Development and validation of a cardiovascular disease risk-prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT)

Abstract

ig data" has the potential to support personalized or precision medicine through more complex riskprediction algorithms with more predictors.1-3 These data can be used to accurately assess disease risk across subgroups with distinct characteristics or health profiles -including situations where a health profile represents only a fraction of the overall population.Furthermore, compared with more commonly used clinical data or epidemiology studies, large population health surveys have the potential to generate predictive algorithms that are more patient-oriented, have the potential to perform better across socioeconomic groups, and can be used for both population and clinical purposes.First, population health surveys emphasize sociodemographic profile and health behaviours. These patient-oriented risks are common for multiple chronic diseases. 4 Risks are all ascertained using self-response questions and validated for use in a broad community setting. This allows people to calculate their own risk in a nonclinical setting -reducing the burden on clinicians to collect and perform risk calculation. Second, algorithms developed using entire populations should be better calibrated (i.e., predictive risk closely approximating real or observed risk) and generalizable (i.e., better performing in a wide range of settings). As well, there are opportunities to recalibrate risk algorithms using population data that are not feasible with clinical data. AbstrAct 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.

How do cardiovascular risk prediction equations developed among 30–74 year olds perform in older age groups? A validation study in 125 000 people aged 75–89 years

“…Similarly, a Canadian risk tool derived from 100 000 health survey respondents aged 20–105 years was validated in 10-year age bands up to 69 years of age, with individuals aged 70 years or older evaluated as one group 7. The European SCORE O.P.…”

“…As a result, recently available CVD risk equations such as QRISK3 in the United Kingdom (UK),6 the Canadian Cardiovascular Disease Population Risk Tool (CVDPORT)7 and Systematic COronary Risk Evaluation in Older Persons (SCORE O.P.) in Europe8 have been developed from cohorts with an upper age range extending to individuals in their 80s or beyond.…”

Development and Validation of the Chronic Disease Population Risk Tool (CDPoRT) to Predict Incidence of Adult Chronic Disease

“…Smoking has been found to be a major predictor in similar population risk algorithms for stroke, 12 all-cause mortality, 30 and cardiovascular disease. 13 Body mass index was the most prognostic predictor for a similar diabetes tool. 9 CDPoRT is unique vs other population risk algorithms because CDPoRT does predict chronic disease burden, this burden is an underestimate because the tool does not consider all chronic diseases or subsequent chronic disease incidence (ie, multimorbidity).…”

Individual risk prediction using data beyond the medical clinic

“…The basis for these predictions will no longer be limited to traditional medical evaluation data, as shown by Manuel and colleagues in their linked paper. 1 It should be focused on actionable information that informs decisions or focused efforts to understand the mechanisms that generate health outcomes. These predictions will be increasingly subject to scrutiny because, whereas the consequences of high-quality predictions will be better outcomes, errors or inappropriately promoted predictions that lead to wrong decisions have an obvious capacity for harm.…”

“…In linked research, Manuel and colleagues advance the field by showing that information from population surveys can produce estimates of cardiovascular disease risk of comparable accuracy to estimates created from data collected within the health care system using more traditional medical risk measures. 1 This approach offers much less expensive data collection and is more easily repeatable than detailed measures that require a physical visit to a clinic and often an evaluation by a specialist. Although in-depth symptoms, physical examinations and specialized medical tests would likely refine such predictions, the survey-based model is efficient and could be used to better rationalize the ordering of medical tests.…”