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Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database
Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918116/ https://www.ncbi.nlm.nih.gov/pubmed/36769515 http://dx.doi.org/10.3390/jcm12030870 |
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author | Chen, Shiyu Hu, Weiwei Yang, Yuhui Cai, Jiaxin Luo, Yaqi Gong, Lingmin Li, Yemian Si, Aima Zhang, Yuxiang Liu, Sitong Mi, Baibing Pei, Leilei Zhao, Yaling Chen, Fangyao |
author_facet | Chen, Shiyu Hu, Weiwei Yang, Yuhui Cai, Jiaxin Luo, Yaqi Gong, Lingmin Li, Yemian Si, Aima Zhang, Yuxiang Liu, Sitong Mi, Baibing Pei, Leilei Zhao, Yaling Chen, Fangyao |
author_sort | Chen, Shiyu |
collection | PubMed |
description | Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients. |
format | Online Article Text |
id | pubmed-9918116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99181162023-02-11 Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database Chen, Shiyu Hu, Weiwei Yang, Yuhui Cai, Jiaxin Luo, Yaqi Gong, Lingmin Li, Yemian Si, Aima Zhang, Yuxiang Liu, Sitong Mi, Baibing Pei, Leilei Zhao, Yaling Chen, Fangyao J Clin Med Article Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients. MDPI 2023-01-21 /pmc/articles/PMC9918116/ /pubmed/36769515 http://dx.doi.org/10.3390/jcm12030870 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Shiyu Hu, Weiwei Yang, Yuhui Cai, Jiaxin Luo, Yaqi Gong, Lingmin Li, Yemian Si, Aima Zhang, Yuxiang Liu, Sitong Mi, Baibing Pei, Leilei Zhao, Yaling Chen, Fangyao Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title | Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title_full | Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title_fullStr | Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title_full_unstemmed | Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title_short | Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database |
title_sort | predicting six-month re-admission risk in heart failure patients using multiple machine learning methods: a study based on the chinese heart failure population database |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918116/ https://www.ncbi.nlm.nih.gov/pubmed/36769515 http://dx.doi.org/10.3390/jcm12030870 |
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