Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platform

Abstract

AbstractBackground and AimsArtificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions lack actionability at an individual patient level, explainability and biological plausibility. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.Methods and ResultsThe AIRE platform was developed in a secondary care dataset of 1,163,401 ECGs from 189,539 patients, using deep learning with a discrete-time survival model to create a subject-specific survival curve using a single ECG. Therefore, AIRE predicts not only risk of mortality, buttime-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil and the UK, including volunteers, primary care and secondary care subjects. AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776)), cardiovascular (CV) death 0.832 (0.831-0.834), non-CV death (0.749 (0.747-0.751)), future ventricular arrhythmia (0.760 (0.756-0.763)), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.889))). Through phenome- and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological aging and metabolic syndrome.ConclusionAIRE is an actionable, explainable and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short- and long-term risk estimation.Graphical Abstract