Purpose
To evaluate critically visual field (VF) improvement in participants in the Collaborative Initial Glaucoma Treatment Study (CIGTS).
Design
Prospective, comparative case series from a randomized clinical trial comparing trabeculectomy and topical medications in treating open-angle glaucoma (OAG).
Methods
A total of 607 subjects with newly diagnosed OAG were identified for study. Baseline and follow-up VF tests were obtained and mean deviation (MD) change from baseline over follow-up was analyzed. Clinically substantial change (loss or improvement) was defined as change from baseline of ≥3 decibels in MD. Baseline factors were inspected to determine their association with VF improvement in repeated measures regression models.
Results
The percentage of participants showing substantial VF improvement over time was similar to that showing VF loss through 5 years after initial treatment, after which VF loss became more frequent. Measures of better intraocular pressure (IOP) control during treatment were significantly predictive of VF improvement, including a lower mean IOP, a lower minimum IOP, and lower sustained levels of IOP over follow-up. Other predictive factors included female sex (odds ratio [OR] = 1.73), visits 1 year prior to cataract extraction (OR = 0.11), and an interaction between treatment and baseline MD wherein surgically treated subjects with worse baseline VF loss were more likely to show VF improvement.
Conclusions
In the CIGTS, substantial VF loss and improvement were comparable through 5 years of follow-up, after which VF loss became more frequent. Predictive factors for VF improvement included several indicators of better IOP control, which supports the postulate that VF improvement was real.
Visual field (VF) loss is a hallmark sign of glaucoma, and its assessment is a standard part of the ophthalmic examination of patients being evaluated or followed for this condition. Measurement of VF loss is performed using some form of VF testing, which in current practice is commonly an automated test. The test involves presenting a sequence of light stimuli to a patient and judging the patient’s ability to detect the stimuli. Its reliability is estimated by recording false-positive and false-negative responses as well as fixation losses, which, if substantial, require retesting. Within-patient variability in the VF test is a well-known phenomenon, and its study has resulted in the recommendation that a finding on one test be confirmed by subsequent repeated testing. For example, in 3 major trials of glaucoma and ocular hypertension treatment, 3 consecutive VF tests were required to confirm a defined change in VF.
Most studies involving treatment of glaucoma have evaluated progression in VF loss to measure treatment efficacy. Our previous investigations using the Collaborative Initial Glaucoma Treatment Study (CIGTS) data have provided information on factors predictive of progressive VF loss. The possibility that VF improvement might occur has received sparse attention. In 1985, Spaeth presented evidence that lowering intraocular pressure (IOP) in patients with glaucoma can result in improvement of VFs. He suggested the possibility that retinal ganglion cells damaged by glaucoma may, with effective treatment, recover their function; thus, not only VF loss but also VF improvement should be considered as real phenomena. If so, VF improvement would not necessarily be an artifact of inherent noise in test taking, which has been the prevailing thought.
The purpose of this study was to evaluate the distribution of VF change over 9 years of follow-up in CIGTS, with a particular focus on those VF tests that demonstrated improvement from a carefully measured baseline. We sought to assess whether improvement was associated with measures indicative of good IOP control, based on the premise that factors previously identified as predictive of VF loss should also be found to associate (in the opposite direction) with VF improvement, if improvement is real.
Methods
The CIGTS was a multicenter randomized clinical trial in which 607 participants with newly diagnosed glaucoma were assigned to initial treatment with topical medications or trabeculectomy. For bilaterally eligible participants, prior to randomization a study eye was selected by the enrolling ophthalmologist to receive the randomized treatment. The VF findings evaluated herein relate to the study eye of each patient. Patients were enrolled between 1993 and 1997 and follow-up continued through 2004. Informed consent to participate was obtained from all patients and institutional review board approval was obtained at all participating centers. The research was HIPAA compliant and adhered to the tenets of the Declaration of Helsinki.
Participants were required to have had at least 1 VF test prior to being screened for eligibility, and had 2 comprehensive baseline examinations that included VF testing. The Humphrey 24-2 full-threshold VF test was administered by personnel who were certified and followed a well-defined protocol for the test. If the 2 baseline VF tests varied by more than 3 units in the scoring or if one was not reliable, a third VF test was obtained. The average of the 2 tests, or the median of 3, was used to characterize the participant’s baseline VF status. Using the same VF test protocol, VF tests were performed at follow-up visits conducted at 3 and 6 months after treatment initiation, and biannually thereafter. Further details on the study’s protocol and participants have been previously reported.
