Supplementary file 1_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.docx

ObjectiveTo develop a machine learning (ML) model for predicting prolonged healing (>8 weeks) in diabetic wounds, focusing on dynamic C-reactive protein (CRP) trajectories.
MethodsThis was a retrospective single-center cohort study. We included 465 patients with type 2 diabetes, standardized wound sizes (5–8 cm2), and debridement alone (2021–2024: training set, n = 325; 2025: temporal validation set, n = 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.
ResultsThe 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.
ConclusionDynamic CRP trajectories, particularly CRP_2nd, are critical for predicting prolonged diabetic wound healing. The GradientBoosting model provides a clinically actionable tool for risk stratification.