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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
JAMA Ophthalmol. Author manuscript; available in PMC 2014 April 1.
Published in final edited form as:
PMCID: PMC3804264

Relationship Between Rates of Binocular Visual Field Loss and Vision-Related Quality of Life in Glaucoma



To evaluate the relationship between binocular rates of visual field change and vision-related quality of life (VRQOL) in glaucoma.


The study included 796 eyes of 398 participants that had diagnosed or suspected glaucoma followed for an average of 7.3 ± 2.0 years. Subjects were recruited from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES). VRQOL was evaluated using the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) at the last follow-up visit. Integrated binocular visual fields (BVF) were calculated from the monocular fields of each patient. Linear regression of mean deviation (MD) values was used to evaluate rates of visual field change during the follow-up period. Logistic regression models were used to investigate the relationship between abnormal VRQOL and rates of visual field change, while adjusting for potentially confounding socio-economic and demographic variables.


Thirty-two patients (8.0%) had abnormal VRQOL as determined by the results of the NEI VFQ-25 questionnaire. Subjects with abnormal VRQOL had significantly faster rates of BVF change than those with normal VRQOL (−0.18 db/year vs. −0.06 dB/year, respectively; P < 0.001). Rates of BVF change were significantly associated with abnormality in VRQOL (OR = 1.31 per 0.1dB/year faster; P = 0.038), after adjustment for confounding variables.


Patients with faster rates of BVF change were at higher risk of reporting abnormal VRQOL. Assessment of rates of BVF change may provide useful information in determining risk of functional impairment in glaucoma.


Glaucoma is a progressive optic neuropathy that may result in significant visual impairment. The loss of vision affects the quality of life and also has economic consequences to the patient and to society.1 The impact of glaucoma on vision-related quality of life (VRQOL) has frequently been investigated using questionnaire-based self-reported assessments, such as the National Eye Institute Visual Function Questionnaire (NEI VFQ-25).25 Previous studies have shown a significant relationship between the severity of visual field defects on standard automated perimetry (SAP) and the results on the NEI VFQ-25 questionnaire, with more severe defects associated with worse scores.2, 5 Patients with visual field loss in both eyes tend to have worse scores on the NEI VFQ-25 questionnaire than those with unilateral defects. For patients with asymmetric degrees of damage, visual field assessment of the less affected eye frequently shows a stronger relationship with questionnaire results than the more affected eye. Such relationship is understandable as one eye may compensate for the loss of vision of the other eye. For this reason, it has been suggested that binocular visual field (BVF) tests may represent the best way to gauge the impact of field losses on quality of life. In the absence of “true” binocular vision tests, one can approximate the BVF by integrating the results of monocular visual fields.6 These integration methods have been shown to agree closely with the “true” BVF, thus offering a practical way for studying rates of BVF change.

Previous studies investigating the relationship between BVF results and NEI VFQ-25 have been conducted using cross-sectional data and have only considered the visual field status at the time of questionnaire administration.2, 5 This ignores the rate at which the patients develop visual field defects. Although significant relationships have been reported between BVF and NEI-VFQ-25, the strength of the association has been far less than perfect. One reason for the imperfect relationship between the severity of visual field damage and patient-reported VRQOL may be the development of compensatory strategies by affected patients.7 It is likely that patients with slowly progressing disease will have more time to adapt to their limited functional status by developing compensatory strategies and, therefore, will be less likely to report an abnormal VRQOL. On the other hand, in patients with rapidly progressing disease, the visual field defects may result in substantial impairment in the ability to perform daily activities without time for development of compensatory strategies. This would be more readily recognized by such a patient and more likely to be reported as an abnormal VRQOL. Therefore, it is reasonable to hypothesize that for two patients with similar degrees of visual field loss, the one with previous history of faster disease progression will report worse VRQOL status than the one with slowly progressing damage. However, to our knowledge, this hypothesis has not been investigated in the literature.

The purpose of the current study was to investigate the relationship between rates of BVF loss and results of patient-reported VRQOL outcomes in glaucoma, as assessed by the NEI VFQ-25 questionnaire.



