Abstract
Purpose:
To quantify and compare the different prevalence rates of specific retinal imaging biomarkers in patients with intermediate AMD (iAMD) and advanced non-neovascular AMD (nnAMD).
Methods:
Cross-sectional study of patients with iAMD and advanced nnAMD. Imaging studies were reviewed for qualitative imaging biomarkers. Choroidal thickness measurements were obtained subfoveally and in 1000 um and 2000 um intervals away from the fovea. The Chi-squared test and Fisher’s exact test were used to compare rates of imaging biomarkers among the two cohorts. P-value of <0.05 was considered significant.
Results:
376 eyes of 197 patients with iAMD and 187 eyes of 97 patients with advanced nnAMD were recruited. There were significantly lower rates of the following imaging biomarkers in the iAMD compared with the advanced nnAMD cohorts: soft drusen (66.0% vs. 84.2%, p = 0.001), calcified drusen (4.3% vs. 40.0%, p < 0.0001), RPD (26.2% vs. 53.3%, p < 0.0001), ORT (0.5% vs. 46.9%, p < 0.0001), RP (1.1% vs. 46.3%, p < 0.0001), pigment migration (53.2% vs. 100%, p < 0.0001), and iRORA (17.9% vs. 80.2%, p < 0.0001). In the iAMD cohort, choroidal thickness was significantly greater at 188 μm (SD: 60) and 194 μm (SD: 69), compared to the advanced nnAMD with measurements of 153 μm (SD: 68), and 161 μm (SD: 76). This difference was statistically significant (p < 0.0001 and p = 0.0002).
Conclusions:
Our results highlight significant differences in imaging biomarkers between both cohorts. Key biomarkers, such as iRORA, RPD, pigment migration, and thinner choroidal thickness, were associated with advanced nnAMD. Identifying these biomarkers early may help target patients who could benefit from new treatments, potentially delaying vision loss.
Keywords: Age-related macular degeneration < RETINA, retinal pathology / research < RETINA, techniques of retinal examination < RETINA, anatomy/biochemistry/physiology < RETINA, retina - medical therapies < RETINA
Introduction
Age-related macular degeneration (AMD) is a leading cause of vision impairment and blindness among the elderly, with an increasing global prevalence that corresponds to our rapidly ageing population.1 AMD progresses from an early or intermediate stage, characterized by drusen and pigmentary changes, to an advanced stage which can be either neovascular (nAMD) or non-neovascular (nnAMD).2 Advanced nnAMD is defined by the presence of geographic atrophy (GA) or complete retinal pigment epithelium (RPE) and outer retina atrophy (cRORA). cRORA is characterized by progressive focal atrophy of the RPE and loss of macular photoreceptors corresponding to irreversible vision loss.2,3 The insidious onset and progressive nature of advanced nnAMD present substantial challenges to timely intervention and management.
The recent FDA approval of Syfovre™ and Izervay™ represents a significant breakthrough in the management of nnAMD. Before this, treatment options for nnAMD were limited, primarily focusing on lifestyle modifications and nutritional supplements.4 Both treatments have been clinically shown to slow down the progression of nnAMD through complement inhibition, opening a promising new avenue for preserving vision in these patients.5,6 This development underscores the importance of early detection of AMD and understanding which imaging biomarkers signify that an eye is most likely to progress to cRORA. As future therapies improve, patients diagnosed with intermediate AMD (iAMD) may stand to gain substantial benefits from the timely initiation of treatment. By identifying specific imaging biomarkers associated with the higher risk of progression to cRORA, clinicians can target patients who are most likely to benefit from this therapeutic option.
Our group, through the University of Colorado’s AMD registry, aimed to quantify and compare the different prevalence rates of specific retinal imaging biomarkers in patients with iAMD and advanced nnAMD. This analysis may enable us to identify patients who are at a greater risk of converting to advanced nnAMD and provide baseline data for longitudinal studies.
