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      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)

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          Abstract

          Risk assessment and risk prediction 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.

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          Most cited references54

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          Decision curve analysis: a novel method for evaluating prediction models.

          Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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            2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR).

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              Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis.

              The benefits of blood pressure lowering treatment for prevention of cardiovascular disease are well established. However, the extent to which these effects differ by baseline blood pressure, presence of comorbidities, or drug class is less clear. We therefore performed a systematic review and meta-analysis to clarify these differences.
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                Author and article information

                Journal
                European Journal of Cardiovascular Nursing
                European Journal of Cardiovascular Nursing
                SAGE Publications
                1474-5151
                1873-1953
                July 25 2019
                October 2019
                June 24 2019
                October 2019
                : 18
                : 7
                : 534-544
                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, Hôpital cardiologique de Lyon, France
                [9 ]Department of Cardiovascular Medicine, Imperial College, UK
                [10 ]Heart Failure Unit, Cardiology, G da Saliceto Hospital, Italy, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
                Article
                10.1177/1474515119856207
                31234638
                0d2dfeb9-6cf3-4ad0-b2cf-a07800192c71
                © 2019

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

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