Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations

• Published in: JACC. Advances (Online) Vol. 2; no. 8
• Main Authors: Siontis, Konstantinos C, Abreau, Sean, Attia, Zachi I, Barrios, Joshua P, Dewland, Thomas A, Agarwal, Priyanka, Balasubramanyam, Aarthi, Li, Yunfan, Lester, Steven J, Masri, Ahmad, Wang, Andrew, Sehnert, Amy J, Edelberg, Jay M, Abraham, Theodore P, Friedman, Paul A, Olgin, Jeffrey E, Noseworthy, Peter A, Tison, Geoffrey H
• Format: Journal Article
• Language: English
• Published: United States 01.10.2023
• Subjects: mavacamten; electrocardiogram; hypertrophic cardiomyopathy; machine learning; artificial intelligence
• ISSN: 2772-963X; 2772-963X
• DOI: 10.1016/j.jacadv.2023.100582

Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM). The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment. We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response. In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], = 0.007). AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.