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  • Does IOP Variability Help Predict POAG?

    By Lynda Seminara
    Selected and Reviewed By: Neil M. Bressler, MD, and Deputy Editors
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    Journal Highlights

    JAMA Ophthalmology, July 2020

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    Whether and how the long-term vari­ability of intraocular pressure (IOP) may contribute to the occurrence of primary open-angle glaucoma (POAG) is not well understood. In a post hoc secondary analysis of two randomized trials, Gordon et al. examined this issue. They found that, for people with untreated ocular hypertension, taking into account the variable long-term IOP data did not seem to improve the ability to predict POAG.

    For this study, the researchers used data from the Ocular Hypertension Treatment Study (OHTS) and the European Glaucoma Prevention Study (EGPS). The model used in these two studies to predict POAG development included baseline values for age, IOP, central corneal thickness, vertical cup-disc ratio, and pattern standard devia­tion (SD). In this analysis, the authors tested whether predictions could be improved by replacing baseline IOP data with mean follow-up IOP, SD of IOP, maximum IOP, range of IOP, or coefficient of variation IOP. They used the C statistic to compare the predictive accuracy of multivariable landmark Cox proportional hazards regression models for the development of POAG.

    The OHTS data consisted of 97 POAG end points from 709 of 819 participants (58.7% women, 25% African American, 69.1% white). Mean age was 55.7 years, and the median fol­low-up period was 6.9 years. EGPS data included 44 POAG end points from 397 of 500 participants in the placebo group (50.1% women, 100% white). The mean age was 57.8 years, and the median follow-up time was 4.9 years. The C statistic for the original predic­tion model was 0.741.

    When the other IOP values were substituted for baseline IOP in the OHTS prediction model, the C statistic was 0.784 for mean follow-up IOP, 0.781 for maximum IOP, 0.745 for SD of IOP, 0.741 for range of IOP, and 0.729 for coefficient of variation IOP. EGPS findings were similar. No measure of IOP variability, when added to the complete prediction model, increased the C statistic by more than 0.007 in either cohort.

    These findings suggest that factoring in long-term IOP variability does not strengthen POAG prediction models. Even so, given that IOP is the only known modifiable risk factor for glau­coma, understanding how its dynamic variation is linked to the onset and pro­gression of POAG could play a crucial role in management, the authors said.

    The original article can be found here.