Abstract 14773: Automated Cardiovascular Calcium Burden: Unlocking the Future of TAVR Prognosis With Artificial Intelligence

Abstract

Background: Calcific valve disease is now understood as a dynamic inflammatory process that alters the tissue’s adaptive response to stress stimuli. Transcatheter aortic valve replacement (TAVR) is a minimally invasive treatment for severe aortic stenosis. Manual analysis of calcifications in pre-TAVR CT scans is time-consuming and subjective. Hypothesis: Deep learning (DL) models can accurately quantify cardiovascular calcification and predict post-procedural outcomes in TAVR patients. We aimed to develop such a model and investigate its correlation with long-term all-cause mortality in TAVR patients. Methods: This post-hoc analysis examined patients from the FRAILTY-AVR trial and the Royal Victoria Hospital TAVR registry. A 3D UNet-based DL pipeline was developed and trained to detect and quantify coronary artery calcification (CAC), aortic valve calcification (AVC), mitral annular calcification (MAC), and thoracic aorta calcification (TAC) based on pre-procedural CT images. Quantification involved segmentation and CT image analysis techniques. Cox regression analysis, adjusting for various covariates, evaluated the primary endpoint of all-cause mortality one year after TAVR. Results: The study included 585 TAVR patients (mean age: 82.5 years; 44% females) with a median follow-up of 506 days. At one year, 27.5%(161) reached the primary endpoint of all-cause mortality. Hazard ratios for MAC, AVC, TAC, and CAC were 1.26 (95% CI: 1.06-1.51, P=0.010), 0.99 (95% CI: 0.83-1.18, P=0.91), 1.07 (95% CI: 0.89-1.29, P=0.46), and 0.89 (95% CI: 0.72-1.09, P=0.26), respectively. Adjustments for covariates did not significantly change these hazard ratios. Mean calcification volumes were 770 mm3 (AVC), 737 mm3 (CAC), 3076 mm3 (TAC), and 629 mm3 (MAC). No substantial correlations were observed between the four calcifications or cardiovascular co-morbidities however, each score may yield meaningful information. Conclusions: High mitral annular calcification, quantified by our AI pipeline, was independently associated with increased one-year post-TAVR mortality. AI enables efficiency, objectivity, and improved insights, enhancing clinical decision-making and personalized care.