Identification of chronic rhinosinusitis phenotypes using cluster analysis

Abstract

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.

Keywords: cluster analysis; phenotype; quality of life; sinusitis; staging.

Figure 1

Dendrogram for development of 5 clusters

Figure 2

Based on the discriminant analysis, the above algorithm can be used to classify patients into the five statistical clusters using simple clinical measures. The likelihood of correct classification using this algorithm is shown in Figure 3 and Table 4. SNOT-22=22-item Sinonasal Outcome Test. Yr=years of age. Productivity loss equals the number of work days missed in last 90 days.

Figure 3

Based on the clinical algorithm developed using discriminant analysis, 89.4% of all individuals are categorized into the appropriate cluster, with individual clusters ranging from 80–96% correct classification. The size of each figure is proportional to the frequency/size of each specific cluster and overlap into other clusters signifies the percentage of misclassification.