ArticleCritical care medicine2018
A Comparative Analysis of Sepsis Identification Methods in an Electronic Database.
Article in Critical care medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Epidemiology and burden of sepsis acquired in hospitals and intensive care units: a systematic review and meta-analysis.Intensive care medicine · 2020Pooled it
- Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study.Frontiers in cellular and infection microbiology · 2022Trial
- Lymphopenia and elevated NLR identify patients with septic shock who may benefit from corticosteroids: a target trial emulation study.Intensive care medicine experimental · 2026Article
- Laboratory-DetectedHealth science reports · 2026Article
- Hospital-Wide Sepsis Detection: A Machine Learning Model Based on Prospectively Expert-Validated Cohort.Journal of clinical medicine · 2026Article
- Development and validation of an explainable machine learning model for mortality prediction in ICU patients with lung cancer.Frontiers in cell and developmental biology · 2026Article
- Sepsis and septic shock case identification from electronic health records: an open-source workflow and comparison of cohorts by criteria.Critical care (London, England) · 2025Article
- Hemoglobin variability predicts mortality in acute kidney injury patients undergoing continuous renal replacement therapy: a multi-database cohort study.Renal failure · 2025Article
- Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.Infection · 2025Article
- Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare.medRxiv : the preprint server for health sciences · 2025Article
- Group-based trajectory modeling of anion gap and mortality in patients with sepsis: a retrospective analysis of the MIMIC-IV database.European journal of medical research · 2025Article
- Relationship between time-weighted average anion gap and mortality in septic shock patients: a retrospective analysis of the MIMIC-IV database.BMC infectious diseases · 2025Article
- Recommended Hematocrit in Patients With Sepsis: An Observational Study.Health science reports · 2025Article
- Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model.NPJ digital medicine · 2025Article
- Parenclitic network mapping predicts survival in critically ill patients with sepsis.Physiological reports · 2025Article
- Validation of a software application using electronic health records for automatic detection of community onset sepsis.Scientific reports · 2025Article
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score.Diagnostics (Basel, Switzerland) · 2025Article
30 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
objectivesTo evaluate the relative validity of criteria for the identification of sepsis in an ICU database.
designRetrospective cohort study of adult ICU admissions from 2008 to 2012.
settingTertiary teaching hospital in Boston, MA. PATIENTS: Initial admission of all adult patients to noncardiac surgical ICUs.
interventionsComparison of five different algorithms for retrospectively identifying sepsis, including the Sepsis-3 criteria. MEASUREMENTS AND MAIN
results11,791 of 23,620 ICU admissions (49.9%) met criteria for the study. Within this subgroup, 59.9% were suspected of infection on ICU admission, 75.2% of admissions had Sequential Organ Failure Assessment greater than or equal to 2, and 49.1% had both suspicion of infection and Sequential Organ Failure Assessment greater than or equal to 2 thereby meeting the Sepsis-3 criteria. The area under the receiver operator characteristic of Sequential Organ Failure Assessment (0.74) for hospital mortality was consistent with previous studies of the Sepsis-3 criteria. The Centers for Disease Control and Prevention, Angus, Martin, Centers for Medicare & Medicaid Services, and explicit coding methods for identifying sepsis revealed respective sepsis incidences of 31.9%, 28.6%, 14.7%, 11.0%, and 9.0%. In-hospital mortality increased with decreasing cohort size, ranging from 30.1% (explicit codes) to 14.5% (Sepsis-3 criteria). Agreement among the criteria was acceptable (Cronbach's alpha, 0.40-0.62).
conclusionsThe new organ dysfunction-based Sepsis-3 criteria have been proposed as a clinical method for identifying sepsis. These criteria identified a larger, less severely ill cohort than that identified by previously used administrative definitions. The Sepsis-3 criteria have several advantages over prior methods, including less susceptibility to coding practices changes, provision of temporal context, and possession of high construct validity. However, the Sepsis-3 criteria also present new challenges, especially when calculated retrospectively. Future studies on sepsis should recognize the differences in outcome incidence among identification methods and contextualize their findings according to the different cohorts identified.
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