Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach
Authors' conclusion
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.
Study context
- Population
- T2D patients with diabetic peripheral neuropathic pain (DPNP) diagnosed via the Michigan Neuropathy Screening Instrument and clinical evaluation
- Participants
- 500 patients
- Comparison
- Training vs Validation
- Outcome
- Independent risk factors for DPNP and nomogram prediction performance
Methodological detailsnomogram, LASSO regression, multivariate logistic regression, SHAP
- Nomogram
- LASSO regression
- Multivariate logistic regression
- SHAP
Performance
As reported in the abstract
Nomogram discrimination (AUC)
| Test or model | AUC |
|---|---|
| Training | 0.863 |
| Validation | 0.813 |
AUC
Training0.863
Validation0.813
- Independent risk factors: age, diabetes duration, BMI, smoking history, fasting blood glucose, hyperlipidemia, AST
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Further findings
Nonlinearity and predictor importance
- Restricted cubic spline (RCS): nonlinear relationship for diabetes duration
- SHAP: diabetes duration the most influential predictor
Original publication
Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach. Diabetes Res Clin Pract. Published 22 March 2026. doi:10.
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