For clinicians and researchers, the ability to predict which children will develop asthma can be a challenging task. The most widely used current asthma prediction tool, the Asthma Predictive Index (API), uses a combination of major and minor criteria to give a yes or no answer as to whether a child will develop asthma. While this tool is excellent at predicting which children will not develop asthma, it has a very low predictability for which children will go on to develop asthma. In order to determine a child’s distinct asthma risk based on their own unique risk factors, a more personalized prediction tool is needed. Utilizing data from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) birth cohort, Dr. Biagini Myers and Dr. Khurana Hershey developed a new, personalized and predictive asthma risk tool called the Pediatric Asthma Risk Score (PARS):
- PARS is calculated by answering just six questions about parental asthma, eczema, early wheezing, wheezing apart from cold, race and sensitization.
- PARS scores range from 1-14 and correspond to an asthma risk (by 7 years of age) ranging from 3% to 79%.
- PARS is superior to the API with an 11% increase in the ability to detect children that will develop asthma, specifically children with mild-to-moderate asthma risk. These are arguably the children most likely to respond to prevention strategies.
- PARS was replicated in the Isle of Wight birth cohort, a population recruited 10 years prior to CCAAPS on a different continent, demonstrating its robustness.
PARS is the most accurate asthma predictive tool to-date applicable in an office setting. PARS is available as a web application at: https://pars.research.cchmc.org and is also available for download in the Apple App Store and on Google Play.
Asthma Research team members showcasing the new PARS mobile application. Pictured left to right:
Row 1: J. Biagini Myers, L. Martin, T. Gonzalez, A. Baatyrbek kyzy, X. Ren, D. Spagna, A. Kothari, L. Murrison, G. Khurana Hershey
Row 2: A. Herr, J. Kroner, G. Hill, T. Mersha, M. Sherenian