Cited by 244Sepsis Diagnosis and TreatmentMachine Learning in HealthcareAcute Kidney Injury Research

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A computational approach to early sepsis detection

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Calvert, Jacob S., Price, Daniel A., Chettipally, Uli K., Barton, Christopher W., Feldman, Mitchell D., Hoffman, Jana L., Jay, Melissa, Das, Ritankar·2016·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2016.05.003

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Abstract

Objective To develop high-performance early sepsis prediction technology for the general patient population. Methods Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission. Results A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance. Conclusions Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.

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Authors: Calvert, J. S., Price, D. A., Chettipally, U. K., Barton, C. W., Feldman, M. D., Hoffman, J. L., Jay, M., & Das, R.
Affiliations: Dascena (United States) (US)
University of California, San Francisco (US)
Corresponding author: Hoffman, Jana L.
ORCID: Calvert, Jacob S.
Chettipally, Uli K.
Feldman, Mitchell D.
Hoffman, Jana L.
Das, Ritankar
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2016-07
Volume: 74
Pages: 69-73
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2016.05.003
OpenAlex: W2372800617
Cited by: 244
References: 36
Citations by year: 2026: 13 · 2025: 18 · 2024: 24 · 2023: 21 · 2022: 24 · 2021: 39 · 2020: 35 · 2019: 38 · 2018: 11 · 2017: 15 · 2016: 6
FWCI: 21.90
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 321 d)
Locations: Computers in Biology and Medicine (acceptedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation (1549867)
Topics: Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
Keywords: sepsis prediction, early sepsis detection, systemic inflammatory response syndrome, vital signs, intensive care unit patients, higher-order correlations, insight
MeSH terms: Adult, Critical Care, Diagnosis, Computer-Assisted, Female, Humans, Male, Retrospective Studies, Time Factors, Biomarkers, Sepsis

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A computational approach to early sepsis detection

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