Linear Regression Model for Predicting Dressing-Change Pain in Diabetic Foot Ulcers: Development, Internal Validation, and Personalized Analgesic Care.

This study included 232 patients with diabetic foot ulcers (DFUs) to construct and validate a linear regression model to predict pain during dressing changes and to evaluate the effectiveness of a model-based personalized analgesic care regimen. In stage 1,160 patients were divided into a training set (n = 113) and an internal validation set (n = 47); multivariable linear regression identified ulcer area, ulcer duration, duration of diabetes, and the Self-Rating Anxiety Scale (SAS) score as predictors of continuous dressing-change VAS. The model yielded an adjusted R2 of 0.458 and an RMSE of 1.719 in the training set, while the validation-set RMSE, calibration intercept, calibration slope, and AUC for identifying high pain were 2.058, 1.414, 0.721, and 0.913, respectively. In stage 2 (72 cases), subjects were assigned to a control group and an intervention group. The intervention group received a personalized analgesic regimen based on model-derived risk stratification, while the control group received conventional analgesic care. After 4 weeks, the intervention group had a lower dressing-change VAS score than the control group (3.83 ± 1.61 vs 4.94 ± 1.91, P = 0.010), together with a higher overall adherence rate (86.11% vs 63.89%, P = 0.029), a higher ulcer area reduction rate (41.84% ± 11.01% vs 26.22% ± 7.55%, P < 0.001), and a shorter wound-healing time (32.0 [29.0-35.0] vs 38.0 [34.0-43.5] days, P < 0.001). The model showed useful discrimination for dressing-change pain, and the model-based personalized analgesic regimen improved short-term pain, adherence, and wound-related outcomes; however, external validation and further refinement of model calibration are required before routine clinical implementation.