The Prognostic Value of Artificial Intelligence to Predict Cardiac Amyloidosis in Patients with Severe Aortic Stenosis Undergoing Transcatheter Aortic Valve Replacement

Abstract

Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). CA has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at risk patients. In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analyzed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analysis using Cox regression was performed to compare clinical outcomes between patients with high CA probability versus those with low probability at one year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG-AI algorithm was significantly associated with increased all-cause mortality (HR 1.40, 95%CI 1.01-1.96, p = 0.046) and higher rates of MACE (TIA/Stroke, myocardial infarction, heart failure hospitalizations) (HR 1.36, 95%CI 1.01- 1.82, p = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95%CI 1.13-2.20, p = 0.008) at one-year follow-up. There were no significant differences in TIA/Stroke or myocardial infarction. AI applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.