Computational modeling may improve TAVR planning but clinical translation remains limited
This systematic review evaluates the role of computational modeling derived from patient-specific imaging in planning transcatheter aortic valve replacement (TAVR) compared with standard computed tomography-based anatomical assessment. The review synthesizes evidence on secondary outcomes including paravalvular leak, conduction disturbances, coronary obstruction, and aortic injury.
The authors report that computational models show promise in predicting these complications, potentially aiding procedural planning. However, the evidence base is limited by small study populations, heterogeneous methodologies, and a lack of patient-specific validation. No pooled effect sizes are reported, and the primary outcome is not specified.
Key limitations include the absence of integration into routine clinical workflows and the need for validation against clinically meaningful endpoints. The authors emphasize that future progress will require scalable digital infrastructure and close collaboration between clinicians and engineers to incorporate simulation outputs into Heart Team decision-making.
Clinicians should interpret these findings cautiously, as clinical translation remains limited and the technology is not yet ready for widespread adoption in TAVR planning.