Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 May;11(3):387-398.
doi: 10.1055/s-0040-1710525. Epub 2020 May 27.

A Review of Predictive Analytics Solutions for Sepsis Patients

Andrew K Teng  1 , Adam B Wilcox  1
Affiliations
Review

A Review of Predictive Analytics Solutions for Sepsis Patients

Andrew K Teng et al. Appl Clin Inform. 2020 May.

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.

PubMed Disclaimer

Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) adapted diagram.
See this image and copyright information in PMC

References

    1. Data & Reports | Sepsis | CDC. Centers for Disease Control and Prevention. Available at:https://www.cdc.gov/sepsis/datareports/index.html. Accessed August 11, 2019
    1. O'Brien J. The Cost of Sepsis. Centers for Disease Control and Prevention. Published November 18,2016. Available at:https://blogs.cdc.gov/safehealthcare/the-cost-of-sepsis/. Accessed August 11, 2019
    1. Sepsis. Mayo Clinic. Published November 16,2018. Available at:https://www.mayoclinic.org/diseases-conditions/sepsis/symptoms-causes/sy.... Accessed August 11, 2019
    1. Balk R A. Systemic inflammatory response syndrome (SIRS): where did it come from and is it still relevant today? Virulence. 2014;5(01):20–26. - PMC - PubMed
    1. Singer M, Deutschman C S, Seymour C W et al.The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) JAMA. 2016;315(08):801–810. - PMC - PubMed

Publication types