Live donor nephrectomy. Potential remaining life years lost, quality-adjusted life years (QALYs) lost and added lifetime cumulative risk of ESRD from donation. Overall 0.532-0.884 remaining life years were lost from donating a kidney. This was equivalent to 1.20%-2.34% of remaining life years (or 0.76%-1.51% remaining QALYs). The risk was higher in male and black individuals. The study showed that 1%-5% of average-age current live kidney donors might develop ESRD as a result of nephrectomy. The added risk of ESRD resulted in a loss of only 0.126-0.344 remaining life years. Most of the loss of life was predicted to be associated with chronic kidney disease (CKD) not ESRD. Most events occurred 25 or more years after donation. Reducing the increased risk of death associated with CKD had a modest overall effect on the per cent loss of remaining life years (0.72%-1.9%) and QALYs (0.58%-1.33%). Smoking and obesity reduced life expectancy and increased overall lifetime risks of ESRD in non-donors. However the percentage loss of remaining life years from donation was not very different in those with or without these risk factors. Live kidney donation may reduce life expectancy by 0.5-1 year in most donors. The development of ESRD in donors may not be the only measure of risk as most of the predicted loss of life predates ESRD. The study identifies the potential importance of following donors and treating risk factors aggressively to prevent ESRD and to improve donor survival. © 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.

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.

The risk assessment score in acute whiplash injury predicts outcome and reflects biopsychosocial factors.

PubMed

Kasch, Helge; Qerama, Erisela; Kongsted, Alice; Bach, Flemming W; Bendix, Tom; Jensen, Troels S

2011-12-01

One-year prospective study of 141 acute whiplash patients (WLP) and 40 acute ankle-injured controls. This study investigates a priori determined potential risk factors to develop a risk assessment tool, for which the expediency was examined. The whiplash-associated disorders (WAD) grading system that emerged from The Quebec Task-Force-on-Whiplash has been of limited value for predicting work-related recovery and for explaining biopsychosocial disability after whiplash and new predictive factors, for example, risk criteria that comprehensively differentiate acute WLP in a biopsychosocial manner are needed. Consecutively, 141 acute WLP and 40 ankle-injured recruited from emergency units were examined after 1 week, 1, 3, 6, and 12 months obtaining neck/head visual analog scale score, number of nonpainful complaints, epidemiological, social, psychological data and neurological examination, active neck mobility, and furthermore muscle tenderness and pain response, and strength and duration of neck muscles. Risk factors derived (reduced cervical range of motion, intense neck pain/headache, multiple nonpain complaints) were applied in a risk assessment score and divided into seven risk strata. A receiver operating characteristics curve for the Risk Assessment Score and 1-year work disability showed an area of 0.90. Risk strata and number of sick days showed a log-linear relationship. In stratum 1 full recovery was encountered, but for high-risk patients in stratum 6 only 50% and 7 only 20% had returned to work after 1 year (P < 5.4 × 10). Strength measures, psychophysical pain measurements, and psychological and social data (reported elsewhere) showed significant relation to risk strata. The Risk Assessment score is suggested as a valuable tool for grading WLP early after injury. It has reasonable screening power for encountering work disability and reflects the biopsychosocial nature of whiplash injuries.

Risk Assessment Using Cytochrome P450 Time-Dependent Inhibition Assays at Single Time and Concentration in the Early Stage of Drug Discovery.

PubMed

Kosaka, Mai; Kosugi, Yohei; Hirabayashi, Hideki

2017-09-01

In this article, we proposed a risk assessment strategy for CYP3A time-dependent inhibition (TDI) during drug discovery based on a thorough retrospective study of 13 reference drugs, some of which are known to have in vitro TDI potential but have unknown clinical relevance. First, the traditional parameter k inact /K I , recommended by regulatory authorities for necessity decision making in clinical drug-drug interaction (DDI) studies, was investigated as a predictive index for clinical TDI liability. The cutoff value of 1.1 for k inact /K I , established by the Food and Drug Administration, tended to produce false-positive prediction results for clinical DDI occurrence. The value of 1.25 recommended in the European Medicines Evaluation Agency draft guideline yielded better predictions with only 1 false negative for diltiazem. Second, to enable earlier risk assessment, remaining activity, defined as the residual CYP3A activity in vitro obtained in the screening conditions, was investigated as an alternative index. As a result, the ratios of unbound C max or area under the curve to remaining activity precisely predicted clinical DDI occurrence. In conclusion, we demonstrated the predictive power of k inact /K I and remaining activity values for clinical DDIs. These findings provide insights that enable TDI risk assessment, even during drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Emotional predictors of bowel screening: the avoidance-promoting role of fear, embarrassment, and disgust.

PubMed

Reynolds, Lisa M; Bissett, Ian P; Consedine, Nathan S

2018-05-03

Despite considerable efforts to address practical barriers, colorectal cancer screening numbers are often low. People do not always act rationally, and investigating emotions may offer insight into the avoidance of screening. The current work assessed whether fear, embarrassment, and disgust predicted colorectal cancer screening avoidance. A community sample (N = 306) aged 45+ completed a questionnaire assessing colorectal cancer screening history and the extent that perceptions of cancer risk, colorectal cancer knowledge, doctor discussions, and a specifically developed scale, the Emotional Barriers to Bowel Screening (EBBS), were associated with previous screening behaviours and anticipated bowel health decision-making. Step-wise logistic regression models revealed that a decision to delay seeking healthcare in the hypothetical presence of bowel symptoms was less likely in people who had discussed risk with their doctor, whereas greater colorectal cancer knowledge and greater fear of a negative outcome predicted greater likelihood of delay. Having previously provided a faecal sample was predicted by discussions about risk with a doctor, older age, and greater embarrassment, whereas perceptions of lower risk predicted a lower likelihood. Likewise, greater insertion disgust predicted a lower likelihood of having had an invasive bowel screening test in the previous 5 years. Alongside medical and demographic factors, fear, embarrassment and disgust are worthy of consideration in colorectal cancer screening. Understanding how specific emotions impact screening decisions and behaviour is an important direction for future work and has potential to inform screening development and communications in bowel health.

Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

PubMed

Uttam, Shikhar; Pham, Hoa V; LaFace, Justin; Leibowitz, Brian; Yu, Jian; Brand, Randall E; Hartman, Douglas J; Liu, Yang

2015-11-15

Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test. ©2015 American Association for Cancer Research.

Basic disturbances of information processing in psychosis prediction.

