Age-related macular degeneration (AMD) is the leading cause of irreversible visual impairment and blindness in the elderly worldwide.1 Clinically, both forms of AMD (neovascular AMD and nonexudative AMD) may display geographic atrophy (GA) as their final presentation. Current treatments for exudative AMD — primarily vascular endothelial growth factor (VEGF) inhibitors — are relatively effective and lead to a reduction of subretinal fluid and subsequent improvements in visual acuity (VA).2,3 In contrast, GA currently has no treatment. Owing to the utilization of therapies for exudative AMD, it is relatively easy to quantify the visual impact of this clinical form. However, GA is also a significant public health concern that affects more than 5 million people worldwide and is responsible for 20% of the cases of legal blindness in North America.4 Although there are still no proven or effective treatments to prevent the onset and progression of GA, there are clinical trials underway examining new treatment options for reducing GA progression rates.5
Because disease development and impairment of VA due to GA are gradual progressions, patients may harbor enlarging GA lesions for years before VA is affected. In many cases, nonexudative AMD goes undetected in the early stages. Millions with AMD suffer central vision loss that hinders daily activities and threatens their independence.6 The ultimate goal would be early identification of eyes at greatest risk for progressing to GA, but the subtle nature of GA growth and its current poor association with impairment of VA means many patients with nonexudative AMD are currently not identified until late in the disease process.
Furthermore, ongoing advances in multimodal retinal imaging technology with the identification of various phenotypic characteristics of macular atrophy have created great variability when it comes to identifying and reporting GA progression. Several studies have described GA and its progression with discrepant reports. In the Beaver Dam Eye Study, Klein et al. used stereoscopic fundus photographs to identify late stages of age-related maculopathy. They reported a 2.0% prevalence and a 30% progression rate of age-related maculopathy in a 10-year period.7 Nunes et al., in a prospective study of 30 eyes, showed a GA progression rate of 43.3% during a 1-year period with spectral-domain optical coherence tomography (SD-OCT) en face imaging.8 Additionally, the Fundus Autofluorescence Imaging in Age-Related Macular Degeneration Study (FAM) group reported a progression rate of approximately 90% in 1.80 years using fundus autofluorescence (FAF) in a prospective, longitudinal, multicenter study of 170 eyes with GA.9
In addition to discrepancies in reported GA progression rates, there are limited data on clinical characteristics or demographics of the GA population within the late-AMD patient population. Calijn et al., in a meta-analysis of 42,080 European individuals, reported an increase in prevalence of early AMD and a decrease in prevalence of late AMD after 2006, but did not distinguish between individuals with GA and those with choroidal neovascularization (CNV) for the late AMD group.10 Population-based cohort studies that utilize databases to classify the prevalence of GA rarely report on visual impairment as well, meaning there is limited information available regarding the relationship between the reported prevalence of GA and the clinical impact on VA.11,12
A better understanding of the baseline characteristics and macular changes of the GA patient population can allow for earlier detection and development of effective treatment for this sight-threatening disease. This study aims to address and bridge current knowledge gaps; better characterize patients with GA in routine clinical practice, including mean best-corrected VA (BCVA) and incidence of visual impairment and blindness; and examine interrelationships between GA and CNV.
Patients and Methods
This cross-sectional study was performed at Cole Eye Institute, Cleveland, Ohio, after receiving approval from the Cleveland Clinic Institutional Review Board. All study-related procedures were performed in accordance with good clinical practice (International Conference on Harmonization of Technical Requirements of Pharmaceuticals for Human Use [ICH] E6), applicable U.S. Food and Drug Administration regulations, and the Health Insurance Portability and Accountability Act.
