A Markov modelled pharmacoeconomic analysis of bimatoprost 0.03% in the treatment of glaucoma as an alternative to filtration surgery in Italy

Abstract

ABSTRACTObjective: Glaucoma is generally managed by decreasing the intraocular pressure (IOP) to a level believed to prevent further damage to the optic disc and loss of visual field. This may be achieved medically or surgically. The objective of this pharmacoeconomic analysis was to investigate the 4‐year costs of bimatoprost 0.03% (Lumigan) eye drops as an alternative to filtration surgery (FS) for glaucoma patients on maximum tolerable medical therapy (MTMT).† Lumigan is a registered trade name of Allergan, Inc., USAResearch design and method: A Markov model was designed using effectiveness and resource use data from a randomized clinical trial and expert statements (Delphi panel). The RCT covered 83 patients on MTMT. The model compared bimatoprost with FS. In the bimatoprost model arm patients began treatment with bimatoprost. If target IOP (–20%) was not reached using medical therapy the patient proceeded with FS. In the FS model arm, FS was performed after the first ophthalmologist visit. Unit costs were obtained from an Italian chart and tariffs review (healthcare sector perspective).Results: The RCT showed that 74.7% of the patients delayed the need for FS by 3 months. The Markov model forecasted that 64.2% of the patients could delay the need for FS by 1 year, and forecasted 34.0% could avoid FS after 4 years. The 4‐year cost per patient in the bimatoprost and FS arms was €3438 and €4194, respectively (incremental costs of €755). The major cost drivers for the bimatoprost arm were patients who needed combination therapy or FS if the target IOP was not reached. In the FS arm, the major cost drivers were the initial surgery costs and pressure-lowering medications used as add-on therapy after FS.Conclusions: The analysis shows that in a 4‐year perspective bimatoprost is cheaper compared to FS. In addition, the postponement of FS associated with bimatoprost may have important implications for waiting list planning.