PubMed

Bodatsch, Mitja; Klosterkötter, Joachim; Müller, Ralf; Ruhrmann, Stephan

2013-01-01

The basic symptoms (BS) approach provides a valid instrument in predicting psychosis onset and represents moreover a significant heuristic framework for research. The term "basic symptoms" denotes subtle changes of cognition and perception in the earliest and prodromal stages of psychosis development. BS are thought to correspond to disturbances of neural information processing. Following the heuristic implications of the BS approach, the present paper aims at exploring disturbances of information processing, revealed by functional magnetic resonance imaging (fMRI) and electro-encephalographic as characteristics of the at-risk state of psychosis. Furthermore, since high-risk studies employing ultra-high-risk criteria revealed non-conversion rates commonly exceeding 50%, thus warranting approaches that increase specificity, the potential contribution of neural information processing disturbances to psychosis prediction is reviewed. In summary, the at-risk state seems to be associated with information processing disturbances. Moreover, fMRI investigations suggested that disturbances of language processing domains might be a characteristic of the prodromal state. Neurophysiological studies revealed that disturbances of sensory processing may assist psychosis prediction in allowing for a quantification of risk in terms of magnitude and time. The latter finding represents a significant advancement since an estimation of the time to event has not yet been achieved by clinical approaches. Some evidence suggests a close relationship between self-experienced BS and neural information processing. With regard to future research, the relationship between neural information processing disturbances and different clinical risk concepts warrants further investigations. Thereby, a possible time sequence in the prodromal phase might be of particular interest.

Predicting vascular complications in percutaneous coronary interventions.

PubMed

Piper, Winthrop D; Malenka, David J; Ryan, Thomas J; Shubrooks, Samuel J; O'Connor, Gerald T; Robb, John F; Farrell, Karen L; Corliss, Mary S; Hearne, Michael J; Kellett, Mirle A; Watkins, Matthew W; Bradley, William A; Hettleman, Bruce D; Silver, Theodore M; McGrath, Paul D; O'Mears, John R; Wennberg, David E

2003-06-01

Using a large, current, regional registry of percutaneous coronary interventions (PCI), we identified risk factors for postprocedure vascular complications and developed a scoring system to estimate individual patient risk. A vascular complication (access-site injury requiring treatment or bleeding requiring transfusion) is a potentially avoidable outcome of PCI. Data were collected on 18,137 consecutive patients undergoing PCI in northern New England from January 1997 to December 1999. Multivariate regression was used to identify characteristics associated with vascular complications and to develop a scoring system to predict risk. The rate of vascular complication was 2.98% (541 cases). Variables associated with increased risk in the multivariate analysis included age >or=70, odds ratio (OR) 2.7, female sex (OR 2.4), body surface area <1.6 m(2) (OR 1.9), history of congestive heart failure (OR 1.4), chronic obstructive pulmonary disease (OR 1.5), renal failure (OR 1.9), lower extremity vascular disease (OR 1.4), bleeding disorder (OR 1.68), emergent priority (OR 2.3), myocardial infarction (OR 1.7), shock (1.86), >or=1 type B2 (OR 1.32) or type C (OR 1.7) lesions, 3-vessel PCI (OR 1.5), use of thienopyridines (OR 1.4) or use of glycoprotein IIb/IIIa receptor inhibitors (OR 1.9). The model performed well in tests for significance, discrimination, and calibration. The scoring system captured 75% of actual vascular complications in its highest quintiles of predicted risk. Predicting the risk of post-PCI vascular complications is feasible. This information may be useful for clinical decision-making and institutional efforts at quality improvement.

Melanoma-specific mortality and competing mortality in patients with non-metastatic malignant melanoma: a population-based analysis.

PubMed

Shen, Weidong; Sakamoto, Naoko; Yang, Limin

2016-07-07

The objectives of this study were to evaluate and model the probability of melanoma-specific death and competing causes of death for patients with melanoma by competing risk analysis, and to build competing risk nomograms to provide individualized and accurate predictive tools. Melanoma data were obtained from the Surveillance Epidemiology and End Results program. All patients diagnosed with primary non-metastatic melanoma during the years 2004-2007 were potentially eligible for inclusion. The cumulative incidence function (CIF) was used to describe the probability of melanoma mortality and competing risk mortality. We used Gray's test to compare differences in CIF between groups. The proportional subdistribution hazard approach by Fine and Gray was used to model CIF. We built competing risk nomograms based on the models that we developed. The 5-year cumulative incidence of melanoma death was 7.1 %, and the cumulative incidence of other causes of death was 7.4 %. We identified that variables associated with an elevated probability of melanoma-specific mortality included older age, male sex, thick melanoma, ulcerated cancer, and positive lymph nodes. The nomograms were well calibrated. C-indexes were 0.85 and 0.83 for nomograms predicting the probability of melanoma mortality and competing risk mortality, which suggests good discriminative ability. This large study cohort enabled us to build a reliable competing risk model and nomogram for predicting melanoma prognosis. Model performance proved to be good. This individualized predictive tool can be used in clinical practice to help treatment-related decision making.

The Predictive Effects of Early Pregnancy Lipid Profiles and Fasting Glucose on the Risk of Gestational Diabetes Mellitus Stratified by Body Mass Index.

PubMed

Wang, Chen; Zhu, Weiwei; Wei, Yumei; Su, Rina; Feng, Hui; Lin, Li; Yang, Huixia

2016-01-01

This study aimed at evaluating the predictive effects of early pregnancy lipid profiles and fasting glucose on the risk of gestational diabetes mellitus (GDM) in patients stratified by prepregnancy body mass index (p-BMI) and to determine the optimal cut-off values of each indicator for different p-BMI ranges. A retrospective system cluster sampling survey was conducted in Beijing during 2013 and a total of 5,265 singleton pregnancies without prepregnancy diabetes were included. The information for each participant was collected individually using questionnaires and medical records. Logistic regression analysis and receiver operator characteristics analysis were used in the analysis. Outcomes showed that potential markers for the prediction of GDM include early pregnancy lipid profiles (cholesterol, triacylglycerols, low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratios [LDL-C/HDL-C], and triglyceride to high-density lipoprotein cholesterol ratios [TG/HDL-C]) and fasting glucose, of which fasting glucose level was the most accurate indicator. Furthermore, the predictive effects and cut-off values for these factors varied according to p-BMI. Thus, p-BMI should be a consideration for the risk assessment of pregnant patients for GDM development.

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

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

PubMed Central

Fox, Naomi J.; Marion, Glenn; Davidson, Ross S.; White, Piran C. L.; Hutchings, Michael R.

2012-01-01

Simple Summary Parasitic helminths represent one of the most pervasive challenges to livestock, and their intensity and distribution will be influenced by climate change. There is a need for long-term predictions to identify potential risks and highlight opportunities for control. We explore the approaches to modelling future helminth risk to livestock under climate change. One of the limitations to model creation is the lack of purpose driven data collection. We also conclude that models need to include a broad view of the livestock system to generate meaningful predictions. Abstract Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed. PMID:26486780

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

PubMed

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

2010-12-01

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

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.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

PubMed

Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

2015-04-01

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Childhood Hodgkin International Prognostic Score (CHIPS) Predicts event-free survival in Hodgkin Lymphoma: A Report from the Children’s Oncology Group

PubMed Central

Schwartz, Cindy L.; Chen, Lu; McCarten, Kathleen; Wolden, Suzanne; Constine, Louis S.; Hutchison, Robert E.; de Alarcon, Pedro A.; Keller, Frank G.; Kelly, Kara M.; Trippet, Tanya A.; Voss, Stephan D.; Friedman, Debra L.

