Identification of chronic rhinosinusitis phenotypes using cluster analysis. Issue 5 (17th February 2015) - Soler, Zachary M.; Hyer, J. Madison; Ramakrishnan, Viswanathan; Smith, Timothy L.; Mace, Jess; Rudmik, Luke; Schlosser, Rodney J.
<x xml:space="preserve">Abstract</x> Background Current clinical classifications of chronic rhinosinusitis (CRS) have been largely defined based upon preconceived notions of factors thought to be important, such as polyp or eosinophil status. Unfortunately, these classification systems have little correlation with symptom severity or treatment outcomes. Unsupervised clustering can be used to identify phenotypic subgroups of CRS patients, describe clinical differences in these clusters and define simple algorithms for classification. Methods A multi‐institutional, prospective study of 382 patients with CRS who had failed initial medical therapy completed the Sino‐Nasal Outcome Test (SNOT‐22), Rhinosinusitis Disability Index (RSDI), Medical Outcomes Study Short Form‐12 (SF‐12), Pittsburgh Sleep Quality Index (PSQI), and Patient Health Questionnaire (PHQ‐2). Objective measures of CRS severity included Brief Smell Identification Test (B‐SIT), CT, and endoscopy scoring. All variables were reduced and unsupervised hierarchical clustering was performed. After clusters were defined, variations in medication usage were analyzed. Discriminant analysis was performed to develop a simplified, clinically useful algorithm for clustering. Results Clustering was largely determined by age, severity of patient reported outcome measures, depression, and fibromyalgia. CT and endoscopy varied somewhat among clusters. Traditional clinical measures, including polyp/atopic status, prior surgery, B‐SIT and asthma, did not vary among clusters. A simplified algorithm based upon productivity loss, SNOT‐22 score, and age predicted clustering with 89% accuracy. Medication usage among clusters did vary significantly. Conclusion A simplified algorithm based upon hierarchical clustering is able to classify CRS patients and predict medication usage. Further studies are warranted to determine if such clustering predicts treatment outcomes.