ArticleJournal of medical Internet research2022
Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.
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.
What it found
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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.
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.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- [S3 guideline on sepsis-prevention, diagnosis, therapy, and follow-up care-update 2025].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025Guideline
- Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Article
- Current standard of care for septic shock.Intensive care medicine · 2026Review
- From Data to Decisions: Harnessing Multi-Agent Systems for Safer, Smarter, and More Personalized Perioperative Care.Journal of personalized medicine · 2025Review
- Improving Prognostic Accuracy of MASCC Score with Lactate and CRP Measurements in Febrile Neutropenic Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.BMC medical informatics and decision making · 2025Observational
- AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.Journal of medical Internet research · 2025Article
- AI-driven multi-omics profiling of sepsis immunity in the digestive system.Frontiers in immunology · 2025Review
- Evaluating the impact of common clinical confounders on performance of deep-learning-based sepsis risk assessment.Frontiers in artificial intelligence · 2025Article
- Septic shock in the immunocompromised cancer patient: a narrative review.Critical care (London, England) · 2024Review
- Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review.Journal of medical Internet research · 2023Article
- Audit of a computerized version of the Manchester triage system and a SIRS-based system for the detection of sepsis at triage in the emergency department.International journal of emergency medicine · 2022Article
- 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... · 2022Article
- Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: Scoping Review.JMIR medical informatics · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.