Data Sheet 1_Development and validation of a risk prediction model for painful diabetic peripheral neuropathy in type 2 diabetes mellitus: a multicenter retrospective study.pdf

ObjectiveTo construct and validate a clinical model to predict painful diabetic peripheral neuropathy (PDPN) risk in type 2 diabetes mellitus (T2DM) patients for early identification and intervention in primary care.
MethodsA total of 1,984 patients with T2DM were included in the analysis. After data preprocessing and application of the Synthetic Minority Oversampling Technique (SMOTE) with a 200% oversampling ratio, feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. Six predictive models: multivariable logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and support vector machine (SVM)—were developed and tuned using repeated 5-fold cross-validation. Model performance was evaluated on the independent test cohort using comprehensive discrimination and calibration metrics. To enhance clinical interpretability, a nomogram and SHapley Additive exPlanations (SHAP) analysis were implemented to visualize predictor contributions.
ResultsTen variables were selected as predictors. Among 1,984 patients, 81 (4.08%) had PDPN. LR model demonstrated the most favorable trade-off for screening purposes, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.894 (95% CI: 0.814–0.964), area under the precision–recall curve (PR-AUC) of 0.470 (95% CI: 0.258–0.665), and balanced accuracy of 0.826 (95% CI: 0.667–0.932). SHAP analysis identified musculoskeletal disorders and HbA1c as the most influential predictors. A user-friendly dynamic web-based nomogram was constructed to support clinical implementation.
ConclusionWe established and validated a clinically interpretable model for PDPN risk prediction in patients with T2DM. The dynamic nomogram enables individualized risk estimation and may assist timely intervention in routine practice.