Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE

To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify the contribution of predictive factors using SHapley Additive exPlanations (SHAP) values.

METHODS

This retrospective study of 500 type 2 diabetes patients diagnosed DPNP via the Michigan Neuropathy Screening Instrument and clinical evaluation. Predictors were selected using univariate analysis and LASSO regression, with independent risk factors identified by multivariate logistic regression. Nonlinear relationships were assessed using restricted cubic spline (RCS). The nomogram was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, and decision curve analysis (DCA). SHAP quantified factor importance.

RESULTS

Seven independent risk factors were identified: age, diabetes duration, BMI, smoking history, fasting blood glucose, hyperlipidemia, and AST-highlighting metabolic parameters, especially AST, as key novel contributors. RCS revealed a nonlinear relationship for diabetes duration. The nomogram exhibited strong discrimination (AUCs: 0.863 training, 0.813 validation), good calibration, and strong clinical utility. SHAP confirmed diabetes duration as the most influential predictor.

CONCLUSIONS

This nomogram provides an interpretable tool for early DPNP risk prediction. By quantifying individual risk, it enables clinicians to identify high-risk patients and implement personalized preventive strategies, potentially improving outcomes.