A comprehensive electronic chart review was performed from January 2012 to January 2016 to assess ophthalmic data. Prior to the utilization of International Classification of Diseases, 10th Revision (ICD-10), GA did not have a unique ICD diagnosis code and was generally described in the notes field. Therefore, for this query, patients were included if any AMD code (ICD-9 and ICD-10) was recorded, and all macula examination records were assessed in AMD patient records. Natural language processing was used to search within the record for GA. A direct keyword search for references of “geographic,” “atrophy,” “atrophic,” “GA,” and “advanced dry AMD” was performed. A group of related models, Word2Vec, which identifies words in natural language with similar context, was used. Word2Vec identified candidate terms that appeared in similar contexts as the key words mentioned, even if not included within the same set of notes. Two patient groups were then established, corresponding to each of the methods above. Word Mover's Distance, a statistical technique, was used as a similarity metric, considering any note with at least 50% similarity and at least three words as a match. Matches were grouped as GA cases.
Patients without records of GA were excluded. Posteriorly, GA cases were grouped into three cohorts according to the diagnosis of the fellow-eye: (Group 1) GA:GA; (Group 2) GA:CNV; and (Group 3) GA : early/intermediate AMD. A similar method was used to classify the fellow-eye: for group 2, terms like “choroidal neovascularization,” “CNV,” “disciform,” “scar,” “fibrosis,” “wet,” “SRF,” “SRH,” “hemorrhage,” “lipid,” and “fluid” were used. For group 3, terms like “drusen,” “soft,” and “confluent” were used. Mismatches between the two algorithms were classified as “questionable” and manually assessed. Additional exclusion criteria included: (a) age of 50 years or younger; (b) presence of CNV in both eyes; (c) if no information was found for the fellow eye in the electronic medical records system, or if the fellow eye was not classifiable.
The main outcome was to characterize the visual impairment of GA. Secondary outcomes were interrelationship between GA and CNV, mean BCVA, incidence of visual impairment (defined as the ineligibility for unrestricted driving license, BCVA < 70 ETDRS letters in the better-seeing eye) and legal blindness (defined as BCVA < 20 ETDRS letters in the better-seeing eye) of patients seen at the Cole Eye Institute. BCVA was measured using Snellen charts. Posteriorly, a formula (85+ (50*(LOG(Snellen fraction)))) was used to convert values to ETDRS letters.13
Categorical variables were described using percentages, whereas continuous variables were described using means and standard deviations. Relationships between categorical variables were assessed using Kruskal-Wallis tests (for ordered variables), whereas relationships between continuous variables were assessed using t-tests, one-way analysis of variance test (for normally distributed variables), or Kruskal-Wallis tests (for non-normally distributed variables). Analyses were performed using SPSS Statistics software, version 25 (SPSS, Chicago, IL).
Data from 19,359 patients with AMD were retrieved from the Cole Eye Institute database. Posteriorly, only patients with the presence of GA confirmed by fundus exams were selected, totaling 2,123 charts. After exclusion criteria were applied, 1,045 patients were selected. They were distributed into three cohorts according to the diagnosis of the fellow-eye: group 1 (n = 502), group 2 (n = 303), and group 3 (n = 240).
The whole cohort average age (standard deviation) was 83.6 (± 8) years, whereas subgroups were as follows: group 1 was 84.4 (± 8.2) years, group 2 was 83.2 (± 8) years, and group 3 was 83.1 (± 8) years (P = .02). Females comprised 55.5% of the whole cohort; group 1 was 65.3% female, group 2 was 60.7% female, and group 3 was 51.7% female. Caucasian race compromised 84.8% of the whole cohort, whereas group 1 was 94.2% Caucasian, group 2 was 93.7% Caucasian, and group 3 was 89.2% Caucasian.
The average whole cohort study eye BCVA was 50.4 (± 22.3) letters, whereas group 1 was 50.3 (± 22.1) letters, group 2 was 52.5 (± 21.3) letters, and group 3 was 48.5 (± 23.6) letters (P = .002 for the simultaneous comparison of all groups, and P < .05 for all pairwise comparison). The average whole cohort fellow eye BCVA was 57.3 (± 20.4) letters, whereas group 1 was 51 (± 21.2) letters, group 2 was 56.5 (± 23.1) letters, and group 3 was 66.7 (± 16.9) letters, (P = .004 for the simultaneous comparison of all groups, and P < .05 for all pairwise comparison).
