Characterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms

Abstract

Abstract Background Transthyretin amyloid cardiomyopathy (ATTR-CM) follows an indolent course and may remain undiagnosed for many years before resulting in clinical disease. Purpose To assess the utility of scalable artificial intelligence (AI) methods applied to electrocardiographic (ECG) images and echocardiograms in detecting longitudinal changes that precede the clinical diagnosis of ATTR-CM. Methods Across 5 hospitals of a large U.S.-based hospital system, we identified a total of 234 patients (age 79 [IQR:70-85] years, n=56 (23.9%) women) with ATTR-CM, defined by the presence of a positive bone scintigraphy scan (or pharmacotherapy with an approved transthyretin stabilizer) between 2015 and 2023. Using 10:1 age- and sex-matched controls, we independently trained convolutional neural network algorithms to discriminate ATTR-CM from controls using i) ECG images (AUROC 0.915) or ii) echocardiographic studies by incorporating all available views (AUROC 0.93). In a leave-out testing set of n=82 individuals with serial ECGs and n=49 individuals with serial echocardiograms we characterized longitudinal patterns in the evolution of the AI ECG and echo-based scores, and estimated the percentage of individuals that would have screened positive across distinct time intervals. Results In the leave-out testing set 52.4% and 34.7% of all individuals who went on to receive a diagnosis of ATTR-CM would have screened positive by AI deployed to ECGs and echocardiograms, respectively, 1 to 3 years before their confirmatory testing, with the percentage increasing to 75.6% and 69.4% in the 12 months before their eventual diagnosis (A). Across both modalities, there was a longitudinal increase in AI predictions when comparing the median of all studies performed more than a year before diagnosis versus the median of any studies performed within 12 months of diagnosis (AI-Echo: 0.20 [IQR: 0.04-0.35] to 0.49 [0.24-0.59] & AI-ECG: 0.07 [0.02-0.20] to 0.32 [0.11-0.49], p<0.001 for both) (B), consistent with evolution in the ECG and echocardiographic signature of ATTR-CM before its clinical diagnosis. Conclusions We demonstrate that AI applied directly to ECG images and echocardiography may enable scalable monitoring of subclinical ATTR-CM progression.