A review and a framework of variables for defining and characterizing tinnitus subphenotypes

Eleni Genitsaridi*, Derek J. Hoare, Theodore Kypraios, Deborah A. Hall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
3 Downloads (Pure)

Abstract

Tinnitus patients can present with various characteristics, such as those related to the tinnitus perception, symptom severity, and pattern of comorbidities. It is speculated that this phenotypic heterogeneity is associated with differences in the underlying pathophysiology and personal reaction to the condition. However, there is as yet no established protocol for tinnitus profiling or subtyping, hindering progress in treatment development. This review summarizes data on variables that have been used in studies investigating phenotypic differences in subgroups of tinnitus, including variables used to both define and compare subgroups. A PubMed search led to the identification of 64 eligible articles. In most studies, variables for subgrouping were chosen by the researchers (hypothesis-driven approach). Other approaches included application of unsupervised machine-learning techniques for the definition of subgroups (data-driven), and subgroup definition based on the response to a tinnitus treatment (treatment response). A framework of 94 variable concepts was created to summarize variables used across all studies. Frequency statistics for the use of each variable concept are presented, demonstrating those most and least commonly assessed. This review highlights the high dimensionality of tinnitus heterogeneity. The framework of variables can contribute to the design of future studies, helping to decide on tinnitus assessment and subgrouping.

Original languageEnglish
Article number938
JournalBrain Sciences
Volume10
Issue number12
DOIs
Publication statusPublished - 4 Dec 2020

Keywords

  • Classification
  • Cluster analysis
  • Heterogeneity
  • Latent class analysis
  • Phenotype
  • Subtyping
  • Unsupervised machine learning

ASJC Scopus subject areas

  • Neuroscience(all)

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