Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
NIHR Southampton Biomedical Research Centre, University Hospitals Southampton, Southampton, UK.
Pediatr Allergy Immunol. 2020 Aug;31(6):616-627. doi: 10.1111/pai.13247. Epub 2020 Apr 13.
The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma.
Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective.
Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83).
Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.
在五岁之前无法客观诊断儿童哮喘，这通常会导致学龄前儿童哮喘治疗不足或过度。预测工具可用于估计儿童在学龄期发展为哮喘的风险，从而帮助医生对学龄前儿童进行早期哮喘护理。本综述旨在系统地识别和批判性评估那些开发新的或更新现有的预测模型来预测学龄期哮喘的研究。
检索了三个数据库（MEDLINE、Embase 和 Web of Science Core Collection），以确定截至 2019 年 7 月利用≤5 岁儿童信息预测学龄期儿童（6-13 岁）哮喘的研究。将验证研究作为次要目标进行评估。
共确定了 24 项研究，描述了 2000 年至 2019 年间开发的 26 个预测模型。模型分为回归模型（n=21）和机器学习方法（n=5）。有 9 项研究对 6 个回归模型进行了验证。15 个（21 个中的 15 个）模型需要额外的临床检查。使用受试者工作特征曲线下面积（AUC）评估整体模型性能，范围为 0.66 至 0.87。模型表现出中等程度的预测能力，能够预测哮喘的发生或排除，但不能同时预测。在进行外部验证的情况下，模型表现出适度的通用性（AUC 范围：0.62-0.83）。
现有的预测模型表现出中等的预测性能，在独立验证时通常具有适度的通用性。传统方法的局限性已显示出对预测准确性和分辨率的影响。探索新的方法，如机器学习方法，可能会为未来的学龄期哮喘预测解决这些局限性。
