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

John Karlsson Valik, Logan Ward, Hideyuki Tanushi, Kajsa Müllersdorf, Anders Ternhag, Ewa Aufwerber, Anna Färnert, Anders F. Johansson, Mads Lause Mogensen, Brian Pickering, Hercules Dalianis, Aron Henriksson, Vitaly Herasevich, Pontus Nauclér

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number010123
JournalBMJ Quality and Safety
DOIs
StateAccepted/In press - 2020

Keywords

  • adverse events, epidemiology and detection
  • continuous quality improvement
  • critical care
  • information technology
  • nosocomial infections

ASJC Scopus subject areas

  • Health Policy

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    Valik, J. K., Ward, L., Tanushi, H., Müllersdorf, K., Ternhag, A., Aufwerber, E., Färnert, A., Johansson, A. F., Mogensen, M. L., Pickering, B., Dalianis, H., Henriksson, A., Herasevich, V., & Nauclér, P. (Accepted/In press). 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. BMJ Quality and Safety, [010123]. https://doi.org/10.1136/bmjqs-2019-010123