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

Abstract

Background: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often 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 4 distinct study populations drawn from both electronic health records and prospective cohort studies. We deployed 6 image-based AI-ECG models: 5 validated models for the detection of left ventricular systolic dysfunction, aortic stenosis, mitral regurgitation, left ventricular hypertrophy, and a composite model for structural heart disease; and 1 negative control AI-ECG model for biological sex. Additionally, we developed 6 experimental models designed to identify noncardiovascular conditions. Diagnosis codes from electronic health records and cohorts were transformed into interpretable phenotypes using a phenome-wide association study 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 5 AI-ECG models for structural and functional cardiac disorders was more likely to be associated with cardiovascular phenotypes compared with other phenotype groups (odds ratios ranging from 2.16 to 4.41, P<10⁻⁶), whereas 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 noncardiovascular conditions. Correlation of phenotype association patterns between models was high (0.67-0.96). This pattern was consistent across all models and external data sets 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.

Keywords: artificial intelligence; cardiovascular disease; electrocardiography; machine learning; risk prediction.

Figure 1:

Overrepresentation of Associations with Cardiovascular Phenotypes by AI-ECG Panel A: Distribution of significant associations per phenotype groups from the age sex adjusted serial logistic regression for each AI-ECG model in the Yale New Haven Health System and the four community hospitals. Each pie chart represents the proportion of significant phenotype associations across different organ systems or disease categories, as indicated by the color legend. The “Expected” distribution reflects the proportion of all tested phenotypes before applying significance thresholds. Across models, most associations relate to the circulatory system, except for the sex model. Panel B: The odds ratios (OR) for each phenotype category being significantly associated with the AI-ECG models compared with the expected distribution in the Yale New Haven Health System and the four community hospitals. Each marker represents the OR for a significant phenotype within a disease category. The y-axis represents the OR, while the x-axis corresponds to different AI-ECG models. Colors correspond to disease categories, diamonds indicate significant enrichment, and circles represent non-significant enrichment, as displayed in the legend. Abbreviations: AS, aortic stenosis; LVH, left ventricular hypertrophy; LVSD, left ventricular systolic dysfunction; MR, mitral regurgitation; SHD, structural heart disease; OR, odds ratio.

Figure 2:

Associations between phenotypes and AI-ECG Panel A: Scatter plots comparing odds ratios (ORs) between Yale New Haven Hospital (YNHH) and Community Hospitals for Sex and Mitral Regurgitation (MR). Data points corresponding to the circulatory system are highlighted in red. OR values are clipped at the 99th percentile to reduce the influence of outliers. The diagonal dashed line represents perfect agreement between the two cohorts. Panel B: Volcano plots displaying the associations between various phenotypes and AI-ECG model predictions across four cohorts: YNHH (blue), Community Hospitals (green), Outpatient Clinics (red), and UK Biobank (purple). The x-axis represents the odds ratios and is presented on a logarithmic scale, reflecting the effect size and directionality of the association. The y-axis shows the −log₁₀ (p-value) representing the significance of the association. Labeled points indicate the top 6 statistically significant associations. Due to maximal computational precision limits, p-values are capped at 1×10⁻³⁰⁰. Abbreviations: AS, aortic stenosis; LVH, left ventricular hypertrophy; LVSD, left ventricular systolic dysfunction; MR, mitral regurgitation; SHD, structural heart disease; OR, odds ratio.

Figure 3:

Correlation of on-target phenotype association profiles across AI-ECG models in four independent cohorts These heatmaps show Pearson correlation coefficients between odds ratios (ORs) profiles and AI-ECG models across all phenotypes that we defined as being the target for any of the models for each pair of AI-ECG models. Higher correlation coefficients indicate that models exhibit similar phenotypic association patterns across target cardiovascular conditions. Correlations are shown for four cohorts: Yale New Haven Hospitals, community hospitals, outpatient clinics, and the UK Biobank. Strong correlations were observed among cardiovascular disease (CVD) models, with the SHD model consistently showing high similarity with other disease-specific models. The model trained to predict biological sex showed consistently weaker correlation with cardiovascular models, particularly in the UK Biobank, where inverse correlations were observed. AS: Aortic stenosis, LVH: Left ventricular hypertrophy, LVSD: Left ventricular systolic dysfunction, MR: Mitral regurgitation, SHD: Structural heart disease composite model, Sex: Biological sex prediction model.

Figure 4:

Forest plot displaying the results of the Cox Proportional Hazards models This Forest plot displays the hazard ratios (HR) with 95% confidence intervals (CI) for various cardiovascular conditions across four cohorts: UK Biobank (UKB, red), Yale New Haven Hospital (YNHH, blue), Community Hospitals (green), and NEMG outpatient clinics (yellow). Each panel represents a different AI-ECG model prediction, including Sex, Mitral Regurgitation (MR), Left Ventricular Systolic Dysfunction (LVSD), Left Ventricular Hypertrophy (LVH), Aortic Stenosis (AS), and Structural Heart Disease (SHD). The x-axis represents the hazard ratio, with the vertical dashed line at HR = 1.0 indicating no association. The analysis was conducted using age- and sex-adjusted Cox regression models. Abbreviations: LVSD, Left Ventricular Systolic Dysfunction; LVH, Left Ventricular Hypertrophy; MR, Mitral Regurgitation; AS, Aortic Stenosis; SHD, Structural Heart Disease.