- Pediatrics
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Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events
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Yunseob Shin, Kyung-Jae Cho, Yeha Lee, Yu Hyeon Choi, Jae Hwa Jung, Soo Yeon Kim, Yeo Hyang Kim, Young A Kim, Joongbum Cho, Seong Jong Park, Won Kyoung Jhang
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Acute Crit Care. 2022;37(4):654-666. Published online October 26, 2022
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DOI: https://doi.org/10.4266/acc.2022.00976
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 Abstract
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- Background
Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance. Methods: This is a retrospective multicenter cohort study including five tertiary-care academic children’s hospitals. All pediatric patients younger than 19 years admitted to the general ward from January 2019 to December 2019 were included. Using patient electronic medical records, we evaluated the clinical performance of the pDEWS for identifying deterioration events defined as in-hospital cardiac arrest (IHCA) and unexpected general ward-to-pediatric intensive care unit transfer (UIT) within 24 hours before event occurrence. We also compared pDEWS performance to those of the modified pediatric early-warning score (PEWS) and prediction models using logistic regression (LR) and random forest (RF). Results: The study population consisted of 28,758 patients with 34 cases of IHCA and 291 cases of UIT. pDEWS showed better performance for predicting deterioration events with a larger area under the receiver operating characteristic curve, fewer false alarms, a lower mean alarm count per day, and a smaller number of cases needed to examine than the modified PEWS, LR, or RF models regardless of site, event occurrence time, age group, or sex. Conclusions: The pDEWS outperformed modified PEWS, LR, and RF models for early and accurate prediction of deterioration events regardless of clinical situation. This study demonstrated the potential of pDEWS as an efficient screening tool for efferent operation of rapid response teams.
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Citations
Citations to this article as recorded by  - Predicting cardiac arrest after neonatal cardiac surgery
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eClinicalMedicine.2023; 62: 102124. CrossRef - A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques
Seok Young Song, Won-Kee Choi, Sanggyu Kwak
Medicine.2023; 102(37): e35057. CrossRef - An advanced pediatric early warning system: a reliable sentinel, not annoying extra work
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Acute and Critical Care.2022; 37(4): 667. CrossRef
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