In the whole cohort, 0.02% of the patients were eligible for blindness registration, whereas 2.2% of group 1, 3% of group 2, and 0.8% of group 3 met the same criterion (P < .001 for pairwise and simultaneous comparison of all groups). Patients ineligible for unrestricted driver's license registration were 57.7% of the whole cohort, whereas 70.5% of group 1, 59.7% of group 2, and 39.6% of group 3 were ineligible for unrestricted driver's license registration (P < .001 for pairwise and simultaneous comparison of all groups). Other variables can be seen in Table 1.
Variables Extracted and Comparison Between Groups
This study reports the differences in clinical characteristics between subgroups of patients with GA within a large, multidisciplinary ophthalmological practice. Key differences existed between the cohorts, as BCVA in the study eye was worse in group 3 than groups 1 and 2 (P < .05). Additionally, group 3 also had the best BCVA in the fellow eyes. It is known that genetic predisposition is associated with the disease severity of the fellow-eye; therefore, it is possible that this group was majorly compounded by patients with a less-severe AMD variant.14
The impact on social life aspects is evidenced by the finding that 57.7% of all patients with GA were ineligible for unrestricted driver's license registration, with 70.5%, 59.7%, and 39.6% of groups 1, 2, and 3, respectively, ineligible for unrestricted driver's license registration. The lower percentage of ineligible individuals in group 2 and group 3 can be attributed to a higher BCVA score in the fellow eye. In group 3, the improved VA is likely due to the early disease stage, whereas in group 2, treatment of CNV with anti-VEGF injections may have contributed to increased preservation of VA.
It is challenging to characterize patients with late-stage AMD because retrospective studies often use diagnosis codes to recruit their patients. In the U.S., GA codes have only been used since 2015, and prospective studies are laborious, expensive, and time-consuming. Furthermore, GA coding has been recently updated (October 2016) with new codes in an effort to describe the vast phenotype of retinal atrophic lesions (ie, advanced atrophic nonexudative AMD, with or without subfoveal involvement). To our knowledge, few reports have been able to overcome such challenge. Chakravarthy et al., in a retrospective cohort study, used mandated EMR data fields to report ophthalmic health care resource use among patients with GA in the UK.15 With a similar design, this study recruited 4,769 patients with GA. However, they only emphasized characteristics of the 1,901 patients with bilateral GA, which our study suggests may not be generalizable to patients with CNV or early/intermediate AMD in the fellow eye. In addition, by including all patients with various levels of fellow eye disease in our study, we gathered characteristics of a population equivalent to routine clinical practice. The software construction of Chakravarthy et al. was not precisely detailed; therefore, the comparison between algorithms used for automated patient selection is difficult to perform. Similarities between the present study and the U.K. study included the average age (83.6 years vs. 80.3 years, respectively), and a higher prevalence of female patients. Other comparisons between reports are shown in Table 2.
Comparison Between Geographic Atrophy Studies in Routine Clinical Practice
Joachim et al., in a cross-sectional study of three population-based cohorts, reported a positive correlation between progression of unilateral AMD to bilateral involvement and risk factors such as advanced age, positive family history, and smoking status.16 This could explain why group 3 showed the lowest percentage of patients with positive family history of AMD and the lowest average age (P < .05).
Women appeared to have a higher prevalence of late-stage AMD in this study, which supports findings by Owen et al. in an analysis of AMD prevalence, number and incidence in the U.K. population from 2007 to 2009.11 In this analysis, more than 90% of patients were self-declared Caucasians, which is in accordance with previous prevalence reports.1,6
Heterogeneity between reports in prevalence rates can be explained by different study designs, diverse age groups, different diagnostic techniques used, or even differences between populations/nationalities. In the Comparison of AMD Treatments Trials (CATT), of the 1,183 who had evaluable photographs, 81 (0.07%) had GA at baseline.17 The lower prevalence of patients with AMD who presented with GA and CNV in this study can be explained by the rigid inclusion criteria used by the clinical trial, as patients were only included in the trial if VA was between 20/25 and 20/320. Participants with GA in the foveal center of the study eye were also excluded from the study. Variations regarding age groups are also well-reported. Rudnicka et al., in a systematic review of 25 European ancestry population-based studies (1,571 cases), reported AMD prevalence rates from patients ranging from 30 to 98 years old.18 GA prevalence reports ranged from 0.1% in patients 50 years old or younger to 40% in patients older than 90 years of age.
