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 W Turner; Steff C Lewis; Aziz Sheikh; Hilary Pinnock

doi:10.12688/wellcomeopenres.19078.2

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

Wellcome Open Res. 2023 Sep 7:8:195. doi: 10.12688/wellcomeopenres.19078.2. eCollection 2023.

Authors

Luke Daines¹, Laura J Bonnett², Holly Tibble¹, Andy Boyd³, Richard Thomas³, David Price^{4

5}, Steve W Turner^{6

7}, Steff C Lewis⁸, Aziz Sheikh¹, Hilary Pinnock¹

Affiliations

¹ Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK.
² Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK.
³ Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK.
⁴ Observational and Pragmatic Research Institute, Singapore, 573969, Singapore.
⁵ Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, AB25 2ZG, UK.
⁶ Child Health, University of Aberdeen, Aberdeen, AB25 2ZG, UK.
⁷ Women and Children Division, NHS Grampian, Aberdeen, AB25 2ZG, UK.
⁸ Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, EH16 4UX, UK.

Abstract

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.

Keywords: ALSPAC; asthma; children and young people; diagnosis; electronic health records; prediction model; primary care.

Grants and funding

WT_/Wellcome Trust/United Kingdom