Observational studyPediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2020
A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.
Observational study 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, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 34 citations in OpenAlex.
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- Parent's Process of Escalation of Care for Their Deteriorating Children Admitted to Paediatric Wards: A Grounded Theory.Journal of clinical nursing · 2026Article
- Observational
- Signal detection for pediatric patient deterioration with nursing students: An explanatory sequential design mixed-methods study.International journal of nursing studies advances · 2025Article
- Prearrest vital sign abnormalities are associated with adverse outcomes in pediatric ICU cardiac arrest: a get with the guidelines-resuscitation analysis.Resuscitation · 2025Article
- Risk Analytics Clinical Decision Support Decreases Duration of Vasoactive Infusions Following Pediatric Cardiac Surgery: A Multicenter Before and After Clinical Trial.Critical care medicine · 2025Article
- Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Article
- Machine Learning for Predicting Critical Events Among Hospitalized Children.JAMA network open · 2025Article
- Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- Not too sick, not too well: reducing the diagnostic void in pediatric emergency medicine.Pediatric research · 2024Article
- Predicting transfers to intensive care in children using CEWT and other early warning systems.Resuscitation plus · 2024Article
- Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.Hospital pediatrics · 2024Article
- Exploring the potential impact of adding upper limit single trigger MET thresholds to a paediatric early warning scoring tool at a tertiary children's hospital: a retrospective review.Frontiers in pediatrics · 2024Article
- The use of machine learning and artificial intelligence within pediatric critical care.Pediatric research · 2023Review
- Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach.JMIR medical informatics · 2022Article
- Vital signs as physiomarkers of neonatal sepsis.Pediatric research · 2022Review
- Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.Frontiers in pediatrics · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectivesClinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients.
designObservational cohort study.
settingAn urban, tertiary-care medical center. PATIENTS: Patients less than 18 years admitted to the general ward during years 2009-2018.
interventionsNone. MEASUREMENTS AND MAIN
resultsThe primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01).
conclusionsWe developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.
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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.