Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubM...

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Bibliographic Details
Published in:Intensive care medicine 2020-03, Vol.46 (3), p.383-400
Main Authors: Fleuren, Lucas M., Klausch, Thomas L. T., Zwager, Charlotte L., Schoonmade, Linda J., Guo, Tingjie, Roggeveen, Luca F., Swart, Eleonora L., Girbes, Armand R. J., Thoral, Patrick, Ercole, Ari, Hoogendoorn, Mark, Elbers, Paul W. G.
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Anesthesiology
Critical Care Medicine
Diagnostic systems
Diagnostic tests
Diagnostic Tests, Routine
Emergency medical care
Emergency medical services
Emergency Medicine
Heterogeneity
Humans
Infection
Intensive
Intensive care
Learning algorithms
Machine Learning
Medical diagnosis
Medical tests
Medicine
Medicine & Public Health
Meta-analysis
Multivariate analysis
Pain Medicine
Pediatrics
Pneumology/Respiratory System
Predictions
Quality assessment
Quality control
Real time
Retrospective Studies
Reviews
Sepsis
Sepsis - diagnosis
Septic shock
Shock, Septic
Systematic Review
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Summary:Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n  = 15; 54%), followed by hospital wards ( n  = 7; 25%), the emergency department (ED, n  = 4; 14%) and all of these settings ( n  = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.
ISSN:0342-4642
1432-1238
DOI:10.1007/s00134-019-05872-y