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Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease

BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensiv...

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Autores principales: Li, Xunliang, Zhu, Yuyu, Zhao, Wenman, Shi, Rui, Wang, Zhijuan, Pan, Haifeng, Wang, Deguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201999/
https://www.ncbi.nlm.nih.gov/pubmed/37203863
http://dx.doi.org/10.1080/0886022X.2023.2212790
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author Li, Xunliang
Zhu, Yuyu
Zhao, Wenman
Shi, Rui
Wang, Zhijuan
Pan, Haifeng
Wang, Deguang
author_facet Li, Xunliang
Zhu, Yuyu
Zhao, Wenman
Shi, Rui
Wang, Zhijuan
Pan, Haifeng
Wang, Deguang
author_sort Li, Xunliang
collection PubMed
description BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.
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spelling pubmed-102019992023-05-23 Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease Li, Xunliang Zhu, Yuyu Zhao, Wenman Shi, Rui Wang, Zhijuan Pan, Haifeng Wang, Deguang Ren Fail Research Article BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death. Taylor & Francis 2023-05-19 /pmc/articles/PMC10201999/ /pubmed/37203863 http://dx.doi.org/10.1080/0886022X.2023.2212790 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Research Article
Li, Xunliang
Zhu, Yuyu
Zhao, Wenman
Shi, Rui
Wang, Zhijuan
Pan, Haifeng
Wang, Deguang
Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title_full Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title_fullStr Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title_full_unstemmed Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title_short Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
title_sort machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201999/
https://www.ncbi.nlm.nih.gov/pubmed/37203863
http://dx.doi.org/10.1080/0886022X.2023.2212790
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