Abstract / Summary
BackgroundSuperficial vein thrombosis (SVT) is a clinically relevant complication of lower extremity varicose veins. This study aimed to develop and internally validate a parsimonious nomogram for estimating the probability of concurrent SVT at presentation.MethodsThis single-center retrospective study included 423 patients with lower extremity varicose veins, including 91 with SVT. Twenty-four candidate variables were evaluated using LASSO regression, the Boruta algorithm, and a genetic algorithm for feature selection. Variables retained by all three methods were entered into multivariable logistic regression to construct the full model, and a reduced model excluding fibrinogen (FIB) was assessed for parsimony. Model performance was compared with the Caprini score and internally validated using 1,000 bootstrap resamples.ResultsAge, interleukin-6, C-reactive protein, D-dimer, and FIB were identified as consensus-selected variables. The full model had an AUC of 0.844 (95% CI, 0.797–0.891). Removing FIB produced minimal changes in discrimination (ΔAUC = 0.007; 95% CI, −0.004 to 0.017; P = 0.224), calibration, Brier score, or decision curves. The four-variable model was therefore selected for nomogram presentation. Its AUC was 0.837 (95% CI, 0.790–0.884), compared with 0.722 (95% CI, 0.663–0.782) for the Caprini score (ΔAUC = 0.115; 95% CI, 0.040–0.190; P = 0.003). The optimism-corrected AUC was 0.837. Adding the Caprini score to the full model numerically increased the AUC from 0.844 to 0.871 (ΔAUC = 0.027; 95% CI, 0.000–0.055; P = 0.054).ConclusionA nomogram based on age, interleukin-6, C-reactive protein, and D-dimer showed good internally validated performance for estimating the probability of concurrent SVT at presentation. Because validation was limited to bootstrap resampling within a single-center cohort, independent external validation is required before clinical application.