This was an observational cohort study. Participants from this study were included in two prospective longitudinal studies designed to evaluate optic nerve structure and visual function in glaucoma (the African Descent and Glaucoma Evaluation Study [ADAGES] and the Diagnostic Innovations in Glaucoma Study [DIGS]). The 3-site ADAGES collaboration includes the Hamilton Glaucoma Center at the Department of Ophthalmology, University of California San Diego (UCSD), the New York Eye and Ear Infirmary and the Department of Ophthalmology, University of Alabama at Birmingham, Birmingham (UAB). Although DIGS includes only patients recruited at UCSD, the protocol of the two studies is identical. Methodological details have been described previously.8 The University of California San Diego Human Subjects Committee approved all protocols, and methods adhered to the Declaration of Helsinki.

At each visit during follow-up, subjects underwent a comprehensive ophthalmic examination, including review of medical history, best corrected visual acuity (BCVA), slit-lamp biomicroscopy, intraocular pressure (IOP) measurement using Goldmann applanation tonometry, gonioscopy, dilated fundoscopy examination using a 78-diopter (D) lens, stereoscopic optic disc photography, and standard automated perimetry (SAP) with 24-2 Swedish Interactive Threshold Algorithm (Carl Zeiss Meditec, Inc., Dublin, CA). To be included subjects had to have best corrected visual acuity of 20/40 or better, spherical refraction less than ± 5.0 D, cylinder correction less than 3.0 D and open angle with gonioscopy. Subjects with coexisting retinal disease, uveitis or non-glaucomatous optic disc neuropathy were excluded from the study.

We included 796 eyes of 398 participants that had diagnosed or suspected glaucoma at the baseline visit. Eyes were classified as having glaucoma based on repeatable abnormal visual field test results at baseline, defined as a pattern standard deviation (PSD) with P < 0.05, and/or glaucoma hemifield test (GHT) results outside normal limits, regardless of the appearance of the optic disc. Glaucoma suspects were defined as eyes with abnormal-appearing optic discs (presence of neuroretinal rim thinning or localized or diffuse retinal nerve fiber layer defects indicative of glaucoma, i.e., glaucomatous optic neuropathy) by masked stereophotographic assessment without repeatable abnormal SAP results. Glaucoma suspects also included eyes with intraocular pressure greater than 22 mmHg but with healthy-appearing optic discs and without repeatable abnormal SAP results.

Each patient was required to have a minimum of 5-paired (i.e., visual field tests of both eyes at the same day) SAPs during a minimum of 5 years of follow-up before completion of the NEI VFQ-25. The period of follow-up for the study started on October 1998 and extended until January 2012, however, patients entered and exited the study at different dates. The NEI VFQ-25 questionnaires were completed for all patients during the period extending from December 2009 to January 2012. For each patient, only SAPs obtained before the NEI VFQ-25 were included.

Monocular and Binocular Visual Fields

Monocular SAP was performed using the 24-2 Swedish Interactive Threshold Algorithm at all visits during the follow-up period. Only reliable tests (≤ 33% fixation losses and false negatives, and < 15% false positives) were included. In addition, all visual fields were evaluated by the UCSD Visual Field Assessment Center (VisFACT).9 Visual fields were reviewed and excluded in the presence of artifacts such as lid or rim artifacts, fatigue effects, inattention or inappropriate fixation. Visual fields were also reviewed for the presence of abnormalities that could indicate diseases other than glaucoma, such as homonymous hemianopia.

For calculation of the estimated pointwise sensitivities of the BVF, the monocular SAP threshold sensitivities of the right and left eyes were used. The 24-2 stimulus presentation pattern consists of 54 points within the central 24° in a 6° grid bracketing the horizontal and vertical meridians. The two points corresponding to the blind spot and the two most nasal points of each eye were excluded for the calculations of the BVF. This was necessary because these points do not have a spatial correspondent in the visual field of the fellow eye. Therefore, each individual BVF had a total of 48 overlapping points as shown in Figure 1.

Schematic representation of the binocular visual field (BVF) using the summation method to integrate the sensitivities of the right and left visual fields.

The sensitivity for each point of the BVF was estimated using the binocular summation model described by Nelson-Quigg et al.10 According to this model, the binocular sensitivity can be estimated using the following formula:


where Sr and Sl are the monocular threshold sensitivities for corresponding visual field locations of the right and left eyes, respectively. In order to calculate the binocular sensitivity from the formula above, light sensitivity had to be converted to a linear scale (apostilbs) and then converted back to logarithmic scale (decibels).

Evaluation of rates of visual field change during follow-up was performed using the mean deviation (MD). For calculation of monocular and BVF MD, we applied the formula described by Anderson and colleagues11:


where L is the total number of locations, TDi is the total deviation in decibels of the normal age-matched population at location i, and Si2 is the variance of the i point in the same normal age-matched population. We used data from 102 paired visual fields of age-matched subjects from DIGS and ADAGES cohorts to calculate the TDi and the Si2 of monocular and binocular visual fields.

