Development and external validation of a risk prediction tool for recurrent wheeze attacks in preschool children in England

Introduction Almost half of all children experience wheeze before their 6th birthday, with a subset experiencing recurrent attacks that increase healthcare burden. Early identification of children at high risk could enable timely interventions, but diagnostic tools are limited. Aim To develop and validate a prognostic model predicting the risk of recurrent wheeze attacks (primary outcome) and hospitalisations (secondary outcome) within 12 months of an initial preschool acute wheeze presentation. Methods We developed a multivariable logistic regression model using primary care data from 42 820 children in England aged up to 5 years with acute wheeze episodes recorded in the Clinical Practice Research Datalink. Predictors included demographics, clinical history and treatment patterns. The model was validated internally via bootstrapping and externally using the Secure Anonymised Information Linkage Databank in Wales (9703 children). Model performance was assessed using calibration and discrimination metrics. Results In the CPRD development cohort, 39.6% experienced a subsequent wheeze attack and 17.4% were hospitalised. Key predictors were the healthcare setting of initial acute wheeze, prior wheeze history and reliever prescriptions; inhaled corticosteroid prescriptions reduced risk. The model demonstrated excellent calibration across internal bootstrap validation and two external validation cohorts (calibration slope=1.00, 0.94 and 0.86 respectively, calibration-in-the-large=0.00, 0.035 and −0.143) and moderate discrimination (C-statistic=0.634, 0.625 and 0.638 for wheeze). Conclusions Using routinely collected data, the prognostic model shows strong calibration and moderate discrimination for predicting recurrent wheeze in preschool children. Implementation as a web tool could support early risk stratification and guide preventative management.