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三种三维斑点追踪超声心动图参数预测左心室重构的比较。

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

机构信息

Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.

The First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China.

出版信息

J Healthc Eng. 2020 Jul 30;2020:8847144. doi: 10.1155/2020/8847144. eCollection 2020.

Abstract

Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with traditional models. However, no models that contain comprehensive risk factors were assessed, and there are limited data on the comparison of different 3D STE parameters. In this study, we sought to build a machine learning model for predicting LVR in AMI patients after effective percutaneous coronary intervention (PCI) that contains the majority of the clinical risk factors and compare 3D STE parameters values for LVR prediction. We enrolled 135 first-onset AMI patients (120 males, mean age 54 ± 9 years). All patients went through a 3D STE and a traditional transthoracic echocardiography 24 hours after reperfusion. A second echocardiography was repeated at the three-month follow-up to detect LVR (defined as a 20 percent increase in left ventricular end-diastolic volume). Six models were constructed using 15 risk factors. A receiver operator characteristic curve and four performance measurements were used as evaluation methods. Feature importance was used to compare 3D STE parameters. 26 patients (19.3%) had LVR. Our evaluation showed that RF can best predict LVR with the best AUC of 0.96. 3D GLS was the most valuable 3D STE parameters, followed by GCS, global area strain, and global radial strain (feature importance 0.146, 0.089, 0.087, and 0.069, respectively). To sum up, RF models can accurately predict the LVR after AMI, and 3D GLS was the best 3D STE parameters in predicting the LVR.

摘要

三维斑点追踪超声心动图(3D STE)是一种新兴的非侵入性方法,可用于预测急性心肌梗死(AMI)后左心室重构(LVR)。以前的研究分析了传统模型中 3D STE 的预测价值。然而,没有评估包含全面风险因素的模型,并且关于不同 3D STE 参数的比较数据有限。在这项研究中,我们试图建立一个包含大多数临床风险因素的机器学习模型,用于预测经有效经皮冠状动脉介入治疗(PCI)后的 AMI 患者的 LVR,并比较 3D STE 参数值用于 LVR 预测。我们纳入了 135 例首发 AMI 患者(男性 120 例,平均年龄 54±9 岁)。所有患者在再灌注后 24 小时内接受 3D STE 和传统经胸超声心动图检查。在三个月的随访中重复进行第二次超声心动图检查,以检测 LVR(定义为左心室舒张末期容积增加 20%)。使用 15 个危险因素构建了 6 个模型。使用接收者操作特征曲线和四项性能测量作为评估方法。特征重要性用于比较 3D STE 参数。26 例(19.3%)发生 LVR。我们的评估表明,RF 可以使用最佳 AUC 0.96 最佳预测 LVR。3D GLS 是最有价值的 3D STE 参数,其次是 GCS、整体面积应变和整体径向应变(特征重要性分别为 0.146、0.089、0.087 和 0.069)。总之,RF 模型可以准确预测 AMI 后的 LVR,3D GLS 是预测 LVR 的最佳 3D STE 参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7305/7416266/3bf69402207d/JHE2020-8847144.001.jpg

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