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Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2...

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Autores principales: Su, Longxiang, Xu, Zheng, Chang, Fengxiang, Ma, Yingying, Liu, Shengjun, Jiang, Huizhen, Wang, Hao, Li, Dongkai, Chen, Huan, Zhou, Xiang, Hong, Na, Zhu, Weiguo, Long, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288021/
https://www.ncbi.nlm.nih.gov/pubmed/34291058
http://dx.doi.org/10.3389/fmed.2021.664966
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author Su, Longxiang
Xu, Zheng
Chang, Fengxiang
Ma, Yingying
Liu, Shengjun
Jiang, Huizhen
Wang, Hao
Li, Dongkai
Chen, Huan
Zhou, Xiang
Hong, Na
Zhu, Weiguo
Long, Yun
author_facet Su, Longxiang
Xu, Zheng
Chang, Fengxiang
Ma, Yingying
Liu, Shengjun
Jiang, Huizhen
Wang, Hao
Li, Dongkai
Chen, Huan
Zhou, Xiang
Hong, Na
Zhu, Weiguo
Long, Yun
author_sort Su, Longxiang
collection PubMed
description Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016–2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models. Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models. Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.
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spelling pubmed-82880212021-07-20 Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models Su, Longxiang Xu, Zheng Chang, Fengxiang Ma, Yingying Liu, Shengjun Jiang, Huizhen Wang, Hao Li, Dongkai Chen, Huan Zhou, Xiang Hong, Na Zhu, Weiguo Long, Yun Front Med (Lausanne) Medicine Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016–2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models. Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models. Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources. Frontiers Media S.A. 2021-06-28 /pmc/articles/PMC8288021/ /pubmed/34291058 http://dx.doi.org/10.3389/fmed.2021.664966 Text en Copyright © 2021 Su, Xu, Chang, Ma, Liu, Jiang, Wang, Li, Chen, Zhou, Hong, Zhu and Long. 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 Medicine
Su, Longxiang
Xu, Zheng
Chang, Fengxiang
Ma, Yingying
Liu, Shengjun
Jiang, Huizhen
Wang, Hao
Li, Dongkai
Chen, Huan
Zhou, Xiang
Hong, Na
Zhu, Weiguo
Long, Yun
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title_full Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title_fullStr Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title_full_unstemmed Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title_short Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models
title_sort early prediction of mortality, severity, and length of stay in the intensive care unit of sepsis patients based on sepsis 3.0 by machine learning models
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288021/
https://www.ncbi.nlm.nih.gov/pubmed/34291058
http://dx.doi.org/10.3389/fmed.2021.664966
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