Prediction of sepsis patients using machine learning approach: A meta-analysis. (March 2019)

• Record Type: Journal Article
• Authors: Islam, Md. Mohaimenul; Nasrin, Tahmina; Walther, Bruno Andreas; Wu, Chieh-Chen; Yang, Hsuan-Chia; Li, Yu-Chuan
• Is Part Of: Computer methods and programs in biomedicine. Volume 170(2019)
• Page Start: 1
• Page End: 9
• Publication Date: 2019-03
• Subjects: Area under receiver operating curve -- Sepsis -- Machine learning -- Diagnostic odd ratio; SAUROC Summarized area under receiver operating curve -- SIRS Systemic inflammatory response syndrome -- MEWS Modified early warning system -- SOFA Sequential organ failure assessment -- QSOFA Quick sequential organ failure assessment -- DOR Diagnostic odd ratio -- ICU Intensive care unit; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.12.027
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Sepsis is a common and life-threatening syndrome, and a leading cause of morbidity and mortality globally. We performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86–0.92); sensitivity 0.81 (95%CI:0.80–0.81), and specificity 0.72 (95%CI:0.72–0.72). Abstract: Study objective: Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Machine learning models could help to identify potential clinical variables and provide higher performance than existing traditional low-performance models. We therefore performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis. Methods: A comprehensive literature search was conducted through the electronic database (e.g. PubMed, Scopus, Google Scholar, EMBASE, etc.) between January 1, 2000, and March 1, 2018. All the studies published in English and reporting the sepsis prediction using machine learning algorithms were considered in this study. Two authors independently extracted valuable information from the included studies. Inclusion and exclusion of studies were based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: A total of 7 out of 135 studies met all of our inclusion criteria. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86–0.92); sensitivity 0.81 (95%CI:0.80–0.81), and specificity 0.72 (95%CI:0.72–0.72) whereas the pooled SAUROC for SIRS, MEWS, and SOFA was 0.70, 0.50, and 0.78. Additionally, diagnostic odd ratio for machine learning, SIRS, MEWS, and SOFA was 15.17 (95%CI: 9.51–24.20), 3.23 (95%CI: 1.52–6.87), 31.99 (95% CI: 1.54–666.74), and 3.75(95%CI: 2.06–6.83). Conclusion: Our study findings suggest that the machine learning approach had a better performance than the existing sepsis scoring systems in predicting sepsis.