Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation

Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development an...

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Autores principales:	Pei-Chen Lin, Kuo-Tai Chen, Huan-Chieh Chen, Md. Mohaimenul Islam, Ming-Chin Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sepsis septic shock machine learning emergency department intensive care unit Medicine R
Acceso en línea:	https://doaj.org/article/db4f7d1a0f004fad91523c388f3c115a
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spelling	oai:doaj.org-article:db4f7d1a0f004fad91523c388f3c115a2021-11-25T18:06:46ZMachine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation10.3390/jpm111110552075-4426https://doaj.org/article/db4f7d1a0f004fad91523c388f3c115a2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1055https://doaj.org/toc/2075-4426Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.Pei-Chen LinKuo-Tai ChenHuan-Chieh ChenMd. Mohaimenul IslamMing-Chin LinMDPI AGarticlesepsisseptic shockmachine learningemergency departmentintensive care unitMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1055, p 1055 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sepsis septic shock machine learning emergency department intensive care unit Medicine R
spellingShingle	sepsis septic shock machine learning emergency department intensive care unit Medicine R Pei-Chen Lin Kuo-Tai Chen Huan-Chieh Chen Md. Mohaimenul Islam Ming-Chin Lin Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
description	Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.
format	article
author	Pei-Chen Lin Kuo-Tai Chen Huan-Chieh Chen Md. Mohaimenul Islam Ming-Chin Lin
author_facet	Pei-Chen Lin Kuo-Tai Chen Huan-Chieh Chen Md. Mohaimenul Islam Ming-Chin Lin
author_sort	Pei-Chen Lin
title	Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
title_short	Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
title_full	Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
title_fullStr	Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
title_full_unstemmed	Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation
title_sort	machine learning model to identify sepsis patients in the emergency department: algorithm development and validation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/db4f7d1a0f004fad91523c388f3c115a
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Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation

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