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

Daines, Luke ORCID: 0000-0003-0564-4000, Bonnett, Laura J ORCID: 0000-0002-6981-9212, Tibble, Holly ORCID: 0000-0001-7169-4087, Boyd, Andy ORCID: 0000-0002-8614-3728, Thomas, Richard ORCID: 0000-0001-9833-1702, Price, David, Turner, Steve W ORCID: 0000-0001-8393-5060, Lewis, Steff C ORCID: 0000-0003-1210-2314, Sheikh, Aziz ORCID: 0000-0001-7022-3056 and Pinnock, Hilary ORCID: 0000-0002-5976-8386 (2023) Deriving and validating an asthma diagnosis prediction model for children and young people in primary care. Wellcome Open Research, 8. p. 195.

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Official URL: http://dx.doi.org/10.12688/wellcomeopenres.19078.1

Abstract

<ns3:p><ns3:bold>Introduction: </ns3:bold>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.</ns3:p><ns3:p> <ns3:bold>Methods: </ns3:bold>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).</ns3:p><ns3:p> <ns3:bold>Results: </ns3:bold>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.</ns3:p><ns3:p> <ns3:bold>Conclusions: </ns3:bold>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.</ns3:p>

Item Type:	Article
Uncontrolled Keywords:	Pediatric, Bioengineering, Asthma, Clinical Research, Lung, Respiratory, 3 Good Health and Well Being
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	21 Mar 2024 08:54
Last Modified:	12 Apr 2024 03:32
DOI:	10.12688/wellcomeopenres.19078.1
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3179773