2017-01-01

Background Early response to initial chemotherapy in Hodgkin lymphoma (HL) measured by computed tomography (CT) and/or positron emission tomography (PET) after two to three cycles of chemotherapy may inform therapeutic decisions. Risk stratification at diagnosis could, however, allow earlier and potentially more efficacious treatment modifications. Patients and Methods We developed a predictive model for event-free survival (EFS) in pediatric/adolescent HL using clinical data known at diagnosis from 1103 intermediate-risk HL patients treated on Children’s Oncology Group protocol AHOD0031 with doxorubicin, bleomycin, vincristine, etoposide, prednisone, cyclophosphamide (ABVE-PC) chemotherapy and radiation. Independent predictors of EFS were identified and used to develop and validate a prognostic score (Childhood Hodgkin International Prognostic Score [CHIPS]). A training cohort was randomly selected to include approximately half of the overall cohort, with the remainder forming the validation cohort. Results Stage 4 disease, large mediastinal mass, albumin (<3.5), and fever were independent predictors of EFS that were each assigned one point in the CHIPS. Four-year EFS was 93.1% for patients with CHIPS = 0, 88.5% for patients with CHIPS = 1, 77.6% for patients with CHIPS = 2, and 69.2% for patients with CHIPS = 3. Conclusions CHIPS was highly predictive of EFS, identifying a subset (with CHIPS 2 or 3) that comprises 27% of intermediate-risk patients who have a 4-year EFS of <80% and who may benefit from early therapeutic augmentation. Furthermore, CHIPS identified higher risk patients who were not identified by early PET or CT response. CHIPS is a robust and inexpensive approach to predicting risk in patients with intermediate-risk HL that may improve ability to tailor therapy to risk factors known at diagnosis. PMID:27786406

Childhood Hodgkin International Prognostic Score (CHIPS) Predicts event-free survival in Hodgkin Lymphoma: A Report from the Children's Oncology Group.

PubMed

Schwartz, Cindy L; Chen, Lu; McCarten, Kathleen; Wolden, Suzanne; Constine, Louis S; Hutchison, Robert E; de Alarcon, Pedro A; Keller, Frank G; Kelly, Kara M; Trippet, Tanya A; Voss, Stephan D; Friedman, Debra L

2017-04-01

Early response to initial chemotherapy in Hodgkin lymphoma (HL) measured by computed tomography (CT) and/or positron emission tomography (PET) after two to three cycles of chemotherapy may inform therapeutic decisions. Risk stratification at diagnosis could, however, allow earlier and potentially more efficacious treatment modifications. We developed a predictive model for event-free survival (EFS) in pediatric/adolescent HL using clinical data known at diagnosis from 1103 intermediate-risk HL patients treated on Children's Oncology Group protocol AHOD0031 with doxorubicin, bleomycin, vincristine, etoposide, prednisone, cyclophosphamide (ABVE-PC) chemotherapy and radiation. Independent predictors of EFS were identified and used to develop and validate a prognostic score (Childhood Hodgkin International Prognostic Score [CHIPS]). A training cohort was randomly selected to include approximately half of the overall cohort, with the remainder forming the validation cohort. Stage 4 disease, large mediastinal mass, albumin (<3.5), and fever were independent predictors of EFS that were each assigned one point in the CHIPS.  Four-year EFS was 93.1% for patients with CHIPS = 0, 88.5% for patients with CHIPS = 1, 77.6% for patients with CHIPS = 2, and 69.2% for patients with CHIPS = 3. CHIPS was highly predictive of EFS, identifying a subset (with CHIPS 2 or 3) that comprises 27% of intermediate-risk patients who have a 4-year EFS of <80% and who may benefit from early therapeutic augmentation.  Furthermore, CHIPS identified higher risk patients who were not identified by early PET or CT response. CHIPS is a robust and inexpensive approach to predicting risk in patients with intermediate-risk HL that may improve ability to tailor therapy to risk factors known at diagnosis. © 2016 Wiley Periodicals, Inc.

Correlation between Novel Potential Indoor Risk Factors and Frequency of Doctor's Visit for Respiratory Problem in Taiwan's Tropical Environment.

PubMed

Wang, Yu-Hao; Su, Hsing-Hao; Hsu, Lan; Wang, Chung-Yang; Wu, Pi-Hsiung

2018-01-01

With a global rising trend in prevalence of allergic diseases, more attention has been paid to investigation of environmental risk factors. Many risk factors have so far been identified. However, novel risk factors specific to Taiwanese environment and lifestyle were still relatively unknown. To investigate the potential effects of a number of little-known indoor risk factors on the frequency of doctor's visit for respiratory problems in context of Taiwanese environment and lifestyle. A cross-sectional, population-based study was performed on a 861 participants around Kaohsiung area, Taiwan. Survey investigation was employed to assess the household environment and the frequency of doctor's visit for respiratory problems. Participants who performed "daily cleaning" was shown to have a significantly (p=0.007) higher mean number of doctor's visits in comparison to those who did not. Similar observation was made for participants who periodically took out beddings (p=0.042). Age had a significant positive correlation (linear regression β 0.089) with frequency of respiratory problems. The habit of daily cleaning was implicated as a potential indoor risk factor due to the unique nature of Taiwanese cleaning habit and close contact with cleaning supplies, which could serve as chemical irritants. Bedding takeout was predicted to be an indicator of chronic allergies rather than an actual risk factor. However, both were controversial in their role as potential indoor risk factor, and required further examination.

Child Maltreatment and Risky Sexual Behavior.

PubMed

Thompson, Richard; Lewis, Terri; Neilson, Elizabeth C; English, Diana J; Litrownik, Alan J; Margolis, Benyamin; Proctor, Laura; Dubowitz, Howard

2017-02-01

Risky sexual behavior is a serious public health problem. Child sexual abuse is an established risk factor, but other forms of maltreatment appear to elevate risky behavior. The mechanisms by which child maltreatment influence risk are not well understood. This study used data from 859 high-risk youth, followed through age 18. Official reports of each form of maltreatment were coded. At age 16, potential mediators (trauma symptoms and substance use) were assessed. At age 18, risky sexual behavior (more than four partners, unprotected sex, unassertiveness in sexual refusal) was assessed. Neglect significantly predicted unprotected sex. Substance use predicted unprotected sex and four or more partners but did not mediate the effects of maltreatment. Trauma symptoms predicted unprotected sex and mediated effects of emotional maltreatment on unprotected sex and on assertiveness in sexual refusal and the effects of sexual abuse on unprotected sex. Both neglect and emotional maltreatment emerged as important factors in risky sexual behavior. Trauma symptoms appear to be an important pathway by which maltreatment confers risk for risky sexual behavior. Interventions to reduce risky sexual behavior should include assessment and treatment for trauma symptoms and for history of child maltreatment in all its forms.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

PubMed

Shrestha, Akritee; Bergquist, Savannah; Montz, Ellen; Rose, Sherri

2017-12-15

To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. 2012-2013 Truven MarketScan database. We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC + CCS diagnostic variables. Performance was evaluated based on cross-validated R 2 and predictive ratios. Results show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas. Literature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs. © Health Research and Educational Trust.

