Risk Factors of Deep Vein Thrombosis  in Adults with Acute Compartment  Syndrome Following Lower Extremity Fractures

Yang, Shuo; Wang, Tao; Long, Yubin; Guo, Junfei; Hou, Zhiyong

doi:10.1177/10760296231165053

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Acute compartment syndrome (ACS) is a severe orthopedic emergency that affects 1-30.4% of patients with lower extremity fractures, based on previous reports (1)(2)(3). It is characterized by rapidly increased pressure within a closed compartment after trauma, causing bleeding, tissue edema, and reduced limb perfusion.…”

Section: Introductionmentioning

confidence: 99%

Olink proteomics analysis uncovers the landscape of inflammation-related proteins in patients with acute compartment syndrome

Wang,

Yang,

Long

et al. 2023

Front. Immunol.

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PurposeOur primary purpose was to explore the landscape of inflammation-related proteins, and our second goal was to investigate these proteins as potential biomarkers of acute compartment syndrome (ACS), which is a serious complication of tibial fractures.MethodsWe collected sera from 15 healthy subjects (control group, CG) and 30 patients with tibial fractures on admission day, comprising 15 patients with ACS (ACS group, AG) and 15 patients without ACS (fracture group, FG). Ten samples in each group were analyzed by the inflammation panel of Olink Proteomics Analysis, and all samples were verified by an ELISA. Receiver-operating characteristic (ROC) curve analysis was performed to identify the diagnostic ability and cutoff values of potential biomarkers.ResultsOur findings showed that the levels of IL6, CSF-1, and HGF in the FG were significantly higher than those in the CG. Similar results were found between the AG and CG, and their cutoff values for predicting ACS compared with the CG were 9.225 pg/ml, 81.04 pg/ml, and 0.3301 ng/ml, respectively. Furthermore, their combination had the highest diagnostic accuracy. Notably, compared with FG, we only found a higher expression of CCL23 in the AG. Additionally, we identified 35.75 pg/ml as the cutoff value of CCL23 for predicting ACS in patients with tibial fractures.ConclusionWe identified CCL23 as a potential biomarker of ACS in comparison with tibial fracture patients and the significance of the combined diagnosis of IL6, CSF-1, and HGF for predicting ACS compared with healthy individuals. Furthermore, we also found their cutoff values, providing clinicians with a new method for rapidly diagnosing ACS. However, we need larger samples to verify our results.

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Section: Introductionmentioning

confidence: 99%

Olink proteomics analysis uncovers the landscape of inflammation-related proteins in patients with acute compartment syndrome

Wang,

Yang,

Long

et al. 2023

Front. Immunol.

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Establishment and validation of a predictive model for lower extremity deep vein thrombosis in patients with traumatic pelvic fractures

Shi,

Li,

Zhu

et al. 2024

Thrombosis J

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Background Patients with traumatic pelvic fracture (TPF) are at high risk for developing deep vein thrombosis (DVT). However, there is still no unified standard on how to distinguish high-risk groups for DVT in patients with TPF and how to accurately use anticoagulants at present. Objectives This observational study aimed to establish a DVT risk nomogram score (DRNS) model for TPF patients, and to explore the value of the DRNS model as a clinical guideline in the prevention of DVT with low molecular weight heparin (LMWH). Methods Independent risk factors of lower extremity DVT were screened through Lasso regression and logistic regression. A DRNS model was established per this. Results The independent risk factors of DVT included combined femoral fractures, age ≥ 40 years old, BMI (body mass index) ≥ 24 kg/m 2 , ISS score, fibrinogen concentration, and the minimum concentration of ionized calcium within 48 h after admission. The optimal cutoff value for DRNS was 78.5. In the low-risk population of DVT (DRNS < 78.5), there was no statistical significance of variation about the incidence of DVT progression between the LMWH once a day (qd) group and the LMWH once every 12 h (q12h) group, with P = 0.323. In the high-risk population of DVT (DRNS ≥ 78.5), the incidence of DVT progression in the LMWH qd group was significantly higher than that in the LMWH q12h group, with P = 0.002. Conclusions The DRNS model based on independent risk factors of DVT could stratify the risk of DVT for TPF patients, and it was able to provide more precise DVT drug prevention plans for clinicians.

