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• W4386496720 abstract "<ns4:p><ns4:bold>Introduction: </ns4: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.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4: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).</ns4:p><ns4:p> <ns4:bold>Results: </ns4: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.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4: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.</ns4:p>" @default.
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• W4386496720 date "2023-09-07" @default.
• W4386496720 modified "2023-10-18" @default.
• W4386496720 title "Deriving and validating an asthma diagnosis prediction model for children and young people in primary care" @default.
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• W4386496720 doi "https://doi.org/10.12688/wellcomeopenres.19078.2" @default.
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