ABSTRACT
INTRODUCTION: While reliable, quantitative in vitro testing for sensitivity to aeroallergens has been available for decades, such information has largely been ignored in clustering analyses of asthma. Our aim is to explore allergic polysensitization as a possible marker of asthma severity and, as such, to be considered as an integral marker in future asthma clustering analyses. METHODS: We constructed a database of sensitizations to the 25 aeroallergens in our geographic area (zone 1, Northeastern US) using the ImmunoCAP® in vitro assay. We used the Scikit-Learn® machine learning library for model-based clustering to identify allergic polysensitization clusters. Clusters were compared for differences in common office-based clinical markers of asthma. RESULTS: The database consisted of 509 patients. Unbiased machine learning identified ten clusters of increasing allergic polysensitization of varying sizes (n = 1-339) characterized by significant increases in mean serum immunoglobulin E (p < 0.001), peripheral blood eosinophil count (p < 0.001), and DLCO (p = 0.02). There was a significant decline in mean age at presentation (p < 0.001), FEV1/FVC (p = 0.01), and FEF25-75 (p = 0.002) with increasing allergic polysensitization. Finally, we identified two divergent paths for the poly-atopic march, one driven by perennial and the other by seasonal allergens. CONCLUSION: This pilot study showed that allergic polysensitization, using readily available qualitative and quantitative in vitro sensitization data, largely ignored in cluster analyses to date, may add further clinical precision in cluster analyses of asthma. We suggest the methods used here can be applied and tested using larger databases and aeroallergens present in diverse geographic regions.
