Trial reportPediatrics2025
Clinical Decision Support for Septic Shock in the Emergency Department: A Cluster Randomized Trial.
Trial report in Pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled 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.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Surviving Sepsis Campaign International Guidelines for the Management of Sepsis and Septic Shock in Children 2026.Intensive care medicine · 2026Pooled it
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- Management of Sepsis and Septic Shock in Children: Surviving Sepsis Campaign International Guidelines 2026.Indian pediatrics · 2026Review
- Care Pathway and Outcomes in Pediatric Septic Shock: A Narrative Review from Emergency Department Recognition to PICU Management.Children (Basel, Switzerland) · 2026Review
- Comparison of Pediatric Risk of Mortality-III, Phoenix Sepsis, and pediatric Sequential Organ Failure Assessment scores for predicting septic shock in Vietnamese children with sepsis.The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious DiseasesArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
objectivesDelays in septic shock diagnosis cause preventable mortality in children. Evidence is limited around early recognition strategies. The hypothesis was that clinical decision support (CDS) based on machine-learning predictive models would increase the proportion of children receiving septic shock treatment prior to shock onset.
methodsCDS was implemented in a prospective, stepped-wedge, cluster randomized trial in 4 pediatric emergency departments (EDs) over five 10-week periods. The CDS used models identifying children who did not yet have shock but were predicted to be at high risk based on electronic health record data at arrival and after 2 hours. Providers received CDS; effectiveness was evaluated in patients 60 days to 18 years with concern for sepsis. The primary outcome was antibiotic and bolus within 1 hour of sepsis suspicion. Secondary outcomes were time-to-antibiotic, hypotensive septic shock. Implementation outcomes were evaluated in qualitative interviews.
resultsOf 200 354 ED encounters from March 16, 2022, to March 1, 2023, 1331 encounters met inclusion criteria (979 intervention, 352 control arms). Antibiotic and bolus within 1 hour occurred in 39.0% of patients in the intervention arm versus 38.9% of patients in the control arm (adjusted odds ratio [aOR]: 1.07 [0.61-1.88]). There was no difference in outcomes of shock (aOR: 1.12 [0.53-2.46]) or antibiotic timeliness (aHR: 0.85 [0.63-1.16]). Providers reported the CDS felt valuable and unobtrusive (adoption); 6 months after the trial, EDs continued to use the CDS (maintenance).
conclusionsImplementing predictive CDS that infrequently alerted was feasible and acceptable. It did not change the proportion of patients with suspected sepsis who progressed to hypotensive shock.
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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.