Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Luke Daines * (Corresponding Author), LJ Bonnett , Holly Tibble, Andrew Boyd, R Thomas, David Price, Steven W Turner, Steff C Lewis, Aziz Sheikh, Hilary Pinnock

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people.
Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate
predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database
Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.
Original languageEnglish
JournalWellcome open research
Issue number195
Early online date3 May 2023
Publication statusE-pub ahead of print - 3 May 2023


  • Asthma
  • Diagnosis
  • Primary Care
  • Children and Young People
  • Prediction Model
  • Electronic Health Records


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