Relationship Between Intraocular Pressure and the True Rate of Functional and Structural Progression in the United Kingdom Glaucoma Treatment Study

Abstract

Purpose: To investigate the effect of average intraocular pressure (IOP) on the true rate of glaucoma progression (RoP) in the United Kingdom Glaucoma Treatment Study (UKGTS).

Methods: UKGTS participants were randomized to placebo or Latanoprost drops and monitored for up to two years with visual field tests (VF, 24-2 SITA standard), IOP measurements, and optic nerve imaging. We included eyes with at least three structural or functional assessments (VF with <15% false-positive errors). Structural tests measured rim area (RA) with Heidelberg retina tomography (HRT) and average peripapillary retinal nerve fiber layer (pRNFL) thickness with optical coherence tomography (OCT). One eye of 436 patients (222 on Latanoprost) was analyzed. A Bayesian hierarchical model estimated the true RoP of VF and structural metrics, and their correlations, using sign-reversed multivariable exponential distribution. RA and pRNFL measurements were converted to a dB scale, matching the VF metric (mean deviation [MD]). The effect of average IOP on the true RoPs was estimated.

Results: True RoP at the mean average IOP (17 mm Hg) was faster (P < 0.001) for VF-MD (-0.59 [-0.73, -0.48] dB/year) than HRT-RA (-0.05 [-0.07, -0.03] dB/year) and OCT-pRNFL (-0.08 [-0.11, -0.06] dB/year). The proportional acceleration of RoP per mm Hg increase was, however, not significantly different (smallest P = 0.15). Accounting for the structural floor-effect largely eliminated the differences in RoPs (smallest P = 0.25).

Conclusions: VF appeared to deteriorate at a faster rate than structural measurements. However, this could be explained by the floor-effect from nonfunctional tissue. IOP induced a similar acceleration in RoP per mm Hg increase.

Figure 1.

Example of fitting results for one eye. The blue lines represent the estimated trend for the observed rate of progression (random effect predictions). The data points are all reported in the dB scale used for the analysis. For the rim area and the mRFNL, the linear scale is reported as a secondary axis on the right. The vertical axes have been scaled to cover the same range of 8 dB, allowing a direct comparison of the slopes.

Figure 2.

Effect of Goldmann IOP on the RoP of the VF-MD, HRT rim area, and OCT average pRNFL. Note that the HRT and OCT metrics are also converted to a dB scale for these calculations. The equations for the predicted mean of true RoPs (µ) are reported in the graphs. The observed RoP for VF-MD is different from the true RoP because of learning (see Table 2). The panel on the right shows a comparison of the IOP effect on the mean true RoP in log-scale, used for fitting the exponential component of the model. The dots represent simple linear regression rates in dB scale, color coded for the study arm.

Figure 3.

Effect of Goldmann IOP on the RoP of the visual field mean linear sensitivity (in dB scale) and on the floor-compensated HRT rim area and OCT average pRNFL, also converted to a dB scale. The equations for the predicted mean of true RoPs (µ) are reported in the graphs. The observed RoP for VF-MD is different from the true RoP because of learning. The panel on the right shows a comparison of the IOP effect on the mean true RoP in log-scale, used for fitting the exponential component of the model. The dots represent simple linear regression rates in dB scale, color coded for the study arm.

Figure 4.

The prediction for the observed rate of visual field progression obtained from our model, fitted to the UKGTS cohort, overlaid on the data from Medeiros et al.