Rift Valley Fever Prediction and Risk Mapping: 2014-2015 Season

NASA Technical Reports Server (NTRS)

Anyamba, Assaf

2015-01-01

Extremes in either direction (+-) of precipitation temperature have significant implications for disease vectors and pathogen emergence and spread Magnitude of ENSO influence on precipitation temperature cannot be currently predicted rely on average history and patterns. Timing of event and emergence disease can be exploited (GAP) in to undertake vector control and preparedness measures. Currently - no risk for ecologically-coupled RVFV activity however we need to be vigilant during the coming fall season due the ongoing buildup of energy in the central Pacific Ocean. Potential for the dual-use of the RVF Monitor system for other VBDs Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation.

Boredom proneness and emotion regulation predict emotional eating.

PubMed

Crockett, Amanda C; Myhre, Samantha K; Rokke, Paul D

2015-05-01

Emotional eating is considered a risk factor for eating disorders and an important contributor to obesity and its associated health problems. It has been suggested that boredom may be an important contributor to overeating, but has received relatively little attention. A sample of 552 college students was surveyed. Linear regression analyses found that proneness to boredom and difficulties in emotion regulation simultaneously predicted inappropriate eating behavior, including eating in response to boredom, other negative emotions, and external cues. The unique contributions of these variables to emotional eating were discussed. These findings help to further identify which individuals could be at risk for emotional eating and potentially for unhealthy weight gain. © The Author(s) 2015.

Asymmetric specialization and extinction risk in plant-flower visitor webs: a matter of morphology or abundance?

PubMed

Stang, Martina; Klinkhamer, Peter G L; van der Meijden, Eddy

2007-03-01

A recently discovered feature of plant-flower visitor webs is the asymmetric specialization of the interaction partners: specialized plants interact mainly with generalized flower visitors and specialized flower visitors mainly with generalized plants. Little is known about the factors leading to this asymmetry and their consequences for the extinction risk of species. Previous studies have proposed random interactions proportional to species abundance as an explanation. However, the simulation models used in these studies did not include potential biological constraints. In the present study, we tested the potential role of both morphological constraints and species abundance in promoting asymmetric specialization. We compared actual field data of a Mediterranean plant-flower visitor web with predictions of Monte Carlo simulations including different combinations of the potential factors structuring the web. Our simulations showed that both nectar-holder depth and abundance were able to produce asymmetry; but that the expected degree of asymmetry was stronger if based on both. Both factors can predict the number of interaction partners, but only nectar-holder depth was able to predict the degree of asymmetry of a certain species. What is more, without the size threshold the influence of abundance would disappear over time. Thus, asymmetric specialization seems to be the result of a size threshold and, only among the allowed interactions above this size threshold, a result of random interactions proportional to abundance. The simulations also showed that asymmetric specialization could not be the reason that the extinction risk of specialists and generalists is equalized, as suggested in the literature. In asymmetric webs specialists clearly had higher short-term extinction risks. In fact, primarily generalist visitors seem to profit from asymmetric specialization. In our web, specialists were less abundant than generalists. Therefore, including abundance in the simulation models increased the difference between specialists and generalists even more.

Single Motherhood, Alcohol Dependence, and Smoking During Pregnancy: A Propensity Score Analysis.

PubMed

Waldron, Mary; Bucholz, Kathleen K; Lian, Min; Lessov-Schlaggar, Christina N; Miller, Ruth Huang; Lynskey, Michael T; Knopik, Valerie S; Madden, Pamela A F; Heath, Andrew C

2017-09-01

Few studies linking single motherhood and maternal smoking during pregnancy consider correlated risk from problem substance use beyond history of smoking and concurrent use of alcohol. In the present study, we used propensity score methods to examine whether the risk of smoking during pregnancy associated with single motherhood is the result of potential confounders, including alcohol dependence. Data were drawn from mothers participating in a birth cohort study of their female like-sex twin offspring (n = 257 African ancestry; n = 1,711 European or other ancestry). We conducted standard logistic regression models predicting smoking during pregnancy from single motherhood at twins' birth, followed by propensity score analyses comparing single-mother and two-parent families stratified by predicted probability of single motherhood. In standard models, single motherhood predicted increased risk of smoking during pregnancy in European ancestry but not African ancestry families. In propensity score analyses, rates of smoking during pregnancy were elevated in single-mother relative to two-parent European ancestry families across much of the spectrum a priori risk of single motherhood. Among African ancestry families, within-strata comparisons of smoking during pregnancy by single-mother status were nonsignificant. These findings highlight single motherhood as a unique risk factor for smoking during pregnancy in European ancestry mothers, over and above alcohol dependence. Additional research is needed to identify risks, beyond single motherhood, associated with smoking during pregnancy in African ancestry mothers.

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.

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.

PubMed

Ring, Caroline L; Pearce, Robert G; Setzer, R Woodrow; Wetmore, Barbara A; Wambaugh, John F

2017-09-01

The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with physiological parameters for a virtual population. For risk-based prioritization of chemicals, predicted bioactive equivalent doses were compared to demographic-specific inferences of exposure rates that were based on NHANES urinary analyte biomonitoring data. The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average for the total population when compared to previous methods. However, for some combinations of chemical and demographic groups the margin was reduced by as much as three quarters. This TK modeling framework allows targeted risk prioritization of chemicals for demographic groups of interest, including potentially sensitive life stages and subpopulations. Published by Elsevier Ltd.

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

PubMed

Ben-Sasson, Ayelet; Robins, Diana L; Yom-Tov, Elad

2018-04-24

Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned parents. The objective of this study was to test the feasibility of assessing autism spectrum disorder risk in parental concerns from Web-based sources, using automated text analysis tools and minimal standard questioning. Participants were 115 parents with concerns regarding their child's social-communication development. Children were 16- to 30-months old, and 57.4% (66/115) had a family history of autism spectrum disorder. Parents reported their concerns online, and completed an autism spectrum disorder-specific screener, the Modified Checklist for Autism in Toddlers-Revised, with Follow-up (M-CHAT-R/F), and a broad developmental screener, the Ages and Stages Questionnaire (ASQ). An algorithm predicted autism spectrum disorder risk using a combination of the parent's text and a single screening question, selected by the algorithm to enhance prediction accuracy. Screening measures identified 58% (67/115) to 88% (101/115) of children at risk for autism spectrum disorder. Children with a family history of autism spectrum disorder were 3 times more likely to show autism spectrum disorder risk on screening measures. The prediction of a child's risk on the ASQ or M-CHAT-R was significantly more accurate when predicted from text combined with an M-CHAT-R question selected (automatically) than from the text alone. The frequently automatically selected M-CHAT-R questions that predicted risk were: following a point, make-believe play, and concern about deafness. The internet can be harnessed to prescreen for autism spectrum disorder using parental concerns by administering a few standardized screening questions to augment this process. ©Ayelet Ben-Sasson, Diana L Robins, Elad Yom-Tov. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2018.

