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Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations

OBJECTIVE: At present, there is no early prediction model of left ventricular reverse remodeling (LVRR) for people who are in cardiac arrest with an ejection fraction (EF) of ≤35% at first diagnosis; thus, the purpose of this article is to provide a supplement to existing research. MATERIALS AND MET...

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Autores principales: Liu, Lu, Qiao, Cen, Zha, Jun-Ren, Qin, Huan, Wang, Xiao-Rui, Zhang, Xin-Yu, Wang, Yi-Ou, Yang, Xiu-Mei, Zhang, Shu-Long, Qin, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428443/
https://www.ncbi.nlm.nih.gov/pubmed/36061535
http://dx.doi.org/10.3389/fcvm.2022.864312
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author Liu, Lu
Qiao, Cen
Zha, Jun-Ren
Qin, Huan
Wang, Xiao-Rui
Zhang, Xin-Yu
Wang, Yi-Ou
Yang, Xiu-Mei
Zhang, Shu-Long
Qin, Jing
author_facet Liu, Lu
Qiao, Cen
Zha, Jun-Ren
Qin, Huan
Wang, Xiao-Rui
Zhang, Xin-Yu
Wang, Yi-Ou
Yang, Xiu-Mei
Zhang, Shu-Long
Qin, Jing
author_sort Liu, Lu
collection PubMed
description OBJECTIVE: At present, there is no early prediction model of left ventricular reverse remodeling (LVRR) for people who are in cardiac arrest with an ejection fraction (EF) of ≤35% at first diagnosis; thus, the purpose of this article is to provide a supplement to existing research. MATERIALS AND METHODS: A total of 109 patients suffering from heart attack with an EF of ≤35% at first diagnosis were involved in this single-center research study. LVRR was defined as an absolute increase in left ventricular ejection fraction (LVEF) from ≥10% to a final value of >35%, with analysis features including demographic characteristics, diseases, biochemical data, echocardiography, and drug therapy. Extreme gradient boosting (XGBoost), random forest, and logistic regression algorithm models were used to distinguish between LVRR and non-LVRR cases and to obtain the most important features. RESULTS: There were 47 cases (42%) of LVRR in patients suffering from heart failure with an EF of ≤35% at first diagnosis after optimal drug therapy. General statistical analysis and machine learning methods were combined to exclude a number of significant feature groups. The median duration of disease in the LVRR group was significantly lower than that in the non-LVRR group (7 vs. 48 months); the mean values of creatine kinase (CK) and MB isoenzyme of creatine kinase (CK-MB) in the LVRR group were lower than those in the non-LVRR group (80.11 vs. 94.23 U/L; 2.61 vs. 2.99 ng/ml; 27.19 vs. 28.54 mm). Moreover, AUC values for our feature combinations ranged from 97 to 94% and to 87% when using the XGBoost, random forest, and logistic regression techniques, respectively. The ablation test revealed that beats per minute (BPM) and disease duration had a greater impact on the model’s ability to accurately forecast outcomes. CONCLUSION: Shorter disease duration, slightly lower CK and CK-MB levels, slightly smaller right and left ventricular and left atrial dimensions, and lower mean heart rates were found to be most strongly predictive of LVRR development (BPM).
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spelling pubmed-94284432022-09-01 Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations Liu, Lu Qiao, Cen Zha, Jun-Ren Qin, Huan Wang, Xiao-Rui Zhang, Xin-Yu Wang, Yi-Ou Yang, Xiu-Mei Zhang, Shu-Long Qin, Jing Front Cardiovasc Med Cardiovascular Medicine OBJECTIVE: At present, there is no early prediction model of left ventricular reverse remodeling (LVRR) for people who are in cardiac arrest with an ejection fraction (EF) of ≤35% at first diagnosis; thus, the purpose of this article is to provide a supplement to existing research. MATERIALS AND METHODS: A total of 109 patients suffering from heart attack with an EF of ≤35% at first diagnosis were involved in this single-center research study. LVRR was defined as an absolute increase in left ventricular ejection fraction (LVEF) from ≥10% to a final value of >35%, with analysis features including demographic characteristics, diseases, biochemical data, echocardiography, and drug therapy. Extreme gradient boosting (XGBoost), random forest, and logistic regression algorithm models were used to distinguish between LVRR and non-LVRR cases and to obtain the most important features. RESULTS: There were 47 cases (42%) of LVRR in patients suffering from heart failure with an EF of ≤35% at first diagnosis after optimal drug therapy. General statistical analysis and machine learning methods were combined to exclude a number of significant feature groups. The median duration of disease in the LVRR group was significantly lower than that in the non-LVRR group (7 vs. 48 months); the mean values of creatine kinase (CK) and MB isoenzyme of creatine kinase (CK-MB) in the LVRR group were lower than those in the non-LVRR group (80.11 vs. 94.23 U/L; 2.61 vs. 2.99 ng/ml; 27.19 vs. 28.54 mm). Moreover, AUC values for our feature combinations ranged from 97 to 94% and to 87% when using the XGBoost, random forest, and logistic regression techniques, respectively. The ablation test revealed that beats per minute (BPM) and disease duration had a greater impact on the model’s ability to accurately forecast outcomes. CONCLUSION: Shorter disease duration, slightly lower CK and CK-MB levels, slightly smaller right and left ventricular and left atrial dimensions, and lower mean heart rates were found to be most strongly predictive of LVRR development (BPM). Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428443/ /pubmed/36061535 http://dx.doi.org/10.3389/fcvm.2022.864312 Text en Copyright © 2022 Liu, Qiao, Zha, Qin, Wang, Zhang, Wang, Yang, Zhang and Qin. 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
Liu, Lu
Qiao, Cen
Zha, Jun-Ren
Qin, Huan
Wang, Xiao-Rui
Zhang, Xin-Yu
Wang, Yi-Ou
Yang, Xiu-Mei
Zhang, Shu-Long
Qin, Jing
Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title_full Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title_fullStr Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title_full_unstemmed Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title_short Early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
title_sort early prediction of clinical scores for left ventricular reverse remodeling using extreme gradient random forest, boosting, and logistic regression algorithm representations
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428443/
https://www.ncbi.nlm.nih.gov/pubmed/36061535
http://dx.doi.org/10.3389/fcvm.2022.864312
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