Acute and Critical Care

Original Articles

Pediatrics
Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events: 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; DOI: https://doi.org/10.4266/acc.2022.00976

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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.

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

CPR/Resuscitation
Validation of Pediatric Index of Mortality 3 for Predicting Mortality among Patients Admitted to a Pediatric Intensive Care Unit: Jae Hwa Jung, In Suk Sol, Min Jung Kim, Yoon Hee Kim, Kyung Won Kim, Myung Hyun Sohn; Acute Crit Care. 2018;33(3):170-177. Published online August 31, 2018; DOI: https://doi.org/10.4266/acc.2018.00150

12,966 View
711 Download
13 Web of Science
11 Crossref

Abstract PDF

Background
The objective of this study was to evaluate the usefulness of the newest version of the pediatric index of mortality (PIM) 3 for predicting mortality and validating PIM 3 in Korean children admitted to a single intensive care unit (ICU).
Methods
We enrolled children at least 1 month old but less than 18 years of age who were admitted to the medical ICU between March 2009 and February 2015. Performances of the pediatric risk of mortality (PRISM) III, PIM 2, and PIM 3 were evaluated by assessing the area under the receiver operating characteristic (ROC) curve, conducting the Hosmer-Lemeshow test, and calculating the standardized mortality ratio (SMR).
Results
In total, 503 children were enrolled; the areas under the ROC curve for PRISM III, PIM 2, and PIM 3 were 0.775, 0.796, and 0.826, respectively. The area under the ROC curve was significantly greater for PIM 3 than for PIM 2 (P<0.001) and PRISM III (P=0.016). There were no significant differences in the Hosmer-Lemeshow test results for PRISM III (P=0.498), PIM 2 (P=0.249), and PIM 3 (P=0.337). The SMR calculated using PIM 3 (1.11) was closer to 1 than PIM 2 (0.84).
Conclusions
PIM 3 showed better prediction of the risk of mortality than PIM 2 for the Korean pediatric population admitted in the ICU.

Citations

Citations to this article as recorded by

Clinical Features and Management of Status Epilepticus in the Pediatric Intensive Care Unit
Ekin Soydan, Yigithan Guzin, Sevgi Topal, Gulhan Atakul, Mustafa Colak, Pinar Seven, Ozlem Sarac Sandal, Gokhan Ceylan, Aycan Unalp, Hasan Agin
Pediatric Emergency Care.2023; 39(3): 142. CrossRef
Evaluation of the Performance of PRISM III and PIM II Scores in a Tertiary Pediatric Intensive Care Unit
Büşra Uzunay Gündoğan, Oğuz Dursun, Nazan Ülgen Tekerek, Levent Dönmez
Turkish Journal of Pediatric Emergency and Intensive Care Medicine.2023; 10(1): 8. CrossRef
Incidence and Mortality Trends in Critically Ill Children: A Korean Population-Based Study
Jaeyoung Choi, Esther Park, Ah Young Choi, Meong Hi Son, Joongbum Cho
Journal of Korean Medical Science.2023;[Epub] CrossRef
Internal validation and evaluation of the predictive performance of models based on the PRISM-3 (Pediatric Risk of Mortality) and PIM-3 (Pediatric Index of Mortality) scoring systems for predicting mortality in Pediatric Intensive Care Units (PICUs)
Zahra Rahmatinejad, Fatemeh Rahmatinejad, Majid Sezavar, Fariba Tohidinezhad, Ameen Abu-Hanna, Saeid Eslami
BMC Pediatrics.2022;[Epub] CrossRef
Performance of Pediatric Risk of Mortality III and Pediatric Index of Mortality III Scores in Tertiary Pediatric Intensive Unit in Saudi Arabia
Ahmed S. Alkhalifah, Abdulaziz AlSoqati, Jihad Zahraa
Frontiers in Pediatrics.2022;[Epub] CrossRef
Clinical implications of discrepancies in predicting pediatric mortality between Pediatric Index of Mortality 3 and Pediatric Logistic Organ Dysfunction-2
Eui Jun Lee, Bongjin Lee, You Sun Kim, Yu Hyeon Choi, Young Ho Kwak, June Dong Park
Acute and Critical Care.2022; 37(3): 454. CrossRef
Administrative data in pediatric critical care research—Potential, challenges, and future directions
Nora Bruns, Anna-Lisa Sorg, Ursula Felderhoff-Müser, Christian Dohna-Schwake, Andreas Stang
Frontiers in Pediatrics.2022;[Epub] CrossRef
Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission
Bongjin Lee, Kyunghoon Kim, Hyejin Hwang, You Sun Kim, Eun Hee Chung, Jong-Seo Yoon, Hwa Jin Cho, June Dong Park
Scientific Reports.2021;[Epub] CrossRef
Meta-Analysis for the Prediction of Mortality Rates in a Pediatric Intensive Care Unit Using Different Scores: PRISM-III/IV, PIM-3, and PELOD-2
Yaping Shen, Juan Jiang
Frontiers in Pediatrics.2021;[Epub] CrossRef
Simplified Pediatric Index of Mortality 3 Score by Explainable Machine Learning Algorithm
Orkun Baloglu, Matthew Nagy, Chidiebere Ezetendu, Samir Q. Latifi, Aziz Nazha
Critical Care Explorations.2021; 3(10): e0561. CrossRef
Performance of Pediatric Index of Mortality in a Tertiary Care PICU in India
Nisha Toteja, Bharat Choudhary, Daisy Khera, Rohit Sasidharan, Prem Prakash Sharma, Kuldeep Singh
Journal of Pediatric Intensive Care.2021;[Epub] CrossRef

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