The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Open accessAbstract
IMPORTANCE: Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination. OBJECTIVE: To evaluate and, as needed, update definitions for sepsis and septic shock. PROCESS: A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment). KEY FINDINGS FROM EVIDENCE SYNTHESIS: Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant. RECOMMENDATIONS: Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less. CONCLUSIONS AND RELEVANCE: These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.
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Most models reported AUC values exceeding 0.84, with some attaining up to 0.89 for early sepsis prediction.
“Most models reported AUC values exceeding 0.84, with some attaining up to 0.89 for early sepsis prediction [7, 12, 13, 17, 19, 22].”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [7]
In Results/Recommendations, ‘Screening for Patients Likely to Have Sepsis’ (page unavailable), the reported model has an AUROC of 0.81 outside the ICU and 0.66 inside it—not above 0.84. ‘Clinical Criteria to Identify Patients With Sepsis’ (page unavailable) reports AUROCs of 0.64–0.79 for predicting hospital mortality in patients with suspected infection, rather than 0.84–0.89 for early sepsis prediction. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThese interpretability strategies enabled the identification of key physiological variables driving model predictions, such as respiratory rate (RR), Glasgow Coma Scale, mean arterial pressure (MAP), lactate, creatinine, heart rate (HR), blood urea nitrogen (BUN), body temperature, platelet count, and urine output.
“These interpretability strategies enabled the identification of key physiological variables driving model predictions, such as respiratory rate (RR), Glasgow Coma Scale, mean arterial pressure (MAP), lactate, creatinine, heart rate (HR), blood urea nitrogen (BUN), body temperature, platelet count, and urine output [7, 12–18, 22, 24, 25, 39].”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [7]
The Screening for Patients Likely to Have Sepsis section (page unavailable) identifies respiratory rate and Glasgow Coma Scale as variables in a predictive clinical model; the Variable Definitions and Septic Shock sections also discuss several other listed measures. The paper does not say that interpretability strategies identified variables driving model predictions, and it does not substantiate the full list as model-driving variables. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThere are challenges in estimating maternal sepsis burden due to differences in its definitions.
“However, there are challenges in estimating maternal sepsis burden due to differences in its definitions [9, 10].”
From The aetiology and antimicrobial resistance of bacterial maternal infections in Sub-Saharan Africa—a systematic review and meta-analysis (2024) · cites this paper as [9]
The Variable Definitions section describes inconsistent sepsis terminology and coding, and the Implications section says standardized definitions are crucial to accurately appreciating the scale of sepsis. These passages support the general difficulty of estimating sepsis burden, but the paper does not address maternal sepsis specifically. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe Sepsis-3 consensus framework for sepsis coding and research recommends using concomitant orders for blood cultures and antibiotics within a specified period to define suspected infection.
“The Sepsis-3 consensus framework for sepsis coding and research recommends using concomitant orders for blood cultures and antibiotics within a specified period to define suspected infection.4”
From Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model (2026) · cites this paper as [4]
In Results/Recommendations, the paper describes a research cohort in which suspected infection was identified by cultures being sampled and antibiotics being received. It does not recommend concomitant orders within a specified period as a definition for coding and research; the Implications section instead discusses proxies such as antibiotic commencement or clinical probability of infection. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection.
“Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection (1).”
From Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. (2021) · cites this paper as [1]
Laboratory results may not be available in the early stages for use in the ED.
“Our study did not include laboratory results as they may not be available in the early stages for use in the ED, although they are widely used in the ICU to evaluate organ failure and dysfunction.10”
From Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study (2025) · cites this paper as [10]
The diagnostic criteria for sepsis according to Sepsis-3 guidelines include some indicators that are not always available in the ED.
“Therefore, the diagnostic criteria for sepsis according to the Sepsis-3 guidelines include some indicators that are not always available in the ED.10”
From Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study (2025) · cites this paper as [10]
Sepsis was operationalized using suspected infection paired with organ dysfunction, consistent with electronic health record applications of Sepsis-3 concepts.
“Sepsis was operationalized using suspected infection paired with organ dysfunction, which was consistent with electronic health record applications of Sepsis-3 concepts (14, 15).”
From Day-4 antipseudomonal and anti-MRSA de-escalation was not associated with higher mortality in critically ill patients with sepsis (2026) · cites this paper as [15]
Although the 2016 consensus statement abandoned the term severe sepsis, papers prior to the consensus statement targeting severe sepsis were included.
“Although the 2016 consensus statement abandoned the term severe sepsis [19], papers prior to the consensus statement targeting severe sepsis were included.”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [19]
Secondary outcomes were cardiopulmonary arrest, sepsis, unanticipated ICU transfer, and 30-day hospital readmission.
“Secondary outcomes were cardiopulmonary arrest, sepsis51, unanticipated ICU transfer and 30-day hospital readmission52.”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [51]
XAI-enhanced models have consistently shown strong predictive performance in ICU settings, often achieving AUC values above 0.85.
“XAI-enhanced models have consistently shown strong predictive performance in ICU settings, often achieving AUC (Area Under the Receiver Operating Characteristic Curve) values above 0.85 [7, 12, 19].”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [7]
The Results/Recommendations sections on clinical criteria and screening report ICU AUROCs for SOFA, SIRS, and a clinical screening model, but do not evaluate XAI-enhanced models. Searches for XAI, machine learning, and AUROC above 0.85 found no passage addressing the attributed claim. The lower AUROCs for different clinical tools do not establish that XAI models perform otherwise. (LLM, conf=0.76)
Experimental — accuracy not yet measuredOnly a limited number of reviewed studies conducted external or temporal validation, and very few models have been prospectively implemented in real clinical environments.
