/Objectives : The aim of this study was to apply data-driven clustering techniques for the subtyping of tinnitus severity to a retrospective cross-sectional cohort of 564 subjects. Additionally, the response of the resulting phenotypes to an enriched acoustic environment (EAE) treatment was investigated....
✦ The floor
Discussion
Signed responses from readers of the wire.
Tinnitus subtyping via data-driven phenotyping may predict response to Enriched Acoustic Environment therapy, suggesting patient selection criteria should be considered before recommending EAE; this warrants prospective validation before practice change.
Identifying which tinnitus patients benefit most from sound-based therapies could shift the field toward precision tinnitus management and improve treatment efficiency.
- 01564 tinnitus patients were clustered into distinct severity phenotypes using data-driven methods.
- 02Response to Enriched Acoustic Environment (EAE) treatment varied significantly across phenotypes.
- 03Data-driven clustering enables more personalised treatment matching than severity alone.
- 04Study is one of the larger tinnitus phenotyping efforts using an objective treatment endpoint.
- 05EAE is a passive, low-cost sound therapy that enriches the ambient sound environment.
Tinnitus patients can be meaningfully clustered into distinct phenotypes based on severity profiles.
studysupportedDifferent tinnitus phenotypes show different response rates to Enriched Acoustic Environment treatment.
studypartially supported- PMID
- 42651203
- DOI
- 10.3390/brainsci16080894.
- Journal
- Brain Sciences
- Publication type
- research_article
- Evidence level
- 2b
- Sample size
- 564
- Population
- Adults with tinnitus (n=564)
- Intervention
- Enriched Acoustic Environment (EAE) treatment
Primary outcomes
Tinnitus phenotype cluster assignment; Treatment response to EAE by phenotype