Pointwise Methods to Measure Long-term Visual Field Progression in Glaucoma

Abstract

Importance: Rates of visual field (VF) progression vary among patients with glaucoma. Knowing the rate of progression of individual patients would allow appropriately aggressive therapy for patients with high rates of visual loss and protect those with low rates from unnecessary therapy.

Objective: To compare 3 pointwise methods of estimating the rate of VF progression in glaucoma.

Design, setting, and participants: This retrospective, observational cohort study included 729 eyes of 567 consecutive patients with primary open-angle glaucoma who had at least 6 reliable VFs and at least 3 years of follow-up. One hundred seventy-six patients (257 eyes) were treated at a tertiary glaucoma center; in addition, data were collected from 391 participants (472 eyes) in the Advanced Glaucoma Intervention Study. Data were collected from May 1988 to November 2004 and analyzed from October 2018 to February 2019.

Exposures: Estimates of VF progression were measured with guided progression analysis (GPA), pointwise linear regression (PLR), and the glaucoma rate index (GRI). A subgroup analysis was performed in a subset of patients with likely VF progression and likely VF stability.

Main outcomes and measures: Proportion of VF series detected as progressing, estimates of false-positive proportions, time to detect progression, and agreement among measures.

Results: Among the 567 patients included in the analysis, mean (SD) age was 65.6 (9.7) years, 300 (52.9%) were female, and 295 (52.0%) were white. The median baseline mean deviation was -6.7 (interquartile range [IQR], -11.6 to -3.5) dB; the median follow-up time, 8.9 (IQR, 7.3-10.4) years. The proportion of eyes labeled as progressing was 27.7% according to the GPA, 33.5% according to the PLR, and 52.9% according to the GRI; pairwise differences for GRI vs PLR were 20% (95% CI, 17%-23%); for GRI vs GPA, 25% (95% CI, 22%-29%); and for PLR vs GPA, 6% (95% CI, 3%-9%; P < .001 for all comparisons, McNemar test). The shortest median time to progression was with the GRI (8.8 [IQR, 2.4-10.5 years), compared with the GPA and PLR (both >16 years). The hazard ratio of VF progression for GRI vs PLR (reference) was 11.3 (95% CI, 9.2-13.7); for GRI vs GPA (reference), 18.1 (95% CI, 14.5-22.6); and for PLR vs GPA (reference), 1.5 (95% CI, 1.3-1.9; P < .001 for all comparisons, Cox proportional hazards regression). These results held in the subgroup with likely progression; the proportions of progressing eyes were 73.7% (115 of 156) for GPA, 81.4% (127 of 156) for PLR, and 92.9% (145 of 156) for GRI. Pairwise difference for GRI vs PLR was 11.5% (95% CI, 7.4%-17.6%; P < .001, McNemar test); for GRI vs GPA, 19.2% (95% CI, 12.6%-26.4%; P < .001, McNemar test); and for PLR vs GPA, 7.7% (95% CI, 0.3%-15.7%; P = .08, McNemar test).

Conclusions and relevance: These results suggest GRI can detect long-term VF progression in glaucoma earlier than PLR or GPA. Validation with prospective designs may strengthen the generalizability and value of this method.

Figure 1.. Detection of Progression by Each Pointwise Method

GPA indicates guided probability analysis; GRI, glaucoma rate index; and PLR, pointwise linear regression.

Figure 2.. Cumulative Proportion of Eyes With Progression Over Time According to Each Method

The entire population includes 729 eyes; the group with likely progression, 156 eyes. GPA indicates guided probability analysis; GRI, glaucoma rate index; and PLR, pointwise linear regression. ^aP < .001 for all comparisons using Cox proportional hazards regression with mixed-effects model. ^bP < .001 for GRI vs GPA and PLR using Cox proportional hazards regression with mixed-effects model. ^cP = .02 for PLR vs GPA using Cox proportional hazards regression with mixed-effects model.

Figure 3.. Spatially Conserved Heat Color-Coded Maps Showing at Each Location the Proportion of Decay Points According to Each Pointwise Method Criterion

Percentages are calculated as the number of decaying locations divided by the total number of locations. Each map has its own scale of reference, with darker colors representing higher proportions and lighter colors representing lower proportions. GPA indicates guided probability analysis; GRI, glaucoma rate index; and PLR, pointwise linear regression.