Xi Hanxu, Zhang Yudi, Zuo Rui, Li Wei, Zhang Chen, Sun Yongchang, Ji Hong, He Zhiqiang, Chang Chun
Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
Information Management and Big Data Centre, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
J Asthma Allergy. 2025 May 31;18:861-876. doi: 10.2147/JAA.S512405. eCollection 2025.
Asthma is the world's second most prevalent chronic respiratory disease. Current clinical decisions regarding hospitalization for adult asthma patients in emergency departments (EDs) primarily rely on presenting clinical status, acute exacerbation severity, therapeutic response and high-risk factors. Assessing the need for hospitalization of patients with complex comorbidities remains a significant challenge.
This study aims to develop models that integrate various environmental and clinical factors to predict the hospitalization of adult asthma patients in EDs and to interpret these models.
A retrospective analysis was conducted utilizing data from asthma patients at a single ED from 2016 to 2023; the data included demographics, vital signs, illness severity, laboratory test results, and comorbidities, along with environmental variables. Predictive models were constructed using the extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), logistic regression (LR), and random forest (RF). Area under the receiver operating characteristic curve (AUC), accuracy, and F1 score were the primary metrics used to assess model performance.
The analysis included 1140 ED visits. The median age was 51.0 years (interquartile range: 31.0 to 67.0 years), and 56.5% of the patients (644) were female. Overall, 21.8% of patients (249) required hospitalization after their ED visits. The AUC results for predicting hospitalization without external environmental factors were 0.8075 for XGBoost, 0.8233 for LightGBM, 0.7935 for SVM, 0.8033 for LR, and 0.8272 for RF. After integrating ambient air pollutant and meteorological features, the RF model consistently outperformed the other models, achieving an AUC of 0.8555. The most critical parameters for predicting hospitalization were found to be illness severity, oxygen saturation, age, and heart rate.
Machine learning (ML) models based on clinical, meteorological, and air pollution data can rapidly and accurately predict hospitalization of adult asthma patients in EDs.
哮喘是全球第二大常见的慢性呼吸道疾病。目前，急诊科针对成年哮喘患者住院治疗的临床决策主要依赖于当前的临床状况、急性加重的严重程度、治疗反应以及高风险因素。评估合并复杂疾病的患者的住院需求仍然是一项重大挑战。
本研究旨在开发整合各种环境和临床因素的模型，以预测成年哮喘患者在急诊科的住院情况，并对这些模型进行解读。
利用2016年至2023年期间一家急诊科哮喘患者的数据进行回顾性分析；数据包括人口统计学信息、生命体征、疾病严重程度、实验室检查结果和合并症，以及环境变量。使用极端梯度提升（XGBoost）、轻量级梯度提升机（LightGBM）、支持向量机（SVM）、逻辑回归（LR）和随机森林（RF）构建预测模型。受试者工作特征曲线下面积（AUC）、准确率和F1分数是评估模型性能的主要指标。
分析包括1140次急诊科就诊。中位年龄为51.0岁（四分位间距：31.0至67.0岁），56.5%的患者（644例）为女性。总体而言，21.8%的患者（249例）在急诊科就诊后需要住院治疗。在不考虑外部环境因素的情况下，预测住院情况的AUC结果分别为：XGBoost为0.8075，LightGBM为0.8233，SVM为0.7935，LR为0.8033，RF为0.8272。在整合环境空气污染物和气象特征后，RF模型始终优于其他模型，AUC达到0.8555。发现预测住院的最关键参数为疾病严重程度、血氧饱和度、年龄和心率。
基于临床、气象和空气污染数据的机器学习（ML）模型能够快速、准确地预测成年哮喘患者在急诊科的住院情况。