Methods
We conducted a cross-sectional study of patients with iAMD and advanced nnAMD enrolled in the University of Colorado AMD registry (August 2014 to June 2021). The registry and this study are approved by the Colorado Multiple Institutional Review Board and conform to the Declaration of Helsinki. The recruitment, inclusion criteria, and informed consent of each participant in the AMD registry are described in detail in a prior publication.7 Informed consent is obtained from subjects after explaining the nature and possible consequences of the study. The exclusion criteria for patients in the registry are explained elsewhere and include a history of concomitant retinal and uveitic disease.8
Patients were enrolled in our iAMD or advanced nnAMD cohorts by the Ferris/Beckman criteria: iAMD patients had presence of large druse(n) and/or pigmentary changes in at least one eye and absence of choroidal neovascularization (CNV) and cRORA, and (2) advanced nnAMD patients had presence of cRORA in at least one eye with the absence of CNV and/or prior intravitreal injections.9 The presence of cRORA was defined using the Classification of Atrophy Meetings (CAM) criteria as follows: 1) loss of outer retinal layers, 2) loss of RPE, and 3) choroidal hypertransmission of at least 250 micrometers in size in patients with AMD and no other retinal disease.3 Two vitreoretinal specialists confirmed the diagnosis for accurate enrollment in the AMD cohorts using multimodal imaging techniques: fundus autofluorescence (FAF), color fundus photography (CP), and optical coherence tomography (OCT).7
The Eidon True Color Confocal Scanner (iCare Spa, Vantaa, Finland) device obtained 50 or 60-degree color photographs centered on the fovea. The Heidelberg Spectralis (Heidelberg Engineering Inc, Heidelberg, Germany) device obtained a 60-degree fundus autofluorescence with automatic real-time-function (ART) of 50-degrees centered on the fovea. The Heidelberg Spectralis device was used to obtain high-resolution horizontal and vertical enhanced-depth imaging (EDI) line scans (6 mm/30 degree/ART 100) and a 49-line cube scan (6 mm/30 degree/ART 9) centered on the fovea.
Two trained graders performed the research review of these same retinal images for qualitative findings, which included the presence or absence (yes or no) of hard drusen, soft drusen, calcified drusen, cuticular drusen on CP; reticular pseudodrusen (RPD) on FAF; drusenoid pigment epithelial detachment (PED), PED, pigment migration, non-exudative subretinal fluid (SRF), outer retinal tubulations (ORT), retinal pseudocysts (RP), subretinal hyper-reflective material (SHRM), and incomplete RPE and outer retina atrophy (iRORA) on 49-line OCT cube scan. Discrepancies were resolved by a third-trained grader.
The research review of OCT images for quantitative findings was also performed by two trained graders. Choroidal thickness was measured using the high-resolution EDI horizontal and vertical line scans with the Spectralis (Heidelberg Engineering, Germany) device software through the fovea. Choroidal thickness measurements were measured from Bruch’s membrane to the choroidoscleral interface oriented perpendicular to Bruch’s membrane. When the interface was difficult to view, the contrast was adjusted on the device to improve the interface’s visibility. Quantitative choroidal thickness measurements were obtained subfoveally and in intervals of 1000 um up to 2 mm superior, inferior, nasal, and temporal for a total of nine data points for each eye. All choroidal thickness measurements were averaged in each eye. Subfoveal measurements were also evaluated independently.10,11 Discrepancies of greater than 20% difference were resolved by a third-trained grader. If the third grader was within 20% of one of the graders, those two measurements were used, and the disparate measurement was disregarded. If the third measurement was greater than 20% of both graders, then an accurate measurement could not be obtained, and the measurements were not used for analysis. If the third measurement was within 20% of both initial graders, the two closest measurements were used.
Demographic categories and qualitative imaging biomarkers were summarized with basic frequencies and percentages. The Chi-square test and Fisher’s exact test were used to compare rates between the two cohorts of patients having at least one eye with the qualitative imaging biomarker. Multivariable logistic regression models with each qualitative imaging marker as the outcome were run as separate models and included AMD stage, as well as adjustment for visit date and age. Quantitative imaging markers, age and body mass index (BMI) were summarized with means, standard deviations (SD), medians, and interquartile ranges (IQR). Data presented include both eyes for patients who had the same AMD stage in both eyes. Associations between quantitative imaging markers and AMD stage were analyzed with linear modeling utilizing generalized estimating equations with unstructured correlation to account for two eyes being included for most patients. Multivariable linear modeling for quantitative imaging biomarkers were also adjusted for lab date and age. A p-value of <0.05 was considered significant.
