Abstract
TopicTo determine the cumulative incidence and features of glaucoma in Vogt-Koyanagi-Harada (VKH) syndrome patients compared to non-glaucoma patients. Clinical relevanceKnowing the exact burden of secondary glaucoma in VKH could guide its screening and management in clinical practice as a part of the regular follow-up for VKH patients. MethodsThe review protocol was pre-registered on PROSPERO [CRD42023462794]. PubMed, Scopus, Web of Science, EBSCOhost, and Google Scholar were searched for studies reporting the cumulative incidence and features of glaucoma presentation in VKH. A manual search was also conducted to supplement the primary search. Subgroup analyses based on glaucoma type, VKH stage, and patients’ age were conducted. All analyses were conducted using STATA. Fixed- and random-effects models were selected according to the observed heterogeneity. Studies’ methodological quality was determined using the NIH tool. ResultsThe analysis of 7084 eyes revealed a progressive increase in the cumulative incidence of secondary glaucoma over time. The cumulative incidence was lowest at VKH onset (7%) and highest at 15 years (26%). Open-angle (12%; 95%CI: 9-14%) is more common than angle-closure glaucoma (7%; 95%CI: 3-13%). Glaucoma cumulative incidence is highest in the chronic recurrent stage of VKH (33%; 95%CI: 12-59%) and among children <18 years of age (26%; 95%CI: 16-37%). Features associated with glaucoma occurrence in VKH showed comparable rates to non-glaucoma cases. However, a meta-analysis to determine risk factors of glaucoma development in VKH was not feasible secondary to the lack of adjusted risk measures in included studies. Studies' quality was questionable in 5 studies. The certainty of evidence was moderate-to-high. ConclusionThe cumulative incidence of glaucoma increases throughout VKH’s course, with a higher tendency in children, chronic recurrent stages, and long-term follow-up. Future research should focus on examining risk factors of glaucoma development in VKH through adjusted multivariable regression models.
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