Objectives The authors present a method that focuses on cohort matching algorithms for performing patient-to-patient comparisons along multiple echocardiographic parameters for predicting meaningful patient subgroups. Background Recent efforts in collecting multiomics data open numerous opportunities for comprehensive integration of highly heterogenous data to classify a patient's cardiovascular state, eventually leading to tailored therapies. Methods A total of 42 echocardiography features, including 2-dimensional and Doppler measurements, left ventricular (LV) and atrial speckle-tracking, and vector flow mapping data, were obtained in 297 patients. A similarity network was developed to delineate distinct patient phenotypes, and then neural network models were trained for discriminating the phenotypic presentations. Results The patient similarity model identified 4 clusters (I to IV), with patients in each cluster showed distinctive clinical presentations based on American College of Cardiology/American Heart Association heart failure stage and the occurrence of short-term major adverse cardiac and cerebrovascular events. Compared with other clusters, cluster IV had a higher prevalence of stage C or D heart failure (78%; p < 0.001), New York Heart Association functional classes III or IV (61%; p < 0.001), and a higher incidence of major adverse cardiac and cerebrovascular events (p < 0.001). The neural network model showed robust prediction of patient clusters, with area under the receiver-operating characteristic curve ranging from 0.82 to 0.99 for the independent hold-out validation set. Conclusions Automated computational methods for phenotyping can be an effective strategy to fuse multidimensional parameters of LV structure and function. It can identify distinct cardiac phenogroups in terms of clinical characteristics, cardiac structure and function, hemodynamics, and outcomes.

中文翻译：

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一种基于网络的“表型组学”方法，用于从高通量心脏成像数据中发现患者亚型。

目标作者提出了一种方法，该方法侧重于队列匹配算法，用于沿多个超声心动图参数进行患者与患者的比较，以预测有意义的患者亚组。背景 最近在收集多组学数据方面的努力为高度异质性数据的综合整合以对患者的心血管状态进行分类提供了许多机会，最终导致量身定制的治疗。方法 共获得 297 名患者的 42 项超声心动图特征，包括二维和多普勒测量、左心室 (LV) 和心房斑点追踪以及矢量流图数据。开发了一个相似性网络来描绘不同的患者表型，然后训练神经网络模型来区分表型表现。结果 患者相似性模型确定了 4 个集群（I 到 IV），每个集群中的患者根据美国心脏病学会/美国心脏协会心力衰竭分期和短期主要不良心脑血管事件的发生情况表现出独特的临床表现。与其他集群相比，集群 IV 的 C 期或 D 期心力衰竭患病率更高（78%；p < 0.001），纽约心脏协会功能等级 III 或 IV（61%；p < 0.001），以及更高的发生率主要不良心脑血管事件（p < 0.001）。神经网络模型显示出对患者群的稳健预测，独立保持验证集的接收者操作特征曲线下面积从 0.82 到 0.99。结论 用于表型的自动化计算方法可以成为融合 LV 结构和功能的多维参数的有效策略。它可以在临床特征、心脏结构和功能、血流动力学和结果方面识别不同的心脏表型。