Sawada, Hideko MD, PhD; Yoshino, Takaiko MD; Fukuchi, Takeo MD, PhD; Abe, Haruki MD, PhD
Department of Ophthalmology, Niigata University Medical and Dental Hospital, Niigata, Japan
Presented as a poster at the 2010 Annual Meeting of the American Academy of Ophthalmology (Scientific Poster 399) October 2010, Chicago.
Disclosure: Supported in part by a Grant-in-Aid for Scientific Research (23592556) from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The authors declare no conflict of interest.
T.F.: conception and design, writing the article, data collection, provision of materials, patients, or resources, statistical expertise; T.Y.: data collection and literature search; H.A.: administrative, technical, or logistic support.
The Committee for Research Ethics at Niigata University Medical and Dental Hospital approved the study protocol and the study was conducted according to the tenets of the Declaration of Helsinki.
Reprints: Hideko Sawada, MD, PhD, Department of Ophthalmology, Niigata University Medical and Dental Hospital, 1-757 Asahimachi, Niigata 951-8510, Japan (e-mail: email@example.com).
Received November 30, 2011
Accepted June 12, 2012
Many studies have investigated the impact of visual field defects on quality of life (QOL) in glaucoma patients.1–8 To implement treatment strategies with optimal timing, it is essential to understand the status and characteristics of QOL in glaucoma patients. Furthermore, considering that numerous daily activities require vision, it would be especially helpful to evaluate vision-specific QOL and assess its sensitivity to patterns of glaucomatous visual field defects. There are several visual field indexes used for assessing visual field loss in glaucoma patients, including mean deviation (MD), Advanced Glaucoma Intervention Study scores,1 Collaborative Initial Glaucoma Treatment Study scores,9 and Esterman visual field testing.3,10 Another index called the visual field index, or glaucoma progression index,11 is a recently developed index that also has been proved to have a good relationship with QOL in glaucoma patients.12 Of these various indexes, MD is one of the most frequently used with the Humphrey Field Analyzer (HFA), and has been shown in many studies to provide good to moderate correlation in evaluating QOL in glaucoma patients.1,8 Although current visual field indexes used with the HFA are able to detect decreased QOL, the results of their assessments may be nonequivalent among patients with similar glaucoma stages if these patients differ in terms of their visual field impairment. In follow-up patient examinations, we oftentimes notice different level of complaints among patients who have almost the same degree of MD decline but have different patterns of visual field loss. We presume that the same decreased MD status would not bring about the same degree of inconvenience among patients when the locations of their visual field defects are different. The significance of visual field loss should be different among different locations of visual field. Therefore, it would be more beneficial to patients if we were able to divide the visual fields into clusters and evaluate the relationship between vision-specific QOL and each cluster. A few studies have investigated the relationship between visual field defect location and QOL in order to identify essential clusters that contribute more to certain types of QOL.5 In addition, several studies have mapped patterns of visual fields or clustered visual fields.13–15 The use of clustered visual fields should prove advantageous for obtaining data on the status of individuals’ visual field defect, and thus for comparing the severity of decreased QOL among patients.
In the present study, we used an approach involving clustered visual fields to ascertain the relationship between visual field impairment and vision-specific QOL in glaucoma patients.
PARTICIPANTS AND METHODS
We examined 336 eyes of 168 consecutive patients (84 men and 84 women) who were diagnosed with glaucoma and had been treated at the Department of Ophthalmology of Niigata University Medical and Dental Hospital, Ojiya General Hospital, and Niigata Minami Hospital in Japan. The participants were recruited from July 2007 to December 2008. The median age of recruited patients was 61.5±11.9 years (range, 30 to 82 y). Eighty-four cases of normal tension glaucoma (NTG) and 84 cases of primary open-angle glaucoma (POAG) were eligible for this study. The number of patients with visual field defects worse than −6.0 dB in each cluster were counted, and was compared between NTG and POAG patients. In addition, the number of patients with advanced glaucoma stage (total MD<−12.0 dB) was counted and compared between NTG and POAG patients who also had visual field defect in cluster 6. The definition of visual field defects worse than −6.0 and −12.0 dB were made based on the early stage and the advanced stage of glaucomatous visual field defect, which were defined by Anderson and Patella.16 The study complied with the tenets of the World Medical Association’s Declaration of Helsinki and was also approved by the institutional ethics board. Written informed consent documents were completed by all enrolled participants after an explanation of the aims and methods of the study were provided. All patients’ data were kept confidential. Patients who had other ocular diseases or brain disease that could affect visual function were excluded from the study. We also excluded patients who had undergone ocular surgery, including laser therapy within 6 months of the study and cataract patients who were diagnosed with lens opacity stronger than grade 2 in the Emery-Little classification by the slit-lamp examination.
