Observational studyBiomedical journal2022
Development and validation of a deep-learning-based pediatric early warning system: A single-center study.
Observational study in Biomedical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From scores to signals: evolution and innovations in pediatric early warning systems.Acute and critical care · 2026Article
- Digital Twins in Pediatric Infectious Diseases: Virtual Models for Personalized Management.Journal of personalized medicine · 2025Review
- Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.Journal of translational medicine · 2024Article
- Article
- Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.Scientific reports · 2024Observational
- Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study.JTCVS open · 2023Article
- Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development.JMIR cardio · 2023Article
- Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events.Acute and critical care · 2022Article
- About gladiators and a sacred disease.Biomedical journal · 2022Article
- Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.Frontiers in pediatrics · 2022Article
- Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.JMIR medical informatics · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning scores. We developed a deep-learning-based pediatric early warning system (pDEWS) and validated its performance.
methodsThis single-center retrospective observational cohort study reviewed, 50,019 pediatric patients admitted to the general ward in a tertiary-care academic children's hospital from January 2012 to December 2018. They were split by admission date into a derivation and a validation cohort. We developed a pDEWS for the early prediction of cardiopulmonary arrest and unexpected ward-to-pediatric intensive care unit (PICU) transfer. Then, we validated this system by comparing modified pediatric early warning score (PEWS), random forest (RF); an ensemble model of multiple decision trees and logistic regression (LR); a statistical model that uses a logistic function.
resultsFor predicting cardiopulmonary arrest, the pDEWS (area under the receiver operating characteristic curve (AUROC), 0.923) outperformed modified PEWS (AUROC, 0.769) and reduced the mean alarm count per day (MACPD) and number needed to examine (NNE) by 82.0% (from 46.7 to 8.4 MACPD) and 89.5% (from 0.303 to 0.807), respectively. Furthermore, for predicting unexpected ward-to-PICU transfer pDEWS also showed superior performance compared to existing methods.
conclusionOur study showed that pDEWS was superior to the modified PEWS and prediction models using RF and LR. This study demonstrates that the integration of the pDEWS into RRTs could increase operational efficiency and improve clinical outcomes.
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