Abstract 20549: Patient-Specific CT Image-Based Engineering Analysis of Transcatheter Aortic Valve Replacement - Implications for Aortic Root Rupture
Introduction: Despite the increased global experience with transcatheter aortic valve replacement (TAVR), there remain major adverse clinical events. One of the most severe complications of TAVR is aortic rupture. Although several clinical risk factors of TAVR-induced rupture have been identified, the mechanisms remain largely unknown. The objective of this study was to use computational models to predict potential aortic rupture in TAVR patients.
Methods: Pre-procedural CT scans of TAVR patients were used to reconstruct patient-specific finite element (FE) models, which included the aortic root, aortic leaflets, calcification, mitral-aortic intervalvular fibrosa, anterior mitral leaflet, fibrous trigones, and left ventricle. Stent deployment was simulated in a total of 25 patients to evaluate the potential for aortic rupture. Our research design consisted of two phases: Phase One, which was to develop and calibrate FE modeling techniques by retrospectively analyzing 7 Edwards SAPIEN cases with known results; and Phase Two, which was to implement the modeling methodology developed in Phase One to conduct a blind study of 18 cases from a database of 60 patients consisting of 50% rupture cases. For the blind study, FE simulations were completed by researchers blind to the clinical outcomes, and data analysis was conducted by an independent researcher.
Results: Simulations correctly predicted 83% of the rupture cases. The balloon pressure at time of rupture was approximately 3.52 atm and 2.53 atm for SAPIEN 23 and 26 valves, respectively. The average contact force between the stent and native tissue was about 81N.
Conclusion: Our analysis of over 18 patients suggested that the TAVR outcome could depend on the patient-specific aortic sinus shape, calcification volume, shape, location, and orientation. These results demonstrate the potential for simulation-based pre-TAVR planning tools to evaluate device performance and improve clinical outcomes.
Author Disclosures: Q. Wang: None. C. Martin: None. S. Kodali: None. J. Leipsic: None. P. Blanke: None. J. Webb: None. M. Leon: None. M. Williams: None. C. Primiano: None. W. Sun: Ownership Interest; Significant; Dura Biotech.
This research has received full or partial funding support from the American Heart Association.
- © 2014 by American Heart Association, Inc.