Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.
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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.
Published Version (Please cite this version)10.1093/eurheartj/ehy653
Publication InfoBlazer, Daniel; Pencina, Michael; Pennells, Lisa; Kaptoge, Stephen; Wood, Angela; Sweeting, Mike; ... Di Angelantonio, Emanuele (2019). Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. European heart journal, 40(7). pp. 621-631. 10.1093/eurheartj/ehy653. Retrieved from https://hdl.handle.net/10161/18955.
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Professor of Psychiatry and Behavioral Sciences
I am currently semi-retired. Most of my recent work has been focused on roles with the National Academy of Medicine (former Institute of Medicine). I have chaired three committees during the past four years, one on the mental health and substance use workforce, one on cognitive aging, and one on hearing loss in adults. I currently also chair the Board on the Health of Select Populations for the National Academies. In the past I have been PI on a number of research
Professor of Biostatistics & Bioinformatics
As vice dean of data science and information technology, Dr. Pencina is responsible for developing and implementing quantitative science strategies as they pertain to the education and training, and laboratory, clinical science, and data science missions of the School of Medicine. He leads the School’s IT strategic direction and investments, working in collaboration with the vice presidents and chief information officers of Duke Health and Duke University’s Office of Information T
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