Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
Open accessAbstract
OBJECTIVES: Earlier treatment of sepsis leads to decreased mortality. Epic is an electronic medical record providing a predictive alert system for sepsis, the Epic Sepsis Model (ESM) Inpatient Predictive Analytic Tool. External validation of this system is lacking. This study aims to evaluate the ESM as a sepsis screening tool and determine whether an association exists between ESM alert system implementation and subsequent sepsis-related mortality. DESIGN: Before-and-after study comparing baseline and intervention period. SETTING: Urban 746-bed academic level 1 trauma center. PATIENTS: Adult acute care inpatients discharged between January 12, 2018, and July 31, 2019. INTERVENTIONS: During the before period, ESM was turned on in the background, but nurses and providers were not alerted of results. The system was then activated to alert providers of scores greater than or equal to 5, a set point determined using receiver operating characteristic curve analysis (area under the curve, 0.834; p < 0.001). MEASUREMENTS AND MAIN RESULTS: Primary outcome was mortality during hospitalization; secondary outcomes were sepsis order set utilization, length of stay, and timing of administration of sepsis-appropriate antibiotics. Of the 11,512 inpatient encounters assessed by ESM, 10.2% (1,171) had sepsis based on diagnosis codes. As a screening test, the ESM had sensitivity, specificity, positive predictive value, and negative predictive value rates of 86.0%, 80.8%, 33.8%, and 98.11%, respectively. After ESM implementation, unadjusted mortality rates in patients with ESM score greater than or equal to 5 and who had not yet received sepsis-appropriate antibiotics declined from 24.3% to 15.9%; multivariable analysis yielded an odds ratio of sepsis-related mortality (95% CI) of 0.56 (0.39–0.80). CONCLUSIONS: In this single-center before-and-after study, utilization of the ESM score as a screening test was associated with a 44% reduction in the odds of sepsis-related mortality. Due to wide utilization of Epic, this is a potentially promising tool to improve sepsis mortality in the United States. This study is hypothesis generating, and further work with more rigorous study design is needed.
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The Epic Sepsis model shows divergent performance across hospitals: it is critiqued in a Michigan hospital and affirmed as effective in Greenville.
“The widely adopted Epic Sepsis model exemplifies these divergent outcomes. Wong et al. [21] critique the model’s performance when applied to a Michigan, U.S. hospital and raise concerns about sepsis care quality, while Cull et al. [45] affirm its effectiveness using data from Greenville, U.S.”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [45]
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- Authors
- Cull, J., Brevetta, R., Gerac, J., Kothari, S., & Blackhurst, D.
- Affiliations
- Prisma Health (US)
- Corresponding author
- Cull, John
- Venue
- Critical Care Explorations
- Published
- 2023-06-30
- Volume
- 5
- Issue
- 7
- Pages
- e0941
- ISSN
- 2639-8028 (ISSN-L)
- PMC
- PMC10317482
- OpenAlex
- W4382751277
- Cited by
- 37
- References
- 32
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 10 · 2023: 1
- FWCI
- 7.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Critical Care Explorations (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- sepsis, sepsis prediction model, electronic health record alerts, sepsis screening, sepsis mortality, model validation
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