Does machine-learning phenotyping identify distinct risk profiles for cardiovascular death and heart failure hospitalizations in patients with severe functional mitral regurgitation undergoing transcatheter edge-to-edge repair?
Machine learning phenotyping can successfully stratify risk for cardiovascular death and heart failure hospitalizations among patients with severe functional mitral regurgitation undergoing transcatheter edge-to-edge repair.
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: ), 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.
D’Ascenzo et al. (Thu,) studied this question.
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