Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York.
Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Centre, Cairo, Egypt; Department of Internal Medicine, Bronx Lebanon Hospital Center, Bronx, New York.
JACC Cardiovasc Imaging. 2019 Jul;12(7 Pt 1):1149-1161. doi: 10.1016/j.jcmg.2018.02.005. Epub 2018 Apr 18.
This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance.
Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification.
The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age 65 ± 17 years; 55.3% women; ejection fraction 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications.
Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p < 0.001). Further cluster analysis in 387 patients with DD helped to classify the severity of DD into 2 groups, with good agreement with conventional classification (kappa = 0.619, p < 0.001). Survival analyses of patients assessed by both clustering algorithms for screening and grading DD showed improved prediction of event-free survival by clusters over conventional classification for all-cause mortality and cardiac mortality, even after accounting for a multivariable, balanced propensity score.
An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.
本研究旨在探索用于评估左心室（LV）舒张功能障碍（DD）的超声心动图变量的自然聚类，以分离高风险表型模式并评估其预后意义。
LV DD 的评估在心血管疾病的管理和预后中很重要。数据驱动方法，如聚类分析，可用于分离相似的病例，而无需预先设定风险分层算法的限制。
该研究纳入了 866 例连续患者的便利样本，这些患者因心肌功能评估（年龄 65 ± 17 岁；55.3%为女性；射血分数 60 ± 9%）而接受了超声心动图 DD 评估参数的检查，这些参数是根据常规指南建议获得的。使用 Ward 链接方法对这些参数进行无监督的层次聚类分析。比较了常规分类和基于聚类的分类之间的主要不良心血管事件、住院和死亡率。
用于筛查 866 例患者中 559 例 DD 存在的聚类算法识别出了 2 个不同的组，与常规分类的一致性适度（kappa=0.41，p<0.001）。对 387 例 DD 患者的进一步聚类分析有助于将 DD 的严重程度分为 2 组，与常规分类的一致性良好（kappa=0.619，p<0.001）。对使用两种聚类算法筛查和分级 DD 的患者进行生存分析显示，与常规分类相比，聚类在预测全因死亡率和心脏死亡率的无事件生存率方面有了改善，即使在考虑了多变量、平衡倾向评分后也是如此。
对用于评估 LV DD 的超声心动图变量进行的无监督评估显示出分组的独特模式。这些自然聚类模式可能更好地识别具有相似风险的患者群体，将其纳入临床实践可能有助于消除不确定的结果并提高临床预后预测。
