Dynamic C-reactive protein trajectories predict prolonged healing time in diabetic wounds: a machine learning model based on a single-center cohort with standardized wound size

Objective: To develop a machine learning (ML) model for predicting prolonged healing (>8 weeks) in diabetic wounds, focusing on dynamic C-reactive protein (CRP) trajectories. Methods: = 140). Serial CRP was measured at admission (CRP), post-antibiotic preoperatively (CRP_2nd), and postoperatively at discharge (CRP_3rd). Therapeutic response variables (therapeutic_response_1/2/all) were calculated as percentage changes in serial CRP levels across treatment phases, reflecting anti-inflammatory/antimicrobial efficacy. LASSO regression selected features, 12 ML models were constructed, and performance was evaluated via AUC, sensitivity, and specificity. SHAP analysis interpreted predictions. Results: The GradientBoosting model exhibited superior performance (validation set: accuracy = 0.9357, sensitivity = 0.8689, specificity = 0.9873). LASSO regression identified 15 key variables [including CRP_2nd, CRP_3rd, albumin (ALB)]. SHAP analysis revealed CRP_2nd as the most influential predictor (mean absolute SHAP value = 0.460), with elevated CRP_2nd/CRP_3rd associated with prolonged healing and higher ALB/favorable therapeutic responses as protective factors. Conclusion: Dynamic CRP trajectories, particularly CRP_2nd, are critical for predicting prolonged diabetic wound healing. The GradientBoosting model provides a clinically actionable tool for risk stratification.