DYT1 dystonia increases risk taking in humans

PubMed Central

Arkadir, David; Radulescu, Angela; Raymond, Deborah; Lubarr, Naomi; Bressman, Susan B; Mazzoni, Pietro; Niv, Yael

2016-01-01

It has been difficult to link synaptic modification to overt behavioral changes. Rodent models of DYT1 dystonia, a motor disorder caused by a single gene mutation, demonstrate increased long-term potentiation and decreased long-term depression in corticostriatal synapses. Computationally, such asymmetric learning predicts risk taking in probabilistic tasks. Here we demonstrate abnormal risk taking in DYT1 dystonia patients, which is correlated with disease severity, thereby supporting striatal plasticity in shaping choice behavior in humans. DOI: http://dx.doi.org/10.7554/eLife.14155.001 PMID:27249418

Health risk perception, optimistic bias, and personal satisfaction.

PubMed

Bränström, Richard; Brandberg, Yvonne

2010-01-01

To examine change in risk perception and optimistic bias concerning behavior-linked health threats and environmental health threats between adolescence and young adulthood and how these factors related to personal satisfaction. In 1996 and 2002, 1624 adolescents responded to a mailed questionnaire. Adolescents showed strong positive optimistic bias concerning behaviorlinked risks, and this optimistic bias increased with age. Increase in optimistic bias over time predicted increase in personal satisfaction. The capacity to process and perceive potential threats in a positive manner might be a valuable human ability positively influencing personal satisfaction and well-being.

Plasma Lipidomic Profiles Improve on Traditional Risk Factors for the Prediction of Cardiovascular Events in Type 2 Diabetes Mellitus.

PubMed

Alshehry, Zahir H; Mundra, Piyushkumar A; Barlow, Christopher K; Mellett, Natalie A; Wong, Gerard; McConville, Malcolm J; Simes, John; Tonkin, Andrew M; Sullivan, David R; Barnes, Elizabeth H; Nestel, Paul J; Kingwell, Bronwyn A; Marre, Michel; Neal, Bruce; Poulter, Neil R; Rodgers, Anthony; Williams, Bryan; Zoungas, Sophia; Hillis, Graham S; Chalmers, John; Woodward, Mark; Meikle, Peter J

2016-11-22

Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization-tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework. Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval [CI], 0.678-0.682) to 0.700 (95% CI, 0.698-0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219-0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738-0.742) to 0.760 (95% CI, 0.757-0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317-0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease). The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus. URL: https://clinicaltrials.gov. Unique identifier: NCT00145925. © 2016 American Heart Association, Inc.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Procalcitonin as a potential predicting factor for prognosis in bacterial meningitis.

PubMed

Park, Bong Soo; Kim, Si Eun; Park, Si Hyung; Kim, Jinseung; Shin, Kyong Jin; Ha, Sam Yeol; Park, JinSe; Kim, Sung Eun; Lee, Byung In; Park, Kang Min

2017-02-01

We investigated the potential role of serum procalcitonin in differentiating bacterial meningitis from viral meningitis, and in predicting the prognosis in patients with bacterial meningitis. This was a retrospective study of 80 patients with bacterial meningitis (13 patients died). In addition, 58 patients with viral meningitis were included as the disease control groups for comparison. The serum procalcitonin level was measured in all patients at admission. Differences in demographic and laboratory data, including the procalcitonin level, were analyzed between the groups. We used the mortality rate during hospitalization as a marker of prognosis in patients with bacterial meningitis. Multiple logistic regression analysis showed that high serum levels of procalcitonin (>0.12ng/mL) were an independently significant variable for differentiating bacterial meningitis from viral meningitis. The risk of having bacterial meningitis with high serum levels of procalcitonin was at least 6 times higher than the risk of having viral meningitis (OR=6.76, 95% CI: 1.84-24.90, p=0.004). In addition, we found that high levels of procalcitonin (>7.26ng/mL) in the blood were an independently significant predictor for death in patients with bacterial meningitis. The risk of death in patients with bacterial meningitis with high serum levels of procalcitonin may be at least 9 times higher than those without death (OR=9.09, 95% CI: 1.74-47.12, p=0.016). We found that serum procalcitonin is a useful marker for differentiating bacterial meningitis from viral meningitis, and it is also a potential predicting factor for prognosis in patients with bacterial meningitis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

PubMed

Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Pesticides drive risk of micropollutants in wastewater-impacted streams during low flow conditions.

PubMed

Munz, Nicole A; Burdon, Francis J; de Zwart, Dick; Junghans, Marion; Melo, Laura; Reyes, Marta; Schönenberger, Urs; Singer, Heinz P; Spycher, Barbara; Hollender, Juliane; Stamm, Christian

2017-03-01

Micropollutants enter surface waters through various pathways, of which wastewater treatment plants (WWTPs) are a major source. The large diversity of micropollutants and their many modes of toxic action pose a challenge for assessing environmental risks. In this study, we investigated the potential impact of WWTPs on receiving ecosystems by describing concentration patterns of micropollutants, predicting acute risks for aquatic organisms and validating these results with macroinvertebrate biomonitoring data. Grab samples were taken upstream, downstream and at the effluent of 24 Swiss WWTPs during low flow conditions across independent catchments with different land uses. Using liquid chromatography high resolution tandem mass spectrometry, a comprehensive target screening of almost 400 organic substances, focusing mainly on pesticides and pharmaceuticals, was conducted at two time points, and complemented with the analysis of a priority mixture of 57 substances over eight time points. Acute toxic pressure was predicted using the risk assessment approach of the multi-substance potentially affected fraction, first applying concentration addition for substances with the same toxic mode of action and subsequently response addition for the calculation of the risk of the total mixture. This toxic pressure was compared to macroinvertebrate sensitivity to pesticides (SPEAR index) upstream and downstream of the WWTPs. The concentrations were, as expected, especially for pharmaceuticals and other household chemicals higher downstream than upstream, with the detection frequency of plant protection products upstream correlating with the fraction of arable land in the catchments. While the concentration sums downstream were clearly dominated by pharmaceuticals or other household chemicals, the acute toxic pressure was mainly driven by pesticides, often caused by the episodic occurrence of these compounds even during low flow conditions. In general, five single substances explained much of the total risk, with diclofenac, diazinon and clothianidin as the main drivers. Despite the low predicted acute risk of 0%-2.1% for affected species, a significant positive correlation with macroinvertebrate sensitivity to pesticides was observed. However, more effect data for pharmaceuticals and a better quantification of episodic pesticide pollution events are needed for a more comprehensive risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Risk assessment and the prevention of radicalization from nonviolence into terrorism.