Results
The iAMD cohort consisted of 376 eyes from 197 patients with a mean age of 76.3 years (SD = 7.0), while the nnAMD cohort included 187 eyes from 97 patients, with a higher mean age of 82.3 years (SD = 7.0). The age difference between the two groups was statistically significant (p < 0.0001). Table 1 presents the additional demographic characteristics of our study. There were no significant differences between the two cohorts with regards to sex, race/ethnicity, family history of AMD, body mass index, or smoking status.
Table 1.
Characteristics of the study cohorts.
| iAMD Cohort N = 197 | Advanced nnAMD Cohort N = 97 | p-value | |
|---|---|---|---|
| Mean (SD) Age | 76.3 (7.0) | 82.3 (7.0) | <0.0001 |
| Sex | 0.30 | ||
| Male | 77 (39.1%) | 44 (45.4%) | |
| Female | 120 (60.9%) | 53 (54.6%) | |
| Race/ethnicity | 0.42 | ||
| White | 189 (95.9%) | 91 (93.8%) | |
| Other | 8 (4.1%) | 6 (6.2%) | |
| Family history of AMD | 0.37 | ||
| Yes | 73 (37.1%) | 28 (28.9%) | |
| No | 99 (50.2%) | 56 (57.7%) | |
| Uncertain | 25 (12.7%) | 13 (13.4%) | |
| Mean (SD) body mass index | (n = 191) 27.0 (5.3) | (n = 84) 26.7 (5.0) | 0.65 |
| Smoking | 0.43 | ||
| Never | 91 (46.2%) | 39 (40.2%) | |
| Current | 7 (3.6%) | 2 (2.1%) | |
| Former | 99 (50.2%) | 56 (57.7%) |
iAMD: Intermediate Age-related Macular Degeneration; nnAMD: non-neovascular Age-related Macular Degeneration; AMD: Age-related Macular Degeneration; SD: Standard deviation.
Our study revealed significant differences in several imaging biomarkers between patients in the iAMD and advanced nnAMD cohorts (Tables 2 and 3). After adjusting for age and visit date, there were significantly lower rates of the following imaging biomarkers in the iAMD compared with the advanced nnAMD cohorts: soft drusen (66.0% vs. 84.2%, p = 0.001), calcified drusen (4.3% vs. 40.0%, p < 0.0001, RPD measured on FAF (26.2% vs. 53.3%, p < 0.0001), ORT (0.5% vs. 46.9%, p < 0.0001), RP (1.1% vs. 46.3%, p < 0.0001), pigment migration (53.2% vs. 100%, p < 0.0001), and iRORA (17.9% vs. 80.2%, p < 0.0001). Conversely, rates of SHRM were higher in the iAMD cohort compared with the advanced nnAMD cohort (19.0% vs. 8.3%, p = 0.01 after adjustment). Rates of hard drusen, cuticular drusen, non-exudative SRF, drusenoid PED, and PED were not significantly different between groups.
Table 2.
Patients with the presence of specific imaging biomarkers in iAMD and advanced nnAMD cohorts.
