Berger, Daria O; Pedersen, Eva S L; Mallet, Maria C; de Jong, Carmen C M; Usemann, Jakob; Regamey, Nicolas; Spycher, Ben D; Ardura-Garcia, Cristina; Kuehni, Claudia E (2022). External Validation of the Predicting Asthma Risk in Children (PARC) tool in a clinical cohort. Pediatric pulmonology, 57(11), pp. 2715-2723. Wiley-Blackwell 10.1002/ppul.26088
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Pediatric_Pulmonology_-_2022_-_Berger_-_External_Validation_of_the_Predicting_Asthma_Risk_in_Children_PARC_tool_in_a.pdf - Accepted Version Available under License Publisher holds Copyright. Download (421kB) | Preview |
INTRODUCTION
The Predicting Asthma Risk in Children (PARC) tool uses questionnaire-based respiratory symptoms collected from preschool children to predict asthma risk 5 years later. The tool was developed and validated in population cohorts but not validated using a clinical cohort. We aimed to externally validate the PARC tool in a paediatric pulmonology clinic setting.
METHODS
The Swiss Paediatric Airway Cohort (SPAC) is a prospective cohort of children seen in paediatric pulmonology clinics across Switzerland. We included children aged 1-6 years with cough or wheeze at baseline who completed the 2-year follow up questionnaire. The outcome was defined as current wheeze plus use of asthma medication. We assessed performance using: sensitivity, specificity, negative and positive predictive value (NPV, PPV), area under the curve (AUC), scaled Brier's score and Nagelkerke's R2 scores. We compared performance in SPAC to that in the original population, the Leicester Respiratory Cohort (LRC).
RESULTS
Among 346 children included, 125 (36%) reported the outcome after 2 years. At a PARC score of 4: sensitivity was higher (95% vs 79%), specificity lower (14% vs 57%), and NPV and PPV comparable (0.84 vs. 0.87 and 0.37 vs.0.42) in SPAC versus LRC. AUC (0.71 vs 0.78), R2 (0.18 vs 0.28) and Brier's scores (0.13 vs 0.22) were lower in SPAC.
CONCLUSIONS
The PARC tool shows some clinical utility, particularly for ruling out the development of asthma in young children, but performance limitations highlight the need for new prediction tools to be developed specifically for the clinical setting. This article is protected by copyright. All rights reserved.
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