Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair: the MITRA-AI study

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 Consecutive patients undergoing TEER from 2009 to 2020 were included in the MITRA-AI registry. The primary endpoint and results 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/m2), lower left ventricular ejection fraction (<35%), and more prevalent ischaemic aetiology compared with low-risk phenotype patients. Within low-risk groups, permanent atrial fibrillation amplified that of HF hospitalizations. In the Mitrascore cohort, the incidence of the primary endpoint was 48, 52, 35, and 42% across clusters. 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.