PubMed

Sarma, Kiran M

2017-04-01

This article considers the challenges associated with completing risk assessments in countering violent extremism. In particular, it is concerned with risk assessment of those who come to the attention of government and nongovernment organizations as being potentially on a trajectory toward terrorism and where there is an obligation to consider the potential future risk that they may pose. Risk assessment in this context is fraught with difficulty, primarily due to the variable nature of terrorism, the low base-rate problem, and the dearth of strong evidence on relevant risk and resilience factors. Statistically, this will lead to poor predictive value. Ethically, it can lead to the labeling of an individual who is not on a trajectory toward violence as being "at risk" of engaging in terrorism and the imposing of unnecessary risk management actions. The article argues that actuarial approaches to risk assessment in this context cannot work. However, it further argues that approaches that help assessors to process and synthesize information in a structured way are of value and are in line with good practice in the broader field of violence risk assessment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry.

PubMed

Rowlinson, Steve; Jia, Yunyan Andrea

2014-04-01

Existing heat stress risk management guidelines recommended by international standards are not practical for the construction industry which needs site supervision staff to make instant managerial decisions to mitigate heat risks. The ability of the predicted heat strain (PHS) model [ISO 7933 (2004). Ergonomics of the thermal environment analytical determination and interpretation of heat stress using calculation of the predicted heat strain. Geneva: International Standard Organisation] to predict maximum allowable exposure time (D lim) has now enabled development of localized, action-triggering and threshold-based guidelines for implementation by lay frontline staff on construction sites. This article presents a protocol for development of two heat stress management tools by applying the PHS model to its full potential. One of the tools is developed to facilitate managerial decisions on an optimized work-rest regimen for paced work. The other tool is developed to enable workers' self-regulation during self-paced work.

The Heinz Nixdorf Recall study and its potential impact on the adoption of atherosclerosis imaging in European primary prevention guidelines.

PubMed

Mahabadi, Amir A; Möhlenkamp, Stefan; Moebus, Susanne; Dragano, Nico; Kälsch, Hagen; Bauer, Marcus; Jöckel, Karl-Heinz; Erbel, Raimund

2011-10-01

Non-contrast-enhanced computed tomography (CT) imaging of the heart enables noninvasive quantification of coronary artery calcification (CAC), a surrogate marker of the atherosclerotic burden in the coronary artery tree. Multiple studies have underlined the ability of CAC score for individual risk stratification and, accordingly, the American Heart Association recommended cardiac CT for risk assessment in individuals with an intermediate risk of cardiovascular events as measured by Framingham Risk Score. However, limitations in transcribing risk stratification algorithms based on American cohort studies into European populations have been acknowledged in the past. Moreover, data on implications for reclassification into higher- or lower-risk groups based on CAC scores were lacking. The Heinz Nixdorf Recall (HNR) study is a population-based cohort study that investigated the ability of CAC scoring in risk prediction for major cardiovascular events above and beyond traditional cardiovascular risk factors. According to Heinz Nixdorf Recall findings, CAC can be used for reclassification, especially in those in the intermediate-risk group, to advise on lifestyle changes for the reclassified low-risk category, or to implement intensive treatments for the reclassified high-risk individuals. This article discusses the present findings of the Heinz Nixdorf Recall Study with respect to the current literature, risk stratification algorithms, and current European guidelines for risk prediction.

From Risk Assessment to Risk Management: Matching Interventions to Adolescent Offenders’ Strengths and Vulnerabilities

PubMed Central

Singh, Jay P.; Desmarais, Sarah L.; Sellers, Brian G.; Hylton, Tatiana; Tirotti, Melissa; Van Dorn, Richard A.

2013-01-01

Though considerable research has examined the validity of risk assessment tools in predicting adverse outcomes in justice-involved adolescents, the extent to which risk assessments are translated into risk management strategies and, importantly, the association between this link and adverse outcomes has gone largely unexamined. To address these shortcomings, the Risk-Need-Responsivity (RNR) model was used to examine associations between identified strengths and vulnerabilities, interventions, and institutional outcomes for justice-involved youth. Data were collected from risk assessments completed using the Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) for 120 adolescent offenders (96 boys and 24 girls). Interventions and outcomes were extracted from institutional records. Mixed evidence of adherence to RNR principles was found. Accordant to the risk principle, adolescent offenders judged to have more strengths had more strength-based interventions in their service plans, though adolescent offenders with more vulnerabilities did not have more interventions targeting their vulnerabilities. With respect to the need and responsivity principles, vulnerabilities and strengths identified as particularly relevant to the individual youth's risk of adverse outcomes were addressed in the service plans about half and a quarter of the time, respectively. Greater adherence to the risk and need principles was found to predict significantly the likelihood of externalizing outcomes. Findings suggest some gaps between risk assessment and risk management and highlight the potential usefulness of strength-based approaches to intervention. PMID:25346561

Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl

USGS Publications Warehouse

Matchett, Elliott L.; Casazza, Michael L.; Fleskes, Joseph; Kelman, T.; Cadena, M.; Pitesky, M.

2017-01-01

Wildlife researchers frequently study resource and habitat selection of wildlife to understand their potential habitat requirements and to conserve their populations. Understanding wildlife spatial-temporal distributions related to habitat have other applications such as to model interfaces between wildlife and domestic food animals in order to mitigate disease transmission to food animals. The highly pathogenic avian influenza (HPAI) virus represents a significant risk to the poultry industry. The Central Valley of California offers a unique geographical confluence of commercial poultry and wild waterfowl, which are thought to be a key reservoir of avian influenza (AI). Therefore, understanding spatio-temporal distributions of waterfowl could improve our understanding of potential risk of HPAI exposure from a commercial poultry perspective. Using existing radio-telemetry data on waterfowl (U.S. Geological Survey) in combination with habitat and vegetation data based on Geographic Information Systems (GIS), we are developing GIS-based statistical models that predict the probability of waterfowl presence (Habitat Suitability Mapping). Near-real-time application can be developed using recent habitat data derived from Landsat imagery (acquired by satellites and publically available through the U.S. Geological Survey) to predict temporally- and spatially-varying distributions of waterfowl in the Central Valley. These results could be used to provide decision support for the poultry industry in addressing potential risk of HPAI exposure related to waterfowl proximity.

Transforming RNA-Seq data to improve the performance of prognostic gene signatures.

PubMed

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

PubMed Central

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. PMID:24416353

Safety factors predictive of job satisfaction and job retention among home healthcare aides.

PubMed

Sherman, Martin F; Gershon, Robyn R M; Samar, Stephanie M; Pearson, Julie M; Canton, Allison N; Damsky, Marc R

2008-12-01

Although many of the well known work characteristics associated with job satisfaction in home health care have been documented, a unique aspect of the home health care aides' (HHA) work environment that might also affect job satisfaction is the fact that their workplace is a household. To obtain a better understanding of the potential impact of the risks/exposures/hazards within the household environment on job satisfaction and job retention in home care, we recently conducted a risk assessment study. Survey data from a convenience sample of 823 New York City HHAs were obtained and analyzed. Household/job-related risks, environmental exposures, transportation issues, threats/verbal and physical abuse, and potential for violence were significantly correlated with HHA job satisfaction and job retention. Addressing the modifiable risk factors in the home health care household may improve job satisfaction and reduce job turnover in this work population.

