Assessment of Disease Status and Treatment Response With Artificial Intelligence−Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy

Although hypertrophic cardiomyopathy (HCM) causes significant morbidity and is a leading cause of sudden death in adolescents, initial detection remains difficult. Echocardiography is an important diagnostic modality for HCM, but since the electrocardiogram (ECG) is more widely accessible, we created an ECG-based AI algorithm to achieve a fully automated diagnosis of HCM. We ran the AI-ECG algorithms on ECGs from a pre-treatment group and an on-treatment group from the phase 2 PIONEER-OLE trial of the cardiac myosin inhibitor, mavacamten in patients with obstructive HCM. We found that AI-ECG HCM scores correlated with disease status in patients with obstructive HCM on mavacamten treatment. Disease status was measured by decreases over time in left ventricular outflow tract gradients and NT-proBNP levels. Therefore, AI-ECGs might hold promise as a potential tool for monitoring disease status, cardiac hemodynamics, and drug therapeutic response.