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

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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.684004

DOI=10.3389/fcvm.2021.684004

ISSN=2297-055X

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 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 was able to early differentiate LVRR and non-LVRR in patients with first-diagnosed DCM before drug therapy, facilitating disease management and invasive therapy selection. Multicenter prospective study is necessary for further validation.