Abstract
Background and Aims
Early detection is important given the availability of new disease-modifying therapies and the high prevalence of transthyretin amyloid cardiomyopathy (ATTR-CM) among patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). We developed a multimodal artificial intelligence (AI) model for early detection of ATTR-CM using chest computed tomography (CT), echocardiography, and electrocardiography. This approach may provide a scalable strategy for preclinical monitoring.
Methods
This retrospective study included patients who underwent technetium-99m–pyrophosphate scintigraphy (PYP) at two academic medical centers: Columbia University Irving Medical Center and Weill Cornell Medicine. ATTR-CM status was determined using a composite reference standard incorporating PYP scan interpretation, laboratory tests, and endomyocardial biopsy results when available. The diagnostic performance of the model was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and predictive values at various thresholds.