Analysis of Factors Affecting the Healing Time of Chronic Refractory Wounds and Construction of a Prediction Model.

Abstract

This study aimed to comprehensively analyze the factors affecting the healing time of chronic refractory wounds, then establish a clinical prediction model and verify its performance. A retrospective analysis was conducted on the clinical data of 166 patients with chronic refractory wounds who met the inclusion criteria at a tertiary hospital in Changsha (from October 2021 to December 2023). The wound healing time was defined as the days of hospital stay until meeting the discharge standard. The collected information includes: diabetes status, average daily hospital expenses, wound type, admission route, availability of medical insurance, age, gender, education level, average daily dressing changes during hospitalization, smoking status, blood platelet level at admission, albumin level at admission, hemoglobin level at admission, creatinine level at admission, and prothrombin time. Then, univariate and multivariate logistic regression analyses were conducted to explore the risk factors affecting the healing time. Subsequently, a risk prediction model was constructed in the form of nomogram based on the risk factors identified, and the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were employed to evaluate the prediction performance and calibration of the model. The results of multivariate logistic regression analysis indicate that the factors affecting the healing time of chronic refractory wounds include male gender (OR: 2.86, 95% CI: 1.03-7.93, P < .05), diabetes history (OR: 4.05, 95% CI: 1.11-14.85, P < .05), reduced average daily dressing changes during hospitalization (OR: 0.54, 95% CI: 0.31-0.96, P < .05), elevated blood platelet level (OR: 1.01, 95% CI: 1.00-1.01, P < .05), lowered albumin level (OR: 0.87, 95% CI: 0.78-0.97, P < .05), lowered hemoglobin level (OR: 0.97, 95% CI: 0.95-1.00, P < .05), and lowered creatinine level (OR: 0.99, 95% CI: 0.99-1.00, P < .05). The ROC curve shows that the area under the curve (AUC) of the model is 0.761, indicating good prediction. The DCA curve suggests good clinical applicability.