Clustering of Visual Fields and Data Collection
Corrected visual acuity was measured in all subjects. Visual field examination was performed on the bilateral eyes of all patients. We used the visual field data of HFA program 30-2, which were collected within 6 months of study inception. Only reliable visual field data that met the following criteria were used: fixation losses <20% and false-negative or false-positive errors <25%. We mapped the central 30 degrees of the visual field as previously reported by Suzuki et al,13 and divided this area into 10 clusters (Fig. 1). Specifically, we divided the upper and lower hemifields into 5 clusters each—the upper and lower paracentral visual fields were designated as clusters 1 and 6, the upper and lower arcuate fields as clusters 2 and 7, the upper and lower nasal fields as clusters 3 and 8, the upper and lower temporal fields as clusters 5 and 10, and the upper and lower peripheral fields as clusters 4 and 9. The scores for each of the 10 clusters were the averaged MD scores of all the tested points within the cluster.
We assessed patients’ vision-specific QOL by using the Japanese version of the 25-item National Eye Institute Visual Function Questionnaire (NEI VFQ-25).17 The NEI-VFQ-25 is composed of 12 subscales and a composite score.18–20 Each subscale measures the status of vision-related activities, social functioning, and emotional well-being. The 12 subscales include general health, general vision, ocular pain, near vision, distance vision, social function, mental health, role limitation, dependency, driving, color vision, and peripheral vision. The composite score is the averaged score of the 12 subscales, excluding that of general health. All patients were asked to complete the NEI VFQ-25. The questionnaire was self-administered; an interviewer read questions out loud only if patients asked for help.
We used a single linear regression analysis to assess the relationship between the scores of 10 visual field clusters and the NEI VFQ-25 scores. NEI VFQ-25 scores were used as independent variables for comparison with the dependent variables of MD scores. P values <0.0001 were considered to be statistically significant. We defined correlations as “good” when the correlation coefficient was ≥0.4 and “poor” when <0.4. We applied χ2 test to compare the percentage of patients who had visual field defects with MD <−6.0 dB in each cluster between NTG and POAG patients. P<0.05 was considered significant. All statistical analyses were carried out using statistical analysis software, SPSS version 14 for Windows (SPSS Inc., Chicago, IL).
Visual Function Data of Participants
The average best-corrected visual acuity in log10MAR±SD was −0.03±0.14 (range, −0.08 to 0.70) in the better eye, and 0.13±0.62 (range, −0.08 to no light perception) in the worse eye. The average MD score±SD was −8.54±8.25 dB (range, 2.15 to −30.38 dB) in the better eye and −14.52±8.01 dB (range, 0.08±−32.09 dB) in the worse eye (Fig. 2). As presented in Table 1, the average MD scores±SD in the visual field clusters ranged from −3.19 dB in cluster 10 to −13.03 dB in cluster 3 in the better eye, and from −5.69 dB in cluster 10 to −20.28 dB in cluster 3 in the worse eye. The average MD scores±SD in the better eye was −9.69±9.18 dB in POAG patients and −7.38±7.06 dB in NTG patients. The ratio of patients who had visual field defects worse than −6.0 dB in each cluster was larger in POAG patients than NTG patients in every clustered visual field except for cluster 1 (Table 2). Significant difference between the 2 groups was detected in clusters 4, 5, 6, 8, 9, and 10. Although the ratio of patients with total MD <−12.0 dB was similar between POAG (28.6%) and NTG (27.4%) patients, the ratio of patients with MD <−6.0 dB in cluster 6 was larger in POAG patients (27.4%) than NTG patients (14.3%). In addition, the percentage of patients who met both criteria was larger (19.0%) in POAG patients than NTG patients (8.3%) (Table 3).
Statistical Analysis of the Relationship Between the NEI VFQ-25 and Clustered Visual Fields
In the better eye, the composite scores of all 10 clusters showed significant correlations (P<0.0001) with the NEI VFQ-25. Clusters 6 and 9 were the most strongly correlated with the NEI VFQ-25—the composite and 7 out of 12 subscales had good correlation coefficients (r>0.4), and all subscale scores except for general health and ocular pain were significantly correlated to the NEI VFQ-25 (P<0.0001). Cluster 2 was also strongly correlated to the NEI VFQ-25, with good correlation coefficients (r>0.4) seen in the composite and 4 subscales. In contrast, cluster 10 correlated least strongly with the NEI VFQ-25, having poor correlation coefficients and was significantly related to the NEI VFQ-25 only in the composite and 2 subscales, including role limitation and dependency (Table 4).