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Research on risk factors for deep vein thrombosis in elderly patients with upper limb fractures and establishment of Nomogram prediction model

Wang,

Liu,

Zhang

et al. 2024

Preprint

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Purpose In this study, we aimed to investigate the risk factors for the development of deep vein thrombosis (DVT) in elderly patients with upper extremity fractures, construct and validate a Nomogram prediction model. Methods Based on the inclusion and exclusion criteria, we retrospectively analyzed 359 patients with upper limb fractures over the age of 70 who were admitted to the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from September 2020 to September 2023. All the patients underwent color Doppler vascular ultrasound of all four limbs to determine the presence or absence of upper and lower limb venous thrombosis before surgery. The patients were divided into DVT and non-thrombosis groups based on thrombosis. Along with the prevalent complications in clinical elderly patients, the sex, smoking, the use of indwelling needle, hypertension, fracture site, hyperlipidemia, atrial fibrillation, diabetes, premature beat, body mass index (BMI), preoperative plasma D-dimer levels, and hemoglobin levels of the patients in the two groups were analyzed by performing univariate and multivariate analysis. We also investigated the relationship between different factors and DVT in patients. All patients were divided into a modeling group (n = 251) and validation group (n = 108) in a ratio of 7:3. Logistic regression was used to construct a Nomogram prediction model and internal validation was performed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were drawn to evaluate the predictive efficiency, accuracy and clinical benefit of the nomogram model, and the validation group was used to evaluate the feasibility of the nomogram. Results In total, 38 cases of DVT were found in 359 patients, and the incidence rate of thrombosis was 10.58%. High blood pressure, hyperlipidemia, diabetes, anemia, BMI > 25kg/m2 and shoulder periarticular fracture are independent risk factors for deep vein thrombosis in upper limb fractures. The results of univariate and multivariate analysis revealed significant differences in terms of smoking, hyperlipidemia, diabetes, atrial fibrillation, anemia, fracture location, indwelling needle, and BMI between the two groups (p < 0.05). The periarticular fracture of the shoulder joint is a common site for the occurrence of DVT. No significant difference was found in terms of plasma D-dimer levels and premature beats with thrombosis between the two groups (p < 0.05). Furthermore, we also found that DVT is not associated with a history of hypertension but is associated with blood pressure levels, which showed significant differences between the two groups (p < 0.05). The ROC curve analysis showed that the area under the curve (AUC) of the prediction nomograph model was 0.972(95%CI: 0.952–0.992)for the modeling group and 0.860༈95%CI༚0.699-1.000༉for the validation group, with a sensitivity of 96.3% and 81.8%, and a specificity of 88.8% and 87.6%, respectively, with a good discrimination. The calibration curve of two groups showed that the predictive probability of the model was in high consistency with the actual incidence. The decision curve analysis (DCA) results in both groups showed that the nomograph model had good net returns. Conclusions The high-risk factors for the development of DVT in elderly patients with upper extremity fracture include smoking, high blood pressure, diabetes mellitus, hyperlipidemia, atrial fibrillation, anemia, BMI > 25kg/m2, indwelling needle, and periprosthetic shoulder fracture. These factors should be paid attention to in clinical settings, and the relevant evaluation methods should be improved, to prevent the occurrence of DVT and pulmonary embolism (PE).

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Risk Factors of Deep Vein Thrombosis in Adults with Acute Compartment Syndrome Following Lower Extremity Fractures

Cited by 5 publications

References 41 publications

Olink proteomics analysis uncovers the landscape of inflammation-related proteins in patients with acute compartment syndrome

Olink proteomics analysis uncovers the landscape of inflammation-related proteins in patients with acute compartment syndrome

Establishment and validation of a predictive model for lower extremity deep vein thrombosis in patients with traumatic pelvic fractures

Research on risk factors for deep vein thrombosis in elderly patients with upper limb fractures and establishment of Nomogram prediction model

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