A clinical prediction model to support the diagnosis of asthma in children and young people in UK primary care

05 Aug 2021
Respiratory conditions
  • Asthma
Respiratory topics
  • Children
Type of resource
Dublin 2021
Luke Daines, Usher Institute, The University of Edinburgh, United Kingdom
Clinical Research Results AimMaking an accurate diagnosis of asthma in children can be challenging, and mis-diagnosis of asthma is common. We aimed to derive and internally validate a clinical prediction model to support health professionals in primary care weigh up the probability of an asthma diagnosis in children and young people presenting with symptoms suggestive of asthma. MethodsA dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) enhanced with data from routinely collected health records. Individuals with at least three inhaled corticosteroid prescriptions in one year and a diagnostic asthma Read code were designated as having asthma. Potential candidate predictors were included if data were available for at least 60% of participants. Remaining missing data were handled using multiple imputation. The prediction model was derived using logistic regression. Bootstrap re-sampling was used to internally validate the model. Results11,972 individuals aged <25 years (49% female) were included, of whom 994 (8%) met the criteria for asthma. Model performance was good: the area under the receiver operating characteristic (AUROC) was 0.86 (Figure 1; 95% CI 0.85 to 0.87); the calibration slope was 1.00 (95% CI 0.95 to 1.05). The items included in the model were wheeze, cough, breathlessness, hay fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, previous prescription of a short acting beta agonist and the recording of lung function/reversibility testing in the past. ConclusionInformation readily available from a patient’s electronic health records can support primary care clinicians weigh up the likelihood of a child/young person having asthma. Before implementation, we plan to externally validate the prediction model, develop it into a user-friendly clinical decision support software, and test the feasibility of the system in clinical practice. Implementation Science/Service Development Research Ideas on Respiratory Conditions and Tobacco Dependency Abstract Declaration of Interest This work was funded by the Chief Scientist Office, Scotland (CAF/17/01) with support from the Asthma UK Centre for Applied Research. References and Clinical Trial Registry Information