首页|Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning

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BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

SepsisMachine learningEmergency departmentTriageInformatics

Jingyuan Xie、Jiandong Gao、Mutian Yang、Ting Zhang、Yecheng Liu、Yutong Chen、Zetong Liu、Qimin Mei、Zhimao Li、Huadong Zhu、Ji Wu

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Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

Center for Big Data and Clinical Research,Institute for Precision Medicine,Tsinghua University,Beijing 100084,China

Institute of Medical Technology,Peking University Health Science Center,Beijing 100091,China

Emergency Department,State Key Laboratory of Complex Severe and Rare Diseases,Peking Union Medical College Hospital,Chinese Academy of Medical Science and Peking Union Medical College,Beijing 100005,China

Department of Health Care,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100005,China

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National Key Research and Development Program of ChinaCAMS Innovation Fund for Medical Sciences

2021YFC25008032021-I2M-1-056

2024

10.5847/wjem.j.1920-8642.2024.074

世界急诊医学杂志(英文)
浙江大学,浙江大学医学院附属第二医院

世界急诊医学杂志(英文)

ISSN：1920-8642
年,卷(期)：2024.15(5)