Foam-in-Vein: Characterisation of Blood Displacement Efficacy of Liquid Sclerosing Foams.

Sclerotherapy is among the least invasive and most commonly utilised treatment options for varicose veins. Nonetheless, it does not cure varicosities permanently and recurrence rates are of up to 64%. Although sclerosing foams have been extensively characterised with respect to their bench-top properties, such as bubble size distribution and half-life, little is known about their flow behaviour within the venous environment during treatment.

Foam-in-vein: A review of rheological properties and characterization methods for optimization of sclerosing foams.

Varicose veins are chronic venous defects that affect >20% of the population in developed countries. Among potential treatments, sclerotherapy is one of the most commonly used. It involves endovenous injection of a surfactant solution (or foam) in varicose veins, inducing damage to the endothelial layer and subsequent vessel sclerosis.

In vitro and ex vivo evaluation of the biological performance of sclerosing foams.

Since the first reports on foam sclerotherapy, multiple studies have been conducted to determine the physical properties and behavior of foams, but relatively little is known about their biological effects on the endothelial cells lining the vessel wall. Moreover, a systematic comparison of the biological performance of foams produced with different methods has not been carried out yet. Herein, a 2D in vitro method was developed to compare efficacy of commercially available polidocanol injectable foam (PEM, Varithena) and physician-compounded foams (PCFs).

Physical Vein Models to Quantify the Flow Performance of Sclerosing Foams.

Foam sclerotherapy is clinically employed to treat varicose veins. It involves intravenous injection of foamed surfactant agents causing endothelial wall damage and vessel shrinkage, leading to subsequent neovascularization. Foam production methods used clinically include manual techniques, such as the Double Syringe System (DSS) and Tessari (TSS) methods.