Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review - Moor, Michael; Rieck, Bastian; Horn, Max; Jutzeler, Catherine R.; Borgwardt, Karsten (Frontiers in Medicine, 2021)
Background: Sepsis is among the leading causes of death in intensive care units (ICU) worldwide; its recognition, particularly in the early stages of the disease, remains a medical challenge. The existence of digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance early sepsis recognition. 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 literature for machine learning-driven sepsis onset prediction for ICU patients. 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 non-ICU patients were excluded. Study appraisal and synthesis methods: A systematic review was performed according to the PRISMA guidelines, followed by a quality assessment of all eligible studies. Results: Out of 974 identified articles, 22 and 21 met inclusion criteria for the systematic review and quality assessment, respectively. Numerous 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%). The majority of the studies (n=19) employed an offline training scenario combined with horizon evaluation, while two studies implemented an online scenario. Massive inter-study heterogeneity in terms of models, sepsis definitions, prediction time windows, and outcomes precluded a meta-analysis. Only 2 studies provided source code and data sources. 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 optimise the early prediction of sepsis through digital biomarker discovery. This review highlights several shortcomings of current approaches, including low comparability and reproducibility. We gather recommendations how these challenges can be addressed before deploying models in prospective analyses.