Related Experiment Video
Updated: May 6, 2026
Protocol to Create Chronic Wounds in Diabetic Mice
Published on: September 25, 2019
Dynamic C-reactive protein trajectories predict prolonged healing time in diabetic wounds: a machine learning model
Sichao Jiang1, Qixuan Song1, Junhuan Wang2
1Department of Orthopaedic Surgery, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
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:
This 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.
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.
