Daines Luke, Bonnett Laura J, Boyd Andy, Turner Steve, Lewis Steff, Sheikh Aziz, Pinnock Hilary
Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK.
Department of Biostatistics, University of Liverpool, Liverpool, UK.
Wellcome Open Res. 2020 Mar 24;5:50. doi: 10.12688/wellcomeopenres.15751.1. eCollection 2020.
Accurately diagnosing asthma can be challenging. Uncertainty about the best combination of clinical features and investigations for asthma diagnosis is reflected in conflicting recommendations from international guidelines. One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis. However, systematic review evidence identifies that existing models for asthma diagnosis are at high risk of bias and unsuitable for clinical use. Being mindful of previous limitations, this protocol describes plans to derive and validate a prediction model for use by healthcare professionals to aid diagnostic decision making during assessment of a child or young person with symptoms suggestive of asthma in primary care. A prediction model will be derived using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and linked primary care electronic health records (EHR). Data will be included from study participants up to 25 years of age where permissions exist to use their linked EHR. Participants will be identified as having asthma if they received at least three prescriptions for an inhaled corticosteroid within a one-year period and have an asthma code in their EHR. To deal with missing data we will consider conducting a complete case analysis. However, if the exclusion of cases with missing data substantially reduces the total sample size, multiple imputation will be used. A multivariable logistic regression model will be fitted with backward stepwise selection of candidate predictors. Apparent model performance will be assessed before internal validation using bootstrapping techniques. The model will be adjusted for optimism before external validation in a dataset created from the Optimum Patient Care Research Database. This protocol describes a robust strategy for the derivation and validation of a prediction model to support the diagnosis of asthma in children and young people in primary care.
准确诊断哮喘可能具有挑战性。国际指南中相互矛盾的建议反映出，对于哮喘诊断的临床特征和检查的最佳组合存在不确定性。一种解决方案可能是建立一个临床预测模型，以帮助卫生专业人员估计哮喘诊断的概率。然而，系统评价证据表明，现有的哮喘诊断模型存在较高的偏倚风险，不适合临床使用。鉴于以往的局限性，本方案描述了推导和验证一个预测模型的计划，供医疗保健专业人员在基层医疗中评估有哮喘症状的儿童或年轻人时辅助诊断决策。将使用来自阿冯父母与儿童纵向研究（ALSPAC）的数据以及相关的基层医疗电子健康记录（EHR）来推导预测模型。数据将包括年龄在25岁以下且允许使用其相关EHR的研究参与者。如果参与者在一年内至少收到三份吸入性糖皮质激素处方且其EHR中有哮喘代码，则将其确定为患有哮喘。为了处理缺失数据，我们将考虑进行完整病例分析。然而，如果排除有缺失数据的病例会大幅减少总样本量，则将使用多重填补法。将使用向后逐步选择候选预测变量的方法拟合多变量逻辑回归模型。在使用自举技术进行内部验证之前，将评估模型的表观性能。在由最佳患者护理研究数据库创建的数据集中进行外部验证之前，将对模型进行乐观性调整。本方案描述了一种稳健的策略，用于推导和验证一个预测模型，以支持基层医疗中儿童和年轻人哮喘的诊断。
