RT Journal Article
SR Electronic
T1 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
JF BMJ Quality & Safety
JO BMJ Qual Saf
FD BMJ Publishing Group Ltd
SP 735
OP 745
DO 10.1136/bmjqs-2019-010123
VO 29
IS 9
A1 John Karlsson Valik
A1 Logan Ward
A1 Hideyuki Tanushi
A1 Kajsa Müllersdorf
A1 Anders Ternhag
A1 Ewa Aufwerber
A1 Anna Färnert
A1 Anders F Johansson
A1 Mads Lause Mogensen
A1 Brian Pickering
A1 Hercules Dalianis
A1 Aron Henriksson
A1 Vitaly Herasevich
A1 Pontus Nauclér
YR 2020
UL http://qualitysafety.bmj.com/content/29/9/735.abstract
AB 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.