Institute of Applied Mechanics, National Taiwan University, Taipei City, Taiwan.
Department of Pediatrics, National Taiwan University Hospital, Taipei City, Taiwan.
Pediatr Pulmonol. 2024 May;59(5):1256-1265. doi: 10.1002/ppul.26897. Epub 2024 Feb 14.
This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making.
Retrospective cohort study conducted at a single tertiary hospital.
This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia.
Two prediction models were developed using tree-structured machine learning algorithms. The primary outcomes were ICU mortality and 24-h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set.
A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24-h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69-0.91) and 0.92 (95% CI, 0.86-0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO), or higher partial pressure of carbon dioxide (PCO2) were observed.
This study demonstrated that the machine learning models for predicting ICU mortality and 24-h ICU mortality in children with pneumonia have the potential to support decision-making, especially in resource-limited settings.
本研究旨在预测入住重症监护病房（ICU）的肺炎患儿的死亡率，以辅助决策。
单中心回顾性队列研究。
本研究纳入 2010 年至 2019 年期间因肺炎入住国立台湾大学医院儿科 ICU 的患儿。
采用树状结构机器学习算法开发了两种预测模型。主要结局指标为 ICU 死亡率和 24 小时 ICU 死亡率。从电子病历中收集了共 33 个特征，包括人口统计学、基础疾病、生命体征和实验室数据。使用开发数据集构建机器学习模型，并使用保留测试数据集计算性能矩阵。
最终队列共纳入 1231 例肺炎患儿 ICU 入住。ICU 死亡率模型和 24 小时 ICU 死亡率模型的受试者工作特征曲线下面积（AUROC）分别为 0.80（95%置信区间 [CI]，0.69-0.91）和 0.92（95% CI，0.86-0.92）。基于特征重要性，本研究中开发的模型如果观察到血压、外周毛细血管血氧饱和度（SpO2）或更高的二氧化碳分压（PCO2）降低，倾向于预测随后 24 小时内死亡率增加。
本研究表明，预测肺炎患儿 ICU 死亡率和 24 小时 ICU 死亡率的机器学习模型有可能支持决策，特别是在资源有限的情况下。
