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The Simple 10-Item Predicting Asthma Risk in Children Tool to Predict Childhood Asthma-An External Validation
Eva S L Pedersen1, Ben D Spycher1, Carmen C M de Jong1
1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Insights
The Predicting Asthma Risk in Children (PARC) tool accurately predicts childhood asthma risk using preschool symptoms. External validation confirms PARC
Area of Science:
- Pediatric respiratory health
- Clinical prediction modeling
- Epidemiology
Background:
- External validation is crucial for assessing the generalizability of prediction models.
- The Predicting Asthma Risk in Children (PARC) tool was developed using the Leicestershire Respiratory Cohort (LRC).
- PARC utilizes preschool respiratory symptoms to predict asthma development by school age.
Purpose of the Study:
- To perform an external validation of the PARC prediction tool.
- To assess the generalizability of PARC in a different population cohort.
- To confirm the predictive accuracy of PARC for childhood asthma.
Main Methods:
- The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort was used for external validation.
- Inclusion criteria, prediction score items, and asthma outcomes were standardized between cohorts.
- Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, AUC, Brier score, and R-squared.
Main Results:
- The validation population comprised 2690 children; 14% developed asthma.
- PARC demonstrated similar discriminative performance in ALSPAC (AUC=0.77) compared to LRC (AUC=0.78).
- The optimal score cutoff (4) yielded high sensitivity (69%) and specificity (76%); minor variations in criteria did not significantly impact performance.
Conclusions:
- The PARC tool is a validated instrument for predicting asthma in population-based cohorts.
- PARC performs consistently across different cohorts, indicating good generalizability.
- The tool is ready for evaluation in clinical practice settings.
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.
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