Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair

Aims Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. Methods and results Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 +/- 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 +/- 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 +/- 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 +/- 0.360 cm(2)) and highest systolic pulmonary artery pressures (68.4 +/- 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 +/- 10.4%) and enlarged LVESD (53.2 +/- 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 +/- 113 mL; right atrial area: 46.0 +/- 8.83 cm(2)) although LVEF was only slightly reduced (51.5 +/- 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. Conclusion ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.

Automated summary

This study aims to capture the cardiopulmonary complexity of patients with mitral regurgitation (MR) using a machine-learning (ML)-based phenotyping approach. The study found considerable heterogeneity in cardiac damage among MR patients depending on underlying factors and comorbidities. The ML model identified four phenotypes and correlated them with 5-year outcomes.