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A Pediatric Asthma Risk Score to better predict asthma development in young children
Jocelyn M Biagini Myers1, Eric Schauberger2, Hua He3
1Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.
Insights
A new tool, the Pediatric Asthma Risk Score (PARS), accurately predicts asthma development in young children. This score offers improved prediction for mild-to-moderate risk cases, aiding early intervention strategies.
Area of Science:
- Pediatric Allergy and Immunology
- Respiratory Medicine
- Epidemiology
Background:
- Asthma phenotyping lacks reliable prediction of natural history, hindering primary prevention and early intervention.
- Current asthma outcome prediction tools exhibit poor accuracy, necessitating improved alternatives.
Purpose of the Study:
- To develop a quantitative, personalized tool for predicting asthma development in young children.
- To enhance early identification of children at risk for asthma.
Main Methods:
- Utilized data from the Cincinnati Childhood Allergy and Air Pollution Study (n=762) birth cohort to identify asthma predictors.
- Constructed the Pediatric Asthma Risk Score (PARS) by integrating demographic and clinical data.
- Validated PARS against the Asthma Predictive Index (API) and replicated findings in the Isle of Wight birth cohort.
Main Results:
- PARS demonstrated reliable asthma prediction in the initial cohort (sensitivity=0.68, specificity=0.77) and validation cohort (sensitivity=0.67, specificity=0.79).
- PARS showed improved prediction for mild-to-moderate risk children compared to API.
- Key predictors included early wheezing, sensitization to allergens, and African American race.
Conclusions:
- The Pediatric Asthma Risk Score (PARS) is a robust, valid, and generalizable tool for predicting childhood asthma.
- PARS offers significant advantages over the API, particularly for identifying children with mild-to-moderate asthma risk.
- Improved prediction in this common subgroup may facilitate more effective asthma prevention strategies.
Background:
Asthma phenotypes are currently not amenable to primary prevention or early intervention because their natural history cannot be reliably predicted. Clinicians remain reliant on poorly predictive asthma outcome tools because of a lack of better alternatives.
Objective:
We sought to develop a quantitative personalized tool to predict asthma development in young children.
Methods:
Data from the Cincinnati Childhood Allergy and Air Pollution Study (n = 762) birth cohort were used to identify factors that predicted asthma development. The Pediatric Asthma Risk Score (PARS) was constructed by integrating demographic and clinical data. The sensitivity and specificity of PARS were compared with those of the Asthma Predictive Index (API) and replicated in the Isle of Wight birth cohort.
Results:
PARS reliably predicted asthma development in the Cincinnati Childhood Allergy and Air Pollution Study (sensitivity = 0.68, specificity = 0.77). Although both the PARS and API predicted asthma in high-risk children, the PARS had improved ability to predict asthma in children with mild-to-moderate asthma risk. In addition to parental asthma, eczema, and wheezing apart from colds, variables that predicted asthma in the PARS included early wheezing (odds ratio [OR], 2.88; 95% CI, 1.52-5.37), sensitization to 2 or more food allergens and/or aeroallergens (OR, 2.44; 95% CI, 1.49-4.05), and African American race (OR, 2.04; 95% CI, 1.19-3.47). The PARS was replicated in the Isle of Wight birth cohort (sensitivity = 0.67, specificity = 0.79), demonstrating that it is a robust, valid, and generalizable asthma predictive tool.
Conclusions:
The PARS performed better than the API in children with mild-to-moderate asthma. This is significant because these children are the most common and most difficult to predict and might be the most amenable to prevention strategies.
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