Evidence map›Paper›PMID 35195528›Full record

ArticleJournal of medical Internet research2022

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.

Khalia Ackermann, Jannah Baker, Malcolm Green, Mary Fullick, Hilal Varinli, Johanna Westbrook, Ling Li

Open access · goldAbstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.

  1. Pooled it
  2. [S3 guideline on sepsis-prevention, diagnosis, therapy, and follow-up care-update 2025].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025
    Guideline
  3. Pooled it
  4. Article
  5. Current standard of care for septic shock.Intensive care medicine · 2026
    Review
  6. Review
  7. Article
  8. Observational
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  10. Review
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  12. Review
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  15. Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information.Ethical and philosophical issues in medical imaging, multimodal learning and fusion across scales for clinical decision support, and topological data analysis for biomedical imaging : 1st International Workshop, EPIMI 2022, 12th Interna... · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Khalia AckermannCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID 0000-0001-9868-9456
Jannah BakerCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID 0000-0002-2208-6584
Malcolm GreenClinical Excellence Commission, Sydney, Australia.ORCID 0000-0001-6055-9592
Mary FullickClinical Excellence Commission, Sydney, Australia.ORCID 0000-0002-7739-1658
Hilal VarinlieHealth New South Wales, Sydney, Australia.ORCID 0000-0002-9411-0053
Johanna WestbrookCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID 0000-0003-1083-8192
Ling LiCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID 0000-0002-1642-142X
Macquarie University · AUMental Health Commission · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis is a significant cause of morbidity and mortality worldwide. Early detection of sepsis followed promptly by treatment initiation improves patient outcomes and saves lives. Hospitals are increasingly using computerized clinical decision support (CCDS) systems for the rapid identification of adult patients with sepsis.

objectiveThis scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of adult inpatients with sepsis.

methodsThe protocol for this scoping review was previously published. A total of 10 electronic databases (MEDLINE, Embase, CINAHL, the Cochrane database, LILACS [Latin American and Caribbean Health Sciences Literature], Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and PQDT [ProQuest Dissertations and Theses]) were comprehensively searched using terms for sepsis, CCDS, and detection to identify relevant studies. Title, abstract, and full-text screening were performed by 2 independent reviewers using predefined eligibility criteria. Data charting was performed by 1 reviewer with a second reviewer checking a random sample of studies. Any disagreements were discussed with input from a third reviewer. In this review, we present the results for adult inpatients, including studies that do not specify patient age.

resultsA search of the electronic databases retrieved 12,139 studies following duplicate removal. We identified 124 studies for inclusion after title, abstract, full-text screening, and hand searching were complete. Nearly all studies (121/124, 97.6%) were published after 2009. Half of the studies were journal articles (65/124, 52.4%), and the remainder were conference abstracts (54/124, 43.5%) and theses (5/124, 4%). Most studies used a single cohort (54/124, 43.5%) or before-after (42/124, 33.9%) approach. Across all 124 included studies, patient outcomes were the most frequently reported outcomes (107/124, 86.3%), followed by sepsis treatment and management (75/124, 60.5%), CCDS usability (14/124, 11.3%), and cost outcomes (9/124, 7.3%). For sepsis identification, the systemic inflammatory response syndrome criteria were the most commonly used, alone (50/124, 40.3%), combined with organ dysfunction (28/124, 22.6%), or combined with other criteria (23/124, 18.5%). Over half of the CCDS systems (68/124, 54.8%) were implemented alongside other sepsis-related interventions.

conclusionsThe current body of literature investigating the implementation of CCDS systems for the early detection of adult inpatients with sepsis is extremely diverse. There is substantial variability in study design, CCDS criteria and characteristics, and outcomes measured across the identified literature. Future research on CCDS system usability, cost, and impact on sepsis morbidity is needed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24899.

Indexed as

Decision Support Systems, ClinicalSepsisAdultCohort StudiesEarly DiagnosisHumansInpatientsclinical decision support systemsearly detection of diseaseelectronic health recordspatient safetysepsissepsis care pathway

Identifiers

PMID35195528
PMCPMC8908200
OpenAlexW3208088492

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.