Abstract
To develop and validate a clinical prediction model for Iliac Vein Compression Syndrome (IVCS) in outpatients with Varicose veins of the lower extremities (VVLE), to aid clinical decision-making and early identification of high-risk patients. A retrospective cohort study was conducted, including 732 outpatients diagnosed with VVLE between 2014 and 2023. Independent predictors of IVCS were identified through multivariable logistic regression, and a nomogram was developed. The model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Four independent predictors for IVCS were identified: history of deep vein thrombosis (DVT), history of vascular interventions, pain symptoms, and Clinical Etiological Anatomical Pathophysiological (CEAP) grade. The nomogram showed strong performance, with an area under the ROC curve (AUC) of 0.79 in the training set and 0.74 in the validation set. of 0.79 in the training set and 0.74 in the validation set. Calibration curves confirmed good agreement between predicted and observed outcomes. DCA demonstrated the clinical utility of the model across different risk thresholds. A simple and cost-effective nomogram for predicting IVCS in VVLE patients was developed and validated. This tool helps outpatient clinicians identify high-risk IVCS patients early, supporting personalized treatment strategies. Further validation is needed, but the model holds promise for improving early diagnosis and patient outcomes.