Harmon Ira, Brailsford Jennifer, Sanchez-Cano Isabel, Fishe Jennifer
Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, Florida.
Department of Emergency Medicine, University of Florida College of Medicine - Jacksonville, Jacksonville, Florida.
Prehosp Emerg Care. 2025;29(1):10-21. doi: 10.1080/10903127.2024.2352583. Epub 2024 May 21.
Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study's objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations.
This is a retrospective observational study of patient encounters for ages 2-18 years from the ESO Data Collaborative between 2018 and 2021. We modified two existing rule-based pediatric asthma CPs and created three new CPs (one rule-based and two machine learning-based). Two pediatric emergency medicine physicians independently reviewed encounters to assign labels of asthma exacerbation or not. Taking that labeled encounter data, a 50/50 train/test split was used to create training and test sets from the labeled data. A 90/10 split was used to create a small validation set from the training set. We used specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and macro F to compare performance across all CP models.
After applying the inclusion and exclusion criteria, 24,283 patient encounters remained. The machine-learning models exhibited the best performance for the identification of pediatric asthma exacerbations. A multi-layer perceptron-based model had the best performance in all metrics, with an F score of 0.95, specificity of 1.00, sensitivity of 0.91, negative predictive value of 0.98, and positive predictive value of 1.00.
We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest.
哮喘急性发作是儿童急诊医疗服务（EMS）接诊的常见原因。因此，儿童哮喘急性发作的院前管理已被指定为EMS研究的重点。然而，由于哮喘症状的异质性，尤其是在儿童中，从院前记录中准确识别儿童哮喘急性发作是细微且困难的。因此，本研究的目的是开发一种针对院前的儿童哮喘可计算表型（CP），该表型能够准确识别儿童哮喘急性发作的院前接诊情况。
这是一项对2018年至2021年期间ESO数据合作组织中2至18岁患者接诊情况的回顾性观察研究。我们修改了两个现有的基于规则的儿童哮喘CP，并创建了三个新的CP（一个基于规则，两个基于机器学习）。两名儿科急诊医学医生独立审查接诊情况，以确定是否为哮喘急性发作。利用标记的接诊数据，采用50/50的训练/测试分割从标记数据中创建训练集和测试集。采用90/10的分割从训练集中创建一个小的验证集。我们使用特异性、敏感性、阳性预测值（PPV）、阴性预测值（NPV）和宏F来比较所有CP模型的性能。
应用纳入和排除标准后，剩余24283例患者接诊情况。机器学习模型在识别儿童哮喘急性发作方面表现出最佳性能。基于多层感知器的模型在所有指标中表现最佳，F分数为0.95，特异性为1.00，敏感性为0.91，阴性预测值为0.98，阳性预测值为1.00。
我们修改了现有的并开发了新的儿童哮喘CP，以回顾性识别院前儿童哮喘急性发作的接诊情况。我们发现基于机器学习的模型大大优于基于规则的模型。鉴于机器学习模型的高性能，开发和应用基于机器学习的CP用于其他病症和疾病，有助于通过准确识别感兴趣病症的患者来加速EMS研究，并最终改善临床护理。
