Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting

Xiangkun Xie; Mingwei Yang; Shan Xie; Xiaoying Wu; Yuan Jiang; Zhaoyu Liu; Huiying Zhao; Yangxin Chen; Yuling Zhang; Jingfeng Wang

doi:10.3389/fcvm.2021.684004

Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting

Front Cardiovasc Med. 2021 Aug 4:8:684004. doi: 10.3389/fcvm.2021.684004. eCollection 2021.

Authors

Xiangkun Xie^{1

2}, Mingwei Yang^{1

2

3}, Shan Xie⁴, Xiaoying Wu^{1

2}, Yuan Jiang^{1

2}, Zhaoyu Liu⁴, Huiying Zhao⁴, Yangxin Chen^{1

2}, Yuling Zhang^{1

2}, Jingfeng Wang^{1

2}

Affiliations

¹ Cardiovascular Medicine Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
² Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China.
³ Cardiovascular Medicine Department, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
⁴ Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

Introduction: Left ventricular reverse remodeling (LVRR) is associated with decreased cardiovascular mortality and improved cardiac survival and also crucial for therapeutic options. However, there is a lack of an early prediction model of LVRR in first-diagnosed dilated cardiomyopathy. Methods: This single-center study included 104 patients with idiopathic DCM. We defined LVRR as an absolute increase in left ventricular ejection fraction (LVEF) from >10% to a final value >35% and a decrease in left ventricular end-diastolic diameter (LVDd) >10%. Analysis features included demographic characteristics, comorbidities, physical sign, biochemistry data, echocardiography, electrocardiogram, Holter monitoring, and medication. Logistic regression, random forests, and extreme gradient boosting (XGBoost) were, respectively, implemented in a 10-fold cross-validated model to discriminate LVRR and non-LVRR, with receiver operating characteristic (ROC) curves and calibration plot for performance evaluation. Results: LVRR occurred in 47 (45.2%) patients after optimal medical treatment. Cystatin C, right ventricular end-diastolic dimension, high-density lipoprotein cholesterol (HDL-C), left atrial dimension, left ventricular posterior wall dimension, systolic blood pressure, severe mitral regurgitation, eGFR, and NYHA classification were included in XGBoost, which reached higher AU-ROC compared with logistic regression (AU-ROC, 0.8205 vs. 0.5909, p = 0.0119). Ablation analysis revealed that cystatin C, right ventricular end-diastolic dimension, and HDL-C made the largest contributions to the model. Conclusion: Tree-based models like XGBoost were able to early differentiate LVRR and non-LVRR in patients with first-diagnosed DCM before drug therapy, facilitating disease management and invasive therapy selection. A multicenter prospective study is necessary for further validation. Clinical Trial Registration:http://www.chictr.org.cn/usercenter.aspx (ChiCTR2000034128).

Keywords: heart failure; idiopathic dilated cardiomyopathy; machine learning; predictive model; reverse remodeling.