Time-to-event risk prediction of dual sensory loss in middle-aged patients with symptomatic knee osteoarthritis: model development, preliminary external validation, and explainability analysis.

内容摘要

Knee osteoarthritis (KOA) is a highly prevalent chronic condition that substantially impairs functional capacity and quality of life among middle-aged and older adults. Sensory loss, including hearing and vision loss, is another major health concern in aging populations. Dual sensory loss (DSL), the coexistence of visual and auditory impairment, leads to more severe clinical consequences than single sensory deficits, largely due to disrupted sensory integration and diminished neural compensatory mechanisms. Emerging evidence indicates that osteoarthritis is linked to progressive deterioration of auditory and visual function, highlighting the need for early identification of individuals at risk. Therefore, this study aimed to develop a machine learning-based time-to-event prediction model for DSL among middle-aged patients with symptomatic KOA and to externally validate its performance in an independent hospital-based cohort, then identify its risk factors through interpretable analysis, providing essential evidence to support early preventive interventions. Data from the China Health and Retirement Longitudinal Study (N = 605) were utilized in model development phase. After data preprocessing steps, we trained and tested four time-to-event ML algorithms. Model performance was evaluated in 10-fold cross-validation by using the concordance index (C-index), Brier scores and calibration plots. A sensitivity analysis was conducted by redefining DSL using a broader cutoff and re-training all models under the same cross-validation framework to assess the robustness and stability of the finding. An independent hospital-based cohort (N = 195) was used for preliminary external validation. The optimal model was further evaluated by the decision curve analysis (DCA) to assess its clinical utility and interpreted with SHapley Additive exPlanations (SHAP) to quantify feature contributions and directional effects. 15 variables with the highest predictive capacity were retained. The DeepSurv model demonstrated superior performance in both the construction and validation phase, achieving C-index exceeding 0.8, with a Brier score below 0.1. The sensitivity analyses results were largely consistent with our primary findings, supporting the robustness of the associations. SHAP analysis revealed self-rated health and sleep duration as the most important predictors, both negatively influencing DSL risk. The DeepSurv model effectively predicts time-to-event risk of DSL in KOA patients, highlighting subjective health perception and sleep duration as critical modifiable factors. These findings support the development of targeted early preventive strategies in clinical practice to preserve sensory function and reduce the long-term disease burden associated with KOA.