| iAMD Cohort N = 197 | Advanced nnAMD Cohort N = 97 | Univariate p-valuea | Age-adjusted p-value | |
|---|---|---|---|---|
| Total have Imaging Biomarker (%) | Total have Imaging Biomarker (%) | - | ||
| CP Hard Drusen | 175/188 (93.1%) | 89/95 (93.7%) | 0.85 | 0.61 |
| CP Soft Drusen | 124/188 (66.0%) | 80/95 (84.2%) | 0.001 | 0.001 |
| CP Calcified Drusen | 8/188 (4.3%) | 38/95 (40.0%) | <0.0001 | <0.0001 |
| CP Cuticular Drusen | 11/188 (5.8%) | 9/95 (9.5%) | 0.26 | 0.35 |
| FAF RPD | 49/187 (26.2%) | 48/95 (53.3%) | <0.0001 | <0.0001 |
| OCT Outer Retinal Tubulation | 1/190 (0.5%) | 45/96 (46.9%) | <0.0001 | <0.0001 |
| OCT Retinal Pseudocysts | 2/189 (1.1%) | 44/95 (46.3%) | <0.0001 | <0.0001 |
| OCT SRF | 5/190 (2.6%) | 5/96 (5.2%) | 0.31b | 0.20 |
| OCT SHRM | 36/190 (19.0%) | 8/96 (8.3%) | 0.02 | 0.01 |
| OCT Drusenoid PED | 83/190 (43.7%) | 43/96 (44.8%) | 0.86 | 0.65 |
| OCT PED | 15/190 (7.9%) | 10/96 (10.4%) | 0.48 | 0.76 |
| OCT Pigment Migration | 101/190 (53.2%) | 96/96 (100%) | <0.0001 | NC |
| OCT iRORA | 34/190 (17.9%) | 77/96 (80.2%) | <0.0001 | <0.0001 |
iAMD: Intermediate Age-related Macular Degeneration; nnAMD: non-neovascular Age-related Macular Degeneration; CP: Color Photography; FAF: Fundus Autofluorescence; RPD: reticular pseudodrusen; OCT: optical coherence tomography; SRF: subretinal fluid; SHRM: subretinal hyper-reflective material; PED: pigment epithelial detachment; iRORA: incomplete retinal pigment epithelial and outer retinal atrophy.
p-values from Chi-square test unless otherwise noted.
Fisher’s exact test.
NC: not calculable due to zero cell size.
Bold type highlights statistically significant findings.
Table 3.
Quantitative imaging markers in the iAMD and advanced nnAMD cohorts.
| iAMD Cohort | Advanced nnAMD Cohort | p-value | Age-adjusted p-value | |
|---|---|---|---|---|
| Average CT | ||||
| # Eyes | 376 | 178 | ||
| Mean (SD) | 188 (60) | 153 (68) | <0.0001 | 0.008 |
| Median (IQR) | 178 (146, 218) | 141 (108, 182) | ||
| Subfoveal CT | ||||
| # Eyes | 374 | 167 | ||
| Mean (SD) | 194 (69) | 161 (76) | 0.0002 | 0.02 |
| Median (IQR) | 188 (146, 230) | 144 (104, 202) |
iAMD: Intermediate Age-related Macular Degeneration; nnAMD: non-neovascular Age-related Macular Degeneration; CT: Choroidal Thickness.
As shown in Table 3, average and subfoveal choroidal thickness measurements were significantly higher in the iAMD cohort compared with the advanced nnAMD cohort. The mean for average choroidal thickness and subfoveal choroidal thickness were 188 μm (SD: 60) and 194 μm (SD: 69) respectively for the iAMD cohort and 153 μm (SD: 68) and 161 μm (SD: 76) respectively for the advanced nnAMD cohort. The difference in these measurements between the two cohorts was statistically significant (p < 0.0001 and p = 0.0002) and remained significant after adjusting for age and lab date (p = 0.008 and p = 0.02).
Discussion
The results of our study provide updated prevalence rates of imaging biomarkers in patients with iAMD and advanced nnAMD in a larger cohort. We found several significant differences in imaging biomarkers between patients with iAMD and advanced nnAMD. Notably, the presence of soft drusen, calcified drusen, RPD, ORT, RP, pigment migration, and iRORA were found to be more prevalent in advanced nnAMD patients. SHRM was seen in fewer advanced nnAMD patients. Choroidal thickness was thinner in patients with nnAMD. Some of these imaging biomarkers have been previously associated with disease progression and severity and may aid in predicting the risk of conversion to advanced nnAMD.