National Eye Institute Visual Function Questionnaire

VRQOL was evaluated using the NEI VFQ-25. This questionnaire was designed to assess the dimensions of self-reported vision-target health status that are relevant for subjects with chronic eye diseases.12, 13 The NEI VFQ-25 consists of a base set of 25 vision targeted questions representing 11 vision-related constructs, plus an additional single-item general health rating question. The vision-related scales are: general vision, near and distance vision activities, ocular pain, vision-related social function, vision-related role function, vision-related mental health, vision-related dependency, driving difficulties, color vision and peripheral vision. Each subscale consists of a minimum of 1 item and a maximum of 4 items. The standard algorithm used to score the results has a scale ranging from 0 to 100, with higher scores representing better visual functioning and well-being. In this format, scores represent the achieved percentage of the total possible score, e.g., a score of 50 represents 50% of the highest possible score. Thirty-six subjects did not answer questions related to driving difficulties as they were not active drivers. Missing data was imputed using multivariable linear regression using information from the other NEI VFQ-25 variables.

We defined abnormal VRQOL as the presence of a score less than 50 on any one of the 10 vision-related subscales of the NEI VFQ-25, excluding the subscale related to ocular pain. This corresponds to approximately 2 standard deviations below the scores previously described for a normal reference population.13

Demographic, Clinical and Socio-economic Variables

Socio-economic and clinical questionnaires were also administered to patients at the time of the NEI VFQ-25. These questionnaires contained a survey about demographics, history of ocular and medical conditions, marital status, health insurance coverage, degree of education and income. As these variables could potentially affect patient perceptions about VRQOL, they were included as potentially confounding factors in the analysis of the relationship between BVF change and NEI VFQ-25 results. These variables were categorized for inclusion in the multivariable models as marital status (married [yes/no]), presence of health insurance (yes/no), degree of education (at least high school degree [yes/no]) and income (less than $25,000/year [yes/no]). Seventy-eight subjects refused to provide income information. Missing data was imputed based on a multivariable logistic regression using data from the other socio-economic variables. For co-morbidities, we investigated the presence or history of the following conditions: diabetes mellitus, arthritis, high blood pressure, heart disease, stroke, depression, asthma and cancers. A simple summation score was used to create a co-morbidity index.14 Visual acuity was measured using an Early Treatment Diabetic Retinopathy (ETDRS) chart and logMAR measurements were used in the analyses. Diagnostic categorization at baseline (glaucoma [yes/no]) was also included in the analyses, as glaucomatous patients and glaucoma suspects could have different perceptions about VRQOL.

During follow-up patients were treated at the discretion of the attending the ophthalmologist. History of topic anti-glaucomatous treatment during follow-up was also included as a potentially confounding variable in multivariable analysis.

Statistical Analysis

Descriptive statistics included mean and standard deviation for normally distributed variables and median, first quartile and third quartile for non-normally distributed variables.

The relationship between changes in BVF MD and abnormality in VRQOL was initially investigated using a random-intercept random-slope linear mixed model with a t-distribution for random effects.1521 In these models, the average evolution of a specific response is described using some function of time, and subject-specific deviations from this average evolution are introduced by random intercepts and random slopes, allowing for different baseline values and different rates of change for each eye. In the linear mixed model, BVF MD values were considered as the dependent variable. Presence of abnormality in VRQOL was included as a fixed-effect covariate with a value of 1 if the patient had an abnormal VRQOL as determined by the NEI VFQ-25 and a value of 0 if in the patient had a normal VRQOL. Time was included as a continuous predictor. The two-way interaction between time and VRQOL was included in the model in order to evaluate whether there was a significant difference in longitudinal BVF MD over time between those with normal and abnormal VRQOL. Rates of change were calculated for the BVF and for monocular visual field exams. For each patient, eyes with faster (more negative slope) and slower rates of change (more positive slope) were identified as the worse and better eye, respectively.

We were interested whether rates of BVF change were predictive of abnormality in VRQOL. For this analysis, we investigated the relationship between VRQOL (dependent variable) and rates of BVF change using a multivariable logistic model, after adjusting for potentially confounding demographic, clinical and socio-economic variables described above. We also adjusted for the BVF MD value closest to the NEI VFQ-25, in order to determine whether rates of BVF change could provide statistically independent information besides that provided by the most recent BVF in predicting abnormality in VRQOL.