Correlations between the NEI VFQ-25 and sectors in the worse eye were weaker than those of sectors in the better eye. Correlation coefficients were poor overall. The sector that correlated most strongly with QOL was cluster 6, which had good correlation coefficients (r>0.4) in the composites and 4 subscales. Clusters 7 and 9 had good correlation coefficients (r>0.4) in the composites and 3 subscales. The central upper visual field within 10 degrees of the fixation point, that is, clusters 1 and 2, did not show a significant relationship with the NEI VFQ-25 at all (Table 5).
Near and distant vision were significantly correlated with only clusters 6 and 7 in both the better eye and the worse eye. With regard to driving, however, all clusters in the upper hemifield of the better eye, particularly cluster 5 in the better eye (r=0.509), showed good correlation coefficients (r>0.4).
The results of the current study showed a significant correlation between vision-specific QOL and clustered visual fields in both the better and worse eyes. Although significance was different among clusters, correlations with QOL were generally higher in clusters in the better eye. In particular, correlation coefficients in the lower paracentral visual field (cluster 6) and the lower peripheral visual field (cluster 9) in the better eye were higher for several subscales, including general vision, near vision, distance vision, social function, mental health, role limitation, and driving. As for driving, the upper hemifield in the better eye was strongly correlated. In contrast, most correlation coefficients in the worse eye, particularly the upper hemifield, were low overall. Equivalent correlation coefficient values between the better and worse eyes were only observed in the lower hemifield for near vision, distant vision, and driving. These results indicate that the lower hemifield in the better eye is more important in vision-specific QOL. Although visual function in the worse eye seems to contribute less to vision-specific QOL, impairment of the worse eye is also considered to negatively impact on QOL in certain situations.
The HFA program 30-2 is frequently used in clinical settings to assess glaucomatous visual field progression. Although MD reflects decreased QOL well overall, it does not demonstrate a visual field section that provides specific information on some aspect of QOL status. In order to identify important clusters for QOL, it is essential to investigate the relationship between each cluster and QOL. This was done by Sumi et al,5 which proved the important cluster of visual field for QOL in glaucomatous visual fields. They conducted an evaluation based on clustered visual fields that were originally categorized. They divided the central 30 degrees into 3 clusters, including the portion of the visual field within 10 degrees of the fixation point, that between 11 and 20 degrees, and that between 21 and 30 degrees. Then, the central 10 degrees of the visual field was further divided into 6 clusters.5 Their results showed that the visual field in the lower hemifield within 5 degrees of the fixation point in the better eye significantly contributed to patients’ visual disability. The results of the current study showed particular significance of the lower paracentral visual field in the better eye, which was consistent with these previous findings.
Clustering of the visual fields in glaucomatous eyes has been performed by several investigators. Weber et al14 studied the relationship between morphologically damaged segments of the optic nerve head and visual function to create a “functional disc map.” Garway-Heath et al15 have created a map that demonstrated the anatomic relationship between visual field test points and corresponding regions of the optic nerve head. These studies focused on the anatomic changes in the optic nerve head to create visual field mappings. In contrast to these methods, Suzuki et al13 used a mathematical algorithm to divide the central 30 degrees of the visual field into 15 sectors based on the interpoint correlation of tested deviations. Although they performed the analysis without the anatomic assumption of a retinal fiber layer, their sector distribution results were found to be compatible with the distribution of the nerve fiber layers. In the present study, we used the mapping techniques of Suzuki et al13 and arranged it to divide the central 30 degrees into 10 clusters.
The results of this study showed that the upper hemifield in the better eye was important for driving, except for the lower paracentral (cluster 6) visual field. Among all 10 clusters, the upper temporal visual field (cluster 5) had the strongest correlation with the driving score of the NEI VFQ-25. We concluded from this result that the upper temporal visual field is crucial for driving, for example in terms of recognizing objects and pedestrians a few meters away. Patients with upper hemifield impairment in the better eye are thus more likely to have difficulty in driving than those with lower hemifield impairment. The current study showed that overall, the better eye had higher correlation coefficients with driving than the worse eye. We also found that correlation coefficients of the upper hemifield in the better eye with driving were higher than those of the lower hemifield. In the worse eye, the good correlation coefficient was found in cluster 10 for driving only among the other subscales. Moreover, the correlation coefficients were generally poor in most of the clusters in the worse eye. These results suggested that the vision required for driving may be dependent on the better eye rather than the worse eye. In addition, temporal field may be required for driving to recognize objects ahead.