In our advanced nnAMD group, we discovered a significantly higher occurrence of iRORA (discreet from the cRORA lesions), which aligns with past research suggesting that iRORA is a precursor to cRORA. Corradetti et al. confirmed that 90% of newly occurring iRORA transformed into cRORA within a span of 24 months. Additionally, they discovered that OCT biomarkers, such as intraretinal hyper-reflective foci (IHRF) or pigment migration, were independent predictors of a faster conversion from iRORA to cRORA within 12 months.12 This finding is consistent with other studies that have identified IHRF/pigment migration as one of the strongest risk factors for advanced AMD progression.13–15 Multiple studies have found that RPD can also be a significant risk factor for the development of cRORA.16–19 It has been shown that eyes with RPD display a decrease in choroidal thickness, significant deficits in choriocapillaris flow, and compromised blood flow in the choroid. These factors may make these eyes more susceptible to developing cRORA, as the RPE and photoreceptors rely heavily on choroidal perfusion.13 Our findings of increased rates of pigment migration and RPD in eyes of our advanced nnAMD cohort support these findings.
It is not surprising to find a higher prevalence of RP, ORT, and calcified drusen in the advanced nnAMD group. RP has been shown to correspond to an intermediate stage of the atrophic process in cRORA characterized by the loss of Müller cells before thinning of the neurosensory retina.20 ORT, first identified in autopsy eyes with neovascular AMD, are understood to be a photoreceptor structural response to injury. Typically appearing in late-stage AMD, their presence is associated with severe photoreceptor loss, advanced nnAMD, and decreased visual acuity.21,22 These two findings suggest a more advanced stage of the disease and may indicate the need for earlier intervention. According to prior research, patients with AMD and calcified drusen are more likely to develop advanced AMD. Our study supports this finding, as we discovered that 40% of the advanced nnAMD group had calcified drusen compared to only 4.3% of the iAMD group (p < 0.0001). A study by Tan et al. highlights calcified drusen as a strong and independent risk factor for advanced AMD progression, with a 6.36 OR at 12 months, suggesting its potential prognostic value for neovascular and nnAMD.23 Furthermore, Mano et al. also found that calcified drusen had a significant association with geographic atrophy, suggesting that this type of drusen is associated with atrophic RPE.24
SHRM was seen in fewer advanced nnAMD patients (8.3%) compared to iAMD (19%) in our study, which is in agreement with prior studies; while it has been reported that SHRM is seen in eyes with fibroatrophic scars, it is infrequently seen in eyes with cRORA.25 In a prior investigation by this group, utilizing the same advanced nnAMD cohort, our group observed a correlation between reduced systemic VEGF levels and presence of SHRM. Our deduction is that the SHRM identified within our cohort could potentially signify a specific type of SHRM observed in nnAMD-affected eyes. Further research is needed to help differentiate between the different types of SHRM in AMD patients. SHRM’s association with low levels of VEGF but higher prevalence in iAMD than advanced nnAMD may suggest that this nnAMD type of SHRM could be a precursor to atrophy or advancing disease. However, longitudinal studies on these eyes are needed to establish any connection and better understand the role SHRM plays.
Our study revealed a significant difference in choroidal thickness measurements between patients with advanced nnAMD and iAMD even after adjusting for age. Those with advanced nnAMD had a significantly lower choroidal thickness. The choroid plays a vital role in supporting the RPE and photoreceptor cells, and changes in choroidal thickness have been linked to the development of AMD.10,11,26 Previous research indicates that choroidal thickness decreases in the advanced stages of nnAMD. Lee et al. discovered a significant correlation between thinner subfoveal choroid and greater geographic atrophy size, and AMD progression. They concluded that choroidal thickness could potentially predict disease progression in nnAMD patients.27 Interestingly, using just the subfoveal choroidal thickness seemed to be enough to show significance between the two groups. However, there was a stronger statistical significance when the average choroidal thickness was used. Further, research is needed to determine if subfoveal choroidal thickness can be a reasonable proxy to doing more complex and time-consuming choroidal thickness measurements (ie. averaging multiple data points as used in our study, area measurements, or volume renderings).
