Beyond Static Planning: Computational Predictive Modeling to Avoid Coronary Artery Occlusion in TAVR.

ABSTRACT

BACKGROUND: Coronary artery occlusion (CO) during transcatheter aortic valve replacement (TAVR) is a devastating complication. The objective is to assess the clinical impact of a novel computational predictive modeling algorithm for CO during TAVR planning. METHODS: From January 2020 to December 2022, 116 patients (7.6%) undergoing TAVR evaluation were deemed at increased risk of CO based on traditional criteria. Patients underwent prospective computational modeling (DASI Simulations) to assess risk of CO during TAVR; procedural modifications and clinical results were reviewed retrospectively. RESULTS: Of the 116 patients at risk for CO by traditional methodology, 53 had native aortic stenosis(45.7%), 47 a previous surgical AVR (40.5%), and 16 a prior TAVR (13.8%). Transcatheter valve choice, size, and/or implantation depth was modeled for all patients. Computational modeling predicted an increased risk of CO based in 39/116 (31.9%). Within this sub-cohort, 29 patients proceeded with TAVR. Procedural modifications to augment risk of CO included BASILICA (n=10), chimney coronary stents (n=8), coronary access without stent (n=3). There were no episodes of coronary compromise among patients following TAVR, either for those predicted to be at high risk of CO (with procedural modifications) or predicted low risk (standard TAVR). CONCLUSIONS: Utilization of preoperative simulations for TAVR in patient-specific geometry through computational predictive modeling of CO was an effective enhancement to procedure planning.