• Hansol Chang, Weon Jung, Juhyung Ha, Jae Yong Yu, Sejin Heo, Gun Tak Lee, Jong Eun Park, Se Uk Lee, Sung Yeon Hwang, Hee Yoon, Won Chul Cha, Tae Gun Shin, and Taerim Kim.
• Smart Health Lab, Research Institute of Future Medicine, Samsung Medical Center, Seoul, South Korea.
• Shock. 2023 Sep 1; 60 (3): 373378373-378.

AbstractObjective/Introduction : Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods : The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multilayer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results : Data of 89,250 visits of patients who met prespecified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion : We developed a latent shock prediction model based on 24 h of vital-sign sequence that changed with time and predicted the results by individual.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.

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