Tinnitus Subtyping and Personalized Treatment via Audiometric and Psychometric Clustering.

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 average (PTA), uncomfortable loudness levels, Tinnitus Handicap Inventory (THI), visual analog scales for subjective symptoms, Beck Depression 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, PTA_R=14.7±8.1 dB); Cluster 2 (20.9%, n=65): hearing loss-dominant tinnitus (THI=53.6±22.4, PTA_R=35.4±15.9 dB); Cluster 3 (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 (β=1.45, p<0.001) and approached significance in Cluster 2 (β=1.67, p=0.081), while BDI was significant in Cluster 3 (β=-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.