Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Department of Pediatrics, National Medical Center, Seoul, Republic of Korea.
Sci Rep. 2024 Feb 27;14(1):4707. doi: 10.1038/s41598-024-55528-1.
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.
早期发现病情恶化的患者对于预防危及生命的事件和改善临床结局非常重要。人们已经努力去发现或预防心肺复苏等重大事件，但之前开发的工具通常很复杂且耗时，因此不切实际。为了解决这个问题，我们设计了这项研究，旨在创建一个使用简化变量预测危急事件的深度学习预测模型。
这项回顾性观察性研究纳入了 2020 年至 2022 年期间在一家三级儿童医院普通病房住院的年龄小于 18 岁的患者。危急事件定义为心肺复苏、非计划转入重症监护病房或死亡。住院期间测量的生命体征、测量间隔、性别和年龄用于训练危急事件预测模型。使用年龄特异性 z 分数将正常范围的变异性按年龄进行标准化。整个数据集按照 8:2 的比例分为训练数据集和测试数据集，并在每个数据集上进行模型学习和测试。
所开发模型的预测性能表现出色，接收器操作特征曲线下面积为 0.986，精度-召回曲线下面积为 0.896。我们使用简化变量开发了一种具有出色预测能力的深度学习模型，能够有效地预测危急事件，同时减轻医务人员的工作负担。然而，由于这是一项单中心试验，没有进行外部验证，因此需要进一步研究。
