Analysis of risk factors related to chronic non‐healing wound infection and the construction of a clinical prediction model

Abstract This study is aimed to analyse the risk factors associated with chronic non‐healing wound infections, establish a clinical prediction model, and validate its performance. Clinical data were retrospectively collected from 260 patients with chronic non‐healing wounds treated in the plastic surgery ward of Shanxi Provincial People's Hospital between January 2022 and December 2023 who met the inclusion criteria. Risk factors were analysed, and a clinical prediction model was constructed using both single and multifactor logistic regression analyses to determine the factors associated with chronic non‐healing wound infections. The model's discrimination and calibration were assessed via the concordance index (C‐index), receiver operating characteristic (ROC) curve and calibration curve. Multivariate logistic regression analysis identified several independent risk factors for chronic non‐healing wound infection: long‐term smoking (odds ratio [OR]: 4.122, 95% CI: 3.412–5.312, p < 0.05), history of diabetes (OR: 3.213, 95% CI: 2.867–4.521, p < 0.05), elevated C‐reactive protein (OR: 2.981, 95% CI: 2.312–3.579, p < 0.05), elevated procalcitonin (OR: 2.253, 95% CI: 1.893–3.412, p < 0.05) and reduced albumin (OR: 1.892, 95% CI: 1.322–3.112, p < 0.05). The clinical prediction model's C‐index was 0.762, with the corrected C‐index from internal validation using the bootstrap method being 0.747. The ROC curve indicated an area under the curve (AUC) of 0.762 (95% CI: 0.702–0.822). Both the AUC and C‐indexes ranged between 0.7 and 0.9, suggesting moderate‐to‐good predictive accuracy. The calibration chart demonstrated a good fit between the model's calibration curve and the ideal curve. Long‐term smoking, diabetes, elevated C‐reactive protein, elevated procalcitonin and reduced albumin are confirmed as independent risk factors for bacterial infection in patients with chronic non‐healing wounds. The clinical prediction model based on these factors shows robust performance and substantial predictive value.