ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2025
Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.
Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Early prediction of unplanned critical care transfers in children using EHR-based ensemble machine learning.JAMIA open · 2026Article
- Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study.JMIR formative research · 2026Article
- Machine learning of laboratory parameters to predict mortality risk in pediatric hemophagocytic lymphohistiocytosis: a retrospective single-center study.BMC pediatrics · 2026Article
- Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2026Article
- Development and validation of a machine learning model for critical progression risk in pediatric severe community-acquired pneumonia.Scientific reports · 2025Article
- Explaining alerts from a pediatric risk prediction model using clinical text.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Machine Learning for Predicting Critical Events Among Hospitalized Children.JAMA network open · 2025Article
- Based on C-CHEWS study on the application value of comprehensive perioperative nursing based on scoring in critically ill children with pediatric cardiovascular surgery.Frontiers in pediatrics · 2025Article
- Artificial intelligence in transitional care: practice, promise, and pitfalls-a scoping review.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivesTo describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the risk of the direct ward to the ICU transfer within 12 hours, and the associated improved outcomes among hospitalized children.
designPre- vs. post-implementation study.
settingAn urban, tertiary-care, academic hospital. PATIENTS: Pediatric (age < 18 yr) admissions from May 1, 2019, to April 30, 2023.
interventionsNone. MEASUREMENTS AND MAIN
resultsPatients were divided into baseline, pre-pCART implementation (May 1, 2019, to April 30 2021), and post-pCART implementation (May 1, 2021, to April 30, 2023) cohorts. First-ward admissions with a high-risk score (pCART score ≥ 92) were considered as the main cohort. The primary outcome was the occurrence of critical events, defined as invasive mechanical ventilation, vasoactive drug administration, or death within 12 hours of the first high-risk pCART score. There were 2763 and 3943 patients in the baseline and implementation cohorts, respectively. pCART implementation was associated with a decrease in the percentage of the primary outcome from baseline 1.4% to 0.4% (p < 0.001), which converted to more than two-thirds lower adjusted odds of the primary outcome (odds ratio, 0.22 [95% CI, 0.11-0.40]; p < 0.001). pCART implementation was also associated with a decreased prevalence of critical events at 24 and 48 hours after a first high-risk score. We failed to identify any association between cohort period and overall hospital and ICU length-of-stay, number of ICU transfers, and time to ICU transfer. However, there was a difference in hospital length-of-stay among a subpopulation of patients transferred to the ICU (median 6 vs. 7 d; p < 0.001). Analysis of compliance metrics indicates sustained compliance achievements over time.
conclusionsThe deployment of pCART, a ML-based pediatric risk stratification tool, for clinical decision support for pediatric ward patients, was associated with lower odds of critical events among high-risk patients.
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