Neural network-derived electrocardiographic features have prognostic significance and important phenotypic and genotypic associations

Abstract

Background Subtle prognostically-important ECG features may not be apparent to physicians. In the course of supervised machine learning (ML), many thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. Hypothesis Novel neural network (NN)-derived ECG features can predict future cardiovascular disease and mortality Methods and Results We extracted 5120 NN-derived ECG features from an AI-ECG model trained for six simple diagnoses and applied unsupervised machine learning to identify three phenogroups. In the derivation cohort (CODE, 1,558,421 subjects), the three phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared to phenogroup A (HR 1.20, 95% CI 1.17-1.23, p < 0.0001). The predictive ability of the phenogroups was retained in a group with physician confirmed normal ECGs. We externally validated our findings in five diverse cohorts (Figure) and found phenogroup B had a significantly greater risk of mortality in all cohorts. Phenome-wide association study (PheWAS) showed phenogroup B had a higher rate of future AF, ischaemic heart disease, AV block, heart failure, VT, and cardiac arrest. Phenogroup B had increased cardiac chamber volumes and decreased cardiac output. A single-trait GWAS yielded four loci. SCN10A, SCN5A and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. Gradient-weighted Class Activation Mapping (Grad-CAM) identified the terminal QRS and terminal T wave as important regions of the ECG for identification of phenogroup B. Conclusion NN-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.