Evidence map›Paper›PMID 35522467›Full record

ArticleJMIR medical informatics2022

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: Scoping Review.

Khalia Ackermann, Jannah Baker, Marino Festa, Brendan McMullan, Johanna Westbrook, Ling Li

Open access · goldAbstract readScoping Review
In one paragraph

Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
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

6 authors at 3 institutions in 1 country.

Khalia AckermannCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID https://orcid.org/0000-0001-9868-9456
Jannah BakerCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID https://orcid.org/0000-0002-2208-6584
Marino FestaKids Critical Care Research, Department of Paediatric Intensive Care, Children's Hospital at Westmead, Sydney, Australia.ORCID https://orcid.org/0000-0001-6541-7841
Brendan McMullanDepartment of Immunology and Infectious Diseases, Sydney Children's Hospital, Randwick, Sydney, Australia.ORCID https://orcid.org/0000-0001-5144-3416
Johanna WestbrookCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID https://orcid.org/0000-0003-1083-8192
Ling LiCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia.ORCID https://orcid.org/0000-0002-1642-142X
Macquarie University · AUChildren's Hospital at Westmead · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis is a severe condition associated with extensive morbidity and mortality worldwide. Pediatric, neonatal, and maternal patients represent a considerable proportion of the sepsis burden. Identifying sepsis cases as early as possible is a key pillar of sepsis management and has prompted the development of sepsis identification rules and algorithms that are embedded in computerized clinical decision support (CCDS) systems.

objectiveThis scoping review aimed to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of pediatric, neonatal, and maternal inpatients at risk of sepsis.

methodsMEDLINE, Embase, CINAHL, Cochrane, Latin American and Caribbean Health Sciences Literature (LILACS), Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and ProQuest Dissertations and Theses Global (PQDT) were searched by using a search strategy that incorporated terms for sepsis, clinical decision support, and early detection. Title, abstract, and full-text screening was performed by 2 independent reviewers, who consulted a third reviewer as needed. One reviewer performed data charting with a sample of data. This was checked by a second reviewer and via discussions with the review team, as necessary.

resultsA total of 33 studies were included in this review-13 (39%) pediatric studies, 18 (55%) neonatal studies, and 2 (6%) maternal studies. All studies were published after 2011, and 27 (82%) were published from 2017 onward. The most common outcome investigated in pediatric studies was the accuracy of sepsis identification (9/13, 69%). Pediatric CCDS systems used different combinations of 18 diverse clinical criteria to detect sepsis across the 13 identified studies. In neonatal studies, 78% (14/18) of the studies investigated the Kaiser Permanente early-onset sepsis risk calculator. All studies investigated sepsis treatment and management outcomes, with 83% (15/18) reporting on antibiotics-related outcomes. Usability and cost-related outcomes were each reported in only 2 (6%) of the 31 pediatric or neonatal studies. Both studies on maternal populations were short abstracts.

conclusionsThis review found limited research investigating CCDS systems to support the early detection of sepsis among pediatric, neonatal, and maternal patients, despite the high burden of sepsis in these vulnerable populations. We have highlighted the need for a consensus definition for pediatric and neonatal sepsis and the study of usability and cost-related outcomes as critical areas for future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24899.

Indexed as

computerized clinical decision supportearly detection of diseaseelectronic health recordspatient safetysepsissepsis care pathway

Identifiers

PMID35522467
PMCPMC9123549
OpenAlexW4226268064

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.