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 of this study was to assess the clinical impact of a computational predictive modeling algorithm for CO during TAVR planning.

Methods: From January 2020 to December 2022, 116 patients (7.6%) who underwent TAVR evaluation were deemed to be at increased risk of CO on the basis of traditional criteria. Patients underwent prospective computational modeling (DASI Simulations) to assess their 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 valve stenosis (45.7%), 47 had undergone previous surgical AVR (40.5%), and 16 had undergone previous TAVR (13.8%). Transcatheter valve choice, size, or implantation depth was modeled for all patients. Computational modeling predicted an increased risk of CO in 39 of 116 (31.9%) patients. Within this subcohort, 29 patients proceeded with TAVR. Procedural modifications to augment the risk of CO included bioprosthetic or native aortic scallop intentional laceration to prevent iatrogenic coronary artery obstruction during TAVR (n = 10), chimney coronary stents (n = 8), and coronary access without stents (n = 3). There were no episodes of coronary artery compromise among patients after TAVR, either for those predicted to be at high risk of CO (with procedural modifications) or those predicted to be at low risk (standard TAVR).

Conclusions: The use of preoperative simulations for TAVR in patient-specific geometry through computational predictive modeling of CO is an effective enhancement to procedure planning.