An explainable machine learning-based prediction model for sarcopenia in elderly Chinese people with knee osteoarthritis.

BACKGROUND

Sarcopenia is an age-related progressive skeletal muscle disease that leads to loss of muscle mass and function, resulting in adverse health outcomes such as falls, functional decline, and death. Knee osteoarthritis (KOA) is a common chronic degenerative joint disease among elderly individuals who causes joint pain and functional impairment. These two conditions often coexist in elderly individuals and are closely related. Early identification of the risk of sarcopenia in KOA patients is crucial for developing intervention strategies and improving patient health.

METHODS

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), selecting symptomatic KOA patients aged 65 years and above and analyzing a total of 95 variables. Predictive factors were screened via least absolute shrinkage and selection operator (LASSO) regression and logistic regression. Eight machine learning algorithms were employed to construct predictive models, with internal cross-validation and independent test validation performed. The final selected model was analyzed via the SHapley Additive exPlanations (SHAP) method to enhance interpretability and clinical applicability. To facilitate clinical use, we developed a web application based on this model ( http://106.54.231.169/ ).

RESULTS

The results indicate that six predictive factors-body mass index, upper arm length, marital status, total cholesterol, cystatin C, and shoulder pain-are closely associated with the risk of sarcopenia in KOA patients. CatBoost demonstrated excellent overall performance in both calibration analyses and probability estimates, reflecting accurate and dependable predictions. The final results on the independent test set (accuracy = 0.8902; F1 = 0.8627; AUC = 0.9697; Brier score = 0.0691) indicate that the model possesses strong predictive performance and excellent generalization ability, with predicted probabilities closely aligning with actual occurrence rates and thereby underscoring its reliability.

CONCLUSION

From the perspective of public health and aging, this study constructed an interpretable sarcopenia risk prediction model on the basis of routine clinical data. This model can be used for early screening and risk assessment of symptomatic KOA patients, assisting health departments and clinicians in the early detection and follow-up of relevant populations, thereby improving the quality of life and health outcomes of elderly individuals.