JMIR Publications

ABSTRACT

Background:

Age-related hearing loss (ARHL) is associated with severe negative outcomes, including social isolation, depression, and cognitive decline. Despite this, routine ARHL screening is often neglected in primary care due to low awareness, resource limitations, and inefficiencies. A practical risk assessment tool could effectively address this gap.

Objective:

To develop and validate a user-friendly nomogram for identifying older adults at high risk of ARHL in community settings, thereby facilitating targeted screening and timely interventions.

Methods:

This multicenter retrospective cohort study included 34,983 older adults from three primary healthcare centers in Beijing (January 2020 – October 2023). Data from Center A (N=18,707) were used for model development, with external validation performed on cohorts from Center B (N=11,008) and Center C (N=5,268). LASSO and logistic regression identified the final six predictors. Model performance was evaluated via discrimination, calibration, and decision-curve analysis, leading to the development of a web-based nomogram

Results:

In the training cohort, 1,177 participants (6.3%) reported hearing difficulties. Six key predictors were identified: age, education, exercise frequency, physical function, dietary habits, and hypertension. The multivariate logistic regression model demonstrated strong performance in internal validation (AUC = 0.806; sensitivity = 0.774; specificity = 0.820). External validation confirmed its generalizability (AUCs = 0.720 and 0.747). Decision-curve analysis highlighted significant clinical net benefit. A user-friendly online prediction webpage was also developed.

Conclusions:

This study successfully developed and validated a dynamic, online nomogram for predicting ARHL in older adults using a large, multicenter dataset. Comprising six readily available predictors, the model exhibits superior accuracy and robust validation, offering a convenient, practical, and web-based tool for proactive risk identification and targeted interventions in primary care.