Zhao Yan, Patel Jenil, Xu Ximing, Zhang Guangli, Li Qinyuan, Yi Liangqin, Luo Zhengxiu
National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China.
Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, Chongqing, China.
Pediatr Pulmonol. 2023 May;58(5):1391-1400. doi: 10.1002/ppul.26331. Epub 2023 Feb 24.
To develop and validate a clinical prediction model to identify school-age asthma in preschool asthmatic children.
In this retrospective prognosis cohort study, asthmatic children aged 3-5 years were enrolled with at least 2 years of follow-up, and their potential variables at baseline and the prognosis of school-age asthma were collected from medical records. A clinical prediction model was developed using Logistic regression. The performance of prediction model was assessed and quantified by discrimination of the area under the receiver operating characteristic curve (AUC) and calibration of Brier score. The model was validated by the temporal-validation method.
In the development dataset, 2748 preschool asthmatic children were included for model development, and 883 (32.13%) children were translated to school-age asthma. The independent prognostic variables with an increased risk for school-age asthma were used to develop the prediction model, including: age, parental asthma, early frequent wheezing, allergic rhinitis, eczema, allergic conjunctivitis, obesity, and aeroallergen of dust mite. While assessing model performance, the discrimination power of AUC was moderate [0.788 (0.770-0.805)] with sensitivity (81.5%) and specificity (60.9%), and the calibration of Brier score was 0.169, supporting the calibration ability. In the temporal-validation dataset of 583 preschool asthmatic children, our model showed satisfactory discrimination (AUC 0.818) and calibration (Brier score 0.150). The prediction model was presented by the web-based calculator (https://casthma.shinyapps.io/dynnomapp/) and a nomogram for clinical application.
In preschool asthmatic children, our prediction model could be used to predict the risk of school-age asthma.
开发并验证一种临床预测模型，以识别学龄前哮喘儿童中的学龄期哮喘。
在这项回顾性预后队列研究中，纳入了3至5岁的哮喘儿童，进行至少2年的随访，并从病历中收集其基线时的潜在变量和学龄期哮喘的预后情况。使用逻辑回归开发临床预测模型。通过受试者操作特征曲线下面积（AUC）的辨别力和Brier评分的校准来评估和量化预测模型的性能。采用时间验证法对模型进行验证。
在开发数据集中，纳入2748名学龄前哮喘儿童进行模型开发，其中883名（32.13%）儿童转变为学龄期哮喘。使用与学龄期哮喘风险增加相关的独立预后变量来开发预测模型，包括：年龄、父母哮喘、早期频繁喘息、过敏性鼻炎、湿疹、过敏性结膜炎、肥胖以及尘螨空气过敏原。在评估模型性能时，AUC的辨别力中等[0.788（0.770 - 0.805）]，敏感性为81.5%，特异性为60.9%，Brier评分的校准值为0.169，支持校准能力。在583名学龄前哮喘儿童的时间验证数据集中，我们的模型显示出令人满意的辨别力（AUC 0.818）和校准（Brier评分0.150）。预测模型通过基于网络的计算器（https://casthma.shinyapps.io/dynnomapp/）和用于临床应用的列线图呈现。
在学龄前哮喘儿童中，我们的预测模型可用于预测学龄期哮喘的风险。
