RT Journal Article SR Electronic T1 Development and validation of a cardiovascular disease risk-prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT) JF Canadian Medical Association Journal JO CMAJ FD Canadian Medical Association SP E871 OP E882 DO 10.1503/cmaj.170914 VO 190 IS 29 A1 Douglas G. Manuel A1 Meltem Tuna A1 Carol Bennett A1 Deirdre Hennessy A1 Laura Rosella A1 Claudia Sanmartin A1 Jack V. Tu A1 Richard Perez A1 Stacey Fisher A1 Monica Taljaard YR 2018 UL http://www.cmaj.ca/content/190/29/E871.abstract AB BACKGROUND: Routinely 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.METHODS: We 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.RESULTS: We 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).INTERPRETATION: The 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