A New Biomarker of Aging Derived From Electrocardiograms Improves Risk Prediction of Incident Cardiovascular Disease.

Background: A biomarker of cardiovascular aging, derived from a deep learning algorithm applied to digitized 12-lead electrocardiograms, has recently been introduced. This biomarker, δ-age, is defined as the difference between predicted electrocardiogram age and chronological age.

Objective: The purpose of this study was to assess the potential value of δ-age in enhancing the performance of primary prevention models for cardiovascular disease that incorporate traditional cardiovascular risk factors.

Methods: In this cohort study, we included 7,108 men and women from the Norwegian Tromsø Study in 2015 to 16, with follow-up through 2021 for incident fatal and nonfatal myocardial infarction (MI) and hemorrhagic or cerebral stroke. We used Cox proportional hazards regression models, Harrell's concordance statistic (C-index), and the net reclassification improvement.

Results: During a median follow-up of 5.9 years, we observed 155 cases of MI and 141 strokes. In men and women combined,HR per SD increment in δ-age, after adjustment for traditional risk factors included in the Norwegian risk model for acute cerebral stroke and myocardial infarction (NORRISK 2) score, was 1.24 (95% CI: 1.09-1.41) for the combined outcome, with similar HRs for MI and stroke. In men, the HR was significant for MI and in women for stroke. The C-index increased significantly but modestly when δ-age was added to a model with traditional risk factors. The net reclassification improvement was 26.0% (95% CI: 13.3%-38.1%) for the combined outcome, 17.5% (95% CI: 0.6%-33.5%) for MI, and 37.2% (95% CI: 20.1%-53.0%) for stroke.

Conclusions: Incorporating δ-age into primary prevention risk prediction models significantly improved performance beyond traditional cardiovascular risk factors for the combined outcome and separately for MI and stroke.