Predictors for surgical site infection after fasciotomy in patients with acute leg compartment syndrome

医学 筋膜切开术 骨科手术 单变量分析 逻辑回归 接收机工作特性 体质指数 内科学 外科 腹部外科 多元分析 不利影响
作者
Shuo Yang,Yubin Long,Tao Wang,Junfei Guo,Zhiyong Hou
出处
期刊:Journal of Orthopaedic Surgery and Research [Springer Nature]
卷期号:18 (1) 被引量:2
标识
DOI:10.1186/s13018-023-03589-9
摘要

Abstract Background Surgical site infection (SSI) is one of the most common complications of orthopedic surgery, which can result in fever, pain, and even life-threatening sepsis. This study aimed to determine the predictors of SSI after fasciotomy in patients with acute leg compartment syndrome (ALCS). Methods We collected information on 125 ALCS patients who underwent fasciotomy in two hospitals between November 2013 and January 2021. Patients with SSI were considered as the SSI group and those without SSI as the non-SSI group. Univariate analysis, logistic regression analysis, and receiver operating characteristic (ROC) curve analyses were used to evaluate patient demographics, comorbidities, and admission laboratory examinations. Results In our research, the rate of SSI (26 of 125) was 20.8%. Several predictors of SSI were found using univariate analysis, including body mass index (BMI) ( p = 0.001), patients with open fractures ( p = 0.003), and patients with a history of smoking ( p = 0.004). Besides, the levels of neutrophil ( p = 0.022), glucose ( p = 0.041), globulin ( p = 0.010), and total carbon dioxide were higher in the SSI group than in the non-SSI group. According to the results of the logistic regression analysis, patients with open fractures ( p = 0.023, OR 3.714), patients with a history of smoking ( p = 0.010, OR 4.185), and patients with a higher BMI ( p = 0.014, OR 1.209) were related predictors of SSI. Furthermore, ROC curve analysis indicated 24.69 kg/m 2 as the cut-off value of BMI to predict SSI. Conclusions Our results revealed open fractures, BMI, and smoking history as independent risk factors for SSI following fasciotomy in patients with ALCS and determined the cut-off value of BMI, enabling us to individualize the evaluation of the risk for SSI to implement early targeted treatments.

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