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Development and validation of a deep-learning-based pediatric early warning system: A single-center study
Seong Jong Park1, Kyung-Jae Cho2, Oyeon Kwon2
1Department of Pediatrics, Asan Medical Center Children's Hospital, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
Insights
A new deep-learning pediatric early warning system (pDEWS) significantly improves early detection of clinical deterioration in children. The pDEWS outperforms existing scores, enhancing patient safety and hospital efficiency.
Area of Science:
- Pediatric critical care medicine
- Artificial intelligence in healthcare
- Clinical informatics
Background:
- Early detection of clinical deterioration is crucial for improving patient outcomes.
- Rapid response teams (RRTs) and early warning scores are established methods for intervention.
- Existing systems require enhancement for greater accuracy and efficiency.
Purpose of the Study:
- To develop and validate a deep-learning-based pediatric early warning system (pDEWS).
- To assess the performance of pDEWS in predicting critical events in pediatric patients.
- To compare pDEWS against existing early warning scores and predictive models.
Main Methods:
- A retrospective observational cohort study of 50,019 pediatric patients.
- Development of a pDEWS using deep learning techniques.
- Validation by comparing pDEWS with modified pediatric early warning score (PEWS), random forest (RF), and logistic regression (LR) models.
Main Results:
- pDEWS demonstrated superior performance in predicting cardiopulmonary arrest (AUROC 0.923 vs. 0.769 for modified PEWS).
- pDEWS significantly reduced alarm burden (82.0% reduction in MACPD) and improved efficiency (89.5% reduction in NNE).
- pDEWS also showed superior performance in predicting unexpected ward-to-PICU transfers compared to existing methods.
Conclusions:
- The developed pDEWS is a highly effective tool for early detection of critical events in pediatric patients.
- Integration of pDEWS into RRTs can enhance operational efficiency.
- pDEWS has the potential to significantly improve clinical outcomes in pediatric care.
Background:
Early 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.
Methods:
This 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.
Results:
For 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.
Conclusion:
Our 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.