All statistical analyses were performed with commercially available software (Stata version 11, StataCorp, College Station, TX). The alpha level (type I error) was set at 0.05.


This study included 3,678 calculated BVFs of 398 subjects followed for an average of 7.3 ± 2.0 years before the completion of the NEI VFQ-25 questionnaires. Average age at the questionnaire visit was 65.7 ± 11.6 years. Two hundred twenty two (55.8%) patients were Caucasian, 167 (42.0%) were African-American and 9 (2.2%) had other ethnicity. Two hundred thirty four (58.8%) were female and 164 (41.2%) were male. The average number of visits during follow-up was 9.2 ± 3.2.

Thirty-two patients (8.0%) had abnormal VRQOL as determined by the results of the NEI VFQ-25 questionnaire. Table 1 shows baseline clinical and demographic characteristics for subjects with normal and abnormal VRQOL. Subjects with abnormal VRQOL had significantly faster rates of BVF change than those without normal VRQOL (−0.18 db/year vs. −0.06 dB/year, respectively; P < 0.001). The follow-up time was similar between groups (7.4 ± 2.0 vs. 6.8 ± 1.8, P = 0.130). Table 2 shows results of the NEI VFQ-25 component scores in subjects who had normal VRQOL versus those who had abnormal VRQOL. Statistically significant differences were found for all vision-related subscales.

Table 1
Baseline clinical and demographic characteristics of subjects with normal and abnormal vision-related quality of life (VRQOL).
Table 2
Results of the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) in subjects with normal and abnormal vision-related quality of life (VRQOL).

We investigated whether rates of BVF change were indicative of abnormality in VRQOL, after adjusting for BVF MD closest to the NEI VFQ-25 questionnaire and other demographic, clinical and socio-economic variables in a logistic regression model. Table 3 shows the results for the univariable and multivariable logistic regression analyses. In univariable regressions, rate of BVF change (odds ratio [OR] = 1.23 per 0.1dB/year faster; P = 0.001), lower BVF MD closest to the questionnaire visit (OR = 1.10 per 1 dB lower; P = 0.027), younger age (OR = 1.39 per decade younger; P = 0.008), and lower income (OR = 3.67; P < 0.001) were significantly associated with abnormality in VRQOL. Being married at the time of the VRQOL questionnaire was associated with a decrease in the odds of having abnormal VRQOL (OR = 0.43; P = 0.029). Higher educational level (OR = 2.01; P = 0.097), follow-up period (OR = 0.85 per year longer; P = 0.133), higher comorbity index (OR = 1.34 per additional comorbity; P = 0.074) and African-American ancestry (OR = 1.63; P = 0.185) tended to be associated with abnormal VRQOL, but the association did not reach statistical significance. All other variables had univariable associations with P values greater than 0.20. All variables with P < 0.20 were entered in the multivariable model as shown in Table 3. Rate of BVF change was still significantly associated with abnormality in VRQOL even after adjustment for degree of BVF loss (MD at the visit closest to questionnaire administration) and other confounding variables (OR = 1.31 per 0.1dB/year faster; P = 0.038). Each 0.1dB/year faster slope of BVF change was associated with a 31% increase in the odds of abnormality in VRQOL. Age (OR = 1.62 per decade younger; P = 0.008) and income (OR = 2.87; P = 0.015) were also significantly associated with abnormal VRQOL in the multivariable logistic model. Figure 2 shows the relationship between probability of reporting abnormal VRQOL and slope of BVF MD over time. Subjects with faster rates of BVF change had a higher chance of reporting abnormality in VRQOL.

Relationship between probability of reporting abnormal vision-related quality of life (VRQOL) and slope of binocular visual field (BVF) mean deviation (MD) over time. Subjects with faster rates of BVF change had a higher probability of reporting abnormal ...
Table 3
Results of the univariable and multiviable logistic regression models for prediction of abnormality in vision-related quality of life (VRQOL).

We also evaluated whether the rate of visual field change of the worse and the better eye were independently associated with abnormal VRQOL, after adjusting for the MD and for the same demographic, clinical, and socio-economic variables specified earlier. Rates of visual field change for the worse (OR = 1.12 per 0.1 dB/year faster; P = 0.102) and better eye (OR = 1.14 per 0.1dB/year faster; P = 0.103) were not statistically associated with abnormal VRQOL.