Another explanation for the differences reported in the prevalence variability may lay in the diagnostic method used. Although pioneer studies used solely color fundus photographs (CFP),19 current reports have used a diverse diagnostic arsenal. FAF has been largely used in GA studies.20–25 Due to its high-contrast retinal images and distinction between undamaged retina and retinal pigment epithelium (RPE), FAF is particularly valuable for recognizing and reproducing semiautomated quantifications of atrophic areas.26 SD-OCT is another imaging modality that is commonly used and has been corroborated to evaluate and quantify atrophy.9,27–29 A relatively new approach for imaging GA with SD-OCT, known as the sub-RPE slab, generates an en face image from light reflected from beneath the RPE.9,29 Using only light that penetrates into the choroid results in images with higher contrast at the borders of GA, making GA regions more distinct. The combined analysis of sub-RPE slab and cross-sectional images ensures that the area of perceived GA measured corresponds to the loss of photoreceptors and RPE, which correlates with the loss of visual function.30 Interestingly, the use of OCT to assess GA was much more frequent in this study. Although FAF was performed less than 1% of the time, macular OCT was performed in more than half of all baseline encounters. This could be due to the ability of SD-OCT imaging to determine lesion sizes and borders with reasonable precision, in addition to being more accurate than FAF in defining foveal involvement.31 SD-OCT is also an important tool to monitor AMD; it can identify disruptions of the surrounding outer retinal layers and the presence of retinal tubulations, anatomical hallmarks that may assist in the prediction of atrophy progression.21
Differences in GA prevalence between ethnic populations have also been previously reported. Varma et al., in a cross-sectional, population-based study, reported a GA prevalence of 0.15% in Latinos 40 years and older based in Los Angeles.32 Cachulo et al., in a study of the same design, reported that GA accounted for 0.27% of a Portuguese population-based sample.33 Song et al., in a systematic review of cross-sectional studies, reported a GA prevalence in the Chinese population ranging from 0.15% (in people ages 45 to 49 years) to 1.09% (in people ages 85 to 89 years)34 Wilde et al., in a cross-sectional, population-based study, reported that GA occurred in 2.5% of an elderly U.K. Caucasian population.35 Finally, Andersen et al., in a study of the same design, reported a prevalence of pure GA in one or both eyes in 2.3% of an elderly population born in Greenland.36 Although ethnic differences in the prevalence of GA were not addressed in the current study, it could be an important factor for future studies as the database for patients with GA grows.
The drawbacks of this study are the ones common to retrospective studies, such as information bias (medical history records may contain incomplete information) and selection bias (ie, patients with GA and good central VA may not be referred into the hospital eye care). Since the data were obtained at a large referral center, they may not be representative of the distribution of GA in the community. Additionally, this database was created using clinical examination only. No confirmation was done using OCT; thus, misdiagnosis may have occurred. Finally, patients with GA and CNV were excluded to avoid mistaking other types of atrophy with true GA; therefore, a significant number of GA cases may not have been included in this analysis. The study strengths are the use of a large real-world patient cohort and the use of clinician-reported GA diagnoses instead of ICD codes, since codes may not faithfully represent the broad varieties of existing disease phenotypes. Although formal validation of the natural language process was not performed, disagreement between the two algorithms was manually confirmed by a retinal physician. Additionally, access to patient medical records when generating this database allowed important clinical information, such as baseline VA and background risk factors, to be reported in addition to GA prevalence.
Characterizing patients with GA in the real-world clinical setting is critical. Although no approved drugs are currently available to significantly reduce atrophy progression and its associated vision loss, efforts in this direction have been made.36,37 When GA treatments become accessible, attempts must be made to intercede at a point that will permit patients to preserve their independence. Additionally, overcoming current knowledge gaps will assist us with potential insights into the etiology of AMD.
