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Appl Clin Inform 2020; 11(03): 387-398
DOI: 10.1055/s-0040-1710525
Review Article
Georg Thieme Verlag KG Stuttgart · New York

A Review of Predictive Analytics Solutions for Sepsis Patients

Andrew K. Teng
1   Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, Washington, United States
,
Adam B. Wilcox
1   Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, Washington, United States
› Author Affiliations Funding This work was supported by the U.S. Department of Health and Human Services, National Library of Medicine Training Grant T15LM007442
Further Information

Publication History

02 October 2019

02 April 2020

Publication Date:
27 May 2020 (online)

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Abstract

Background Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.

Objective To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality.

Methods and Results We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24–198,833) and feature size (range: 2–285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes.

Conclusion Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.

Keywords

sepsis - predictive analytics - machine learning - algorithms - data modeling

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in this project.


 

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