Key result
Machine-learning-derived phenotyping identified four distinct clusters among patients undergoing MV TEER, with 5-year survival ranging from 60.9% in the reference cluster to 23.8% (P<0.001).
Why the study?
Patients with MR present with considerable heterogeneity in cardiac damage, prompting the use of a machine-learning approach to capture their cardiopulmonary complexity.
Does machine learning-based phenotyping using echocardiographic data predict 5-year survival in patients undergoing MV TEER for mitral regurgitation?
Observational (n=1,426)
Yes
Does machine learning-based phenotyping using echocardiographic data predict 5-year survival in patients undergoing MV TEER for mitral regurgitation?
Absolute Event Rate: 23.8% vs 60.9%
p-value: p=<0.001
Machine learning-enabled phenotyping based on echocardiographic data identifies distinct clinical clusters with significantly different 5-year survival rates in patients undergoing MV TEER, refining risk stratification.
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 cm2) 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 cm2) 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.
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Trenkwalder et al. (2023) conducted an observational in Mitral regurgitation (n=1,426). Machine-learning-derived phenotypes (Cluster 4) vs. Cluster 1 (preserved LVEF and regular LVESD) was evaluated on 5-year survival (p=<0.001). Machine-learning-derived phenotyping identified four distinct clusters among patients undergoing MV TEER, with 5-year survival ranging from 60.9% in the reference cluster to 23.8% (P<0.001).
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