Cargando…

Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study

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 sepsi...

Descripción completa

Detalles Bibliográficos
Autores principales: Cull, John, Brevetta, Robert, Gerac, Jeff, Kothari, Shanu, Blackhurst, Dawn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317482/
https://www.ncbi.nlm.nih.gov/pubmed/37405252
http://dx.doi.org/10.1097/CCE.0000000000000941
_version_ 1785067881209266176
author Cull, John
Brevetta, Robert
Gerac, Jeff
Kothari, Shanu
Blackhurst, Dawn
author_facet Cull, John
Brevetta, Robert
Gerac, Jeff
Kothari, Shanu
Blackhurst, Dawn
author_sort Cull, John
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10317482
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-103174822023-07-04 Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study Cull, John Brevetta, Robert Gerac, Jeff Kothari, Shanu Blackhurst, Dawn Crit Care Explor Original Clinical Report 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. Lippincott Williams & Wilkins 2023-06-30 /pmc/articles/PMC10317482/ /pubmed/37405252 http://dx.doi.org/10.1097/CCE.0000000000000941 Text en Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Clinical Report
Cull, John
Brevetta, Robert
Gerac, Jeff
Kothari, Shanu
Blackhurst, Dawn
Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title_full Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title_fullStr Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title_full_unstemmed Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title_short Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
title_sort epic sepsis model inpatient predictive analytic tool: a validation study
topic Original Clinical Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317482/
https://www.ncbi.nlm.nih.gov/pubmed/37405252
http://dx.doi.org/10.1097/CCE.0000000000000941
work_keys_str_mv AT culljohn epicsepsismodelinpatientpredictiveanalytictoolavalidationstudy
AT brevettarobert epicsepsismodelinpatientpredictiveanalytictoolavalidationstudy
AT geracjeff epicsepsismodelinpatientpredictiveanalytictoolavalidationstudy
AT kotharishanu epicsepsismodelinpatientpredictiveanalytictoolavalidationstudy
AT blackhurstdawn epicsepsismodelinpatientpredictiveanalytictoolavalidationstudy