Tinnitus risk factors and its evolution over time: a cohort study
Hobeika, L.; Fillingim, M.; Tanguay-Sabourin, C.; Roy, M.; Londero, A.; Samson, S.; Vachon-Presseau, E.
Show abstract
BackgroundSubjective tinnitus is an auditory percept unrelated to an external sound source. The lack of curative treatments and limited understanding of its risk factors complicate the prevention and management of this distressing symptom. This study seeks to identify socio-demographic, psychological, and health-related risk factors predicting tinnitus presence (how often individuals perceive tinnitus) and severity separately, and their evolution over time. MethodsUsing the UK Biobank dataset which encompasses data on the socio-demographic, physical, mental and hearing health from more than 170,000 participants, we trained two distinct machine learning models to identify risk scores predicting tinnitus presence and severity separately. These models were used to predict tinnitus over time and were replicated in 463 individuals from the Tinnitus Research Initiative database. FindingMachine learning based approach identified hearing health as a primary risk factor for the presence and severity of tinnitus, while mood, neuroticism, hearing health, and sleep only predicted tinnitus severity. Only the severity model accurately predicted the evolution over nine years, with a large effect size for individuals developing severe tinnitus (Cohens d = 1.10, AUC-ROC = 0.70). To facilitate its clinical applications, we simplified the severity model and validated a five-item questionnaire to detect individuals at risk of developing severe tinnitus. InterpretationThis study is the first to clearly identify risk factors predicting tinnitus presence and severity separately. Hearing health emerges as a major predictor of tinnitus presence, while mental health plays a crucial role in its severity. The successful prediction of the evolution of tinnitus severity over nine years based on socio-emotional, hearing and sleep factors suggests that modifying these factors could mitigate the impact of tinnitus. The newly developed questionnaire represents a significant advancement in identifying individuals at risk of severe tinnitus, for which early supportive care would be crucial. FundingHorizon Europe Marie Slodowska-Curie Actions, the Fondation des gueules cassees, the Fondation pour lAudition, the Louise and Alan Edwards Foundation, the Canadian Institutes Health Research, the Institut TransMedTech and the Canada First Research Excellence Fund.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Digital thErapy For Improved tiNnitus carE Study (DEFINE): Protocol for a Randomised Controlled Trial 93%
- Disentangling listening effort and memory load beyond behavioural evidence 89%
- Hearing and cognitive decline in aging differentially impact neural tracking of context-supported versus random speech across linguistic timescales 89%
Similar papers in this journal
- Altered Neural Processing in Middle Frontal Gyrus and Cerebellum During Temporal Recalibration of Action-Outcome Predictions in Schizophrenia Spectrum Disorders 88%
- Early visual processing as a marker of disease, not vulnerability: Event-related potential (ERP) evidence from 22q11.2 deletion syndrome, a population at high risk for schizophrenia 86%
- Who Does What to Whom? Graph Representations of Action-Predication in Speech Relate to Psychopathological Dimensions of Psychosis 85%
Similar papers in this journal
Similar papers in this journal
- Effectiveness of bimodal neuromodulation for tinnitus treatment in a real-world clinical setting in United States: A retrospective chart review 95%
- Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms 85%
- The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy 84%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.