We attempted to explore the association between metformin use and hearing loss in in a large-scale study. This retrospective multicenter cohort study assessed the data of patients with type 2 diabetes mellitus (DM) aged over 40 years using the Observational Health Data Science and Informatics open-source software and the Common Data Model database from 1 January 2002 to 31 December 2019. Each participant was selected using the ICD-10-CM diagnosis code E11 for type 2 DM with sensorineural hearing loss. The participants were divided into metformin and non-metformin users. The outcome measure was the first occurrence of hearing loss after the diagnosis of DM as measured by the CDM cohort study. A total of 80,596 patients, including 46,152 metformin users and 34,444 non-metformin users from three hospitals were assessed. After calibration, we compared the risk of hearing loss using Kaplan–Meier curves, and found significant differences between the groups. The calibrated hazard ratio in the three hospitals (0.79 [95% confidence interval, 0.57–1.12]) was summarized. These findings suggest that the probability of hearing loss-free survival in the metformin user group is higher than that in the non-metformin user group.

Background: This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. Methods: We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. Results: The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Conclusions: Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.