Acute and Critical Care

Pediatrics
Corrigendum to: Development of a deep learning model for predicting critical events in a pediatric intensive care unit: In Kyung Lee, Bongjin Lee, June Dong Park; Received March 15, 2024 Accepted March 15, 2024 Published online April 1, 2024; DOI: https://doi.org/10.4266/acc.2023.01424.e1 [Epub ahead of print]; Corrects: Acute Crit Care 2024;39(1):186

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Pediatrics
Development of a deep learning model for predicting critical events in a pediatric intensive care unit: In Kyung Lee, Bongjin Lee, June Dong Park; Acute Crit Care. 2024;39(1):186-191. Published online February 20, 2024; DOI: https://doi.org/10.4266/acc.2023.01424; Correction in: https://doi.org/

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Abstract PDF: Background
Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Successful Brain Dead Donor Management with CRRT: A Case Report: Sang Hyun Lim, Young Joo Lee, Han Bum Joe, Jae Moung Lee, In Kyung Lee; Korean J Crit Care Med. 2012;27(4):286-289.; DOI: https://doi.org/10.4266/kjccm.2012.27.4.286

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Brain death results in adverse pathophysiologic effects in many brain-dead donors with cardiovascular instability. We experienced a brain-dead donor with continuous renal replacement therapy (CRRT) who was in a severe metabolic, electrolyte derangement and poor pulmonary function. The thirty-nine-year-old male patient with subarachnoid hemorrhage and intraventricular hemorrhage was admitted into the intensive care unit (ICU). After sudden cardiac arrest, he went into a coma state and was referred to as a potential organ donor. When he was transferred, his vital sign was unstable even under the high dose of inotropics and vasopressors. Even with aggressive treatment, the level of blood sugar was 454 mg/dl, serum K+ 7.1 mEq/L, lactate 5.33 mmol/L and PaO2/FiO2 60.3. We decided to start CRRT with the mode of continuous venovenous hemodiafiltration (CVVHDF). After 12 hours of CRRT, vital sign was maintained well without vasopressors, and blood sugar, serum potassium and lactate levels returned to 195 of PaO2/FiO2. Therefore, he was able to donate his two kidneys and his liver.

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Activation Policy for Brain-dead Organ Donation
Young-Joo Lee
The Ewha Medical Journal.2015; 38(1): 1. CrossRef

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