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Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data

BACKGROUND: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstr...

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Autores principales: Valik, John Karlsson, Ward, Logan, Tanushi, Hideyuki, Müllersdorf, Kajsa, Ternhag, Anders, Aufwerber, Ewa, Färnert, Anna, Johansson, Anders F, Mogensen, Mads Lause, Pickering, Brian, Dalianis, Hercules, Henriksson, Aron, Herasevich, Vitaly, Nauclér, Pontus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467502/
https://www.ncbi.nlm.nih.gov/pubmed/32029574
http://dx.doi.org/10.1136/bmjqs-2019-010123
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author Valik, John Karlsson
Ward, Logan
Tanushi, Hideyuki
Müllersdorf, Kajsa
Ternhag, Anders
Aufwerber, Ewa
Färnert, Anna
Johansson, Anders F
Mogensen, Mads Lause
Pickering, Brian
Dalianis, Hercules
Henriksson, Aron
Herasevich, Vitaly
Nauclér, Pontus
author_facet Valik, John Karlsson
Ward, Logan
Tanushi, Hideyuki
Müllersdorf, Kajsa
Ternhag, Anders
Aufwerber, Ewa
Färnert, Anna
Johansson, Anders F
Mogensen, Mads Lause
Pickering, Brian
Dalianis, Hercules
Henriksson, Aron
Herasevich, Vitaly
Nauclér, Pontus
author_sort Valik, John Karlsson
collection PubMed
description BACKGROUND: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. METHODS: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review. RESULTS: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. CONCLUSIONS: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.
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spelling pubmed-74675022020-09-15 Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data Valik, John Karlsson Ward, Logan Tanushi, Hideyuki Müllersdorf, Kajsa Ternhag, Anders Aufwerber, Ewa Färnert, Anna Johansson, Anders F Mogensen, Mads Lause Pickering, Brian Dalianis, Hercules Henriksson, Aron Herasevich, Vitaly Nauclér, Pontus BMJ Qual Saf Original Research BACKGROUND: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. METHODS: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review. RESULTS: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. CONCLUSIONS: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards. BMJ Publishing Group 2020-09 2020-02-06 /pmc/articles/PMC7467502/ /pubmed/32029574 http://dx.doi.org/10.1136/bmjqs-2019-010123 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Original Research
Valik, John Karlsson
Ward, Logan
Tanushi, Hideyuki
Müllersdorf, Kajsa
Ternhag, Anders
Aufwerber, Ewa
Färnert, Anna
Johansson, Anders F
Mogensen, Mads Lause
Pickering, Brian
Dalianis, Hercules
Henriksson, Aron
Herasevich, Vitaly
Nauclér, Pontus
Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title_full Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title_fullStr Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title_full_unstemmed Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title_short Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
title_sort validation of automated sepsis surveillance based on the sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467502/
https://www.ncbi.nlm.nih.gov/pubmed/32029574
http://dx.doi.org/10.1136/bmjqs-2019-010123
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