- Friedman DS, O'Colmain BJ, Muñoz B, et al. Prevalence of age-related macular degeneration in the United States. Arch Ophthalmol. 2004;122(4):564–572. doi:10.1001/archopht.122.4.564 [CrossRef]
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- Davis RP, Schefler AC, Murray TG. Concomitant bilateral intravitreal anti-VEGF injections for the treatment of exudative age-related macular degeneration. Clin Ophthalmol. 2010;4:703–707.
- Holz FG, Strauss EC, Schmitz-Valckenberg S, van Lookeren Campagne M. Geographic atrophy. Ophthalmology. 2014;121(5):1079–1091. doi:10.1016/j.ophtha.2013.11.023 [CrossRef]
- Bressler NM, Bressler SB, Congdon NG, et al. Potential public health impact of Age-Related Eye Disease Study results: AREDS report no. 11. Arch Ophthalmol. 2003;121(11):1621–1624. doi:10.1001/archopht.121.11.1621 [CrossRef]
- Klein ML, Ferris FL, Armstrong J, et al. Retinal precursors and the development of geographic atrophy in age-related macular degeneration. Ophthalmology. 2008;115(6):1026–1031. doi:10.1016/j.ophtha.2007.08.030 [CrossRef]
- Kandasamy R, Wickremasinghe S, Guymer R. New treatment modalities for geographic atrophy. Asia Pac J Ophthalmol (Phila). 2017;6(6):508–513.
- Klein R, Klein BE, Linton KL. Prevalence of age-related maculopathy. The Beaver Dam Eye Study. Ophthalmology. 1992;99(6):933–943. doi:10.1016/S0161-6420(92)31871-8 [CrossRef]
- Nunes RP, Gregori G, Yehoshua Z, et al. Predicting the progression of geographic atrophy in age-related macular degeneration with SD-OCT en face imaging of the outer retina. Ophthalmic Surg Lasers Imaging Retina. 2013;44(4):344–359. doi:10.3928/23258160-20130715-06 [CrossRef]
- Colijn JM, Buitendijk GHS, Prokofyeva E, et al. Prevalence of age-related macular degeneration in Europe: The past and the future. Ophthalmology. 2017;124(12):1753–1763. doi:10.1016/j.ophtha.2017.05.035 [CrossRef]
- Owen CG, Jarrar Z, Wormald R, Cook DG, Fletcher AE, Rudnicka AR. The estimated prevalence and incidence of late stage age related macular degeneration in the UK. Br J Ophthalmol. 2012;96(5):752. doi:10.1136/bjophthalmol-2011-301109 [CrossRef]
- Rudnicka AR, Kapetanakis VV, Jarrar Z, et al. Incidence of late-stage age-related macular degeneration in American Whites: Systematic review and meta-analysis. Am J Ophthalmol. 2015;160(1):85–93.e3. doi:10.1016/j.ajo.2015.04.003 [CrossRef]
- Gregori NZ, Feuer W, Rosenfeld P, et al. Novel method for analyzing snellen visual acuity measurements. Retina. 2010;30(7):1046–1050. doi:10.1097/IAE.0b013e3181d87e04 [CrossRef]
- Schick T, Altay L, Viehweger E, et al. Genetics of unilateral and bilateral age-related macular degeneration severity stages. PloS One. 2016;11(6):e0156778. doi:10.1371/journal.pone.0156778 [CrossRef]
- Chakravarthy U, Bailey CC, Johnson RL, et al. Characterizing disease burden and progression of geographic atrophy secondary to age-related macular degeneration. Ophthalmology. 2018;125(6):842–849. doi:10.1016/j.ophtha.2017.11.036 [CrossRef]
