ArticleEuropean heart journal. Digital health2025
Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair: the MITRA-AI study.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Efficacy and safety of PM-AR-T versus edwards MC3 rings in tricuspid regurgitation: A non-inferiority, randomized controlled trial.PloS one · 2025Trial
- Mitral Transcatheter Edge-to-Edge Repair in Patients With Advanced Heart Failure: A Single-Center Experience and Insights Into Anatomical and Clinical Determinants of Procedural Complexity.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Article
- One-year real-world outcomes after vericiguat initiation in heart failure: The ROVER-Japan cohort study.International journal of cardiology. Heart & vasculature · 2026Article
- Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Review
- Epidemiology, pathophysiology, diagnosis and management of atrial functional mitral regurgitation: An expert opinion paper.ESC heart failure · 2025Review
- Diagnosis and Diagnostic Challenges of Secondary Mitral Regurgitation in the Era of Transcatheter Edge-to-Edge Repair of the Mitral Valve.Journal of clinical medicine · 2025Review
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Authors and funding
41 authors.
Funding
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Abstract
Aims: Severe functional mitral regurgitation (FMR) may benefit from mitral transcatheter edge-to-edge repair (TEER), but selection of patients remains to be optimized. Objectives: The aim of this study was to use machine-learning (ML) approaches to uncover concealed connections between clinical, echocardiographic, and haemodynamic data associated with patients' outcomes. Methods and results: Consecutive patients undergoing TEER from 2009 to 2020 were included in the MITRA-AI registry. The primary endpoint was a composite of cardiovascular death or heart failure (HF) hospitalization at 1 year. External validation was performed on the Mitrascore cohort. 822 patients were included. The composite primary endpoint occurred in 250 (30%) patients. Four clusters with decreasing risk of the primary endpoint were identified (42, 37, 25, and 20% from Cluster 1 to Cluster 4, respectively). Clusters were combined into a high-risk (Clusters 1 and 2) and a low-risk phenotype (Clusters 3 and 4). High-risk phenotype patients had larger left ventriculars (LVs) (>107 mL/m Conclusion: A ML analysis identified meaningful clinical phenotypic presentations in FMR undergoing TEER, with significant differences in terms of cardiovascular death and HF hospitalizations, confirmed in an external validation cohort.
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