Development and validation of a cardiovascular disease risk prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT)
Manuel DG, Tuna M, Bennett C, Hennessy D, Rosella L, Sanmartin C, Tu JV, Perez R, Fisher S, Taljaard M. CMAJ. 2018; 190(29):E871-82. Epub 2018 Jul 23.
Background — Large, routinely-collected 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 (survey years 2001 to 2007; follow-up from 2001 to 2012) linked to hospitalization 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 hospitalization or death. Death from a non-cardiovascular cause was considered a competing risk.
Results — We included 104 219 respondents aged 20 to 105 years, representing 98% of the Ontario population. There were 3709 cardiovascular events and 818 478 person-years follow-up in the combined derivation and validation cohorts (5-year cumulative incidence function, male: 0.026, 95% confidence interval (CI) [0.025–0.028]; female: 0.018[0.017–0.019]). The final CVDPoRT algorithm contained 12 variables, was discriminating (male: C-statistic 0.82, 95%CI [0.81–0.83]; female: 0.86 [0.85–0.87]) and was well-calibrated in the overall population (5-year observed cumulative incidence function v. predicted risk,male: 0.28%; female: 0.38%) and in nearly all predefined policy-relevant subgroups.
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.