Figure 3 shows visual field test results of two patients included in the study that had similar BVF damage at the time corresponding to the questionnaire visit. Patient A had an abnormal VRQOL, while patient B had a normal VRQOL. During the follow-up period before the questionnaire administration, patient A had a rate of change of −1.29 dB/year and clearly showed progression in the BVF. Conversely, patient B had a much slower rate of change of −0.22 dB/year during follow-up.

Example illustrating binocular visual field (BVF) results of two patients included in the study that had similar visual field damage at the time of the questionnaire. (A) Patient who reported an abnormal vision-related quality of life (VRQOL) and clearly ...


In the present study, we demonstrated that progressive loss in BVF sensitivity was associated with self-reported abnormal VRQOL, as measured by the NEI VFQ-25 questionnaire. Information provided by rates of BVF change was statistically predictive of abnormality in VRQOL even after adjustment for the degree of BVF loss at the time of questionnaire administration and other demographic and clinical potential confounders. Rates of BVF change also provided more information than monocular rates of change and cross-sectional visual field information for predicting VRQOL. To the best of our knowledge, this is the first study to relate rates of progressive BVF damage to self-reported VRQOL outcomes in glaucoma. These results may have significant implications for evaluation of the risk for developing functional impairment in glaucoma.

Each 0.1dB/year faster rate of BVF change was associated with a 31% increase in the odds of reporting abnormal VRQOL. When we investigated the relationship between monocular rates of visual field change (better and worse eye) and abnormality in VRQOL, we observed that the predictive abilities were inferior to that of the BVF. Moreover, the association between monocular rates of visual field change and abnormality in VRQOL did not reach statistical significance. These results indicate that longitudinal assessment of the integrated BVF seems to provide more information for detecting patients at risk of presenting functional impairment than assessment of monocular visual fields separately. This can be explained by the fact that the BVF combines the sensitivities of both visual fields and, therefore, is likely to be a better representation of the patient’s experience of the external world.

Cross-sectional information provided by the BVF MD was statistically associated with abnormality in VRQOL in the univariable logistic model. This result is in agreement with previous studies that have evaluated the relationship between BVF damage and VRQOL.2, 5, 22 However, cross-sectional information was not statistically associated with abnormality in VRQOL after adjustment for longitudinal information provided by the rate of BVF change and other clinical and demographic characteristics. This may be explained by the fact that having a sequence of visual field tests is likely to provide a more robust evaluation of the visual field status than a single test, which is subject to much more variability and may not always reflect the true existing impairment.23, 24 Our findings show the importance of calculating rates of BVF change instead of relying only on the information provided by a single visual field exam to estimate risks of functional impairment.

Although there is no consensus in the literature about the effects of peripheral vision loss on the ocular motor system, previous studies suggested that patients with peripheral visual field loss tend to ignore the region with scotoma rather than making more eye movements to compensate for the loss.2528 Therefore, it is not surprising that, in the absence of a compensatory search mechanism for the visual field loss, patients progressing at a faster rate in the BVF would experience a greater impact of the disease on their daily activities. Moreover, it is reasonable to admit that glaucomatous patients with visual field losses would develop perceptual plasticity similar to the one that has been reported in individuals suffering from macular degeneration and central visual field damage.7, 29 However, experimental studies suggest that these mechanisms are not immediate and that long-term neuronal reorganization of the visual cortex is necessary.3032 Similarly, the development of other compensatory mechanisms not related to eye movement that would enable patients to cope better with losses of visual function are likely to occur relatively slowly. Therefore, patients with fast progressive disease may not have enough time to develop those compensatory mechanisms and would be the ones most likely to report worse VRQOL scores.

We used estimated BVFs from monocular visual fields instead of “true” BVFs. Although “true” BVFs can be obtained using specific perimetric strategies, they are not routinely performed in clinical practice. Among the several different methods previously proposed for construction of the integrated BVF from monocular fields, we used the summation method as it has been shown to have a superior correlation with the “true” BVF exam compared to other approaches.6 Although the methodology used in our study to calculate BVF rates of change requires some mathematical calculations, it can be easily implemented into the standard software of visual field instruments in order to provide clinicians with information about rates of BVF change, which may help determine risk of functional impairment.

