Epidemiology of hospital-onset versus community-onset sepsis in U.S. hospitals and association with mortality: a retrospective analysis using electronic clinical data.
Open accessAbstract
Objectives: Prior studies have reported that hospital-onset sepsis is associated with higher mortality rates than community-onset sepsis. Most studies, however, have used inconsistent case-finding methods and applied limited risk-adjustment for potential confounders. We used consistent sepsis criteria and detailed electronic clinical data to elucidate the epidemiology and mortality associated with hospital-onset sepsis. Design: Retrospective cohort study. Setting: 136 U.S. hospitals in the Cerner HealthFacts dataset. Patients: Adults hospitalized in 2009–2015. Interventions: None. Measurements and Main Results: We identified sepsis using Centers for Disease Control and Prevention Adult Sepsis Event criteria and estimated the risk of in-hospital death for hospital-onset sepsis versus community-onset sepsis using logistic regression models. In patients admitted without community-onset sepsis, we estimated risk of death associated with hospital-onset sepsis using Cox regression models with sepsis as a time-varying covariate. Models were adjusted for baseline characteristics and severity of illness. Among 2.2 million hospitalizations, there were 95,154 sepsis cases: 83,620 (87.9%) community-onset sepsis and 11,534 (12.1%) hospital-onset sepsis (0.5% of hospitalized cohort). Compared to community-onset sepsis, hospital-onset sepsis patients were younger (median 66 vs 68 yr) but had more comorbidities (median Elixhauser score 14 vs 11), higher Sequential Organ Failure Assessment scores (median 4 vs 3), higher ICU admission rates (61% vs 44%), longer hospital length of stay (median 19 vs 8 d), and higher in-hospital mortality (33% vs 17%) ( p < 0.001 for all comparisons). On multivariate analysis, hospital-onset sepsis was associated with higher mortality versus community-onset sepsis (odds ratio, 2.1; 95% CI, 2.0–2.2) and patients admitted without sepsis (hazard ratio, 3.0; 95% CI, 2.9–3.2). Conclusions: Hospital-onset sepsis complicated one in 200 hospitalizations and accounted for one in eight sepsis cases, with one in three patients dying in-hospital. Hospital-onset sepsis preferentially afflicted ill patients but even after risk-adjustment, they were twice as likely to die as community-onset sepsis patients; in patients admitted without sepsis, hospital-onset sepsis tripled the risk of death. Hospital-onset sepsis is an important target for surveillance, prevention, and quality improvement initiatives.
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Hospital-onset sepsis is more clinically complex and harder to predict than community-acquired sepsis.
“prior research suggesting that hospital-onset sepsis is more clinically complex and harder to predict than community-acquired sepsis.23,24”
From Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model (2026) · cites this paper as [23]
The Results (Patient Characteristics; page not provided) show that hospital-onset sepsis patients had more comorbidities and higher SOFA scores, supporting the description of greater clinical complexity. The Discussion (page not provided) suggests hospital-onset sepsis may be slower to recognize, but it does not establish that it is harder to predict. The paper also distinguishes community-onset sepsis from infections necessarily acquired in the community. (LLM, conf=0.87)
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Previously healthy adults among septic patients: Population-level epidemiology and outcomes
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Implementation of an EMS protocol to improve prehospital sepsis recognition
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The impact of comorbidities and COVID-19 on the evolution of community onset sepsis
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In vivo functional immunoprotection correlates for vaccines against invasive bacteria
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Early Experience with a Novel Strategy for Assessment of Sepsis Risk: The Shock Huddle
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Call for a Singapore National Action Plan for Sepsis (SNAPS): Stop sepsis, save lives
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Recent Advances in Bedside Device-Based Early Detection of Sepsis
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A Snapshot of National Institutes of Health Funding for Sepsis Research: 2019-2023
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Validation of an ICD-Based Algorithm to Identify Sepsis: A Retrospective Study
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Fluid Resuscitation and Initial Management in Patients Presenting with Sepsis in the General Ward
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Epidemiologic Characteristics and Management of Sepsis Among Previously Healthy Patients
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Should hospital-onset Adult Sepsis Event surveillance be routine… or even mandatory?
Rhee, C., & Klompas, M. (2022) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2022.16 · Cited by 3 · see citing works
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Machine learning for predictive analytics
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Exploring the mechanism of polymorphonuclear neutrophils against sepsis based on immune model
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Predicting Sepsis Mortality and Costs Using Medicare Claims: A Method to the Madness*
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Nomogram for predicting the prognosis of tumor patients with sepsis after gastrointestinal surgery
Chen, R.-X., Wu, Z., Li, Z., Wang, H., & Ji, J. (2022) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v14.i9.1771 · Cited by 1 · see citing works
Acinetobacter baumannii Early-Onset Sepsis After Home Delivery Into Toilet Water
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SEP-1 Septic Shock Bundle Guidelines Not Applicable to Inpatients—Reply
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Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study
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What’s Taking So Long? Known Unknowns, Capacity Strain, and Hospital-acquired Sepsis
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Can artificial intelligence and machine learning help us treat sepsis?
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Jarolín, D. M., Quintana, R., & Samudio, M. (2022) · Revista científica ciencias de la salud · doi:10.53732/rccsalud/04.01.2022.54
Improving the recognition of sepsis in primary care
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Improving Sepsis Management Through the Emergency Quality Network Sepsis Initiative
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Exploiting Electronic Data to Advance Knowledge and Management of Severe Infections
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Analysis of factors associated with mortality due to sepsis resulting from device-related infections
Amarante, A. C. A., Júnior, A. L., Ferrari, R. A. P., Kerbauy, G., & Capobiango, J. D. (2024) · Anales de Pediatría (English Edition) · doi:10.1016/j.anpede.2024.07.003
- Authors
- Rhee, C., Wang, R., Zhang, Z., Fram, D., Kadri, S. S., & Klompas, M.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- National Institutes of Health (US)
- Venue
- Critical Care Medicine
- Published
- 2019-09
- Volume
- 47
- Issue
- 9
- Pages
- 1169-1176
- ISSN
- 0090-3493 (ISSN-L)
- PMC
- PMC6697188
- OpenAlex
- W2944935359
- Cited by
- 122
- References
- 37
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 18 · 2023: 18 · 2022: 21 · 2021: 18 · 2020: 11 · 2019: 2
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Centers for Disease Control and Prevention (U54CK000484)
- Agency for Healthcare Research and Quality (K08HS025008)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Infectious Diseases › Infection Control in Healthcare
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Nosocomial Infections in ICU
- Keywords
- community-onset sepsis, sepsis mortality, in-hospital mortality, sepsis epidemiology, hospitalized patients, sepsis risk factors, comorbidities, intensive care unit admission, length of hospital stay, risk adjustment, hospital quality of care, sepsis prevention
- MeSH terms
- Adult, Aged, Aged, 80 and over, Cross Infection, Female, Humans, Intensive Care Units, Length of Stay, Male, Middle Aged, Retrospective Studies, Severity of Illness Index, United States, Comorbidity, Logistic Models, Proportional Hazards Models, Hospital Mortality, Age Distribution, Community-Acquired Infections, Sepsis, Organ Dysfunction Scores
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