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Abstract Background In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer. Methods This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients. Results Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08. Conclusions These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.

Purpose of Review: To critically assess recent evidence concerning osteoporosis fracture risk. Recent Findings: Robust instruments exist for predicting factures incorporating well-documented risk factors especially prior fracture whose magnitude varies with site, occurrence time, and age. Stratifying time-since-prior fracture has resulted in the concept of imminent fracture risk and increased focus on secondary fracture prevention. Secondary fracture prevention recommendations include fracture liaison service, pharmacologic and non-pharmacologic multidisciplinary intervention, and communicating that fractures in older adults are the predictable consequence of underlying osteoporosis rather than unfortunate accidents. Summary: Quality improvement in osteoporosis care includes diagnosing osteoporosis on the basis of clinical fractures rather than exclusively relying on bone density testing; applying diagnostic rather than screening approaches to patients with prior fractures; regularly updating fall and fracture histories; performing a physical exam focused on spinal curvature, posture, and musculoskeletal function; reviewing images to identify prevalent fractures that may have been missed; and general use of fracture risk algorithms at all stages of osteoporosis management. Communicating effectively with patients about osteoporosis and fractures, their consequences, and pharmacological and non-pharmacological management is the cornerstone of high-value care. [ABSTRACT FROM AUTHOR]

Aims There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods and results Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms. Conclusion Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need. View large Download slide View large Download slide [ABSTRACT FROM AUTHOR]

Decision-making for integrated pest management (IPM) in crops requires the assessment of multiple risk factors. Plant disease models have been used to predict disease risk and support decisions about whether and when to protect crops based on environmental conditions. In addition to requiring information about disease risks, correct decision-making also requires answers to several questions. These include: Is the plant susceptible to infection? Is the plant protected by a previous fungicide application? Which fungicide should be used for the specific application? Which dose of the product should be used, and when should it be applied? Obtaining answers to these questions requires a multiple-modelling approach in which models for diseases are combined with those for plant growth and for the effects of fungicides. This review discusses models that predict fungicide efficacy dynamics based on fungicide physical mode of action, fungicide localisation on/within host plants, fungicide effects on the pathogen, and the dynamics of fungicide residues. Empirical and mechanistic models are considered. Empirical models have been mainly developed by fitting equations to field data. Mechanistic models consider the processes that determine fungicide dynamics and effects, and these are generally considered to be superior to empirical models, but parameterisation of mechanistic models is challenging. A new modelling approach is described that combines the main processes of mechanistic models with simple parameterisation based on laboratory experiments, practical knowledge, and technical information. Examples are also provided of systems that calculate fungicide dose and application time. Decision support systems are described as tools that provide farmers with all of the information required for correct decision-making in IPM.

Decision-making for integrated pest management (IPM) in crops requires the assessment of multiple risk factors. Plant disease models have been used to predict disease risk and support decisions about whether and when to protect crops based on environmental conditions. In addition to requiring information about disease risks, correct decision-making also requires answers to several questions. These include: Is the plant susceptible to infection? Is the plant protected by a previous fungicide application? Which fungicide should be used for the specific application? Which dose of the product should be used, and when should it be applied? Obtaining answers to these questions requires a multiple-modelling approach in which models for diseases are combined with those for plant growth and for the effects of fungicides. This review discusses models that predict fungicide efficacy dynamics based on fungicide physical mode of action, fungicide localisation on/within host plants, fungicide effects on the pathogen, and the dynamics of fungicide residues. Empirical and mechanistic models are considered. Empirical models have been mainly developed by fitting equations to field data. Mechanistic models consider the processes that determine fungicide dynamics and effects, and these are generally considered to be superior to empirical models, but parameterisation of mechanistic models is challenging. A new modelling approach is described that combines the main processes of mechanistic models with simple parameterisation based on laboratory experiments, practical knowledge, and technical information. Examples are also provided of systems that calculate fungicide dose and application time. Decision support systems are described as tools that provide farmers with all of the information required for correct decision-making in IPM. [ABSTRACT FROM AUTHOR]

