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

Luke Daines*, Laura J. Bonnett, Holly Tibble, Andy Boyd, Richard Thomas, David Price, Steve Turner, Steff C Lewis, Aziz Sheikh, Hilary Pinnock

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 (OPCRD).

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 <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35.

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
Article number195
Number of pages27
JournalWellcome Open Research
Volume8
Early online date3 May 2023
DOIs
Publication statusPublished - 7 Sept 2023

Keywords / Materials (for Non-textual outputs)

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

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