Lower Limb Muscle Mass Parameters Predict Short-Term Prognosis After Endovascular Intervention for Non-Thrombotic Iliac Vein Compression Syndrome With Varicose Veins
Yanqing Zhan, Chenshu Li, Juan Wang, Peng Qiu, Tingmiao Wu, Zhigang Wang, Hui Ma, Hongji Pu, Jinhua Zhou, Wei ChenBackground: Lower limb muscle mass depletion has been associated with poor outcomes in various vascular diseases, yet its prognostic role in patients with non-thrombotic iliac vein compression syndrome (NIVCS) and varicose veins (VC) undergoing endovascular intervention remains unclear. This study aimed to investigate the predictive value of lower limb muscle mass parameters for short-term prognosis in this patient population.Methods: A total of 241 patients with NIVCS and VC treated at our hospital from January 2022 to December 2024 were enrolled. Patients were stratified into poor prognosis (n = 57) and good prognosis (n = 184) groups based on one-year outcomes. Univariate and binary logistic regression analyses identified independent prognostic factors. Receiver operating characteristic (ROC) curve analysis evaluated the predictive performance of muscle mass parameters.Results: There were statistically significant differences in comparisons of rectus femoris (RF) thickness, RF area, vastus intermedius (VI) thickness, classification, and Hb between the two groups (p < 0.05). Binary logistic regression analysis showed that RF thickness, RF area, and VI thickness were independent factors (p < 0.05). The ROC analysis demonstrated that the combined index of RF thickness, RF area, and VI thickness had an area under the curve of 0.894, with a standard error of 0.021 (95% CI: 0.853–0.936), a Youden index of 0.62, a sensitivity of 86.00%, and a specificity of 76.19%.Conclusion: Lower limb muscle mass parameters, particularly the combined assessment of RF thickness, RF area, and VI thickness, serve as valuable predictors of short-term prognosis following endovascular intervention for NIVCS with VC and may have potential value for preoperative risk stratification and require external validation.