Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
Year: 2023
Type: article
Abstract: 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: < 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.
Source: Critical Care Explorations
Institution: Prisma Health
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W4382751277