15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

          Related collections

          Most cited references39

          • Record: found
          • Abstract: found
          • Article: not found

          Use and misuse of the receiver operating characteristic curve in risk prediction.

          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

            Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys.

              Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be recalibrated and updated for application in different countries with routinely available information.
                Bookmark

                Author and article information

                Journal
                European Journal of Preventive Cardiology
                Eur J Prev Cardiolog
                SAGE Publications
                2047-4873
                2047-4881
                June 24 2019
                June 24 2019
                : 204748731984671
                Affiliations
                [1 ]Centro Nacional de Investigaciones Cardiovasculares (CNIC), Spain
                [2 ]Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV), Spain
                [3 ]Department of Vascular Medicine, University Medical Center Utrecht, The Netherlands
                [4 ]Clinical Research Department Cardiology, Heartcentre Hasselt, Jessa Hospital, Hasselt, Belgium
                [5 ]Department of Nursing, Cyprus University of Technology, Cyprus
                [6 ]Jessa Hospital, Heartcentre Hasselt, Belgium
                [7 ]Faculty of Medicine and Life Sciences, Hasselt University, Belgium
                [8 ]Department of Cardiology, Hoŝpital cardiologique de Lyon, France
                [9 ]Department of Cardiovascular Medicine, Imperial College, UK
                [10 ]Heart Failure Unit, Cardiology, G da Saliceto Hospital, ItalyKeck School of Medicine, University of Southern California, Los Angeles, CA, USA
                Article
                10.1177/2047487319846715
                31234648
                8cfb9174-9a0a-4ba0-a447-1000fe05c158
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

                History

                Comments

                Comment on this article