Artificial intelligence-estimated biological heart age using 12 lead electrocardiogram predicts mortality and cardiovascular outcomes

Abstract Funding Acknowledgements Type of funding sources: None. Main funding source(s): none Background There is a paucity of data on the artificial intelligence-estimated biological ECG heart age (AI ECG-heart age) for predicting cardiovascular outcomes as distinct from the chronological age (CA). Purpose We sought to investigate whether a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs predicted mortality and cardiovascular outcomes. Methods We trained and validated a deep neural network using raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. Additionally, randomly age-sex matched patients with reduced and preserved ejection fraction (EF) were compared. Results The deep neural network was trained to estimate the AI ECG-heart age (the mean absolute error 5.8 ± 3.9 years and R squared of 0.7 (r=0.84, p<0.0001) (Fig 1A). In the Cox proportional-hazards models after adjusting for relevant co-morbidity factors, the subjects with an AI ECG-heart age of seven years older than the chronological age had higher all-cause mortality (HR 1.62 [1.43-1.84]) and major adverse cardiovascular events (MACEs) (HR 1.92 [1.65-2.23]), while those under seven years had an inverse relationship (HR 0.86 [0.77-0.95] for all-cause mortality; HR 0.73 [0.63-0.84] for MACEs) (Fig 2). Subjects with a reduced EF had a substantially higher mean AI ECG heart-age, QRS duration, and corrected QT intervals than those with a preserved EF (all p<0.001) (Fig 1B). Conclusion The biological heart age estimated by AI had a significant impact on mortality and MACE. Those data suggested that the AI ECG-heart age might facilitate the primary prevention and health care for cardiovascular outcomes.