- Joachim N, Colijn JM, Kifley A, et al. Five-year progression of unilateral age-related macular degeneration to bilateral involvement: The Three Continent AMD Consortium report. Br J Ophthalmol. 2017;101(9):1185–1192. doi:10.1136/bjophthalmol-2016-309729 [CrossRef]
- Grunwald JE, Pistilli M, Daniel E, et al. Incidence and growth of geographic atrophy during 5 years of Comparison of Age-Related Macular Degeneration Treatments Trials. Ophthalmology. 2017;124(1):97–104. doi:10.1016/j.ophtha.2016.09.012 [CrossRef]
- Rudnicka AR, Jarrar Z, Wormald R, Cook DG, Fletcher A, Owen CG. Age and gender variations in age-related macular degeneration prevalence in populations of European ancestry: A meta-analysis. Ophthalmology. 2012;119(3):571–580. doi:10.1016/j.ophtha.2011.09.027 [CrossRef]
- Caire J, Recalde S, Velazquez-Villoria A, et al. Growth of geographic atrophy on fundus autofluorescence and polymorphisms of CFH, CFB, C3, FHR1–3, and ARMS2 in age-related macular degeneration. JAMA Ophthalmol. 2014;132(5):528–534. doi:10.1001/jamaophthalmol.2013.8175 [CrossRef]
- Klein R, Klein BEK, Knudtson MD, Meuer SM, Swift M, Gangnon RE. Fifteen-year cumulative incidence of age-related macular degeneration. The Beaver Dam Eye Study. Ophthalmology. 2007;114(2):253–262. doi:10.1016/j.ophtha.2006.10.040 [CrossRef]
- Jaffe GJ, Schmitz-Valckenberg S, Boyer D, et al. Randomized trial to evaluate tandospirone in geographic atrophy secondary to age-related macular degeneration: The GATE Study. Am J Ophthalmol. 2015;160(6):1226–1234. doi:10.1016/j.ajo.2015.08.024 [CrossRef]
- Holz FG, Bindewald-Wittich A, Fleckenstein M, et al. Progression of geographic atrophy and impact of fundus autofluorescence patterns in age-related macular degeneration. Am J Ophthalmol. 2007;143(3):463–472. doi:10.1016/j.ajo.2006.11.041 [CrossRef]
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Variables Extracted and Comparison Between Groups
|Variables||GA:GA (N = 502)||GA:CNV (N = 303)||GA : Early AMD (N = 240)|
|BMI||26.48 (± 5.6)||29.1 (± 5.5)||29.2 (± 5.2)|
|Positive family history of AMD||172 (34.3%)||101 (33.3%)||32 (13.3%)|
|Smoker||31 (6.2%)||15 (4.9%)||11 (4.6%)|
|Former smoker||234 (46.6%)||149 (49.2%)||107 (44.5%)|
|Use AREDS vitamins||138 (27.5%)||69 (22.8%)||57 (23.7%)|
|FA performed||10 (2%)||18 (5.9%)||4 (1.7%)|
|HVF performed||11 (2.2%)||12 (4%)||15 (6.2%)|
|ICG performed||1 (0.2%)||2 (0.7%)||3 (1.2%)|
|Macula OCT performed||245 (48.8%)||194 (64%)||128 (53.3%)|
|Optic nerve OCT performed||19 (3.8%)||6 (2%)||11 (4.6%)|
Comparison Between Geographic Atrophy Studies in Routine Clinical Practice
|Conti et al.||Chakravarthy et al.|
|Variables||GA:GA (n = 502)||GA:CNV (n = 303)||GA : Early AMD (n = 240)||GA:GA (n = 1,901)||GA:CNV (n = 1,696)||GA : Early AMD (n = 1,172)|
|Age (years), mean||84.4 (± 8.2)||83.2 (± 8)||83.1 (± 8)||81 (± 6)||81 (± 5)||79 (± 7)|
|Study eye VA at baseline (ETDRS letters)||50||52||48||45||55||45|
|Fellow eye VA at baseline (ETDRS letters)||51||56||67||64||43||73|
|Eligible for blindnessregistration, (%)||2.2%||3%||0.8%||7.1% %||2.8 %||1.5 %|
|Ineligible to drive, (%)||70.5%||59.7%||39.6%||71.1%||68.8%||44.8%|