Pulse Brain · Growing Health Evidence Index
Tier 4 — Narrative / commentaryPeer-reviewed

Looking towards the future: patient-specific computational modeling to optimize outcomes for transcatheter mitral valve repair

Paul Wong, Andrew D. Wisneski, Amitoj Sandhu, Zhongjie Wang, Vaikom S. Mahadevan, Tom C. Nguyen, Julius M. Guccione

Frontiers in Cardiovascular Medicine · 2023

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Summary

This narrative review examines the application of computational modelling techniques to mitral valve disease management, particularly in the context of emerging transcatheter mitral valve repair (TMVR) technologies. The authors argue that patient-specific finite element and fluid–structure interaction models, informed by clinical imaging, can establish disease atlases and biomechanical profiles to guide clinicians towards optimal intervention selection—either surgical repair or TMVR—as TMVR gradually expands to lower-risk patient populations.

UK applicability

The review addresses a clinical challenge relevant to UK cardiac services, where mitral valve disease management and the integration of TMVR into NHS treatment pathways remain evolving areas. UK cardiac centres may benefit from computational modelling approaches to optimise patient selection and procedural planning, though implementation would require investment in imaging infrastructure and computational capacity.

Key measures

Mitral valve geometry, pathophysiology replication, biomechanics profiles, treatment outcome optimisation

Outcomes reported

The paper reviews computational modelling approaches (finite element method, fluid–structure interactions) applied to mitral valve repair, and discusses how patient-specific models derived from clinical imaging can guide treatment selection between surgical and transcatheter interventions for mitral valve regurgitation.

Theme
General food systems / other
Subject
Other / interdisciplinary
Study type
Narrative Review
Study design
Narrative review
Source type
Peer-reviewed study
Status
Published
System type
Human clinical
DOI
10.3389/fcvm.2023.1140379
Catalogue ID
SNmojg06h5-zue1n2

Topic tags

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