# 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

## TL;DR

This study shows that changes in C-reactive protein levels can predict slow healing in diabetic wounds using a machine learning model.

## Contribution

A novel machine learning model using dynamic C-reactive protein trajectories to predict prolonged healing in diabetic wounds.

## Key findings

- The GradientBoosting model achieved high accuracy (0.9357) in predicting prolonged healing.
- CRP_2nd was identified as the most influential predictor of healing time.
- Higher albumin levels and favorable therapeutic responses were protective factors for healing.

## Abstract

To develop a machine learning (ML) model for predicting prolonged healing (>8 weeks) in diabetic wounds, focusing on dynamic C-reactive protein (CRP) trajectories.

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.

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.

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.