The NEI VFQ-25 was used to assess VRQOL. This questionnaire has been previously validated to investigate quality of life in patients with chronic eye diseases and showed a better correlation with visual impairment than other questionnaires.2, 5, 33 However, it is important to note that there is no consensus on how to classify patients based on the results of the NEI VFQ-25. In our study, we determined that abnormality in VRQOL was present if at least one of the 10 vision-related subscores was below 50, excluding the one related to ocular pain. This would represent at least 2 standard deviations below the scores measured on a reference normal population.13 This classification ensured the identification of a subgroup with significant abnormality in VRQOL, as indicated by the significant differences for all subscales displayed in Table 2. In addition, the general clinical and socio-economic variables associated with abnormal VRQOL in our analyses are in agreement with previous studies.34

Our study has limitations. We did not have longitudinal data on the NEI VFQ-25 questionnaires and the incidence of VRQOL could not be established in our cohort. Therefore, a causal relationship between rates of visual field loss and loss in VRQOL could not be established. However, the design of our study is similar to those that have investigated associations between quality of life and a number of predictive factors in several diseases. Despite their limitations, such investigations can still provide important information about factors associated with a particular response and motivate the conduct of studies with more complex designs. Although the association found in our study was still significant after adjustment for potentially confounding variables, prospective studies determining the time course of development of functional impairment in glaucoma and its associated factors will be paramount. Another limitation of our study is that we assumed a linear rate of BVF change over time. Several studies have suggested that functional changes do not follow a linear course over the natural history of the disease,3538 which might be related to the logarithmic scaling (decibel) of visual field sensitivity data. Nevertheless, the assumption of linear change is probably a reasonable one for short and medium follow-up periods, as performed in clinical practice. Another limitation is that NEI VFQ-25 questionnaires are instruments that provide subjective measure of VRQOL. As a consequence, these questionnaires may not reflect the true impairment experienced by patients in daily activities. It is known, for example, that adults with aging-associated cognitive impairment have a tendency to overestimate functional abilities,39 while subjects with depression tend to underestimate their abilities.40, 41 The use of more objective tests, such as driving simulators,42 could potentially provide a more accurate representation of the impairment experienced by glaucomatous patients on daily activities. Future studies should investigate the relationship between rates of visual field loss and objective measures of functional impairment.

In conclusion, rates of progressive BVF loss were predictive of abnormality in VRQOL and may be helpful in identifying patients at risk for development of functional impairment from glaucoma. Longitudinal information on BVFs was more predictive of VRQOL abnormality than longitudinal monocular information and cross-sectional evaluation of visual field damage.


Supported in part by NIH/NEI grants EY021818 (FAM), EY11008 (LMZ), EY14267 LMZ), CAPES grant BEX 1066/11-0, an unrestricted grant from Research to Prevent Blindness (New York, NY), participant retention incentive grants in the form of glaucoma medication at no cost from Alcon, Allergan, Pfizer, Merck and Santen.

Linda M. Zangwill – Research Instrument support – Heidelberg Engineering, Carl Zeiss Meditec, Optovue, Topcon Inc.

Robert N. Weinreb – Consultant – Meditec-Zeiss

Peter N. Rosen – Financial support – None

Jeffrey M. Liebmann – Financial support – Consultant: Alcon Laboratories, Inc., Allergan, Inc., Diopsys, Inc, Optovue Inc., Glaukos, Inc., Quark, Inc., Merz Laboratories, Inc. Research support: Carl Zeiss Meditec, Inc., Diopsys, Inc., Optovue, Inc., Pfizer, Inc., Alcon Laboratories, Inc., Merck, Linc. Allergan, Inc.,Topcon Medical Systems, Inc., Glaukos, Inc.

Christopher A. Girkin – Financial support – None

Felipe A. Medeiros – Financial support – Carl-Zeiss Meditec, Inc; Heildelberg Engineering, GmBH


Financial Disclosure(s):

The author(s) have made the following disclosure(s):

