Bernard Jérémy, Yanamala Naveena, Shah Rohan, Seetharam Karthik, Altes Alexandre, Dupuis Marlène, Toubal Oumhani, Mahjoub Haïfa, Dumortier Hélène, Tartar Jean, Salaun Erwan, O'Connor Kim, Bernier Mathieu, Beaudoin Jonathan, Côté Nancy, Vincentelli André, LeVen Florent, Maréchaux Sylvestre, Pibarot Philippe, Sengupta Partho P
Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada.
Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
JACC Cardiovasc Imaging. 2023 Oct;16(10):1253-1267. doi: 10.1016/j.jcmg.2023.02.016. Epub 2023 May 10.
Primary mitral regurgitation (MR) is a heterogeneous clinical disease requiring integration of echocardiographic parameters using guideline-driven recommendations to identify severe disease.
The purpose of this preliminary study was to explore novel data-driven approaches to delineate phenotypes of MR severity that benefit from surgery.
The authors used unsupervised and supervised machine learning and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 years (IQR: 1.3-5.3 years) and 6.8 (IQR: 4.0-8.5 years), respectively. The authors compared the phenogroups' incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure).
High-severity (HS) phenogroups from the French cohort (HS: n = 117; low-severity [LS]: n = 126) and the Canadian cohort (HS: n = 87; LS: n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (P = 0.047 and P = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (P = 0.70 and P = 0.50, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C statistic improvement; P = 0.480; and categorical net reclassification improvement; P = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution.
Novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery.
原发性二尖瓣反流（MR）是一种异质性临床疾病，需要根据指南驱动的建议整合超声心动图参数以识别严重疾病。
这项初步研究的目的是探索新的数据驱动方法来描绘从手术中获益的MR严重程度的表型。
作者使用无监督和有监督的机器学习以及可解释人工智能（AI），整合了来自法国（n = 243；开发队列）和加拿大（n = 157；验证队列）的400例原发性MR受试者的24个超声心动图参数，随访时间中位数分别为3.2年（IQR：1.3 - 5.3年）和6.8年（IQR：4.0 - 8.5年）。作者比较了这些表型组相对于传统MR特征的增量预后价值，以及对于全因死亡率这一主要终点，将二尖瓣修复/置换手术时间作为生存分析的协变量（时间依赖性暴露）。
法国队列（高严重度[HS]：n = 117；低严重度[LS]：n = 126）和加拿大队列（HS：n = 87；LS：n = 70）中的高严重度表型组显示，接受手术的HS受试者相较于未接受手术的受试者，无事件生存期有所改善（分别为P = 0.047和P = 0.020）。在两个队列的低严重度表型组中均未观察到类似的手术获益（分别为P = 0.70和P = 0.50）。表型分组在传统上为重度或中度重度MR受试者中显示出增量预后价值（Harrell C统计量改善；P = 0.480；以及分类净重新分类改善；P = 0.002）。可解释人工智能明确了每个超声心动图参数如何对表型组分布产生影响。
新的数据驱动表型分组和可解释人工智能有助于更好地整合超声心动图数据，以识别原发性MR患者，并改善二尖瓣修复/置换手术后的无事件生存期。
