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Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model
BACKGROUND: Chronic heart failure (CHF) comorbid with atrial fibrillation (AF) is a serious threat to human health and has become a major clinical burden. This prospective cohort study was performed to design a risk stratification system based on the light gradient boosting machine (LightGBM) model...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8340471/ https://www.ncbi.nlm.nih.gov/pubmed/34348648 http://dx.doi.org/10.1186/s12872-021-02188-y |
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author | Zheng, Chu Tian, Jing Wang, Ke Han, Linai Yang, Hong Ren, Jia Li, Chenhao Zhang, Qing Han, Qinghua Zhang, Yanbo |
author_facet | Zheng, Chu Tian, Jing Wang, Ke Han, Linai Yang, Hong Ren, Jia Li, Chenhao Zhang, Qing Han, Qinghua Zhang, Yanbo |
author_sort | Zheng, Chu |
collection | PubMed |
description | BACKGROUND: Chronic heart failure (CHF) comorbid with atrial fibrillation (AF) is a serious threat to human health and has become a major clinical burden. This prospective cohort study was performed to design a risk stratification system based on the light gradient boosting machine (LightGBM) model to accurately predict the 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. METHODS: Electronic medical records of hospitalized patients with CHF comorbid with AF from January 2014 to April 2019 were collected. The data set was randomly divided into a training set and test set at a 3:1 ratio. In the training set, the synthetic minority over-sampling technique (SMOTE) algorithm and fivefold cross validation were used for LightGBM model training, and the model performance was performed on the test set and compared using the logistic regression method. The survival rate was presented on a Kaplan–Meier curve and compared by a log-rank test, and the hazard ratio was calculated by a Cox proportional hazard model. RESULTS: Of the included 1796 patients, the 1-, 2-, and 3-year cumulative mortality rates were 7.74%, 10.63%, and 12.43%, respectively. Compared with the logistic regression model, the LightGBM model showed better predictive performance, the area under the receiver operating characteristic curve for 1-, 2-, and 3-year all-cause mortality was 0.718 (95%CI, 0.710–0.727), 0.744(95%CI, 0.737–0.751), and 0.757 (95%CI, 0.751–0.763), respectively. The net reclassification index was 0.062 (95%CI, 0.044–0.079), 0.154 (95%CI, 0.138–0.172), and 0.148 (95%CI, 0.133–0.164), respectively. The differences between the two models were statistically significant (P < 0.05). Patients in the high-risk group had a significantly higher hazard of death than those in the low-risk group (hazard ratios: 12.68, 13.13, 14.82, P < 0.05). CONCLUSION: Risk stratification based on the LightGBM model showed better discriminative ability than traditional model in predicting 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. Individual patients’ prognosis could also be obtained, and the subgroup of patients with a higher risk of mortality could be identified. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources. |
format | Online Article Text |
id | pubmed-8340471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83404712021-08-06 Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model Zheng, Chu Tian, Jing Wang, Ke Han, Linai Yang, Hong Ren, Jia Li, Chenhao Zhang, Qing Han, Qinghua Zhang, Yanbo BMC Cardiovasc Disord Research BACKGROUND: Chronic heart failure (CHF) comorbid with atrial fibrillation (AF) is a serious threat to human health and has become a major clinical burden. This prospective cohort study was performed to design a risk stratification system based on the light gradient boosting machine (LightGBM) model to accurately predict the 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. METHODS: Electronic medical records of hospitalized patients with CHF comorbid with AF from January 2014 to April 2019 were collected. The data set was randomly divided into a training set and test set at a 3:1 ratio. In the training set, the synthetic minority over-sampling technique (SMOTE) algorithm and fivefold cross validation were used for LightGBM model training, and the model performance was performed on the test set and compared using the logistic regression method. The survival rate was presented on a Kaplan–Meier curve and compared by a log-rank test, and the hazard ratio was calculated by a Cox proportional hazard model. RESULTS: Of the included 1796 patients, the 1-, 2-, and 3-year cumulative mortality rates were 7.74%, 10.63%, and 12.43%, respectively. Compared with the logistic regression model, the LightGBM model showed better predictive performance, the area under the receiver operating characteristic curve for 1-, 2-, and 3-year all-cause mortality was 0.718 (95%CI, 0.710–0.727), 0.744(95%CI, 0.737–0.751), and 0.757 (95%CI, 0.751–0.763), respectively. The net reclassification index was 0.062 (95%CI, 0.044–0.079), 0.154 (95%CI, 0.138–0.172), and 0.148 (95%CI, 0.133–0.164), respectively. The differences between the two models were statistically significant (P < 0.05). Patients in the high-risk group had a significantly higher hazard of death than those in the low-risk group (hazard ratios: 12.68, 13.13, 14.82, P < 0.05). CONCLUSION: Risk stratification based on the LightGBM model showed better discriminative ability than traditional model in predicting 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. Individual patients’ prognosis could also be obtained, and the subgroup of patients with a higher risk of mortality could be identified. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources. BioMed Central 2021-08-04 /pmc/articles/PMC8340471/ /pubmed/34348648 http://dx.doi.org/10.1186/s12872-021-02188-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zheng, Chu Tian, Jing Wang, Ke Han, Linai Yang, Hong Ren, Jia Li, Chenhao Zhang, Qing Han, Qinghua Zhang, Yanbo Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title | Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title_full | Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title_fullStr | Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title_full_unstemmed | Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title_short | Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model |
title_sort | time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a lightgbm model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8340471/ https://www.ncbi.nlm.nih.gov/pubmed/34348648 http://dx.doi.org/10.1186/s12872-021-02188-y |
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