Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

TL;DR

The paper assesses whether ECG features, combined with basic demographics, can infer a wide range of cardiac and non-cardiac ICD10-CM diagnoses. By training individual XGBoost classifiers for each code and validating on public datasets with bootstrap-derived confidence intervals, the authors demonstrate AUROCs commonly exceeding 0.7 and, in many cases, approaching 0.95 for non-cardiac conditions. External validation on ECG-VIEW-II confirms generalizability across diverse populations. The findings suggest that ECG carries informative signals beyond traditional cardiac diagnoses, potentially enabling broader, ECG-based screening and triage with interpretable, feature-driven models. The work also highlights a path toward multimodal and waveform-based enhancements in future research.

Abstract

Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.