Several investigators have reported that the pattern of visual field defects was different between NTG and POAG patients. Araie et al21 reported that visual field was more depressed in inferior cecocentral area in NTG patients in late-stage disease. Another report by Caprioli and Spaeth22 also indicated that scotomas in the low-tension patients (intraocular pressure <21 mm Hg) had a steeper slope, closer to fixation and greater depth. Therefore, we analyzed the ratio of patients with visual field defects in cluster 6 between NTG and POAG patients in order to exclude the possibility that significant percentage of NTG patients in this study with visual field defect in cluster 6 skewed the statistic results. The result showed that the number of patients with visual field defect in cluster 6 was larger in POAG patients than NTG patients. We considered that it was simply because the average visual field defect was overall worse in POAG patients than those of NTG patients in the study. As long as patients with higher intraocular pressure tend to have more progressed glaucomatous visual field defect than those with lower intraocular pressure, the result seemed to be reasonable in this recruiting method. Moreover, the number of patients with advanced visual field defect (MD <−12.0 dB), who also had defect in cluster 6 (MD <−6.0 dB), was only 23 (13.7%). These results indicated that cluster 6 was strongly correlated with vision-specific QOL, and it was not because the area was specifically depressed in participants in this study.
Several studies investigating driving performance in glaucoma patients showed a higher incidence of vehicle accidents than in normal-sighted individuals.23,24 Another study conducted by Haymes et al25 investigated on-road driving performance in a real-world setting. They concluded that glaucoma patients were more likely to require intervention by instructors due to difficulty in detecting peripheral objects. Their results showed that MD in the worse eye correlated more strongly with on-road driving performance than MD in the better eye. There were no significant correlations between the location of visual field impairment and driving performance. The group speculated that these were not identified due to a small sample size. McGwin et al26 also compared the better and worse eyes of glaucoma patients in terms of the risk of motor vehicle collisions. They found that moderate to severe visual field impairment in the worse eye significantly increased the odds ratio of collisions. Their results were thus inconsistent with those of the current study. Possible reasons include differences in analysis methods, study populations, and the clinical status of patients. Moreover, as our study has shown a high correlation coefficient in the lower paracentral visual field (cluster 6) of the worse eye, this may have made it difficult to distinguish the difference of correlation with QOL when the entire 30 degrees of the visual field was assessed.
There are several limitations of this study. First, we did not investigate other factors affecting patients’ QOL. Physical abilities for daily living, age, and occupation may be contributing factors. Another limitation of this study was that we did not perform a comparison between each sectors and overall disease severity. This may lead to overestimation of the results if many cases of this study have visual field loss in the inferior paracentral area (sector 6), for example, at the end stage of the disease. However, as we have presented the scatter plot pattern of MD value for all the participants of this study in Figure 2, the severities of the visual field loss were distributed equally from early stage to advanced stage.
To conclude, we explored the correlations between vision-specific QOL and clustered visual fields in glaucoma patients. Although it may be complicated to replicate this analysis in the clinical setting, it would be advantageous for understanding patients’ QOL as it relates more specifically to visual field impairment status. The present data have demonstrated significant relationships between several clusters and various subscales of the NEI VFQ-25. As our study has shown, the visual fields in the better eye, particularly the lower hemifield, were more strongly correlated with QOL than those in the worse eye. Therefore, we should not underestimate the importance of visual field impairment in the better eye or focus too much attention on the worse eye. In addition, we should be aware of the progression of visual field defect when glaucoma patients begin to have scotomas in these areas, and may need to change the treatment strategy when the progression is detected. Clinicians will be able to provide patients more information on how they should be careful in their lives by understanding specific areas of visual field defects, which strongly correlates with vision-specific QOL. We hope that the current results will help clinicians understand the contribution of each visual field cluster to vision-specific QOL in glaucoma patients.
The authors thank Shizuko Yamada, Masayo Endo, Mayumi Saito, Miwako Yoshihara, and Junko Watanabe for the contribution to this study.
1. Gutierrez P, Wilson MR, Johnson C, et al..Influence of glaucomatous visual field loss on health-related quality of life.Arch Ophthalmol.1997;115:777–784.
2. Parrish RK II, Gedde SJ, Scott IU, et al..Visual function and quality of life among patients with glaucoma.Arch Ophthalmol.1997;115:1447–1455.
