Objective To explore the relationship between preoperative nutritional status and postoperative incision healing in elderly patients undergoing hip arthroplasty for hip fractures and to construct a predictive model for poor postoperative incision healing by screening influencing factors.

Methods A retrospective analysis was conducted on the clinical data of 148 elderly patients with hip fractures. Based on the assessment results of the Mini-nutritional Assessment Short-Form (MNA-SF), patients were divided into normal nutrition group (n=94) and malnutrition group (n=54). Postoperative incision healing-related indicators were compared between the two groups. According to the postoperative incision healing status, patients were further divided into poor incision healing group (n=41) and good incision healing group (n=107). A Logistic regression model was used to analyze the influencing factors of postoperative incision healing in patients and to construct a comprehensive index. A receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the comprehensive index for postoperative incision healing in elderly patients with hip fractures.

Results The malnutrition group had longer incision drying and healing times than the normal nutrition group, and a higher proportion of patients with incision skin necrosis and poor incision healing, with statistically significant differences (P < 0.05). Univariate analysis showed that body mass index (BMI), intraoperative blood loss, transferrin, preoperative albumin, lymphocyte count, prognostic nutritional index (PNI), and preoperative nutritional status were influencing factors for postoperative incision healing in patients (P < 0.05). Multivariate Logistic regression analysis revealed that high BMI and large intraoperative blood loss were independent risk factors for poor postoperative incision healing in patients (P < 0.05), while high transferrin, high lymphocyte count, high PNI, and normal preoperative nutritional status were independent protective factors (P < 0.05). ROC curve analysis showed that the areas under the curve for predicting poor postoperative incision healing by BMI, intraoperative blood loss, transferrin, lymphocyte count, PNI, preoperative nutritional status, and the comprehensive index were 0.654, 0.670, 0.634, 0.669, 0.678, 0.652 and 0.818, respectively, with the comprehensive index having the highest predictive value. Delong test results indicated that there were statistically significant differences in the predictive efficacy among the predictive models of BMI-comprehensive index, intraoperative blood loss-comprehensive index, transferrin-comprehensive index, lymphocyte count-comprehensive index, PNI-comprehensive index, and preoperative nutritional status-comprehensive index (P < 0.05).

Conclusion Preoperative malnutrition, high BMI, and large intraoperative blood loss are closely associated with poor incision healing in elderly patients with hip fractures, while good nutritional and immune status are important protective factors for incision healing. Constructing a predictive model by integrating multiple influencing factors can more accurately assess the risk of poor postoperative incision healing.