Cardiovascular Disease Risk Assessment in the Digital Age: From Fixed Risk Equations to Data-Driven, Intervention-Linked Prevention

Abstract

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Its prevention depends on identifying, before the first clinical event, who will benefit from treatment. Over the past six decades, that task has been carried out by a sequence of multivariable risk equations — from the Framingham-derived scores to the US Pooled Cohort Equations, the United Kingdom’s QRISK3, Europe’s SCORE2, and most recently the American Heart Association’s PREVENT equations. Each generation has expanded the input variables, widened the age range, and improved calibration. Yet external validation continues to show systematic miscalibration and weak transportability across populations. This review synthesises how contemporary epidemiology is responding to those limitations on two fronts. First, predictive data sources are broadening: electronic health record-derived machine learning models, wearable and sensor-derived physiological signals, and polygenic risk scores are extending discrimination and covering patients older fixed equations exclude. Second, and less often integrated, digital health interventions — from automated text-message programmes to smartphone-linked blood-pressure management — have demonstrated modest but reproducible reductions in the very risk factors that risk equations estimate: LDL cholesterol, systolic blood pressure, and medication adherence. We argue that the central methodological gap is no longer prediction accuracy in isolation. It is the design of closed-loop systems in which a risk estimate triggers, adapts to, and measures the response of a digital intervention. We examine where the evidence is robust, where disagreements persist over the clinical utility of polygenic scores and the generalisability of wearable-derived data, and what an implementation-focused research agenda should now prioritise.