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Abstract：
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.
摘要：
目的使用无监督机器学习来识别心律失常性二尖瓣脱垂（MVP）风险增加的表型簇。材料和方法这项回顾性研究包括2007年10月至2020年6月期间在15个欧洲三级中心接受钆增强（LGE）心脏MRI的无血流动力学显著二尖瓣返流或左心室（LV）功能障碍的MVP患者。研究终点是持续性室性心动过速的复合终点，心脏性猝死，或者无法解释的晕厥.利用无监督数据驱动的分层k均值算法识别表型簇。通过Cox比例风险模型评估集群与研究终点之间的关联。结果共474例患者(平均年龄,47岁±16[SD]；244名女性，230个雄性），具有两个表型簇。第2组患者（474例中的199例，占42％）的二尖瓣变性更严重（即，双叶MVP和小叶移位)，左右心室重塑,LGE心脏MRI评估的心肌纤维化高于第1组。人口统计学和临床特征(即，症状,Holter监测中的心律失常）在区分两个集群方面的贡献可忽略不计。与第1组相比，第2组患者在39个月的中位随访中发展研究终点的风险明显更高（风险比：3.79[95％CI：1.19，12.12]，P=.02）调整LGE范围后。结论无明显二尖瓣反流或LV功能障碍的MVP患者中,无监督机器学习能够主要基于心脏MRI特征识别具有不同心律失常结局的两个表型簇.这些结果鼓励使用基于深度成像的表型进行MVP中的心律失常风险预测。关键词:磁共振成像,心脏,心脏MRI,二尖瓣脱垂,聚类分析，室性心律失常,心源性猝死,无监督机器学习补充材料可用于本文。©RSNA,2024.
