Inadequate soft tissue coverage and bone loss/comminution are the typical risk factors of surgical site infection in open fractures of the hand: A nomogram prediction model

Background: Different from other parts of long bone fractures, surgical site infections (SSI) often occurs in open fractures of the hand (OFH) due to the anatomical characteristics and injury mechanisms. Our aim of the study is to investigate the particular risk factors of SSI after emergency surgery in OFH and develop a prediction nomogram model. Methods: In our traumatic center, patients with OFH not less than 18 years old were retrieved between October 2020 and April 2024. We excluded patients with other fractures, non-traumatic fractures or surgery before admission. The data of these patients were processed by univariate and multivariate analysis using SPSS (24.0) in order to identify the independent risk factors for SSI. Based on the predictors, the nomogram was constructed and validated by R software (R 4.1.0). Results: The incidence of SSI was 6.96% (43/618). Body mass index (BMI), albumin (ALB), neutrophils (NEU), inadequate soft tissue coverage, and bone loss/comminution were identified as the independent risk factors of post-operative SSI in OFH and enrolled in the prediction nomogram model. The nomogram exhibited a high level of discrimination, with an area under the curve of 0.856 (95%CI 0.790–0.921) in the training group and 0.931 (95%CI 0.848–1.000) in the test group. Hosmer-Lemeshow (H-L) test revealed optimal consistency between the probability of prediction model and the actual probability (training group: X2 = 5.706, P = 0.680; test group: X2 = 3.886, P = 0.867). The calibration curve of both groups demonstrated excellent consistency. Decision curve analysis (DCA) showed favorable applicability of the prediction model. Conclusions: Inadequate soft tissue coverage, serum ALB level, NEU level, bone loss/comminution and BMI were the independent risk factors for post-operative SSI in OFH. The nomogram of this predictors can be used as an effective tool to predict SSI risk in OFH.