Preprocedural CT angiography and machine learning for mortality prediction after transcatheter aortic valve replacement.

Abstract

Prediction of outcomes following transcatheter aortic valve replacement (TAVR) is challenging. Considering that in aortic stenosis outcomes are governed by both valve degeneration and myocardial adverse remodeling, we aimed to evaluate machine-learning leveraging pre-procedural computed tomography (CT) for the prediction of 1-year mortality following TAVR. The analysis included data of consecutive patients who underwent TAVR at a high-volume center between January 2017 and January 2022 and was externally validated on unseen data from 3 international sites. Machine learning by extreme gradient boosting was trained and tested using clinical variables, CT-derived volumetric measurements including myocardial mass, and quantitative fibrocalcific aortic valve characteristics measured using standardized software. The EuroScore II and a separate machine learning risk score based exclusively on baseline clinical characteristics served as comparators. The derivation cohort included 631 consecutive patients (48 % men, 80 ± 8 years old, EuroSCORE II 6.5 [4.6-10.3] %). Machine learning was externally validated on data of 596 patients (48 % men, 81 ± 8 years old, EuroSCORE II 5.4 [4.7-8.1] %). In external validation, the machine learning prognostic risk score had an area under the receiver operator curve of 0.79 (0.74-0.84) which was superior to the EuroSCORE 0.59 (0.53-0.66), and the machine learning risk based on clinical data alone 0.64 (0.59-0.69), p < 0.001 for difference. Machine-learning integrating clinical data and CT-derived imaging characteristics was found to predict 1-year all-cause mortality following TAVR significantly better than clinical variables or clinical risk scores alone; and can help identify patients at higher prognostic risk prior to the procedure.