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

Journal: Journal of healthcare engineering
PMID: 32802300

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.

Authors

  • Junda Zhong
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Peng Liu
    Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China.
  • Shuang Li
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Xiaomin Huang
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Qunhui Zhang
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Jianyu Huang
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.
  • Meixiang Chen
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Zheng Ruan
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Changyu Qin
    Department of Geriatric Cardiology, General Hospital of the Southern Theatre Command, PLA, Guangzhou 510016, China.
  • Lin Xu
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.

Keywords

External Resources

Popular Topics
Recent Journals