A Multicenter Cross-sectional Study on Tinnitus Subtypes and Risk Factors

Overview

Tinnitus affects an estimated 10-15% of the global population and can substantially impair quality of life, yet clinically actionable approaches for subtype identification and risk stratification remain limited. This multicenter, cross-sectional observational study will use de-identified electronic health record (EHR) data from three otolaryngology specialty hospitals in China to address these gaps. All extracted data will be de-identified with direct identifiers removed, and privacy safeguards will be implemented in accordance with institutional policies and applicable regulations to protect patient confidentiality.

Using a prespecified, clinically informed framework, we will classify tinnitus into relevant subtypes, including somatosensory tinnitus, acute vs. chronic tinnitus, pulsatile tinnitus, and sudden hearing loss-related tinnitus. We will first describe the distribution of these subtypes and characterize their demographic, clinical, and laboratory profiles. We will then evaluate associations between candidate risk factors and subtype membership using multivariable analyses to quantify adjusted effects. Finally, we will develop and validate multivariable prediction models using both conventional statistical approaches and machine learning methods to support tinnitus subtype classification. Model performance will be assessed using discrimination, calibration, and clinical utility metrics. By integrating routine clinical data with biomarker information captured in real-world care, this study aims to provide evidence-based tools to improve tinnitus subtype diagnosis and enable more personalized clinical assessment.

Conditions

Interventions