Substantial progression in the extent of VF loss during the CIGTS was a trigger for instituting more aggressive treatment. The amount of change required to warrant further treatment was determined prior to the study’s initiation by convening a panel of glaucoma experts who were presented with side-by-side VFs that showed various increments of VF progression, as measured by the CIGTS VF score. This score has been described previously. In brief, its calculation relied on the probabilities in the 52 locations of the total deviation probability plot, with weighting assigned based on the number of affected adjacent points. The result is a global measure of VF loss on a scale from 0 to 20, with higher scores indicative of more loss. The expert panel determined by consensus that an increment of 3 units in the CIGTS VF score represented a clinically substantial change. In subsequent analyses, a standard output of VF testing, the mean deviation (MD), was found to be highly correlated with the CIGTS VF score, and the MD was also a more sensitive measure of substantial VF defects than the CIGTS VF score. Thus, for this study, we employed a 3 dB change in MD as the minimum incremental change of clinical relevance.
Statistical Methods
The percentages of CIGTS participants who demonstrated at least a 3 dB improvement (gain) in their MD from baseline were plotted by follow-up visit, along with those who demonstrated at least a 3 dB loss for comparison. To address sustained VF gain or loss, we identified visits with gain or loss that were validated by a subsequent gain or loss at their next visit 6 months later. Those without a subsequent 6-month value were not included in either the numerator or denominator at a given study visit point. Standard errors of the binomial proportion estimates (p) were calculated as √(p*(1−p)/n), where p is the proportion of subjects showing gain/loss. For those with sustained gain, we also plotted their MD values over time in spaghetti plots to check consistency of the gains at subsequent time points ( Supplementary Figure 1 , available at AJO.com ).
To investigate whether consecutive measurements showing gain over 5 years of follow-up are consistent with chance occurrence, we simulated the number expected to have gain under the assumption of no real VF change over time, accounting for correlation between visits over time, and compared it with the observed number with gain. Details of this calculation are given in a footnote to the relevant table.
We used repeated measures logistic regression to evaluate factors associated with VF improvement, where the dichotomous outcome was at least a 3 dB gain in MD from baseline at each time point. For example, if a subject had a baseline MD of −5.0 dB and had MD at years 1, 2, 3, and 4 of −2.0, −1.5, −1.0, and −4.0 dB, respectively, the repeated measures outcome for those years would be 1, 1, 1, and 0, where 0 = no improvement and 1= at least a 3 dB improvement. A robust variance estimate was used to account for repeated MD measures in an eye over time. This approach permitted us to evaluate the association of baseline variables with the VF improvement outcome as well as time-dependent (during follow-up) associations of IOP control with VF improvement. Baseline covariates tested in the model included MD, visual acuity (VA), IOP, corneal thickness, treatment, age, sex, race, marital status, diabetes, hypertension, other vascular/cardiac disease, type of open-angle glaucoma, family history of glaucoma, disc hemorrhage, pupillary response, iris color, smoking status, alcohol consumption, and center. Time-dependent covariates investigated included visit number and IOP values (based only on IOP measurements prior to each time point): mean, standard deviation (SD), minimum, maximum, and range of IOP measures; percent of visits with IOP less than 16, 18, 20, or 22 mm Hg; and indicators for whether all visits had IOP less than 16, 18, 20, or 22 mm Hg. Cataract was accounted for in the model as a time-dependent variable indicating a visit within 1 year before cataract surgery to capture the VF decrement and subsequent lower chance of improvement during this period. Our model selection strategy included a best subset selection method ignoring the correlation structure, followed by testing the best of the identified models in the repeated measures setting. This strategy included testing single-variable models. Interactions of all baseline covariates with treatment and time were investigated, whether or not the main effects were significant. Models were run on follow-up data from years 2 through 9. Starting at year 2 insured at least 2 years of summary IOP data to inform those covariates. Cox regression based on time to sustained VF gain was used to identify baseline and time-dependent IOP effects (detailed above) associated with time to first sustained VF gain. Proportional hazards were tested with covariate by time interactions. SAS version 9.3 statistical software (SAS, Cary, North Carolina, USA) was used.
