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

Area of Science:

• Cardiovascular Medicine
• Medical Imaging
• Computational Biology

Background:

• Coronary artery occlusion (CO) is a serious complication during transcatheter aortic valve replacement (TAVR).
• Traditional risk assessment criteria may not fully identify all patients at risk for CO.
• Predictive modeling offers a novel approach to TAVR planning.

Purpose of the Study:

• To evaluate the clinical impact of a computational predictive modeling algorithm for CO during TAVR planning.
• To assess the effectiveness of this technology in identifying high-risk patients and guiding procedural modifications.

Main Methods:

• Prospective computational modeling (DASI Simulations) was used to assess CO risk in 116 TAVR-evaluated patients at increased risk by traditional criteria.
• Procedural modifications were implemented based on modeling predictions.
• Clinical outcomes were retrospectively reviewed.

Main Results:

• Computational modeling identified 31.9% of at-risk patients as having an increased risk of CO.
• Procedural modifications, including scallop intentional laceration and chimney coronary stents, were performed in 29 patients.
• No episodes of coronary artery compromise occurred in any patient post-TAVR, regardless of predicted risk or procedural modifications.

Conclusions:

• Preoperative simulations using computational predictive modeling enhance TAVR planning by assessing patient-specific geometry.
• This approach effectively mitigates the risk of coronary artery occlusion during TAVR.