• Douglas G Manuel, Meltem Tuna, Carol Bennett, Deirdre Hennessy, Laura Rosella, Claudia Sanmartin, Jack V Tu, Richard Perez, Stacey Fisher, and Monica Taljaard.
• Ottawa Hospital Research Institute (Manuel, Tuna, Bennett, Perez, Fisher, Taljaard); Institute for Clinical Evaluative Sciences (Manuel, Tuna, Bennett), Ottawa, Ont.; Institute for Clinical Evaluative Sciences (Tu), Toronto, Ont.; Institute for Clinical Evaluative Sciences (Perez), Hamilton, Ont.; Statistics Canada (Hennessy, Sanmartin); Department of Family Medicine (Manuel) and School of Epidemiology and Public Health (Fisher, Taljaard), University of Ottawa, Ottawa, Ont.; Dalla Lana School of Public Health (Rosella), University of Toronto, Ont.; Sunnybrook Schulich Heart Centre (Tu); Institute of Health Policy, Management, and Evaluation (Tu), University of Toronto, Toronto, Ont. dmanuel@ohri.ca.
• CMAJ. 2018 Jul 23; 190 (29): E871-E882.

BackgroundRoutinely collected data from large population health surveys linked to chronic disease outcomes create an opportunity to develop more complex risk-prediction algorithms. We developed a predictive algorithm to estimate 5-year risk of incident cardiovascular disease in the community setting.MethodsWe derived the Cardiovascular Disease Population Risk Tool (CVDPoRT) using prospectively collected data from Ontario respondents of the Canadian Community Health Surveys, representing 98% of the Ontario population (survey years 2001 to 2007; follow-up from 2001 to 2012) linked to hospital admission and vital statistics databases. Predictors included body mass index, hypertension, diabetes, and multiple behavioural, demographic and general health risk factors. The primary outcome was the first major cardiovascular event resulting in hospital admission or death. Death from a noncardiovascular cause was considered a competing risk.ResultsWe included 104 219 respondents aged 20 to 105 years. There were 3709 cardiovascular events and 818 478 person-years follow-up in the combined derivation and validation cohorts (5-year cumulative incidence function, men: 0.026, 95% confidence interval [CI] 0.025-0.028; women: 0.018, 95% 0.017-0.019). The final CVDPoRT algorithm contained 12 variables, was discriminating (men: C statistic 0.82, 95% CI 0.81-0.83; women: 0.86, 95% CI 0.85-0.87) and was well-calibrated in the overall population (5-year observed cumulative incidence function v. predicted risk, men: 0.28%; women: 0.38%) and in nearly all predefined policy-relevant subgroups (206 of 208 groups).InterpretationThe CVDPoRT algorithm can accurately discriminate cardiovascular disease risk for a wide range of health profiles without the aid of clinical measures. Such algorithms hold potential to support precision medicine for individual or population uses. Study registration: ClinicalTrials.gov, no. NCT02267447.© 2018 Joule Inc. or its licensors.

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