Results
A total of 607 subjects were enrolled in the CIGTS. A description of the sample is given in Table 1 . Briefly, subjects were on average 58 years old at study entry, 55% male, 56% white, and followed for a mean of 7.2 years. Clinical characteristics at baseline included an average MD of −5.4 dB, IOP of 27.5 mm Hg, and VA of 85.7 ETDRS letters (85 letters equates to a 20/20 Snellen VA).
| Continuous Variables | N | Mean (SD) | Min, Max | Median |
|---|---|---|---|---|
| Follow-up (y) | 607 | 7.2 (2.3) | 0.0, 14.5 | 7.7 |
| Age (y) | 607 | 58.0 (10.9) | 28.8, 75.8 | 59.2 |
| MD (dB) | 607 | −5.4 (4.3) | −23.5, 3.4 | −4.4 |
| IOP (mm Hg) | 607 | 27.5 (5.6) | 19.0, 50.0 | 27.0 |
| VA (letters) | 607 | 85.7 (5.7) | 70.0, 99.0 | 86.0 |
| Categorical Variables | Frequency (Percent) |
|---|---|
| Sex | |
| Female | 273 (45.0) |
| Male | 334 (55.0) |
| Race | |
| White | 337 (55.5) |
| Black | 231 (38.1) |
| Asian | 10 (1.7) |
| Other | 29 (4.8) |
| Education | |
| <High school | 128 (21.1) |
| High school | 167 (27.5) |
| >High school | 312 (51.4) |
| Diagnosis | |
| POAG | 550 (90.6) |
| Pseudoexfoliation | 29 (4.8) |
| Pigmentary | 28 (4.6) |
| Diabetes | 102 (16.8) |
| Hypertension | 225 (37.1) |
| Other vascular/cardiac disease | 91 (15.0) |
| Smoking status | |
| Never smoker | 234 (38.6) |
| Ex-smoker | 246 (40.5) |
| Current smoker | 127 (20.9) |
| Immediate family Hx (n = 545) | 201 (36.9) |
| Distant family Hx (n = 461) | 112 (24.3) |
The percentages of CIGTS participants’ study eyes that demonstrated substantial change from baseline—either gain or loss—in study follow-up through 5 years was very similar ( Figure 1 ). For example, at 1, 3, and 5 years after treatment initiation, the percentages showing loss (6.6%, 10.9%, and 14.5%, respectively) and improvement (7.5%, 12.7%, and 13.9%, respectively) differed minimally and were not statistically significant ( P > .20), and the increasing trends in both loss and improvement were similar. Between 5 and 8 years, the percentage with VF loss continued to increase whereas VF improvement remained at the 5-year level, resulting in a higher percentage of patients showing VF loss (19.6%) than improvement (13.5%) at 7 years after treatment initiation. The percentage of VFs showing a 3 dB gain was significantly different from the percentage showing comparable loss at years 7 and 8 (test of equality of binomial proportions; P = .0053 and P = .0088, respectively). Although the percentages of subjects showing a sustained gain or loss are smaller, a similar pattern is present, with 3.9%, 7.0%, and 7.2%% showing sustained gain and 2.9%, 7.3%, and 11.0% showing sustained loss at 1, 3, and 5 years after treatment initiation, respectively.
The tests of equal probability of VF improvement and loss at each time point cannot validate that either loss or gain is real. Thus, we investigated 3 potential alternative explanations for the observed improvement. These included learning effects, variability in MD measures, and chance occurrence in long-term follow-up. Learning effects were evaluated by revisiting previously reported conclusions based on MD values from the 2 baseline CIGTS visits ; 82% of these visits were conducted within 20 days of each other, and all took place within 42 days of each other. These CIGTS results revealed a small, but significant, learning effect (the second VF was an average of 0.57 dB better than the first) for VFs performed within 20 days of each other, but no significant learning effect when the second VF was conducted more than 20 days after the first. As the first follow-up visits took place 3 months after treatment initiation, all follow-up visits took place >20 days from baseline.
Variability was estimated by calculating the percentage of patients with ≥3 dB loss or gain at the second baseline VF compared to the first. Out of 605 patients where the order of fields could be determined, 25 (4.1%) showed loss. Of the 110 fields that were obtained more than 20 days apart (to avoid learning effects), 5 (4.6%) showed gain. Thus, we estimate that approximately 4% of supposed losses or gains may be attributable to variability in VF measurement in the absence of any real change in VFs.
