Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
Br J Radiol. 2021 Nov 1;94(1127):20210259. doi: 10.1259/bjr.20210259. Epub 2021 Aug 31.
Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stratification for these high-risk patients by incorporating various types of data. The aim of this study was to build an ML model to predict adverse events including all-cause deaths and heart transplantation in DCM patients with severely impaired LV systolic function.
One hundred and eighteen patients with DCM and severely reduced LVEFs (<35%) were included. The baseline clinical characteristics, laboratory data, electrocardiographic, and cardiac magnetic resonance (CMR) features were collected. Various feature selection processes and classifiers were performed to select an ML model with the best performance. The predictive performance of tested ML models was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve using 10-fold cross-validation.
Twelve patients died, and 17 patients underwent heart transplantation during the median follow-up of 508 days. The ML model included systolic blood pressure, left ventricular end-systolic and end-diastolic volume indices, and late gadolinium enhancement (LGE) extents on CMR imaging, and a support vector machine was selected as a classifier. The model showed excellent performance in predicting adverse events in DCM patients with severely reduced LVEF (the AUC and accuracy values were 0.873 and 0.763, respectively).
This ML technique could effectively predict adverse events in DCM patients with severely reduced LVEF.
The ML method has superior ability in risk stratification in severe DCM patients.
患有扩张型心肌病（DCM）和严重左心室射血分数（LVEF）降低的患者发生不良心脏事件的风险非常高。机器学习（ML）方法可以通过合并各种类型的数据为这些高危患者进行更有效的风险分层。本研究的目的是构建一个 ML 模型，以预测包括全因死亡和 DCM 患者严重左心室收缩功能障碍患者心脏移植在内的不良事件。
共纳入 118 例 DCM 和严重 LVEF 降低（<35%）的患者。收集基线临床特征、实验室数据、心电图和心脏磁共振（CMR）特征。进行了各种特征选择过程和分类器，以选择性能最佳的 ML 模型。使用 10 折交叉验证评估测试的 ML 模型的预测性能，通过接受者操作特征曲线的曲线下面积（AUC）进行评估。
在中位数为 508 天的随访期间，有 12 例患者死亡，17 例患者接受了心脏移植。ML 模型包括收缩压、左心室收缩末期和舒张末期容积指数以及 CMR 成像上的晚期钆增强（LGE）程度，支持向量机被选为分类器。该模型在预测严重 LVEF 降低的 DCM 患者不良事件方面表现出优异的性能（AUC 和准确率值分别为 0.873 和 0.763）。
这项 ML 技术可以有效地预测严重 LVEF 降低的 DCM 患者的不良事件。
ML 方法在严重 DCM 患者的风险分层方面具有卓越的能力。
