Artificial Intelligence-Enhanced Electrocardiography for Detection and Prediction of Hypertrophic Cardiomyopathy across Monogenic and Polygenic Susceptibility

Background Cascade screening increasingly identifies carriers of pathogenic or likely pathogenic sarcomere variants at risk for hypertrophic cardiomyopathy (HCM) in whom penetrance is incomplete, and surveillance relies on resource-intensive serial imaging. We evaluated whether a validated artificial intelligence-enhanced electrocardiography (AI-ECG) model identifies the HCM phenotype at first clinical assessment, predicts development of HCM during follow-up, and complements polygenic risk. Methods We assembled 1,095 genotype-positive (G+) individuals with pathogenic or likely pathogenic sarcomere variants from Yale-New Haven Hospital (n=119), Erasmus MC (n=858), and Motol University Hospital (n=118). At baseline (first clinical assessment), individuals were classified as phenotype-positive (P+) or phenotype-negative (P−). A previously validated AI-ECG model applied to 12-lead ECG images generated an HCM score. The primary outcome was detection of phenotypic positivity at baseline; secondary analyses included manifest HCM (at baseline or during follow-up) and identifying risk of developing future HCM among G+/P−individuals. In 57,007 UK Biobank participants, we assessed whether AI-ECG adds to an established polygenic risk score (PRS). Results Among 1,095 G+ individuals (median age 46 years [IǪR 34–56]; 52.1% female), 808 (73.8%) were P+ at baseline, 56 (5.1%) developed HCM during follow-up, and 231 (21.1%) remained P−. AI-ECG achieved an AUROC of 0.91 (95% CI 0.89–0.93) for P+ at baseline and 0.92 (95% CI 0.90–0.94) for manifest HCM. At a threshold of 0.15, sensitivity was 0.78, specificity 0.89, PPV 0.95, and NPV 0.59. Among G+/P− individuals, higher AI-ECG scores predicted development of HCM (HR 1.55 per 1-SD; 95% CI 1.28–1.88; p<0.001; adjusted HR 1.38; 95% CI 1.11–1.71; p=0.004). In the UK Biobank, individuals with both high AI-ECG and high PRS had 60-fold higher odds of HCM (adjusted OR 60.2; 95% CI 26.5–137.2), versus 15.0 for high AI-ECG alone and 4.1 for high PRS alone. Conclusions AI-ECG detects the HCM phenotype at baseline in sarcomere variant carriers, predicts development of HCM in G+/P− individuals, and complements PRS in the general population, supporting AI-ECG as a scalable tool to detect HCM and guide surveillance in individuals with monogenic or polygenic susceptibility. Clinical Perspective What Is New? - In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a validated AI-ECG model applied to 12-lead ECG images detected the HCM phenotype at first clinical assessment (AUROC 0.91). - Among G+/P− individuals, a higher AI-ECG score predicted development of HCM during follow-up (HR 1.55 per 1-SD increase), with consistent performance across sites, sexes, age groups, and sarcomere genes. - In 57,007 UK Biobank participants, AI-ECG and polygenic risk provided complementary information; individuals with both a high AI-ECG score and a high polygenic risk score had 60-fold higher odds of HCM. What Are the Clinical Implications? - AI-ECG applied to standard ECG images is a scalable, low-cost tool to stratify the risk of HCM in carriers of a sarcomere variant. - The AI-ECG score may help individualize the intensity of imaging surveillance in G+/P−individuals, focusing resources on those at highest risk of developing HCM. - Combining AI-ECG with polygenic risk may refine identification of high-risk individuals in the general population and might further improve risk stratification.