Published online: December 13, 2018
Asthma affects 25.7 million people in the US including 7.0 million children. Reliable identification of children who are likely to go on to develop asthma in early life is critical since this is when intervention strategies aimed at prevention are most effective. The Asthma Predictive Index (API) developed in 2000 by Castro-Rodriguez et al. is the most widely used and most validated asthma prediction tool. While the API 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 a recently published article in The Journal of Allergy and Clinical Immunology (JACI), Biagini Myers et al. developed a new, personalized and predictive asthma risk algorithm that integrates clinical and demographic factors called the Pediatric Asthma Risk Score (PARS). Utilizing data from the well-established Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) birth cohort, the authors evaluated factors known to be associated with asthma and created a scoring system that returns a continuous and personalized asthma risk score, unlike the API which gives a simple yes/no. By answering just 6 simple questions (regarding parental asthma, eczema, early wheezing, wheeze apart from colds, race and sensitization to two or more allergens), parents and clinicians can calculate a child’s personalized PARS score and determine their risk of developing asthma. The PARS scores range from 1-14 and correspond to an asthma risk (by 7 years of age) ranging from 3% to 79%.
When compared directly to the API, the PARS was superior with an 11% increase in ability to accurately detect children who will go on to develop asthma. The authors replicated the PARS in the Isle of Wight birth cohort, a population recruited 10 years prior to CCAAPS on a different continent with a different demographic, demonstrating the robustness of the PARS. While the PARS and the API comparably detect children at high risk for developing asthma, 43% of asthmatics with a PARS asthma risk <40% were missed completely by the API. These children are arguably the most likely to respond to prevention strategies.
PARS either out performs or is less invasive than all 30 published models predicting asthma, making it the most accurate asthma predictive tool to-date that is 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.
The Journal of Allergy and Clinical Immunology (JACI) is the official scientific journal of the AAAAI, and is the most-cited journal in the field of allergy and clinical immunology.