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February 24, 2023
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Pediatric Asthma Risk Score predicts early asthma across diverse populations

Fact checked byKristen Dowd
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SAN ANTONIO — Researchers validated the Pediatric Asthma Risk Score, a tool that predicts early-life asthma, across several diverse cohorts of children aged 5 to 10 years, according to study results.

These data, presented at the American Academy of Allergy, Asthma & Immunology Annual Meeting, suggest the tool may become the new gold standard in predicting asthma risk among children, according to the researchers.

Child using an asthma inhaler
Researchers validated the Pediatric Asthma Risk Score, a tool that predicts early-life asthma, across several diverse cohorts of children aged 5 to 10 years. Image: Adobe Stock
Jocelyn M. Biagini

“Although the Pediatric Asthma Risk Score (PARS) tool has already been utilized in over 160 countries by more than 31,000 clinicians, by researchers and parents worldwide, these findings further substantiate its clinical utility,” Jocelyn M. Biagini, PhD, associate professor in the division of asthma research at Cincinnati Children’s Hospital Medical Center, told Healio. “Everyday clinicians can utilize PARS to provide parents with a personalized, accurate, quantitative and easy-to-understand asthma risk for their child.”

In a multi-cohort meta-analysis, Biagini and colleagues assessed 5,674 children aged 5 to 10 years from the Children’s Respiratory and Environmental Workgroup to validate PARS — which was developed in 2018 by Biagini and Gurjit Khurana Hershey, MD, PhD, FAAAAI, director of the division of asthma research at Cincinnati Children’s Hospital Medical Center — by comparing it with the Asthma Predictive Index (API).

These children were included in 10 diverse cohorts with different race, ethnicity, sex, cohort type, missing data and birth decades in order to compare each tools’ predictive abilities across different populations reflective of the United States.

In nine of the 10 cohorts, the area under the curve (AUC) for PARS was significantly greater compared with that of API (P range = .01 to < .001).

Further, researchers observed no differences in the PARS AUC based on the cohort type, including for high-risk or average-risk children.

The results of this analysis also showed comparable weights of the six factors used in PARS as those demonstrated in its original analyses.

“These results support that PARS performs with excellent predictive ability and robust performance in populations with substantial heterogeneity,” Biagini told Healio.

For more information:

Jocelyn M. Biagini, PhD, can be reached at jocelyn.biagini@cchmc.org.

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