# Understanding the mechanisms of TAVI durability through computational modelling: a multidisciplinary review

## TL;DR

This paper reviews how computational models can help understand and improve the long-term durability of TAVI valves, especially for younger patients.

## Contribution

The paper provides a multidisciplinary review of computational modeling approaches to assess TAVI durability and highlights the need for better validation and collaboration between clinicians and engineers.

## Key findings

- Computational models can simulate TAVI performance and identify factors affecting durability, such as mechanical stresses and blood flow patterns.
- Current models lack complete validation against clinical outcomes and require uncertainty analysis for reliable predictions.
- Integration of these models into clinical practice could improve patient-specific planning and long-term valve management.

## Abstract

As transcatheter aortic valve implantation (TAVI) expands to younger populations, durability has become a concern, requiring a lifetime rather than a single-procedure perspective. While clinical trials suggest comparable mid-term performance to surgical bioprostheses, data beyond 10 years remain limited, particularly for bicuspid valves, valve-in-valve procedures, and complex anatomies. Computational modelling combines patient anatomy and device design in computer-based simulations to study valve performance under physiological loading. Applied to TAVI, these models can reproduce implantation, evaluate mechanical stresses, and simulate blood flow, providing mechanistic insights into deterioration processes, including altered leaflet loading, stent deformation, and thrombosis-prone flow. Although these simulations do not directly assess durability, they use surrogate metrics linked with these mechanisms, helping identify factors that may influence longevity and guide design and procedural refinements. Clinically, modelling could support patient-specific planning and reintervention strategies, informing decisions across the valve-replacement pathway, an important consideration as younger patients are likely to undergo multiple lifetime procedures. Integrating these tools into pre-procedural planning may help anticipate challenges such as coronary access, annular geometry, and redo feasibility. However, current studies report elements of verification and field-level validation, but none complete a pre-specified, calibrated surrogate-to-outcome validation with uncertainty/sensitivity analysis; thus, durability predictions remain exploratory. Progress needs transparent verification, field checks vs. bench or imaging, surrogate calibration to data, outcome testing in independent cohorts, and routine uncertainty/sensitivity reporting, with close clinician-engineer collaboration. This review underscores the need for a multidisciplinary approach and provides a critical analysis of the available tools and their potential to advance long-term outcomes.

Graphical AbstractUnderstanding the mechanisms of TAVI durability through computational modelling: a multidisciplinary review. Expanding indications for transcatheter aortic valve implantation (TAVI) from high- to low-risk patients, including younger individuals, makes durability and lifetime valve-replacement pathways increasingly important (Panel A). Computational modelling (Panel B) uses anatomical and clinical data together with finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI) to generate surrogate durability metrics (stress, strain, shear, stasis), which require verification and validation through in vitro testing and clinical outcome data (Panel C). These models provide mechanistic insight into durability-related mechanisms, and may ultimately support device/procedure selection, redo TAVI/SAVR strategies, and durability-focused research for lifetime management (Panel D). AS, aortic stenosis; SAVR, surgical aortic valve replacement; SVD, structural valve degeneration.

Understanding the mechanisms of TAVI durability through computational modelling: a multidisciplinary review. Expanding indications for transcatheter aortic valve implantation (TAVI) from high- to low-risk patients, including younger individuals, makes durability and lifetime valve-replacement pathways increasingly important (Panel A). Computational modelling (Panel B) uses anatomical and clinical data together with finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI) to generate surrogate durability metrics (stress, strain, shear, stasis), which require verification and validation through in vitro testing and clinical outcome data (Panel C). These models provide mechanistic insight into durability-related mechanisms, and may ultimately support device/procedure selection, redo TAVI/SAVR strategies, and durability-focused research for lifetime management (Panel D). AS, aortic stenosis; SAVR, surgical aortic valve replacement; SVD, structural valve degeneration.