Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Open accessAbstract
IMPORTANCE: The Third International Consensus Definitions Task Force defined sepsis as "life-threatening organ dysfunction due to a dysregulated host response to infection." The performance of clinical criteria for this sepsis definition is unknown. OBJECTIVE: To evaluate the validity of clinical criteria to identify patients with suspected infection who are at risk of sepsis. DESIGN, SETTINGS, AND POPULATION: Among 1.3 million electronic health record encounters from January 1, 2010, to December 31, 2012, at 12 hospitals in southwestern Pennsylvania, we identified those with suspected infection in whom to compare criteria. Confirmatory analyses were performed in 4 data sets of 706,399 out-of-hospital and hospital encounters at 165 US and non-US hospitals ranging from January 1, 2008, until December 31, 2013. EXPOSURES: Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score, systemic inflammatory response syndrome (SIRS) criteria, Logistic Organ Dysfunction System (LODS) score, and a new model derived using multivariable logistic regression in a split sample, the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score (range, 0-3 points, with 1 point each for systolic hypotension [≤100 mm Hg], tachypnea [≥22/min], or altered mentation). MAIN OUTCOMES AND MEASURES: For construct validity, pairwise agreement was assessed. For predictive validity, the discrimination for outcomes (primary: in-hospital mortality; secondary: in-hospital mortality or intensive care unit [ICU] length of stay ≥3 days) more common in sepsis than uncomplicated infection was determined. Results were expressed as the fold change in outcome over deciles of baseline risk of death and area under the receiver operating characteristic curve (AUROC). RESULTS: In the primary cohort, 148,907 encounters had suspected infection (n = 74,453 derivation; n = 74,454 validation), of whom 6347 (4%) died. Among ICU encounters in the validation cohort (n = 7932 with suspected infection, of whom 1289 [16%] died), the predictive validity for in-hospital mortality was lower for SIRS (AUROC = 0.64; 95% CI, 0.62-0.66) and qSOFA (AUROC = 0.66; 95% CI, 0.64-0.68) vs SOFA (AUROC = 0.74; 95% CI, 0.73-0.76; P < .001 for both) or LODS (AUROC = 0.75; 95% CI, 0.73-0.76; P < .001 for both). Among non-ICU encounters in the validation cohort (n = 66 522 with suspected infection, of whom 1886 [3%] died), qSOFA had predictive validity (AUROC = 0.81; 95% CI, 0.80-0.82) that was greater than SOFA (AUROC = 0.79; 95% CI, 0.78-0.80; P < .001) and SIRS (AUROC = 0.76; 95% CI, 0.75-0.77; P < .001). Relative to qSOFA scores lower than 2, encounters with qSOFA scores of 2 or higher had a 3- to 14-fold increase in hospital mortality across baseline risk deciles. Findings were similar in external data sets and for the secondary outcome. CONCLUSIONS AND RELEVANCE: Among ICU encounters with suspected infection, the predictive validity for in-hospital mortality of SOFA was not significantly different than the more complex LODS but was statistically greater than SIRS and qSOFA, supporting its use in clinical criteria for sepsis. Among encounters with suspected infection outside of the ICU, the predictive validity for in-hospital mortality of qSOFA was statistically greater than SOFA and SIRS, supporting its use as a prompt to consider possible sepsis.
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qSOFA was identified as a predictor of poor outcome in patients with known or suspected infection, but no analysis supported its use as a screening tool.
“Data analysis used to support the recommendations of the Third International Consensus Conference on the Definitions of Sepsis identified qSOFA as a predictor of poor outcome in patients with known or suspected infection, but no analysis was performed to support its use as a screening tool (5).”
From Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. (2021) · cites this paper as [5]
The Results (Performance of New, Simple Criteria) show that qSOFA predicted hospital mortality among patients with suspected infection. The Discussion (Criteria Outside of the ICU; Limitations) says the study did not test whether qSOFA could distinguish patients with infection from those without it, supporting the narrower point that it was not evaluated as a stand-alone infection screen. The broader claim that no analysis supported any screening use goes beyond those passages: the Discussion recommends using qSOFA outside the ICU to consider the possibility of sepsis. (LLM, conf=0.83)
Experimental — accuracy not yet measuredEarlier detection of sepsis can be challenging due to its nonspecific symptoms and the lack of a definitive diagnostic test.
“However, earlier detection of sepsis can be challenging due to its nonspecific symptoms and the lack of a definitive diagnostic test.3,4”
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 [3]
The Abstract (Conclusions and Relevance; page unavailable) says there is no “gold standard” diagnostic test for sepsis. The Discussion (Criteria Outside of the ICU; page unavailable) addresses early recognition and the difficulty of obtaining some clinical and laboratory measures promptly, but our copy does not attribute that difficulty to nonspecific symptoms. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA validated algorithm (sensitivity 91.2%, specificity 89.7%) was used to identify eligible patients.
“A validated algorithm (sensitivity 91.2%, specificity 89.7%) was used to identify eligible patients from electronic health records30,31.”
