Development of a dynamic risk prediction tool for identifying individuals at high risk of cardiovascular diseases: A post-hoc analysis of the multi-ethnic study of atherosclerosis

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BACKGROUND AND AIMS: Cardiovascular disease (CVD) remain the leading cause of mortality and disability. Most risk prediction tools rely on risk factors measured at a single time point, ignoring changes in biomarkers over time. We aimed to develop and validate a dynamic CVD risk prediction tool using joint modelling that incorporates repeated measurements of cardiovascular risk factors. METHODS: We analysed 6637 participants aged 45-84 years from the Multi-Ethnic Study of Atherosclerosis. A Bayesian joint model was used to simultaneously analyze longitudinal risk factor trajectories and time to CVD. Repeated measurements of total cholesterol, systolic blood pressure, HDL-C, and use of lipid-lowering and antihypertensive medications were incorporated as time-varying covariates. Predictive performance was assessed using discrimination, calibration, overall accuracy, and reclassification measures. RESULTS: The joint model identified significant temporal trends, including declining cholesterol (-1.14 mg/dL/year) and increasing HDL-C (0.43 mg/dL/year), while systolic blood pressure remained stable. All risk factors were independently associated with CVD risk. Compared with a conventional model based on a single baseline measurement, the joint model demonstrated improved discrimination, calibration, overall accuracy, and reclassification, with gains becoming more pronounced as longer longitudinal histories were incorporated. A web-based application was developed to support individualized dynamic CVD risk prediction. CONCLUSIONS: Incorporating longitudinal risk factor trajectories provides more accurate CVD risk estimates than conventional models. This approach improves risk stratification and may enhance clinical decision-making for preventive interventions. To our knowledge, this is among the first studies to translate joint modelling methodology into a clinically applicable CVD risk prediction tool.

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