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Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review

Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learn...

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Autores principales: Moor, Michael, Rieck, Bastian, Horn, Max, Jutzeler, Catherine R., Borgwardt, Karsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193357/
https://www.ncbi.nlm.nih.gov/pubmed/34124082
http://dx.doi.org/10.3389/fmed.2021.607952
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author Moor, Michael
Rieck, Bastian
Horn, Max
Jutzeler, Catherine R.
Borgwardt, Karsten
author_facet Moor, Michael
Rieck, Bastian
Horn, Max
Jutzeler, Catherine R.
Borgwardt, Karsten
author_sort Moor, Michael
collection PubMed
description Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from “poor” (satisfying ≤ 40% of the quality criteria) to “very good” (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic Review Registration Number: CRD42020200133.
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spelling pubmed-81933572021-06-12 Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review Moor, Michael Rieck, Bastian Horn, Max Jutzeler, Catherine R. Borgwardt, Karsten Front Med (Lausanne) Medicine Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from “poor” (satisfying ≤ 40% of the quality criteria) to “very good” (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic Review Registration Number: CRD42020200133. Frontiers Media S.A. 2021-05-28 /pmc/articles/PMC8193357/ /pubmed/34124082 http://dx.doi.org/10.3389/fmed.2021.607952 Text en Copyright © 2021 Moor, Rieck, Horn, Jutzeler and Borgwardt. 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
Moor, Michael
Rieck, Bastian
Horn, Max
Jutzeler, Catherine R.
Borgwardt, Karsten
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title_full Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title_fullStr Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title_full_unstemmed Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title_short Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
title_sort early prediction of sepsis in the icu using machine learning: a systematic review
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193357/
https://www.ncbi.nlm.nih.gov/pubmed/34124082
http://dx.doi.org/10.3389/fmed.2021.607952
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