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.