Chance occurrence of consistent gain or loss in long-term follow-up was first evaluated by inspecting a spaghetti plot of VF MD values for subjects with MD improvement from baseline of at least 3 dB, verified by improvement at the next clinical visit (6 months later) (n = 107). This plot shows the gains were for the most part maintained through follow-up ( Supplementary Figure 1 , available at AJO.com ). We also looked at the distribution of consecutive follow-up visits showing gain or loss over the first 5 years compared to what would be expected by chance alone, when assuming no VF change over time and correlation between visits. These values are presented in Table 2 , and show that the probability of having 4 or 5 visits with gain or loss by chance alone is far less than observed, where 18 subjects were observed to have gain (7 expected by chance) and 16 were observed to have loss (<1 expected by chance). Furthermore, 16 and 17 subjects were observed to have gain or loss in 3 out of 5 yearly visits, yet only 14 and 8 were expected to have this result, respectively. We observed many fewer people than would be expected with 1 or 2 visits showing gain or loss, and more than expected with no gain or loss. This would indicate stability in MD scores among most patients.
| # Visits With ≥3 dB Gain or Loss | Gain | Loss | ||
|---|---|---|---|---|
| Observed | Simulated Expected a | Observed | Simulated Expected a | |
| 0 | 306 | 282.7 | 312 | 244.8 |
| 1 | 45 | 76.1 | 46 | 123.5 |
| 2 | 28 | 32.5 | 22 | 35.3 |
| 3 | 16 | 14.4 | 17 | 8.1 |
| 4 | 12 | 6.2 | 12 | 1.2 |
| 5 | 6 | 1.20 | 4 | 0.04 |
a Expected occurrences were simulated assuming no visual field change over time, that is, observed gains or losses were attributable to chance alone. Only CIGTS cases with complete data for annual visits 1–5 were included; the intervening 6-month visits were excluded to increase the sample size with complete data (n = 413). We calculated the expected number of yearly visits with gain (0, 1, 2, 3, 4, or 5), assuming baseline and follow-up MDs had the same marginal mean (−5.4 dB) and standard deviation (4.3 dB). Data were simulated assuming multivariate normality on a transformed scale (ln[−MD+5]) to reduce skewness, with correlation structure observed in the CIGTS data: r = 0.90 between the 2 baseline measures; r = 0.85 between the mean baseline measure and any of the follow-up measures. Data were back-transformed to the raw scale to assess gain and loss. The asymmetrical transformation resulted in minor differences between the expected results for gain and loss. We calculated the number of subjects expected to show gain by chance as the proportion of 10 000 simulated patients in which the VF gain, compared to the average of the 2 baseline measures, was at least 3 dB. These proportions were multiplied by the sample size (413) to yield the expected number of CIGTS subjects with gain by chance alone. Calculations for loss were similar.
We next investigated the relationship between IOP and VF gain or loss. A descriptive look at the average percentage of visits in the first 5 years in which subjects showed a substantial gain (or loss) in MD from baseline, stratified by their maximum IOP during that time, showed an ordinal trend ( Table 3 ). Specifically, subjects with the lowest maximum IOPs (≤13 mm Hg) had on average 18.7% of visits showing a substantial gain in MD compared to 13.4%, 10.6%, and 8.0% in subjects with maximum IOPs of 14–17 mm Hg, 18–21 mm Hg, and ≥22 mm Hg. When looking at the percentage of visits showing substantial loss, those subjects with lower maximum IOP had a smaller percentage of visits showing loss in MD than those with higher maximum IOP.
