Novel treatment approaches for recurrent lower extremity venous insufficiency: Exploring the role of lymph node...

BackgroundRecurrent lower extremity venous insufficiency remains a therapeutic challenge despite advances in surgical and endovenous techniques. Emerging evidence suggests that reflux within the inguinal lymph node venous network (LNVN) may contribute significantly to recurrence. This study evaluates the safety and effectiveness of ultrasound-guided lymph node sclerotherapy as a novel treatment targeting LNVN reflux.MethodsThis prospective observational study included 75 patients with prior treatment for varicose veins who presented with symptomatic recurrence between 2022 and 2024. All patients underwent color Doppler ultrasonography and dynamic lymph node venography to detect LNVN reflux. Eligible patients received ultrasound-guided injections of 0.5% sclerosant into affected lymph nodes. Clinical outcomes were assessed using CEAP classification and duplex ultrasound at 1-year follow-up. Statistical analyses were performed using Wilcoxon signed-rank and chi-square tests.ResultsOf the 75 patients (mean age: 47.4 ± 9.4 years), 80% (n = 60) showed significant clinical improvement. Among patients classified as C3, 90% reported edema resolution. Improvement rates in C4-C6 patients ranged from 70% to 72.2%. Duplex ultrasound demonstrated complete reflux resolution in 64% and partial improvement in 20%. The reduction in reflux was statistically significant (p < .001). No major complications were reported; minor injection-site inflammation occurred in 8% of cases.ConclusionLymph node sclerotherapy appears to be a safe and effective outpatient intervention for recurrent venous insufficiency associated with LNVN dysfunction. By directly addressing a previously under-recognized source of reflux, this technique offers a minimally invasive and targeted alternative to conventional reinterventions. Further multicenter trials are warranted to confirm long-term outcomes and validate its role in standard venous treatment algorithms.