Syfovre™ and Izervay™ have emerged as promising therapeutic options for delaying the progression of advanced nnAMD, targeting the complement cascade at the C3 and C5 levels, respectively. Notably, Syfovre™ displayed encouraging results in OAKS and DERBY trials by significantly reducing the growth of GA.6 Izervay™ pegol showcased its potential in the GATHER1 and GATHER2 trials.5 Yet, recent caution about cases of devastating occlusive retinal vasculitis after intravitreal Syfovre™ injections underscores the pivotal role of precise patient selection.28 By integrating high-risk imaging biomarkers, we can identify patients who are more susceptible to AMD progression. This allows us to pinpoint those who would benefit the most from therapeutic strategies, while also considering the potential risks associated with treatment.
Despite the insights provided by our study, there are noteworthy limitations, including its cross-sectional design, which prevents the establishment of causality between imaging biomarkers and AMD progression. In future studies, we plan to follow these patients longitudinally to determine which baseline imaging biomarkers are associated with conversion of iAMD to advanced nnAMD, and what baseline characteristics are associated with faster growth rates of cRORA lesion(s) in patients with advanced nnAMD. Further limitations include that the subjects were collected from a single clinical center, which may impact the generalizability of our results. Additionally, there was an age difference between the cohorts, though this was accounted for in our statistical analysis. Notable strengths of our prospective study include a large sample size for both cohorts, rigorous patient selection, and AMD phenotyping with consistent high-quality multimodal imaging.
In summary, this study quantifies prevalence rates of imaging biomarkers between iAMD and advanced nnAMD and provides significant evidence supporting the difference in qualitative and quantitative imaging biomarkers between these two groups. By understanding the predictive value of these imaging biomarkers in the progression of AMD, physicians can more effectively target treatment to those patients at higher risk, potentially delaying the onset of severe vision loss.
Acknowledgments
Thank you to the additional members of the University of Colorado Retina Research Group for their contributions to this project: Itzam Marin, MD, Amy Huang, MD, Brandie Wagner, PhD.
Declaration of conflicting interests
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Jennifer L. Patnaik: Orbis (consulting), GoCheckKids (consulting).
Naresh Mandava: Soma Logic (consulting), ONL Therapeutics (consulting), Alcon (patent), 2C Tech (equity), Aurea Medical (equity).
Talisa E. de Carlo Forest: Apellis Pharmaceuticals (previous, stock).
Niranjan Manoharan: Iveric Bio (financial support).
Andres Lisker-Cervantes, Zafar Gill, Ramya Gnanaraj, Vivian Rajeswaren, Anne M. Lynch, Alan G. Palestine, Marc Mathias: No conflict of interest.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research in this publication was supported by the Greenwald Family Research Fund, a Research to Prevent Blindness grant to the Department of Ophthalmology, University of Colorado, and by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535 and by the National Eye Institute of the National Institutes of Health under award number R01EY032456 (AML).
References
- 1.Bourne RR, Stevens GA, White RA, et al. Causes of vision loss worldwide, 1990–2010: a systematic analysis. Lancet Glob Health 2013; 1: e339–e349. [DOI] [PubMed] [Google Scholar]
- 2.Bhutto I and Lutty G. Understanding age-related macular degeneration (AMD): relationships between the photoreceptor/retinal pigment epithelium/Bruch’s membrane/choriocapillaris complex. Mol Aspects Med 2012; 33: 295–317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Sadda SR, Guymer R, Holz FG, et al. Consensus definition for atrophy associated with age-related macular degeneration on OCT: classification of atrophy report 3. Ophthalmology 2018; 125: 537–548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lutein + zeaxanthin and omega-3 fatty acids for age-related macular degeneration: the Age-Related Eye Disease Study 2 (AREDS2) randomized clinical trial. Jama 2013; 309: 2005–2015. [DOI] [PubMed] [Google Scholar]
