Derivation and validation of a widely accessible cardiovascular risk prediction model and derived heart age for the general UK and US populations

There remains a need for tools leveraging widely accessible data compatible with mobile health platforms to provide simple and broadly comprehensible cardiovascular risk estimations. We streamlined the previously published DiCAVA model, originally developed in the UK Biobank (UKB), into a 30-feature tool (DiCAVA-Lite) to predict incident myocardial infarction, stroke, heart failure, angina, and transient ischemic attack in primary prevention settings. We then validated DiCAVA-Lite in the All of Us (AoU) using Cox proportional hazard regression models and an incident composite cardiovascular outcome. We adapted established methods for translating this tool to a “heart age”, which communicates the age of a healthy person with equivalent cardiovascular risk to that of the index individual. Finally, we validated the heart age in the UKB. In the UKB dataset (n=466,052), the mean age was 56 years (SD: 8) with 55.9% females, while in AoU (n=216,985), the mean age was 50 years (SD: 17) with 63.2% females. The DiCAVA-Lite model demonstrated moderate discrimination, with the area under the curve (AUC) of 0.740 (95% CI: 0.735–0.745) (vs. 0.747 [95% CI: 0.744–0.748] for DiCAVA) in UKB and 0.770 (95% CI: 0.765–0.773) in external validation in AoU. Heart age demonstrated discriminative performance equivalent to the DiCAVA-Lite model. In UKB , the mean heart age was 60 among individuals who developed events vs. 55 in those who did not. In AoU , the corresponding ages were 57 and 50, respectively. These findings support DiCAVA-Lite and the corresponding heart age as an accessible and interpretable tool for CVD risk assessment and communication in the general US and UK primary prevention populations.