Why the study?
Comprehensive risk-factor models for predicting left ventricular remodeling after AMI were lacking, and comparative data on different 3D STE parameters were limited.
Can 3D speckle tracking echocardiography parameters combined with machine learning models accurately predict left ventricular remodeling in patients after acute myocardial infarction?
Can 3D speckle tracking echocardiography parameters combined with machine learning models accurately predict left ventricular remodeling in patients after acute myocardial infarction?
Machine learning models, specifically Random Forest, using 3D speckle tracking echocardiography parameters like 3D GLS can accurately predict left ventricular remodeling after acute myocardial infarction.
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.
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Zhong et al. (2020) studied this question.
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