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes. [ABSTRACT FROM AUTHOR]

Abstract: Background and aim: This study aims to analyse the risk of cardiovascular events in a local cohort of patients with type 2 diabetes, and to evaluate the prognostic accuracy of four algorithms used to estimate cardiovascular risk: the Framingham study, United Kingdom Prospective Diabetes Study (UKPDS), Riskard study and Progetto Cuore. Method and results: We analysed clinical charts of the Diabetes Clinics of Modena for the period 1991–95. Patients in the age range of 35–65 with type 2 diabetes and no previous cardiovascular disease were eligible. The incidence of new cardiovascular disease was compared with estimated rates deriving from the different functions. A stratification was obtained in subgroups at different cardiovascular risk, allowing comparison between the algorithms. A total of 1532 patients were eligible; women presented a worse cardiovascular risk profile. An absolute 10-year rate of cardiovascular events of 14.9% was observed. Comparing patients with events with event-free subjects, we found significant differences in systolic blood pressure, age at visit, smoking, high-density lipoprotein (HDL)-cholesterol, duration of diabetes, glycosylated haemoglobin (HbA1c) and co-morbidities. Comparing the estimated risk rate according to the different functions, Italian algorithms were more consistent with observed data; however, Progetto Cuore and Riskard show underestimation of events when applied to females. Conclusions: Estimation of cardiovascular risk is dependent on the algorithm adopted and on the baseline risk of the reference cohort. Functions designed for a specific population, including risk variables peculiar for diabetes, should be adopted to increase the performance of such functions which is clearly unsatisfactory at present. [Copyright &y& Elsevier]

Risk scoring tools have been developed from clinical databases to predict expected patient mortality from cardiac surgical procedures. The risk algorithms have been developed and validated from variables that have been demonstrated to be predictive of mortality. At least six risk models have been developed from different databases measuring outcomes of cardiac surgery. These algorithms have then been used to select very high risk patients for conventional aortic valve replacement (AVR) who would be appropriate candidates for transcatheter aortic valve implantation (TAVI). The two most common risk models used for TAVI selection are the logistic EuroSCORE (LES) and the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) algorithms. Although both models are accurate in predicting mortality in low-risk patients, the LES has been clearly demonstrated to overpredict expected mortality by a factor of three in high-risk candidates for AVR. Various factors that also impact mortality but are not included in either algorithm include liver disease, frailty, porcelain aorta, and previous radiation. Despite these shortcomings, risk algorithms are effective models for predicting risk, with the STS-PROM being more accurate in high-risk patients. Ultimately, a new risk algorithm specific for TAVI will need to be developed once sufficient databases are developed. [ABSTRACT FROM AUTHOR]

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The article presents the author's views on patients who are being treated for aortic stenosis. It discusses two effective treatments of aortic stenosis including transcatheter aortic valve implantation (TAVI) vs surgical aortic valve replacement (SAVR) . It further informs about various risk algorithms present for aortic stenosis. It also focuses on the German Aortic Valve Score developed by the German Aortic Valve Registry (GARY) which help in analyzing the available inadequacies.

That individuals with rheumatoid arthritis (RA) have, on average, higher rates of events related to atherosclerotic vascular disease is now well documented and accepted. However, optimal ways to identify RA patients with atherosclerosis, and treat them to mitigate cardiovascular disease (CVD) risk have not been rigorously established. As such, current guidelines for CVD screening and prevention lack an evidence base. In the context of the current understanding of the determinants of atherosclerosis in RA and the current guidelines that have been published, this review focuses on strategies for identifying RA patients at risk of CVD events, and the possible primary and secondary prevention strategies that may be effective., (Copyright © 2015 Elsevier Ltd. All rights reserved.)