BACKGROUND: The pathophysiology of chronic rhinosinusitis (CRS) is not fully understood. In Europe and the United States, major subsets of CRS classification are based on the presence or absence of polyps. Although nasal polyps (NPs) are a critical factor, many other factors also contribute to the pathogenesis of CRS. The aim of this study was to investigate diverse CRS phenotypes using cluster analysis. METHODS: This was a multicenter study examining clinical data from CRS patients treated at five hospitals. The study design was a retrospective analysis of prospectively collected data. Complete data were available for 425/496 patients. Data were subjected to k-means cluster analysis in an attempt to identify the different phenotypes involved in CRS. RESULTS: CRS was divided into four clusters. Cluster 1 (n = 180) and cluster 2 (n = 129) comprised patients with low peripheral eosinophil and mucosal eosinophil counts. However, polyp scores in cluster 2 were higher than cluster 1. Cluster 3 (n = 50) comprised patients with very high mucosal eosinophil counts but low polyp and symptom scores. Finally, subjects in cluster 4 (n = 66) showed severe polyposis. Polyp score and mucosal eosinophil count were the strongest predictors of clustering by discriminant analysis. CONCLUSION: The results of this study identified distinct clinical CRS phenotypes. CRS was classified into four phenotypes based on NPs and mucosal eosinophil counts. Cutoff points for these factors were identified by tree analysis. Additional studies are needed to establish clinical significance of the phenotypes.
BACKGROUND: The pathophysiology of chronic rhinosinusitis (CRS) is not fully understood. In Europe and the United States, major subsets of CRS classification are based on the presence or absence of polyps. Although nasal polyps (NPs) are a critical factor, many other factors also contribute to the pathogenesis of CRS. The aim of this study was to investigate diverse CRS phenotypes using cluster analysis. METHODS: This was a multicenter study examining clinical data from CRSpatients treated at five hospitals. The study design was a retrospective analysis of prospectively collected data. Complete data were available for 425/496 patients. Data were subjected to k-means cluster analysis in an attempt to identify the different phenotypes involved in CRS. RESULTS:CRS was divided into four clusters. Cluster 1 (n = 180) and cluster 2 (n = 129) comprised patients with low peripheral eosinophil and mucosal eosinophil counts. However, polyp scores in cluster 2 were higher than cluster 1. Cluster 3 (n = 50) comprised patients with very high mucosal eosinophil counts but low polyp and symptom scores. Finally, subjects in cluster 4 (n = 66) showed severe polyposis. Polyp score and mucosal eosinophil count were the strongest predictors of clustering by discriminant analysis. CONCLUSION: The results of this study identified distinct clinical CRS phenotypes. CRS was classified into four phenotypes based on NPs and mucosal eosinophil counts. Cutoff points for these factors were identified by tree analysis. Additional studies are needed to establish clinical significance of the phenotypes.
Authors: Zachary M Soler; J Madison Hyer; Viswanathan Ramakrishnan; Timothy L Smith; Jess Mace; Luke Rudmik; Rodney J Schlosser Journal: Int Forum Allergy Rhinol Date: 2015-02-17 Impact factor: 3.858
Authors: Xin Zhang; Ke Deng; Yulai Yuan; Lei Liu; Shuwen Zhang; Changyong Wang; Gang Wang; Hongping Zhang; Lei Wang; Gaiping Cheng; Lisa G Wood; Gang Wang Journal: Nutrients Date: 2022-06-17 Impact factor: 6.706
Authors: B Liao; J-X Liu; Z-Y Li; Z Zhen; P-P Cao; Y Yao; X-B Long; H Wang; Y Wang; R Schleimer; Z Liu Journal: Allergy Date: 2018-01-26 Impact factor: 13.146