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

Abstract

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.

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study

“…Moor et al [ 16 ] point out that it can be difficult to compare studies due to measures such as AUROC or accuracy as they are directly affected by sepsis prevalence. In unbalanced situations, such as in the case of sepsis prediction, where the proportion of patients without sepsis is substantially larger than the proportion of patients with sepsis, the AUPRC should be reported.…”

“…Different parts of the training data were used for development and internal validation of the algorithm in order to avoid overfitting. Random onset matching [ 16 ]—randomly chosen 4-hour sequences, with the last time point up to 3 hours before onset, for patients with sepsis, or at any point during the whole ICU stay, for patients without sepsis—was used. The time points were sampled from a β(10,1) distribution, with ranges for patients without sepsis scaled to match those of their entire stay.…”

“…Fleuren et al [ 15 ] and Moor et al [ 16 ] reviewed previously developed sepsis prediction algorithms; however, they found that very few had been prospectively evaluated in clinical practice, and those that had been, were evaluated in the United States to date. To date, and to the best of our knowledge, only 1 ICU algorithm is available for clinical use [ 17 , 18 ], and another is planned to be prospectively validated [ 19 ].…”

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections

“…Developing risk prediction scores has been an area of significant interest, as they can be used to support medical decisions for infected patients 16 – 21 . Machine learning (ML) algorithms, which belong to the field of artificial intelligence, have been devised using arrays of clinical and/or biological data to increase diagnostic and prognostic accuracy 22 .…”

Deep Learning for Identification of Acute Illness and Facial Cues of Illness

“…The clinical gestalt is also increasingly used as the basis for building deep learning models, with facial pictures being used to identify different genetic syndromes ( 16 ), as well as to detect coronary artery disease in an emergency setting ( 17 ). However, despite a growing number of studies reporting good results of deep learning models trained with a variety of clinical measurements to predict or detect early sepsis, no model has yet included clinical gestalt or facial feature analysis ( 18 , 19 ). One major challenge to the development of a well-performing deep learning algorithm for facial analysis is the datasets' size and quality of the images ( 20 , 21 ).…”