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
Acute Crit Care. 2022;37(4):654-666. Published online October 26, 2022
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.
Citations
Citations to this article as recorded by
Predicting cardiac arrest after neonatal cardiac surgery Alexis L. Benscoter, Mark A. Law, Santiago Borasino, A. K. M. Fazlur Rahman, Jeffrey A. Alten, Mihir R. Atreya Intensive Care Medicine – Paediatric and Neonatal.2024;[Epub] CrossRef
Volumetric regional MRI and neuropsychological predictors of motor task variability in cognitively unimpaired, Mild Cognitive Impairment, and probable Alzheimer's disease older adults Michael Malek-Ahmadi, Kevin Duff, Kewei Chen, Yi Su, Jace B. King, Vincent Koppelmans, Sydney Y. Schaefer Experimental Gerontology.2023; 173: 112087. CrossRef
Predicting sepsis using deep learning across international sites: a retrospective development and validation study Michael Moor, Nicolas Bennett, Drago Plečko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt 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 Young Joo Han Acute and Critical Care.2022; 37(4): 667. CrossRef
With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.
Citations
Citations to this article as recorded by
External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study Kyung-Jae Cho, Kwan Hyung Kim, Jaewoo Choi, Dongjoon Yoo, Jeongmin Kim Critical Care Medicine.2024; 52(3): e110. CrossRef
Evaluation of optimal scene time interval for out-of-hospital cardiac arrest using a deep neural network Seung Jae Shin, Hee Sun Bae, Hyung Jun Moon, Gi Woon Kim, Young Soon Cho, Dong Wook Lee, Dong Kil Jeong, Hyun Joon Kim, Hyun Jung Lee The American Journal of Emergency Medicine.2023; 63: 29. CrossRef
Short-term load forecasting based on CEEMDAN and Transformer Peng Ran, Kun Dong, Xu Liu, Jing Wang Electric Power Systems Research.2023; 214: 108885. CrossRef
Artificial Intelligence in Resuscitation: A Scoping Review Dmitriy Viderman, Yerkin Abdildin, Kamila Batkuldinova, Rafael Badenes, Federico Bilotta Journal of Clinical Medicine.2023; 12(6): 2254. CrossRef
Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model Hung Viet Nguyen, Haewon Byeon Mathematics.2023; 11(9): 2030. CrossRef
Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: A comparison of approaches using different thermographic imaging treatments Alberto Nogales, Fernando Pérez-Lara, Álvaro J. García-Tejedor Multimedia Tools and Applications.2023; 83(14): 42955. CrossRef
Machine learning pre-hospital real-time cardiac arrest outcome prediction (PReCAP) using time-adaptive cohort model based on the Pan-Asian Resuscitation Outcome Study Hansol Chang, Ji Woong Kim, Weon Jung, Sejin Heo, Se Uk Lee, Taerim Kim, Sung Yeon Hwang, Sang Do Shin, Won Chul Cha, Marcus Ong Scientific Reports.2023;[Epub] CrossRef
Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records Bassel Soudan, Fetna F. Dandachi, Ali Bou Nassif Smart Health.2022; 25: 100294. CrossRef
BERT Learns From Electroencephalograms About Parkinson’s Disease: Transformer-Based Models for Aid Diagnosis Alberto Nogales, Alvaro J. Garcia-Tejedor, Ana M. Maitin, Antonio Perez-Morales, Maria Dolores Del Castillo, Juan Pablo Romero IEEE Access.2022; 10: 101672. CrossRef
Artificial intelligence decision points in an emergency department Hansol Chang, Won Chul Cha Clinical and Experimental Emergency Medicine.2022; 9(3): 165. CrossRef
Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database Arom Choi, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayoung Lee, Heejung Hyun, Hyeon Chang Kim, Ji Hoon Kim, Hyuk-Jae Chang Journal of Cardiovascular Development and Disease.2022; 9(12): 430. CrossRef
Short-Term Load Forecasting Based on Ceemdan and Transformer Peng Ran, Kun Dong, Xu Liu, Jing Wang SSRN Electronic Journal .2022;[Epub] CrossRef
A survey of deep learning models in medical therapeutic areas Alberto Nogales, Álvaro J. García-Tejedor, Diana Monge, Juan Serrano Vara, Cristina Antón Artificial Intelligence in Medicine.2021; 112: 102020. CrossRef
A predictive framework in healthcare: Case study on cardiac arrest prediction Samaneh Layeghian Javan, Mohammad Mehdi Sepehri Artificial Intelligence in Medicine.2021; 117: 102099. CrossRef
Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint) Arom Choi, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayong Lee, Heejung Hyun, Ji Hoon Kim, Hyuk-Jae Chang JMIR Medical Informatics.2021;[Epub] CrossRef
Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach Kuan-Han Wu, Fu-Jen Cheng, Hsiang-Ling Tai, Jui-Cheng Wang, Yii-Ting Huang, Chih-Min Su, Yun-Nan Chang PeerJ.2021; 9: e11988. CrossRef
Cardioinformatics: the nexus of bioinformatics and precision cardiology Bohdan B Khomtchouk, Diem-Trang Tran, Kasra A Vand, Matthew Might, Or Gozani, Themistocles L Assimes Briefings in Bioinformatics.2020; 21(6): 2031. CrossRef
Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study Junetae Kim, Yu Rang Park, Jeong Hoon Lee, Jae-Ho Lee, Young-Hak Kim, Jin Won Huh JMIR Medical Informatics.2020; 8(3): e16349. CrossRef
Rapid response systems in Korea Bo Young Lee, Sang-Bum Hong Acute and Critical Care.2019; 34(2): 108. CrossRef