Effectiveness of multiple therapeutic strategies in neovascular glaucoma patients: A PRISMA-compliant network meta-analysis

Abstract

Purpose: Neovascular glaucoma (NVG) is a severe secondary glaucoma with uncontrolled intraocular pressure that leads to serious eye pain and vision loss. Presently, the therapeutic strategies for NVG are diverse, but the therapeutic effects are still not ideal. We performed a network analysis to assess the effect of multiple therapeutic strategies on the treatment of NVG patients.

Methods: We searched public electronic databases through April 2017 using the following keywords "neovascular glaucoma," "iris neovascularization," "hemorrhagic glaucoma," and "random" without language restrictions. The outcome considered in the present analysis was treatment success rate. A network meta-analysis and multilevel mixed-effects logistic regression were used to compare regimens.

Results: We included 27 articles assessing a total of 1884 NVG patients in our analysis. According to the network analysis, interferon and mitomycin plus trabeculectomy (94.9%), glaucoma valve implantation (86.9%), and iris photocoagulation plus trabeculectomy (81.9%) were the most likely to improve treatment success rate in NVG patients. The multilevel logistic regression analysis showed that glaucoma valve, bevacizumab, interferon, cyclophotocoagulation, trabeculectomy, iris photocoagulation, ranibizumab, and mitomycin had advantages in terms of improving treatment success rate in NVG patients. However, the application of retinal photocoagulation and vitrectomy reduced patient treatment success rate.

Conclusion: The regimen including mitomycin, interferon, and trabeculectomy was the most likely to improve the treatment success rate in NVG patients. The application of glaucoma valve and bevacizumab were more beneficial for improving patient treatment success rate as a surgery and as an agent, respectively.

Figure 1

Network of comparisons for treatment success rate in the analysis.

Figure 2

Cumulative ranking plots based on the estimated SUCRA probabilities for treatment success rate. The abbreviations for each therapeutic strategy are described in Table 1.

Figure 3

Comparison-adjusted funnel plot for assessing the results.

Figure 4

Traditional meta-analysis of treatment success rate among regimens that were not entered into the network meta-analysis. The abbreviations for each therapeutic strategy are described in Table 1.

Figure 5

Forest plot of contributions of the different therapeutic strategies to treatment success rate based on the multilevel mixed-effects logistic regression.