Tinnitus Subtyping and Personalized Treatment via Audiometric and Psychometric Clustering

Abstract

Purpose: We aimed to identify distinct tinnitus subtypes and determine optimal treatment approaches based on comprehensive audiometric and psychometric assessments. Materials and Methods: Cluster analysis was performed on data from 311 tinnitus patients. Assessments included pure tone averDepression Inventory (BDI), and Beck Anxiety Inventory. K-means clustering identified three distinct tinnitus subtypes. Multiple regression analyses were conducted to identify predictors of treatment response. Results: Three distinct subtypes were identified: Cluster 1 (41.8%, n=130): severe tinnitus with normal hearing (THI=61.2 +/- 20.6, (37.3%, n=116): mild psychosomatic tinnitus (THI=30.1 +/- 18.5, PTA_R=13.8 +/- 9.1 dB). Treatment efficacy varied significantly by cluster (p<0.01), with diuretics most effective for Cluster 1 (36.2 +/- 38.8% improvement), baclofen for Cluster 2 (41.4 +/- 39.7%), and Indenol for Cluster 3 (39.5 +/- 35.8%). Hearing aids benefited Cluster 2 (26.0 +/- 35.7%) but were detrimental in Clusters 1 and 3. Initial THI score predicted improvement in Cluster 1 ((3=1.45, p<0.001) and approached significance in Cluster 2 ((3=1.67, p=0.081), while BDI was significant in Cluster 3 ((3=-7.76, p=0.030). Conclusion: We confirmed tinnitus heterogeneity with three distinct subtypes showing differential treatment responses. Audiometric testing provides objective criteria for patient classification and treatment selection. A precision medicine approach with cluster-specific strategies may improve tinnitus management.