“Only a limited number of studies conducted external or temporal validation ([7, 15, 18, 22, 24]), and very few models have been prospectively implemented in real clinical environments.”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [7]
The Results/Recommendations sections describe external data-set analyses of sepsis criteria, and Controversies and Limitations calls for prospective validation of qSOFA. Neither section assesses how many reviewed studies used external or temporal validation or how many models were prospectively implemented in clinical practice; searches for those terms found no such assessment. No page numbers were provided in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAn ensemble machine learning model predicting ICU readmissions reached an AUC of 0.86.
“whereas an ensemble machine learning model predicting ICU readmissions reached an AUC of 0.86 [7].”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [7]
The Results/Recommendations sections report AUROCs for sepsis screening and hospital-mortality prediction, not ICU readmission prediction. Searches for an ensemble machine-learning model, ICU readmissions, and an AUC of 0.86 found no passage addressing the claim. (LLM, conf=0.77)
Experimental — accuracy not yet measuredIn this pragmatic trial, several methods were employed to control for hospital-patient encounters in which the patient was transferred between units randomized as intervention and usual care.
“In this pragmatic trial, we employed several methods to control for hospital–patient encounters in which the patient was transferred between units that were randomized as intervention and usual care51.”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [51]
The Results/Recommendations section on clinical criteria describes analyses of electronic health records and sepsis scores, not a pragmatic trial with units randomized to intervention or usual care (page unavailable). Searches for patient transfers and methods to control for encounters spanning randomized units found no passage addressing those methods. The sentence describes the citing authors’ own trial, which this source cannot verify. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSepsis is a life-threatening acute organ dysfunction caused by secondary infection.
“Sepsis is a life-threatening acute organ dysfunction caused by secondary infection (Singer et al., 2016; Evans et al., 2021).”
From The modulatory effects of gut microbes and metabolites on blood-brain barrier integrity and brain function in sepsis-associated encephalopathy (2023) · cites this paper as Singer et al., 2016
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Clinical applications of artificial intelligence in sepsis: A narrative review
Schinkel, M., Paranjape, K., Nannan Panday, R. S., Skyttberg, N., & Nanayakkara, P. W. B. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103488 · Cited by 130 · see citing works
- Authors
- Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., Hotchkiss, R. S., Levy, M. M., Marshall, J. C., Martin, G. S., Opal, S. M., Rubenfeld, G. D., van der Poll, T., Vincent, J.-L., & Angus, D. C.
- Affiliations
- University College London (GB)
- Feinstein Institute for Medical Research (US)
- University of Pittsburgh (US)
- Department of Critical Care Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- Jena University Hospital (DE)
- Austin Hospital (AU)
- Vanderbilt University (US)
- Hôpital Cochin (FR)
- Emory University (US)
- Washington University in St. Louis (US)
- Brown University (US)
- University of Toronto (CA)
- Grady Memorial Hospital (US)
- Sunnybrook Health Science Centre (CA)
- Department of Infectious Diseases, Academisch Medisch Centrum, Amsterdam, the Netherlands
- Erasmus Hospital (BE)
- Corresponding author
- Deutschman, Clifford S.
- ORCID
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2016-02-23
- Volume
- 315
- Issue
- 8
- Pages
- 801
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC4968574
- OpenAlex
- W2280404143
- Semantic Scholar
- e7768429a390df77ca4706555324f30964ed9347
- Cited by
- 28,250
- References
- 46
- Citations by year
- 2026: 3254 · 2025: 3509 · 2024: 3575 · 2023: 3271 · 2022: 3046 · 2021: 3142 · 2020: 2745 · 2019: 2267 · 2018: 1625 · 2017: 1261 · 2016: 497 · 2015: 7 · 2014: 4 · 2013: 8 · 2012: 4
- FWCI
- 1766.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- American Thoracic Society
- AstraZeneca
- University of Pennsylvania
- Pennsylvania State University
- CSL Behring
- European Respiratory Society
- American Association of Critical-Care Nurses
- Wellcome Trust
- European Resuscitation Council
- European Commission
- Intensive Care Society
- Academy of Medical Royal Colleges
- European Society of Clinical Microbiology and Infectious Diseases
- Universitätsklinikum Jena
- European Society of Intensive Care Medicine
- InflaRx
- National Institutes of Health
- Centers for Disease Control and Prevention
- Defence Science and Technology Laboratory
- National Institute of General Medical Sciences (K23GM104022, R01GM044118)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Life Sciences › Immunology and Microbiology › Immunology › Inflammation biomarkers and pathways
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- sepsis, septic shock, Sepsis-3, sepsis definitions, organ dysfunction, SOFA score, systemic inflammatory response syndrome, lactate, vasopressors, sepsis mortality, sepsis incidence, severe sepsis, Delphi consensus, clinical criteria
- MeSH terms
- Blood Pressure, Delphi Technique, Humans, Lactates, Terminology as Topic, Sensitivity and Specificity, Shock, Septic, Vasoconstrictor Agents, Biomarkers, Hospital Mortality, Peer Review, Research, Systemic Inflammatory Response Syndrome, Sepsis, Advisory Committees, International Classification of Diseases, Respiratory Rate, Organ Dysfunction Scores
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