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 3 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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Evaluating the Discriminative Capacity of a Random Forest Predictive Model for Deep Vein Thrombosis in Tibial Plateau Fracture Patients

Liu,

Xing,

Jiang

et al. 2024

Preprint

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Introduction: This study aims to construct an efficient random forest predictive model to address the knowledge limitations in predicting deep vein thrombosis (DVT) among tibial plateau fracture (TPF) patients. DVT, being a critical and potentially life-threatening consequence, often necessitates intricate clinical management. Materials and Methods This investigation retrospectively examined adult patients who underwent surgical intervention for tibial plateau fractures in our institution from June 2020 to December 2023. Among the 562 patients who underwent surgical intervention, 231 were included in the study cohort, subsequently divided into training and testing cohorts in a 70:30 ratio. The training cohort utilized the R software to construct a random forest predictive model, which was then validated in the testing group. Furthermore, logistic regression analysis was conducted in this study to acquire feature selection variables and the area under the curve (AUC), evaluating the credibility and discriminative capacity of the random forest algorithm. Results As for the discriminative capacity of the random forest predictive model, in the training cohort, the 95% confidence interval (CI), area under the curve (AUC), sensitivity, specificity, F1, and Balanced Accuracy were respectively (0.9775, 1), 1.0000, 1.0000, 1.0000, 1.0000, and 1.0000. Correspondingly, in the testing cohort, these metrics were (0.7326, 0.9176), 0.901, 0.8696, 0.8261, 0.7843, and 0.8478. Conclusion Utilizing the discerningly chosen " important " variables, this study employed the random forest algorithm to craft a predictive model, demonstrating exceptional discriminative prowess. These identified " important " variables, serving as predictive factors, aid clinicians in the identification of high-risk DVT patients. This, in turn, facilitates timely intervention, fortification of care, and enhancement of prognostic outcomes for patients with fractures.

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Correlation between ABO blood type and lower extremity deep vein thrombosis in elderly patients following hip fracture

Cui

2024

Am J Transl Res

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

Cited by 3 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

Evaluating the Discriminative Capacity of a Random Forest Predictive Model for Deep Vein Thrombosis in Tibial Plateau Fracture Patients

Correlation between ABO blood type and lower extremity deep vein thrombosis in elderly patients following hip fracture

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