Malahfji Maan, Tan Xin, Kaolawanich Yodying, Saeed Mujtaba, Guta Andrada, Reardon Michael J, Zoghbi William A, Polsani Venkateshwar, Elliott Michael, Kim Raymond, Li Meng, Shah Dipan J
Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
Department of Statistics, Rice University, Houston, Texas, USA.
JACC Cardiovasc Imaging. 2025 May;18(5):557-568. doi: 10.1016/j.jcmg.2025.01.006. Epub 2025 Mar 26.
Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically.
The aim of this study was to first use unsupervised machine learning to identify unique patient phenoclusters in AR, and subsequently evaluate their prognostic relevance.
Clinical and cardiac magnetic resonance (CMR) characterization of moderate or severe AR patients was performed across 4 U.S.
Data from 2 centers were used for derivation of phenoclusters and validation was performed in the other 2. The outcome was all-cause death. An unsupervised clustering pipeline, Partition Around Medoids, used 23 clinical and CMR variables to derive patient clusters independent of outcomes.
Included were 972 patients with mean age 62 ± 23.2 years, 754 (78%) male, 680 (70%) trileaflet valve, and 330 (34%) underwent valve surgery. Over a median follow-up of 2.58 years (Q1-Q3: 1.03-5.50 years), the overall mortality rate was 12%. Four clusters were derived: 1) a younger predominantly male phenotype with majority of bicuspid aortic valve and high extent of left ventricular (LV) remodeling (1% mortality); 2) older male patients with predominantly tricuspid valves and intermediate outcomes (10% mortality); 3) older predominantly male patients with the highest burden of comorbidities, LV scarring, and dysfunction (22% mortality); and 4) a phenotype of predominantly female patients with high mortality and relatively higher symptoms burden, relatively lower extent of LV remodeling, and rate of aortic valve replacement (20% mortality). The clustering algorithm was independently associated with survival after adjustment for time-dependent aortic valve replacement and traditional risk markers of prognosis in patients with AR (C statistic 0.77 vs 0.75; P = 0.009 in the validation cohort).
Unique patient phenoclusters of AR are described using a machine learning approach leveraging comprehensive CMR and clinical characterization. This approach may be an opportunity for a precision medicine approach to enhance risk stratification of patients with AR. Female patients with AR pose a unique phenotype with high mortality, which deserves greater attention.
目前的治疗模式假定主动脉瓣反流（AR）患者为同质群体，但临床上观察到疾病进展过程和预后各不相同。
本研究的目的是首先使用无监督机器学习来识别AR患者中独特的表型聚类，随后评估它们与预后的相关性。
在美国4个中心对中重度AR患者进行临床和心脏磁共振（CMR）特征分析。2个中心的数据用于推导表型聚类，另外2个中心进行验证。结局为全因死亡。一种无监督聚类方法，围绕中心点划分法，使用23个临床和CMR变量来推导独立于结局的患者聚类。
纳入972例患者，平均年龄62±23.2岁，754例（78%）为男性，680例（70%）为三叶瓣，330例（34%）接受了瓣膜手术。中位随访2.58年（四分位间距：1.03 - 5.50年），总死亡率为12%。推导得到4个聚类：1）年轻的以男性为主的表型，多数为二叶式主动脉瓣且左心室（LV）重塑程度高（死亡率1%）；2）年龄较大的男性患者，主要为三叶瓣且预后中等（死亡率10%）；3）年龄较大的以男性为主的患者，合并症负担、LV瘢痕形成和功能障碍程度最高（死亡率22%）；4）以女性患者为主的表型，死亡率高，症状负担相对较高，LV重塑程度相对较低，主动脉瓣置换率低（死亡率20%）。在对AR患者的时间依赖性主动脉瓣置换和传统预后风险标志物进行调整后，聚类算法与生存率独立相关（验证队列中C统计量为0.77对0.75；P = 0.009）。
利用综合CMR和临床特征，通过机器学习方法描述了AR患者独特的表型聚类。这种方法可能为精准医学方法提供机会，以加强AR患者的风险分层。患有AR的女性患者呈现出死亡率高的独特表型，值得更多关注。