3. Jampel HD.Glaucoma patients’ assessment of their visual function and quality of life.Trans Am Ophthalmol Soc.2001;99:301–317.
4. Jampel HD, Schwartz A, Pollack I, et al..Glaucoma patients’ assessment of their visual function and quality of life.J Glaucoma.2002;11:154–163.
5. Sumi I, Shirato S, Matsumoto S, et al..The relationship between visual disability and visual field in patients with glaucoma.Ophthalmology.2003;110:332–339.
6. Nelson P, Aspinall P, Papasouliotis O, et al..Quality life in glaucoma and its relationship with visual function.J Glaucoma.2003;12:139–150.
7. Magacho L, Lima FE, Nery AC, et al..Quality of life in glaucoma patients: regression analysis and correlation with possible modifiers.Ophthalmic Epidemiol.2004;11:263–270.
8. McKean-Cowdin R, Wang Y, Wu J, et al..Impact of visual field loss on health-related quality of life in glaucoma. The Los Angeles Latino Eye Study.Ophthalmology.2008;115:941–948.
9. Mills RP.Correlation of quality of life with clinical symptoms and signs at the time of glaucoma diagnosis.Trans Am Ophthalmol Soc.1998;96:753–812.
10. Esterman B.Functional scoring of the binocular field.Ophthalmology.1982;89:1226–1234.
11. Bengtsson B, Heijl A.A visual field index for calculation of glaucoma rate of progression.Am J Ophthalmol.2008;145:343–353.
12. Sawada H, Yoshino T, Fukuchi T, et al..Evaluation of the relationship between quality of vision and the visual function index in Japanese glaucoma patients.Graefes Arch Clin Exp Ophthalmol.2011;249:1721–1727.
13. Suzuki Y, Araie M, Ohashi Y.Sectorization of the central 30° visual field in glaucoma.Ophthalmology.1993;100:69–75.
14. Weber J, Dannheim F, Dannheim D.The topographical relationship between optic disc and visual field in glaucoma.Acta Ophthalmol (Copnh).1990;68:568–574.
15. Garway-Heath DF, Poinoosawmy D, Fitzke FW, et al..Mapping the visual field to the optic disc in normal tension glaucoma eyes.Ophthalmology.2000;107:1809–1815.
16. Anderson DR, Patella VM.Automated Static Perimetry.1999:2nd ed.St Louis:Mosby;121–190.
17. Suzukamo Y, Oshika T, Yuzawa M, et al..Psychometric properties of the 25-item National Eye Institute Visual Function Questionnaire (NFI VFQ-25), Japanese version.Health Qual Life Outcomes.2005;26:65.
18. Mangione CM, Berry S, Spritzer K, et al..Identifying the content area for the 51-item National Eye Institute Visual Function Questionnaire: results from focus groups with visually impaired persons.Arch Ophthalmol.1998;116:227–233.
19. Mangione CM, Lee PP, Pitts J, et al..Psychometric properties of the National Eye Institute Visual Function Questionnaire (NEI-VFQ).Arch Ophthalmol.1998;116:1496–1504.
20. Mangione CM, Lee PP, Gutierrez PR, et al..Development of the 25-Item National Eye Institute Visual Function Questionnaire.Arch Ophthalmol.2001;119:1050–1058.
21. Araie M, Hori J, Koseki N.Comparison of visual field defects between normal-tension and primary open-angle glaucoma in the late stage of the disease.Graefes Arch Clin Exp Ophthalmol.1995;233:610–616.
22. Caprioli J, Spaeth GL.Comparison of visual field defects in the low-tension glaucomas with those in the high-tension glaucomas.Am J Ophthalmol.1984;97:730–737.
23. Owsley C, McGwin G Jr, Ball K.Vision impairment, eye disease, and injurious motor vehicle crashes in the elderly.Ophthalmic Epidemiol.1998;5:101–113.
24. Haymes SA, LeBlanc RP, Nicolela MT, et al..Risk of falls and motor vehicle collisions in glaucoma.Invest Ophthalmol Vis Sci.2007;48:1149–1155.
25. Haymes SA, Leblanc RP, Nicolela MT, et al..Glaucoma and on-road driving performance.Invest Ophthalmol Vis Sci.2008;49:3035–3041.
26. McGwin G Jr, Xie A, Mays A, et al..Visual field defects and the risk of motor vehicle collisions among patients with glaucoma.Invest Ophthalmol Vis Sci.2005;46:4437–4441.
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