- 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, and Pontus Nauclér.
- Division of Infectious Diseases, Department of Medicine, Solna (MedS), Karolinska Institutet, Stockholm, Sweden email@example.com.
- BMJ Qual Saf. 2020 Sep 1; 29 (9): 735-745.
BackgroundSurveillance 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.MethodsA 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.ResultsIn 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.ConclusionsA 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.© 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.
Knowledge, pearl, summary or comment to share?
You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
- Superscript can be denoted by
- Numbered or bulleted lists can be created using either numbered lines
1. 2. 3., hyphens
- Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)inline.
- Or use an inline reference
[^1]to refer to a longer footnote elseweher in the document
[^1]: This is a long footnote..