| Maximum IOP in First 5 Years | % Visits 3 dB Gain | % Visits 3 dB Loss | % Difference | MD Change at 5 Years |
|---|---|---|---|---|
| Mean (SD) | ||||
| ≤13 mm Hg (n = 24) | 18.7 (30.0) | 6.3 (14.0) | 12.5 (36.2) | 0.39 (5.38) |
| 14–17 mm Hg (n = 80) | 13.4 (24.4) | 6.3 (16.1) | 7.1 (31.6) | 0.65 (2.79) |
| 18–21 mm Hg (n = 151) | 10.6 (19.0) | 11.7 (20.6) | −1.2 (31.2) | 0.01 (3.60) |
| ≥22 mm Hg (n = 221) | 8.0 (17.1) | 10.6 (19.1) | −2.5 (27.4) | −0.46 (3.67) |
Results of a logistic regression that evaluated the association of baseline factors with VF improvement are shown in Table 4 . The factors evaluated were the same as those previously tested for association with VF loss, as reported by Musch and associates. Main effects without interactions were found for sex (female subjects were more likely to show VF improvement, OR = 1.73; 95% CI, 1.17, 2.56) and an indicator variable for a visit conducted 1 year prior to cataract extraction (VF improvement was much less likely at this time, OR = 0.11; 95% CI, 0.02, 0.62). A significant interaction between baseline MD and treatment is shown in Figure 2 A, wherein participants treated with surgery who presented at baseline with more substantial VF loss were more likely to show VF improvement than those with comparable VF loss treated medically. An interaction of baseline vascular or cardiac disease (other than hypertension) with time ( Figure 2 B) indicated that participants with these conditions were increasingly less likely over time to show VF improvement than those who did not have these conditions.
| Main Effects | Repeated Measures Logistic Regression of Visit-Specific Visual Field Improvement | Cox Regression of Time to Sustained (for 2 Consecutive Visits) Visual Field Improvement | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P Value b | HR | 95% CI | P Value b | |
| Sex (female vs male) | 1.73 | (1.17, 2.56) | .0061 | 1.77 | (1.20, 2.61) | .0039 |
| Cataract a | 0.11 | (0.02, 0.62) | .0127 | 0.45 | (0.06, 3.25) | .4304 |
| Baseline MD (per dB) | See interaction with treatment | 0.90 | (0.87, 0.94) | <.0001 | ||
| Interactions | ||||||
| Treatment × baseline MD | .0403 | No significant interactions | ||||
| Surgery vs medicine @ MD 0 = −5 dB | 0.92 | (0.62, 1.34) | ||||
| Surgery vs medicine @ MD 0 = −10 dB | 1.29 | (0.81, 2.05) | ||||
| Surgery vs medicine @ MD 0 = −15 dB | 1.82 | (0.90, 3.89) | ||||
| Other vascular disease*time | .0136 | |||||
| Vascular disease vs none @ 3 years | 1.00 | (0.57, 1.77) | ||||
| Vascular disease vs none @ 5 years | 0.59 | (0.30, 1.19) | ||||
| Vascular disease vs none @ 7 years | 0.35 | (0.13, 0.96) | ||||
| IOP variables c | ||||||
| Mean IOP d | 1.30 | (1.10, 1.54) | .0024 | 1.23 | (1.05, 1.43) | .0086 |
| Standard deviation IOP d | 1.05 | (0.90, 1.23) | .5520 | 0.99 | (0.83, 1.18) | .9048 |
| Maximum IOP d | 1.20 | (1.00, 1.45) | .0540 | 1.22 | (1.00, 1.48) | .0467 |
| Minimum IOP d | 1.21 | (1.04, 1.41) | .0149 | 1.21 | (1.04, 1.42) | .0148 |
| Range IOP d | 1.04 | (0.89, 1.21) | .6225 | 1.02 | (0.82, 1.28) | .8286 |
| % IOP <16 mm Hg e | 1.17 | (1.04, 1.31) | .0092 | 1.13 | (1.02, 1.24) | .0205 |
| % IOP <18 mm Hg e | 1.20 | (1.05, 1.37) | .0091 | 1.18 | (1.04, 1.33) | .0091 |
| % IOP <20 mm Hg e | 1.30 | (1.05, 1.61) | .0162 | 1.18 | (1.00, 1.39) | .0501 |
| % IOP <22 mm Hg e | 1.18 | (0.90, 1.54) | .2421 | 1.15 | (0.93, 1.41) | .2055 |
| All IOP <16 mm Hg | 1.57 | (1.08, 2.27) | .0172 | 1.41 | (0.93, 2.13) | .1066 |
| All IOP <18 mm Hg | 1.08 | (0.70, 1.68) | .7164 | 1.53 | (1.03, 2.26) | .0345 |
| All IOP <20 mm Hg | 1.93 | (1.34, 2.78) | .0004 | 1.65 | (1.09, 2.51) | .0188 |
| All IOP <22 mm Hg | 1.34 | (0.95, 1.88) | .0922 | 1.36 | (0.86, 2.16) | .1890 |
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