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

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: Thi...

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Autores principales: Xie, Xiangkun, Yang, Mingwei, Xie, Shan, Wu, Xiaoying, Jiang, Yuan, Liu, Zhaoyu, Zhao, Huiying, Chen, Yangxin, Zhang, Yuling, Wang, Jingfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371915/
https://www.ncbi.nlm.nih.gov/pubmed/34422921
http://dx.doi.org/10.3389/fcvm.2021.684004
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author Xie, Xiangkun
Yang, Mingwei
Xie, Shan
Wu, Xiaoying
Jiang, Yuan
Liu, Zhaoyu
Zhao, Huiying
Chen, Yangxin
Zhang, Yuling
Wang, Jingfeng
author_facet Xie, Xiangkun
Yang, Mingwei
Xie, Shan
Wu, Xiaoying
Jiang, Yuan
Liu, Zhaoyu
Zhao, Huiying
Chen, Yangxin
Zhang, Yuling
Wang, Jingfeng
author_sort Xie, Xiangkun
collection PubMed
description 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).
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spelling pubmed-83719152021-08-19 Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting Xie, Xiangkun Yang, Mingwei Xie, Shan Wu, Xiaoying Jiang, Yuan Liu, Zhaoyu Zhao, Huiying Chen, Yangxin Zhang, Yuling Wang, Jingfeng Front Cardiovasc Med Cardiovascular Medicine 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). Frontiers Media S.A. 2021-08-04 /pmc/articles/PMC8371915/ /pubmed/34422921 http://dx.doi.org/10.3389/fcvm.2021.684004 Text en Copyright © 2021 Xie, Yang, Xie, Wu, Jiang, Liu, Zhao, Chen, Zhang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Xie, Xiangkun
Yang, Mingwei
Xie, Shan
Wu, Xiaoying
Jiang, Yuan
Liu, Zhaoyu
Zhao, Huiying
Chen, Yangxin
Zhang, Yuling
Wang, Jingfeng
Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting
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
title_full Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting
title_fullStr Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting
title_full_unstemmed Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting
title_short Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting
title_sort early prediction of left ventricular reverse remodeling in first-diagnosed idiopathic dilated cardiomyopathy: a comparison of linear model, random forest, and extreme gradient boosting
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371915/
https://www.ncbi.nlm.nih.gov/pubmed/34422921
http://dx.doi.org/10.3389/fcvm.2021.684004
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