Partner aggression in high-risk families from birth to age 3 years: associations with harsh parenting and child maladjustment.

PubMed

Graham, Alice M; Kim, Hyoun K; Fisher, Philip A

2012-02-01

Aggression between partners represents a potential guiding force in family dynamics. However, research examining the influence of partner aggression (physically and psychologically aggressive acts by both partners) on harsh parenting and young child adjustment has been limited by a frequent focus on low-risk samples and by the examination of partner aggression at a single time point. Especially in the context of multiple risk factors and around transitions such as childbirth, partner aggression might be better understood as a dynamic process. In the present study, longitudinal trajectories of partner aggression from birth to age 3 years in a large, high-risk, and ethnically diverse sample (N = 461) were examined. Specific risk factors were tested as predictors of aggression over time, and the longitudinal effects of partner aggression on maternal harsh parenting and child maladjustment were examined. Partner aggression decreased over time, with higher maternal depression and lower maternal age predicting greater decreases in partner aggression. While taking into account contextual and psychosocial risk factors, higher partner aggression measured at birth and a smaller decrease over time independently predicted higher levels of maternal harsh parenting at age 3 years. Initial level of partner aggression and change over time predicted child maladjustment indirectly (via maternal harsh parenting). The implications of understanding change in partner aggression over time as a path to harsh parenting and young children's maladjustment in the context of multiple risk factors are discussed.

Partner Aggression in High-Risk Families From Birth to Age 3: Associations With Harsh Parenting and Child Maladjustment

PubMed Central

Graham, Alice M.; Kim, Hyoun K.; Fisher, Philip A.

2012-01-01

Aggression between partners represents a potential guiding force in family dynamics. However, research examining the influence of partner aggression (physically and psychologically aggressive acts by both partners) on harsh parenting and young child adjustment has been limited by a frequent focus on low risk samples and by the examination of partner aggression at a single time point. Especially in the context of multiple risk factors and around transitions such as childbirth, partner aggression might be better understood as a dynamic process. In the present study, longitudinal trajectories of partner aggression from birth to age 3 years in a large, high-risk, and ethnically diverse sample (N = 461) were examined. Specific risk factors were tested as predictors of aggression over time, and the longitudinal effects of partner aggression on maternal harsh parenting and child maladjustment were examined. Partner aggression decreased over time, with higher maternal depression and lower maternal age predicting greater decreases in partner aggression. While taking into account contextual and psychosocial risk factors, higher partner aggression measured at birth and a smaller decrease over time independently predicted higher levels of maternal harsh parenting at age 3 years. Initial level of partner aggression and change over time predicted child maladjustment indirectly (via maternal harsh parenting). The implications of understanding change in partner aggression over time as a path to harsh parenting and young children's maladjustment in the context of multiple risk factors are discussed. PMID:22201248

Informed renesting decisions: the effect of nest predation risk.

PubMed

Pakanen, Veli-Matti; Rönkä, Nelli; Thomson, Robert L; Koivula, Kari

2014-04-01

Animals should cue on information that predicts reproductive success. After failure of an initial reproductive attempt, decisions on whether or not to initiate a second reproductive attempt may be affected by individual experience and social information. If the prospects of breeding success are poor, long-lived animals in particular should not invest in current reproductive success (CRS) in case it generates costs to future reproductive success (FRS). In birds, predation risk experienced during breeding may provide a cue for renesting success. Species having a high FRS potential should be flexible and take predation risk into account in their renesting decisions. We tested this prediction using breeding data of a long-lived wader, the southern dunlin Calidris alpina schinzii. As predicted, dunlin cued on predation risk information acquired from direct experience of nest failure due to predation and ambient nest predation risk. While the overall renesting rate was low (34.5%), the early season renesting rate was high but declined with season, indicating probable temporal changes in the costs and benefits of renesting. We develop a conceptual cost-benefit model to describe the effects of the phase and the length of breeding season on predation risk responses in renesting. We suggest that species investing in FRS should not continue breeding in short breeding seasons in response to predation risk but without time constraints, their response should be similar to species investing in CRS, e.g. within-season dispersal and increased nest concealment.

Using clinical symptoms to predict adverse maternal and perinatal outcomes in women with preeclampsia: data from the PIERS (Pre-eclampsia Integrated Estimate of RiSk) study.

PubMed

Yen, Tin-Wing; Payne, Beth; Qu, Ziguang; Hutcheon, Jennifer A; Lee, Tang; Magee, Laura A; Walters, Barry N; von Dadelszen, Peter

2011-08-01

Preeclampsia is a leading cause of maternal morbidity. The clinical challenge lies in predicting which women with preeclampsia will suffer adverse outcomes and would benefit from treatment, while minimizing potentially harmful interventions. Our aim was to determine the ability of maternal symptoms (i.e., severe nausea or vomiting, headache, visual disturbance, right upper quadrant pain or epigastric pain, abdominal pain or vaginal bleeding, and chest pain or dyspnea) to predict adverse maternal or perinatal outcomes. We used data from the PIERS (Pre-eclampsia Integrated Estimate of RiSk) study, a multicentre, prospective cohort study designed to investigate the maternal risks associated with preeclampsia. Relative risks and receiver operating characteristic (ROC) curves were assessed for each preeclampsia symptom and outcome pair. Of 2023 women who underwent assessment, 52% experienced at least one preeclampsia symptom, with 5.2% and 5.3% respectively experiencing an adverse maternal or perinatal outcome. No symptom and outcome pair, in either of the maternal or perinatal groups, achieved an area under the ROC curve value > 0.7, which would be necessary to demonstrate a discriminatory predictive value. Maternal symptoms of preeclampsia are not independently valid predictors of maternal adverse outcome. Caution should be used when making clinical decisions on the basis of symptoms alone in the preeclamptic patient.

Quantifying predictive capability of electronic health records for the most harmful breast cancer

NASA Astrophysics Data System (ADS)

Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S.

2018-03-01

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and LassoLR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

Quantifying predictive capability of electronic health records for the most harmful breast cancer.

PubMed

Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S

2018-02-01

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

A Novel Nomogram to Predict the Prognosis of Patients Undergoing Liver Resection for Neuroendocrine Liver Metastasis: an Analysis of the Italian Neuroendocrine Liver Metastasis Database.

