Table 1_Analysis of risk factors and predictive value of a nomogram for peripheral arterial disease in patients with type 2 diabetes.docx

BackgroundPeripheral arterial disease (PAD) is a common macrovascular complication of type 2 diabetes mellitus (T2DM) that contributes to lower-limb morbidity and increased cardiovascular mortality. Early risk stratification is essential to guide screening and preventive measures; however, no comprehensive tool currently integrates demographic, clinical and hematologic factors to predict PAD in T2DM.
MethodsIn this retrospective cohort study, 426 adults with T2DM treated between January 2020 and December 2024 were stratified by PAD status (PAD, n = 136; non-PAD, n = 290). Risk factors were identified by multivariable logistic regression. A nomogram was constructed using the rms package in R and internally validated via bootstrap resampling (n = 1 000). Discrimination was assessed by area under the receiver operating characteristic curve (AUC) and concordance index (C-index), and calibration by Hosmer–Lemeshow goodness-of-fit and calibration plots.
ResultsEleven independent predictors were incorporated: age; smoking; alcohol use; diabetes duration; systolic blood pressure; high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); antihypertensive use; white blood cell count; platelet distribution width (PDW); and large platelet ratio (LPR). The nomogram achieved an AUC of 0.826 (95% CI 0.768–0.895), sensitivity of 78.6% and specificity of 89.6%. Internal validation yielded a bias-corrected C-index of 0.795 (95% CI 0.756–0.893), and Hosmer–Lemeshow P = 0.913, indicating good calibration.
ConclusionsThe proposed nomogram demonstrates robust discrimination and calibration for individualized PAD risk prediction in T2DM, supporting its potential to optimize targeted screening and preventive strategies pending external validation.