The Simple 10-Item Predicting Asthma Risk in Children Tool to Predict Childhood Asthma—An External Validation

Background: External validation of prediction models is important to assess generalizability to other populations than the one used for model development. The Predicting Asthma Risk in Children (PARC) tool, developed in the Leicestershire Respiratory Cohort (LRC), uses information on preschool respiratory symptoms to predict asthma at school age. Objective: We performed an external validation of PARC using the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods: We defined inclusion criteria, prediction score items at baseline and asthma at follow-up in ALSPAC to match those used in LRC using information from parent-reported questionnaires. We assessed performance of PARC by calculating sensitivity, specificity, predictive values, likelihood ratios, area under the curve (AUC), Brier score and Nagelkerke's R2. Sensitivity analyses varied inclusion criteria, scoring items, and outcomes. Results: The validation population included 2690 children with preschool respiratory symptoms of whom 373 (14%) had asthma at school age. Discriminative performance of PARC was similar in ALSPAC (AUC = 0.77, Brier score 0.13) as in LRC (0.78, 0.22). The score cutoff of 4 showed the highest sum of sensitivity (69%) and specificity (76%) and positive and negative likelihood ratios of 2.87 and 0.41, respectively. Changes to inclusion criteria, scoring items, or outcome definitions barely altered the prediction performance. Conclusions: Performing equally well in the validation cohort as in the development cohort, PARC is a valid tool for predicting asthma in population-based cohorts. Its use in clinical practice is ready to be tested.