Tracking the Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Using Artificial Intelligence-Enabled Electrocardiography and Echocardiography

Background And Aims: The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale pre-clinical testing. Artificial intelligence (AI)-enabled transthoracic echocardiography (TTE) and electrocardiography (ECG) may provide a scalable strategy for pre-clinical monitoring.

Methods: This was a retrospective analysis of individuals referred for nuclear cardiac amyloid testing at Yale-New Haven Health System (YNHHS, internal cohort) and Houston Methodist Hospitals (HMH, external cohort). Deep learning models trained to discriminate ATTR-CM from age/sex-matched controls on TTE videos (AI-Echo) and ECG images (AI-ECG) were deployed to generate study-level ATTR-CM probabilities (0-100%). Longitudinal trends in AI-derived probabilities were examined using age/sex-adjusted linear mixed models, and their discrimination of future disease was evaluated across preclinical stages.

Results: Among 984 participants at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across cohorts and modalities, AI-derived ATTR-CM probabilities from 7,352 TTEs and 32,205 ECGs diverged as early as 3 years before diagnosis in cases versus controls ( ≤0.004). Among those with both AI-Echo and AI-ECG available one-to-three years nuclear testing (n=433 [YNHHS] and 174 [HMH]), a double-negative screen at a 0.05 threshold (164 [37.9%] and 66 [37.9%], vs all else) had 90.9% and 85.7% sensitivity (specificity of 40.3% and 41.2%), whereas a double-positive screen (78 [18.0%] and 26 [14.9%], vs all else) had 85.5% and 88.9% specificity (sensitivity of 60.6% and 42.9%).

Conclusions: AI-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its pre-clinical course.