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

Abstract Background Severe functional mitral regurgitation (FMR) may benefit from transcatheter mitral valve repair (TMVR), but selection of patients remains to be optimised. Objectives The aim of this study was to use Machine-Learning (ML) approaches to uncover concealed connections between clinical, echocardiographic, and hemodynamic data associated with patients' outcomes. Methods Consecutive patients undergoing TMVR from 2009 to 2020 were included in the MITRA-AI registry. Eleven relevant clinical and echocardiographic variables were selected in a clustering-based model. The primary endpoint was a composite of cardiovascular death or heart failure hospitalisation at one year, while its single components were secondary endpoints. External validation was performed on the Mitrascore dataset and comparison between clustering and Mitrascore was performed with Net Reclassification Index (NRI). Moreover cluster assignment was performed also on a cohort of patients with moderate-to-severe MR treated with medical therapy alone. Results 822 patients were included in the derivation cohort. 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 LVs (> 107 ml/m2), lower LVEF (< 35%) and more prevalent ischemic aetiology (51% to 60% vs. 30% to 40%) compared to low-risk phenotype patients. Moreover, within the high-risk group, patients with diabetes mellitus and with advanced age were at increased risk. Within the low-risk group, ischemic aetiology increased the risk of cardiovascular death, while permanent atrial fibrillation amplified that of HF hospitalizations. In the Mitrascore validation cohort of 1119 patients, incidence of the primary end point varied consistently (48%, 52%, 35% and 42%). Clustering achieved a NRI in 27% of the patients in the derivation cohort and 7% in the validation group compared to Mitrascore. In the validation group of 207 patients treated medically, the incidence of the primary endpoint decreased from the first to the fourth cluster (53%, 42%, 30% and 30%). Conclusions A ML analysis identified meaningful clinical phenotypic presentations in FMR undergoing TMVR, with significant differences in terms of cardiovascular death and heart failure hospitalizations, confirmed in an external validation cohort. ML phenotyping also discriminated prognosis of medically treated patients with severe MR.Central Figure