Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction

Abstract

Introduction: Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers.

Methods: We included four distinct study populations, drawn from both electronic health records (EHR) and prospective cohort studies. We deployed six image-based AI-ECG models, including five validated models for the detection of left ventricular systolic dysfunction (LVSD), aortic stenosis (AS), mitral regurgitation (MR), left ventricular hypertrophy (LVH), a composite model for structural heart disease (SHD), and a negative control AI-ECG model for biological sex. Additionally, we developed six experimental models designed to identify non-cardiovascular conditions. Diagnosis codes from EHR and cohorts were transformed into interpretable phenotypes using a phenome-wide association study (PheWAS) framework. We assessed associations of AI-ECG probabilities with cross-sectional phenotypes using logistic regression, and with new-onset cardiovascular diseases using Cox regression. Pearson correlation coefficients were calculated to compare phenotypic signatures.

Results: The study included one random ECG from 233,689 individuals (mean age 59±18 years, 130,084 [56%] women) across sites. Each of the five AI-ECG models was more likely to be associated with cardiovascular phenotypes compared with other phenotype groups (odds ratios ranging from 2.16 to 4.41, p<10□), while the sex model did not show a similar pattern. All AI-ECG models were significantly associated with their respective target phenotype, but also showed similar or stronger associations with a broad range of other cardiovascular phenotypes. Phenotypic associations were similar across AI-ECG models trained for different conditions, which was not observed in models for non-cardiovascular conditions. Correlation of phenotype association patterns between models was high (r = 0.65-0.99). This pattern was consistent across all models, external datasets, and in both cross-sectional and prospective analyses.

Conclusions: Despite being developed to detect specific cardiovascular conditions, AI-ECG models detect the presence and predict the future development of a broad range of cardiovascular diseases with similar propensity. This challenges their role as binary diagnostic tools and instead supports their use as broader cardiovascular biomarkers.