Renato Lisboa – None

Yeoun Sook Chun – None


1. Weinreb RN, Khaw PT. Primary open-angle glaucoma. Lancet. 2004;363(9422):1711–1720. [PubMed]
2. McKean-Cowdin R, Wang Y, Wu J, Azen SP, Varma R. Impact of visual field loss on health-related quality of life in glaucoma: the Los Angeles Latino Eye Study. Ophthalmology. 2008;115(6):941–948. e941. [PubMed]
3. Spaeth G, Walt J, Keener J. Evaluation of quality of life for patients with glaucoma. Am J Ophthalmol. 2006;141(1 Suppl):S3–S14. [PubMed]
4. Jampel HD. Glaucoma patients' assessment of their visual function and quality of life. Trans Am Ophthalmol Soc. 2001;99:301–317. [PMC free article] [PubMed]
5. van Gestel A, Webers CA, Beckers HJ, van Dongen MC, Severens JL, Hendrikse F, et al. The relationship between visual field loss in glaucoma and health-related quality-of-life. Eye. 2010;24(12):1759–1769. [PubMed]
6. Nelson-Quigg JM, Cello K, Johnson CA. Predicting binocular visual field sensitivity from monocular visual field results. Invest Ophthalmol Vis Sci. 2000;41(8):2212–2221. [PubMed]
7. Huxlin KR. Perceptual plasticity in damaged adult visual systems. Vision Res. 2008;48(20):2154–2166. [PubMed]
8. Racette L, Liebmann JM, Girkin CA, Zangwill LM, Jain S, Becerra LM, et al. African Descent and Glaucoma Evaluation Study (ADAGES): III. Ancestry differences in visual function in healthy eyes. Arch Ophthalmol. 2010;128(5):551–559. [PMC free article] [PubMed]
9. Sample PA, Girkin CA, Zangwill LM, Jain S, Racette L, Becerra LM, et al. The African Descent and Glaucoma Evaluation Study (ADAGES): design and baseline data. Arch Ophthalmol. 2009;127(9):1136–1145. [PMC free article] [PubMed]
10. Nelson-Quigg JM, Cello K, Johnson CA. Predicting binocular visual field sensitivity from monocular visual field results. Invest Ophthalmol Vis Sci. 2000;41(8):2212–2221. [PubMed]
11. Anderson DR. The single field printout with Statpac analysis. In: Kist K, editor. Automated Static Perimetry. St Louis: Mosby; 1992. p. 84.
12. Mangione CM, Lee PP, Gutierrez PR, Spritzer K, Berry S, Hays RD. Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol. 2001;119(7):1050–1058. [PubMed]
13. Mangione CM, Lee PP, Pitts J, Gutierrez P, Berry S, Hays RD. Psychometric properties of the National Eye Institute Visual Function Questionnaire (NEI-VFQ). NEI-VFQ Field Test Investigators. Arch Ophthalmol. 1998;116(11):1496–1504. [PubMed]
14. Globe DR, Varma R, Torres M, Wu J, Klein R, Azen SP. Self-reported comorbidities and visual function in a population-based study: the Los Angeles Latino Eye Study. Arch Ophthalmol. 2005;123(6):815–821. [PubMed]
15. Medeiros FA, Zangwill LM, Alencar LM, Bowd C, Sample PA, Susanna R, Jr, et al. Detection of glaucoma progression with stratus OCT retinal nerve fiber layer, optic nerve head, and macular thickness measurements. Invest Ophthalmol Vis Sci. 2009;50(12):5741–5748. [PMC free article] [PubMed]
16. Medeiros FA, Zangwill LM, Alencar LM, Sample PA, Weinreb RN. Rates of progressive retinal nerve fiber layer loss in glaucoma measured by scanning laser polarimetry. Am J Ophthalmol. 2010;149(6):908–915. [PubMed]
17. Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med. 1997;16(20):2349–2380. [PubMed]
18. Feldman HA. Families of lines: random effects in linear regression analysis. J Appl Physiol. 1988;64(4):1721–1732. [PubMed]
19. Medeiros FA, Alencar LM, Zangwill LM, Bowd C, Vizzeri G, Sample PA, et al. Detection of progressive retinal nerve fiber layer loss in glaucoma using scanning laser polarimetry with variable corneal compensation. Invest Ophthalmol Vis Sci. 2009;50(4):1675–1681. [PMC free article] [PubMed]
20. Medeiros FA, Leite MT, Zangwill LM, Weinreb RN. Combining structural and functional measurements to improve detection of glaucoma progression using Bayesian hierarchical models. Invest Ophthalmol Vis Sci. 2011;52(8):5794–5803. [PMC free article] [PubMed]
21. Lee KJ, Thompson SG. Flexible parametric models for random-effects distributions. Stat Med. 2008;27(3):418–434. [PubMed]