PubMed

Ruzzenente, Andrea; Bagante, Fabio; Bertuzzo, Francesca; Aldrighetti, Luca; Ercolani, Giorgio; Giuliante, Felice; Ferrero, Alessandro; Torzilli, Guido; Grazi, Gian Luca; Ratti, Francesca; Cucchetti, Alessandro; De Rose, Agostino M; Russolillo, Nadia; Cimino, Matteo; Perri, Pasquale; Cataldo, Ivana; Scarpa, Aldo; Guglielmi, Alfredo; Iacono, Calogero

2017-01-01

Even though surgery remains the only potentially curative option for patients with neuroendocrine liver metastases, the factors determining a patient's prognosis following hepatectomy are poorly understood. Using a multicentric database including patients who underwent hepatectomy for NELMs at seven tertiary referral hepato-biliary-pancreatic centers between January 1990 and December 2014, we sought to identify the predictors of survival and develop a clinical tool to predict patient's prognosis after liver resection for NELMs. The median age of the 238 patients included in the study was 61.9 years (interquartile range 51.5-70.1) and 55.9 % (n = 133) of patients were men. The number of NELMs (hazard ratio = 1.05), tumor size (HR = 1.01), and Ki-67 index (HR = 1.07) were the predictors of overall survival. These variables were used to develop a nomogram able to predict survival. According to the predicted 5-year OS, patients were divided into three different risk classes: 19.3, 55.5, and 25.2 % of patients were in low (>80 % predicted 5-year OS), medium (40-80 % predicted 5-year OS), and high (<40 % predicted 5-year OS) risk classes. The 10-year OS was 97.0, 55.9, and 20.0 % in the low, medium, and high-risk classes, respectively (p < 0.001). We developed a novel nomogram that accurately (c-index >70 %) staged and predicted the prognosis of patients undergoing liver resection for NELMs.

Breastfeeding duration and offspring conduct problems: The moderating role of genetic risk.

PubMed

Jackson, Dylan B

2016-10-01

A sizable body of research has examined associations between breastfeeding and various facets of offspring development, including childhood behavioral problems. Notwithstanding the number of studies on the topic, breastfeeding has not consistently been linked to child misbehaviors. Moreover, empirical examinations of whether breastfeeding is differentially predictive of conduct problems among individuals with varying degrees of genetic risk are lacking. The present study examines whether a short duration of breastfeeding and genetic risk interact to predict conduct problems during childhood. A genetically informative design is employed to examine a subsample of twins from the Early Childhood Longitudinal Study: Birth Cohort (ECLS-B), a nationally representative sample of American children. The findings suggest that a shorter duration of breastfeeding only enhances the risk of offspring conduct problems among children who possess high levels of genetic risk. Conversely, longer breastfeeding durations were found to protect against childhood behavioral problems when genetic risk was high. Indicators of genetic risk may help to distinguish individuals whose behavioral development is most sensitive to the duration of breastfeeding. Future research should seek to replicate and extend these findings by considering genetic factors as potential markers of differential susceptibility to breastfeeding duration. Copyright © 2016 Elsevier Ltd. All rights reserved.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

Adolescent sexual victimization: a prospective study on risk factors for first time sexual assault.

PubMed

Bramsen, Rikke Holm; Lasgaard, Mathias; Koss, Mary P; Elklit, Ask; Banner, Jytte

2012-09-01

The present study set out to investigate predictors of first time adolescent peer-on-peer sexual victimization (APSV) among 238 female Grade 9 students from 30 schools in Denmark. A prospective research design was utilized to examine the relationship among five potential predictors as measured at baseline and first time APSV during a 6-month period. Data analysis was a binary logistic regression analysis. Number of sexual partners and displaying sexual risk behaviors significantly predicted subsequent first time peer-on-peer sexual victimization, whereas a history of child sexual abuse, early sexual onset and failing to signal sexual boundaries did not. The present study identifies specific risk factors for first time sexual victimization that are potentially changeable. Thus, the results may inform prevention initiatives targeting initial experiences of APSV.

LDL electronegativity index: a potential novel index for predicting cardiovascular disease.

PubMed

Ivanova, Ekaterina A; Bobryshev, Yuri V; Orekhov, Alexander N

2015-01-01

High cardiovascular risk conditions are frequently associated with altered plasma lipoprotein profile, such as elevated low-density lipoprotein (LDL) and LDL cholesterol and decreased high-density lipoprotein. There is, however, accumulating evidence that specific subclasses of LDL may play an important role in cardiovascular disease development, and their relative concentration can be regarded as a more relevant risk factor. LDL particles undergo multiple modifications in plasma that can lead to the increase of their negative charge. The resulting electronegative LDL [LDL(-)] subfraction has been demonstrated to be especially atherogenic, and became a subject of numerous recent studies. In this review, we discuss the physicochemical properties of LDL(-), methods of its detection, atherogenic activity, and relevance of the LDL electronegativity index as a potential independent predictor of cardiovascular risk.

LDL electronegativity index: a potential novel index for predicting cardiovascular disease

PubMed Central

Ivanova, Ekaterina A; Bobryshev, Yuri V; Orekhov, Alexander N

2015-01-01

High cardiovascular risk conditions are frequently associated with altered plasma lipoprotein profile, such as elevated low-density lipoprotein (LDL) and LDL cholesterol and decreased high-density lipoprotein. There is, however, accumulating evidence that specific subclasses of LDL may play an important role in cardiovascular disease development, and their relative concentration can be regarded as a more relevant risk factor. LDL particles undergo multiple modifications in plasma that can lead to the increase of their negative charge. The resulting electronegative LDL [LDL(–)] subfraction has been demonstrated to be especially atherogenic, and became a subject of numerous recent studies. In this review, we discuss the physicochemical properties of LDL(–), methods of its detection, atherogenic activity, and relevance of the LDL electronegativity index as a potential independent predictor of cardiovascular risk. PMID:26357481

A validation study of a clinical prediction rule for screening asymptomatic chlamydia and gonorrhoea infections among heterosexuals in British Columbia.

PubMed

Falasinnu, Titilola; Gilbert, Mark; Gustafson, Paul; Shoveller, Jean

2016-02-01

One component of effective sexually transmitted infections (STIs) control is ensuring those at highest risk of STIs have access to clinical services because terminating transmission in this group will prevent most future cases. Here, we describe the results of a validation study of a clinical prediction rule for identifying individuals at increased risk for chlamydia and gonorrhoea infection derived in Vancouver, British Columbia (BC), against a population of asymptomatic patients attending sexual health clinics in other geographical settings in BC. We examined electronic records (2000-2012) from clinic visits at seven sexual health clinics in geographical locations outside Vancouver. The model's calibration and discrimination were examined by the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow (H-L) statistic, respectively. We also examined the sensitivity and proportion of patients that would need to be screened at different cut-offs of the risk score. The prevalence of infection was 5.3% (n=10 425) in the geographical validation population. The prediction rule showed good performance in this population (AUC, 0.69; H-L p=0.26). Possible risk scores ranged from -2 to 27. We identified a risk score cut-off point of ≥8 that detected cases with a sensitivity of 86% by screening 63% of the geographical validation population. The prediction rule showed good generalisability in STI clinics outside of Vancouver with improved discriminative performance compared with temporal validation. The prediction rule has the potential for augmenting triaging services in STI clinics and enhancing targeted testing in population-based screening programmes. 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/

Using multiscale texture and density features for near-term breast cancer risk analysis

PubMed Central

Sun, Wenqing; Tseng, Tzu-Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi

2015-01-01

Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. PMID:26127038

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.

PubMed

Fernandez, Ana; Salvador-Carulla, Luis; Choi, Isabella; Calvo, Rafael; Harvey, Samuel B; Glozier, Nicholas

2018-01-01

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population. We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement. Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

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