The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
A computational approach to early sepsis detection
Open accessAbstract
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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“numerous models were tested on the freely accessible MIMIC database [27, 33, 52–59]”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [52]
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Early goal-directed therapy in the treatment of severe sepsis and septic shock
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2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference
Conference, F. T. I. S. D., Levy, M. M., Fink, M. P., Marshall, J. C., Abraham, E. H., Angus, D. C., Cook, D., Cohen, J., Opal, S. M., Vincent, J., et al. (2003) · Intensive Care Medicine · doi:10.1007/s00134-003-1662-x · Cited by 2,944
A randomized trial of protocol-based care for early septic shock.
The ProCESS Investigators (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1401602 · Cited by 2,414
Goal-Directed Resuscitation for Patients with Early Septic Shock
The ARISE Investigators and the ANZICS Clinical Trials Group (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1404380 · Cited by 2,055
Regional Variation in Out-of-Hospital Cardiac Arrest Incidence and Outcome
Nichol, G. (2008) · JAMA · doi:10.1001/jama.300.12.1423 · Cited by 1,979
Validation of a modified Early Warning Score in medical admissions
Subbe, C. P. (2001) · QJM · doi:10.1093/qjmed/94.10.521 · Cited by 1,788
Trial of early, goal-directed resuscitation for septic shock.
Mouncey, P. R., Osborn, T. M., Power, G. S., Harrison, D. A., Sadique, M. Z., Grieve, R. D., Jahan, R., Harvey, S. E., Bell, D., Bion, J. F., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500896 · Cited by 1,604
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Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956
Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-019-05872-y · Cited by 755
Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561
Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M. D., Barton, C., et al. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.5909 · Cited by 509
Self-citationLauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448
Metadata
- 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.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 74
- Pages
- 69-73
- ISSN
- 0010-4825 (ISSN-L)
- 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
- 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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