National Heart and Lung Institute, Imperial College London, London, UK.
Department of Paediatrics, Imperial College London, London, UK.
Clin Exp Allergy. 2019 Apr;49(4):410-418. doi: 10.1111/cea.13336. Epub 2019 Feb 7.
There is uncertainty about the clinical usefulness of currently available asthma predictive tools. Validation of predictive tools in different populations and clinical settings is an essential requirement for the assessment of their predictive performance, reproducibility and generalizability. We aimed to critically appraise asthma predictive tools which have been validated in external studies.
We searched MEDLINE and EMBASE (1946-2017) for all available childhood asthma prediction models and focused on externally validated predictive tools alongside the studies in which they were originally developed. We excluded non-English and non-original studies. PROSPERO registration number is CRD42016035727.
From 946 screened papers, eight were included in the review. Statistical approaches for creation of prediction tools included chi-square tests, logistic regression models and the least absolute shrinkage and selection operator. Predictive models were developed and validated in general and high-risk populations. Only three prediction tools were externally validated: the Asthma Predictive Index, the PIAMA and the Leicester asthma prediction tool. A variety of predictors has been tested, but no studies examined the same combination. There was heterogeneity in definition of the primary outcome among development and validation studies, and no objective measurements were used for asthma diagnosis. The performance of tools varied at different ages of outcome assessment. We observed a discrepancy between the development and validation studies in the tools' predictive performance in terms of sensitivity and positive predictive values.
Validated asthma predictive tools, reviewed in this paper, provided poor predictive accuracy with performance variation in sensitivity and positive predictive value.
目前可用的哮喘预测工具的临床实用性存在不确定性。在不同人群和临床环境中验证预测工具是评估其预测性能、可重复性和通用性的必要条件。我们旨在批判性地评估已在外部研究中验证的哮喘预测工具。
我们在 MEDLINE 和 EMBASE（1946-2017 年）中搜索了所有可用的儿童哮喘预测模型，并重点关注与最初开发它们的研究一起进行的外部验证的预测工具。我们排除了非英语和非原始研究。PROSPERO 注册号为 CRD42016035727。
从 946 篇筛选出的论文中，有 8 篇被纳入综述。用于创建预测工具的统计方法包括卡方检验、逻辑回归模型和最小绝对收缩和选择算子。预测模型是在一般人群和高危人群中开发和验证的。只有三种预测工具经过外部验证：哮喘预测指数、PIAMA 和莱斯特哮喘预测工具。已经测试了各种预测因子，但没有研究检验过相同的组合。发展和验证研究中主要结局的定义存在异质性，并且没有使用客观测量来诊断哮喘。工具在不同的结局评估年龄的性能存在差异。我们观察到开发和验证研究之间在工具的敏感性和阳性预测值方面的预测性能存在差异。
本文综述的经过验证的哮喘预测工具提供了较差的预测准确性，在敏感性和阳性预测值方面存在性能差异。
