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Journal of Clinical Monitoring and Computing
Published in: Journal of Clinical Monitoring and Computing 6/2022

25-01-2022 | Septicemia | Original Research

Prediction of sepsis onset in hospital admissions using survival analysis

Authors: Brandon DeShon, Benjamin Dummitt, Joshua Allen, Byron Yount

Published in: Journal of Clinical Monitoring and Computing | Issue 6/2022

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Abstract

To determine the efficacy of modern survival analysis methods for predicting sepsis onset in ICU, emergency, medical/surgical, and TCU departments. We performed a retrospective analysis on ICU, med/surg, ED, and TCU cases from multiple Mercy Health hospitals from August 2018 to March 2020. Patients in these departments were monitored by the Mercy Virtual vSepsis team and sepsis cases were determined and documented in the Mercy EHR via a rule-based engine utilizing clinical data. We used survival-based modeling methods to predict sepsis onset in these cases. The three survival methods that were used to predict the onset of severe sepsis and septic shock produced AUC values > 0.85 and each provided a median lead time of > 20 h prior to disease onset. This methodology improves upon previous work by demonstrating excellent model performance when generalizing survival-based prediction methods to both severe sepsis and septic shock as well as non-ICU departments.
IRB Information
Trial Registration ID: 1,532,327-1.
Trial Effective Date: 12/02/2019.
Appendix
Available only for authorised users
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Metadata
Title
Prediction of sepsis onset in hospital admissions using survival analysis
Authors
Brandon DeShon
Benjamin Dummitt
Joshua Allen
Byron Yount
Publication date
25-01-2022
Publisher
Springer Netherlands
Published in
Journal of Clinical Monitoring and Computing / Issue 6/2022
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-022-00804-6

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