From Machine learning predicts sepsis deterioration trajectories. (2026) · cites this paper as [30]
The Methods section, “Defining a Cohort With Suspected Infection” (page unavailable), describes identifying patients from EHR data using the timing of antibiotic orders and body-fluid cultures. It does not report that this identification algorithm was validated with 91.2% sensitivity and 89.7% specificity; those figures are unsupported by this paper. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn the original derivation study, only 24% of infected patients had a qSOFA score 2 or 3, but these patients accounted for 70% of poor outcomes.
“In the original derivation study, authors found that only 24% of infected patients had a qSOFA score 2 or 3, but these patients accounted for 70% of poor outcomes (5).”
From Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. (2021) · cites this paper as [5]
Data analysis used to support the Third International Consensus Conference on Definitions of Sepsis identified qSOFA as a predictor of poor outcome in patients with known or suspected infection, but no analysis was performed to support its use as a screening tool.
“Data analysis used to support the recommendations of the Third International Consensus Conference on the Definitions of Sepsis identified qSOFA as a predictor of poor outcome in patients with known or suspected infection, but no analysis was performed to support its use as a screening tool (5).”
From Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. (2021) · cites this paper as [5]
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 [14]
Calvert et al. was one of the first to study machine learning to identify sepsis in an ICU population, and Seymour et al. assessed the sepsis-3 criteria.
“Calvert et al. [33] was one of the first to study machine learning to identify sepsis in an ICU population and Seymour et al. [34] assessed the sepsis-3 criteria.”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [34]
The analysis supporting the 3rd International Consensus Conference identified qSOFA as a predictor of poor outcome in patients with known or suspected infection, but no analysis supported its use as a screening tool.
“Data analysis used to support the recommendations of the 3rd International Consensus Conference on the Definitions of Sepsis identified qSOFA as a predictor of poor outcome in patients with known or suspected infection, but no analysis was performed to support its use as a screening tool [5].”
From Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021 (2021) · cites this paper as [5]
In the original qSOFA derivation study, only 24% of infected patients had a qSOFA score of 2 or 3, but these patients accounted for 70% of poor outcomes.
“In the original derivation study, authors found that only 24% of infected patients had a qSOFA score 2 or 3, but these patients accounted for 70% of poor outcomes [5].”
From Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021 (2021) · cites this paper as [5]
Validated scores such as the quick SOFA may identify patients who are at greater risk for a poor outcome.
“validated scores such as the quick SOFA ([systolic blood pressure ≤100 mm Hg, respiratory rate ≥22/min, altered mental state with Glasgow coma scale <15]: the presence of ≥2 points near the onset of infection is associated with a greater risk of death or prolonged intensive care unit stay] may identify patients who are at greater risk for a poor outcome.”
From 2019 update of the EULAR recommendations for the management of systemic lupus erythematosus (2019) · cites this paper as [146]
Guidelines for management of surgical patients during the SARS-CoV-2 pandemic are based solely on expert opinion.
“Guidelines have been published for the management of surgical patients during the SARS-CoV-2 pandemic,9, 10, 11 but they are based solely on expert opinion.”
From Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study (2020) · cites this paper as [11]
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- Authors
- Seymour, C. W., Liu, V. X., Iwashyna, T. J., Brunkhorst, F. M., Rea, T. D., Scherag, A., Rubenfeld, G., Kahn, J. M., Shankar-Hari, M., Singer, M., Deutschman, C. S., Escobar, G. J., & Angus, D. C.
- Affiliations
- University of Pittsburgh (US)
- Kaiser Permanente (US)
- University of Michigan (US)
- Jena University Hospital (DE)
- University of Washington (US)
- Sunnybrook Health Science Centre (CA)
- Critical Care Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, England
- University College London (GB)
- Feinstein Institute for Medical Research (US)
- Corresponding author
- Seymour, Christopher W.
- ORCID
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2016-02-23
- Volume
- 315
- Issue
- 8
- Pages
- 762
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC5433435
- OpenAlex
- W2282181907
- Semantic Scholar
- a99beb10867b97f6cadb82dfb81e3c34155d42f6
- Cited by
- 3,939
- References
- 43
- Citations by year
- 2026: 336 · 2025: 309 · 2024: 386 · 2023: 417 · 2022: 375 · 2021: 517 · 2020: 451 · 2019: 399 · 2018: 338 · 2017: 270 · 2016: 132 · 2015: 3 · 2013: 2
- FWCI
- 356.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- National Institute of General Medical Sciences (K23GM104022, K23GM112018)
- 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, sepsis definitions, clinical criteria, qSOFA, SOFA score, systemic inflammatory response syndrome, in-hospital mortality, ICU length of stay, organ dysfunction, suspected infection, predictive validity, construct validity, electronic health records, sepsis mortality prediction model, early warning scores
- MeSH terms
- Adult, Female, Humans, Hypotension, Infections, Intensive Care Units, Length of Stay, Male, Pennsylvania, Regression Analysis, Retrospective Studies, Reproducibility of Results, Hospital Mortality, Systemic Inflammatory Response Syndrome, Sepsis, Lactic Acid, Consensus, Tachypnea, Organ Dysfunction Scores
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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