22. Jampel HD, Friedman DS, Quigley H, Miller R. Correlation of the binocular visual field with patient assessment of vision. Invest Ophthalmol Vis Sci. 2002;43(4):1059–1067. [PubMed]
23. Artes PH, Chauhan BC. Longitudinal changes in the visual field and optic disc in glaucoma. Prog Retin Eye Res. 2005;24(3):333–354. [PubMed]
24. Vesti E, Johnson CA, Chauhan BC. Comparison of different methods for detecting glaucomatous visual field progression. Invest Ophthalmol Vis Sci. 2003;44(9):3873–3879. [PubMed]
25. Crabb DP, Smith ND, Rauscher FG, Chisholm CM, Barbur JL, Edgar DF, et al. Exploring eye movements in patients with glaucoma when viewing a driving scene. PloS one. 2010;5(3):e9710. [PMC free article] [PubMed]
26. Wiecek E, Pasquale LR, Fiser J, Dakin S, Bex PJ. Effects of peripheral visual field loss on eye movements during visual search. Frontiers in psychology. 2012;3:472. [PMC free article] [PubMed]
27. Vargas-Martin F, Peli E. Eye movements of patients with tunnel vision while walking. Invest Ophthalmol Vis Sci. 2006;47(12):5295–5302. [PMC free article] [PubMed]
28. Luo G, Vargas-Martin F, Peli E. The role of peripheral vision in saccade planning: learning from people with tunnel vision. Journal of vision. 2008;8(14):25, 21–28. [PMC free article] [PubMed]
29. Zur D, Ullman S. Filling-in of retinal scotomas. Vision Res. 2003;43(9):971–982. [PubMed]
30. Calford MB, Wright LL, Metha AB, Taglianetti V. Topographic plasticity in primary visual cortex is mediated by local corticocortical connections. J Neurosci. 2003;23(16):6434–6442. [PubMed]
31. Darian-Smith C, Gilbert CD. Axonal sprouting accompanies functional reorganization in adult cat striate cortex. Nature. 1994;368(6473):737–740. [PubMed]
32. Darian-Smith C, Gilbert CD. Topographic reorganization in the striate cortex of the adult cat and monkey is cortically mediated. J Neurosci. 1995;15(3 Pt 1):1631–1647. [PubMed]
33. Varma R, Wu J, Chong K, Azen SP, Hays RD. Impact of severity and bilaterality of visual impairment on health-related quality of life. Ophthalmology. 2006;113(10):1846–1853. [PubMed]
34. Broman AT, Munoz B, Rodriguez J, Sanchez R, Quigley HA, Klein R, et al. The impact of visual impairment and eye disease on vision-related quality of life in a Mexican-American population: proyecto VER. Invest Ophthalmol Vis Sci. 2002;43(11):3393–3398. [PubMed]
35. Hood DC, Kardon RH. A framework for comparing structural and functional measures of glaucomatous damage. Prog Retin Eye Res. 2007;26(6):688–710. [PMC free article] [PubMed]
36. Ajtony C, Balla Z, Somoskeoy S, Kovacs B. Relationship between visual field sensitivity and retinal nerve fiber layer thickness as measured by optical coherence tomography. Invest Ophthalmol Vis Sci. 2007;48(1):258–263. [PubMed]
37. Strouthidis NG, Vinciotti V, Tucker AJ, Gardiner SK, Crabb DP, Garway-Heath DF. Structure and function in glaucoma: The relationship between a functional visual field map and an anatomic retinal map. Invest Ophthalmol Vis Sci. 2006;47(12):5356–5362. [PubMed]
38. Medeiros FA, Zangwill LM, Bowd C, Mansouri K, Weinreb RN. The Structure and Function Relationship in Glaucoma: Implications for Detection of Progression and Measurement of Rates of Change. Invest Ophthalmol Vis Sci. 2012 [PMC free article] [PubMed]
39. Rubenstein LZ, Schairer C, Wieland GD, Kane R. Systematic biases in functional status assessment of elderly adults: effects of different data sources. J Gerontol. 1984;39(6):686–691. [PubMed]
40. Kuriansky JB, Gurland BJ, Fleiss JL. The assessment of self-care capacity in geriatric psychiatric patients by objective and subjective methods. J Clin Psychol. 1976;32(1):95–102. [PubMed]
41. Kiyak HA, Teri L, Borson S. Physical and functional health assessment in normal aging and in Alzheimer's disease: self-reports vs family reports. The Gerontologist. 1994;34(3):324–330. [PubMed]
42. Medeiros FA, Weinreb RN, E RB, Rosen PN. Driving simulation as a performance-based test of visual impairment in glaucoma. J Glaucoma. 2012;21(4):221–227. [PMC free article] [PubMed]