Department of Cardiology, German Heart Center Munich, Technical University of Munich, Lazarettstrasse 36, 80636 Munich, Germany.
DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstrasse 8a & 9, 80336 Munich, Germany.
Eur Heart J Cardiovasc Imaging. 2023 Apr 24;24(5):574-587. doi: 10.1093/ehjci/jead013.
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.
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.
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.
患有二尖瓣反流（MR）的患者，其心脏损伤具有相当大的异质性，取决于潜在病因、疾病进展和合并症。本研究旨在通过使用基于机器学习（ML）的表型分析方法来捕捉其心肺复杂性。
从 1426 名接受二尖瓣经导管缘对缘修复术（MV TEER）治疗 MR 的患者中获得数据。该 ML 模型使用 609 名患者（推导队列）进行开发，并在来自两个外部机构的 817 名患者中进行验证。表型基于超声心动图数据，ML 衍生的表型与 5 年结果相关。无监督凝聚聚类在推导队列中揭示了四种表型：第 1 组显示保留的左心室射血分数（LVEF；56.5 ± 7.79%）和规则的左心室收缩末期直径（LVESD；35.2 ± 7.52mm）；第 1 组的 5 年生存率（以下作为参考）为 60.9%。第 2 组表现为保留的 LVEF（55.7 ± 7.82%），但显示最大的二尖瓣有效反流口面积（0.623 ± 0.360 cm2）和最高的收缩期肺动脉压（68.4 ± 16.2mmHg）；5 年生存率为 43.7%（P 值：0.032）。第 3 组的特征是左心室射血分数受损（31.0 ± 10.4%）和左心室收缩末期直径增大（53.2 ± 10.9mm）；5 年生存率降低至 38.3%（P 值：<0.001）。第 4 组虽然左心室射血分数仅略有降低（51.5 ± 11.0%），但存在双心房扩张（左心房容积：312 ± 113mL；右心房面积：46.0 ± 8.83cm2），预后最差（23.8%；P 值：<0.001）。重要的是，ML 衍生的表型的预后意义得到了外部验证。
ML 支持的表型分析可捕捉到二尖瓣瓣外心脏损伤的复杂性，而不一定按顺序发生。这种新的表型分析方法可在未来对接受 MV TEER 的患者进行更精细的风险分层。
