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/m2 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.

Purpose The predictors of muscle necrosis after acute compartment syndrome (ACS) remain debated. This study aimed to investigate the predictors for muscle necrosis in ACS patients. Methods We collected data on ACS patients following fractures from January 2010 to November 2022. Patients were divided into the muscle necrosis group (MG) and the non-muscle necrosis group (NG). The demographics, comorbidities, and admission laboratory indicators were computed by univariate analysis, logistic regression analysis, and receiver-operating characteristic (ROC) curve analysis. Results In our study, the rate of MN was 37.6% (83 of 221). Univariate analysis showed that numerous factors were associated with muscle necrosis following ACS. Logistic regression analysis indicated that crush injury ( p = 0.007), neutrophil (NEU, p = 0.001), creatine kinase myocardial band (CKMB, p = 0.047), and prothrombin time (PT, p = 0.031) were risk factors. Additionally, ROC curve analysis identified 11.415 10 9 /L, 116.825 U/L, and 12.51 s as the cut-off values for NEU, CKMB, and PT to predict muscle necrosis, respectively. Furthermore, the combination of NEU, CKMB, and PT had the highest diagnostic accuracy. Conclusions Our findings showed that crush injury and the level of NEU, CKMB, and PT were risk factors for muscle necrosis after ACS. Additionally, we also identified the cut-off values of NEU, CKMB, and PT and found the combination of crush injury, PT, and NEU with the highest diagnostic accuracy, helping us individualize the assessment risk of muscle necrosis to manage early targeted interventions.

BackgroundBlisters are tense vesicles or bullae that arise on swollen skin and are found in a wide range of injuries. As a complication of fracture, fracture blisters are considered soft tissue injuries, which often lead to adverse effects such as prolonged preoperative waiting time and increased risk of surgical site infection. However, our previous study found that in patients with acute compartment syndrome, fracture blisters may be a form of compartment pressure release, but the specific mechanism has not been revealed. Here, we mapped out the proteomic landscape of fracture blister fluid for the first time and compared its expression profile to cupping and burn blisters.MethodsFirst, fluid samples were collected from 15 patients with fracture blisters, 7 patients with cupping blisters, and 9 patients with burn blisters. Then, the expression levels of 92 inflammatory proteins were measured using the Olink Target 96 Inflammation panel. Protein profiles were compared across the three groups using Differential Protein Expression Analysis and Principal Component Analysis (PCA).ResultsFracture blisters had significantly higher levels of 50 proteins in comparison to cupping and 26 proteins in comparison to burn blisters. Notably, PCA showed fracture blisters closely resembled the protein expression profile of burn blisters but were distinct from the protein expression profile of cupping blisters.ConclusionOur study provides the first characterization of fracture blister fluid using proteomics, which provides a valuable reference for further analysis of the difference between blisters caused by fractures and those caused by other pathogenic factors. This compendium of proteomic data provides valuable insights and a rich resource to better understand fracture blisters.

This study aimed to identify the risk factors of deep vein thrombosis (DVT) in adults with acute compartment syndrome (ACS) following lower extremity fractures. We collected data on adults with ACS following lower extremity fractures in our hospital from November 2013 to January 2021. Patients were divided into the DVT group and the non-DVT group according to whether they had DVT or not. The demographics, comorbidities, and admission laboratory examinations were computed by univariate analysis, logistic regression analysis, and receiver operating characteristic (ROC) curve analysis. In our study, the rate of DVT (26 of 110) was 23.6%. Univariate analysis showed that numerous factors were associated with the formation of DVT. Logistic regression analysis showed that patients with multiple fractures ( P = .015, OR = 5.688), patients with a history of hypertension ( P = .011, OR = 16.673), and patients with a higher BMI ( P = .008, OR = 1.950) and FDP ( P = .013, OR = 1.031) were relevant predictors of DVT. ROC curve analysis indicated 24.73 kg/m2 and 28.33 μg/mL were the cutoff values of BMI and FDP to predict the DVT, respectively. Furthermore, the combination of BMI and FDP had the highest diagnostic accuracy. Our findings identified multiple fractures, BMI, and FDP as independent risk factors for DVT in patients with ACS following lower extremity fractures and determined the cutoff values of BMI and FDP, helping us individualize the assessment of the risk of DVT to manage early targeted interventions.