- 5.Heier J, Khanani A, Eichenbaum D, et al. GATHER2 Pivotal Phase 3 study results: safety of intravitreal avacincaptad pegol in geographic atrophy. In: American academy of ophthalmology annual meeting, 2022. [Google Scholar]
- 6.Heier JS, Lad EM, Holz FG, et al. Pegcetacoplan for the treatment of geographic atrophy secondary to age-related macular degeneration (OAKS and DERBY): two multicentre, randomised, double-masked, sham-controlled, phase 3 trials. Lancet 2023; 402: 1434–1448. [DOI] [PubMed] [Google Scholar]
- 7.Lynch AM, Patnaik JL, Cathcart JN, et al. Colorado age-related macular degeneration registry: design and clinical risk factors of the cohort. Retina 2019; 39: 656–663. [DOI] [PubMed] [Google Scholar]
- 8.Marin AI, Poppelaars F, Wagner BD, et al. Sex and age-related differences in complement factors among patients with intermediate age-related macular degeneration. Transl Vis Sci Technol 2022; 11: 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ferris FL 3rd, Wilkinson CP, Bird A, et al. Clinical classification of age-related macular degeneration. Ophthalmology 2013; 120: 844–851. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mrejen S and Spaide RF. The relationship between pseudodrusen and choroidal thickness. Retina 2014; 34: 1560–1566. [DOI] [PubMed] [Google Scholar]
- 11.Nishida Y, Fujiwara T, Imamura Y, et al. Choroidal thickness and visual acuity in highly myopic eyes. Retina 2012; 32: 1229–1236. [DOI] [PubMed] [Google Scholar]
- 12.Corradetti G, Corvi F, Nittala MG, et al. Natural history of incomplete retinal pigment epithelial and outer retinal atrophy in age-related macular degeneration. Can J Ophthalmol 2021; 56: 325–334. [DOI] [PubMed] [Google Scholar]
- 13.Flores R, Carneiro A, Tenreiro S, et al. Retinal progression biomarkers of early and intermediate age-related macular degeneration. Life (Basel) 2021; 12: 36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Curcio CA, Zanzottera EC, Ach T, et al. Activated retinal pigment epithelium, an optical coherence tomography biomarker for progression in age-related macular degeneration. Invest Ophthalmol Vis Sci 2017; 58: Bio211–bio226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Christenbury JG, Folgar FA, O’Connell RV, et al. Progression of intermediate age-related macular degeneration with proliferation and inner retinal migration of hyperreflective foci. Ophthalmology 2013; 120: 1038–1045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hirabayashi K, Yu HJ, Wakatsuki Y, et al. OCT Risk factors for development of atrophy in eyes with intermediate age-related macular degeneration. Ophthalmol Retina 2023; 7: 253–260. [DOI] [PubMed] [Google Scholar]
- 17.Xu X, Liu X, Wang X, et al. Retinal pigment epithelium degeneration associated with subretinal drusenoid deposits in age-related macular degeneration. Am J Ophthalmol 2017; 175: 87–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zweifel SA, Spaide RF, Curcio CA, et al. Reticular pseudodrusen are subretinal drusenoid deposits. Ophthalmology 2010; 117: 303–312.e1. [DOI] [PubMed] [Google Scholar]
- 19.Ly A, Yapp M, Nivison-Smith L, et al. Developing prognostic biomarkers in intermediate age-related macular degeneration: their clinical use in predicting progression. Clin Exp Optom 2018; 101: 172–181. [DOI] [PubMed] [Google Scholar]
- 20.Cohen SY, Dubois L, Nghiem-Buffet S, et al. Retinal pseudocysts in age-related geographic atrophy. Am J Ophthalmol 2010; 150: 211–217.e1. [DOI] [PubMed] [Google Scholar]
- 21.Schaal KB, Freund KB, Litts KM, et al. Outer retinal tubulation in advanced age-related macular degeneration: optical coherence tomographic findings correspond to histology. Retina 2015; 35: 1339–1350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Preti RC, Govetto A, Filho RGA, et al. Optical coherence tomography analysis of outer retinal tubulations: sequential evolution and pathophysiological insights. Retina 2018; 38: 1518–1525. [DOI] [PubMed] [Google Scholar]
- 23.Tan ACS, Pilgrim MG, Fearn S, et al. Calcified nodules in retinal drusen are associated with disease progression in age-related macular degeneration. Sci Transl Med 2018; 10: eaat4544. DOI: 10.1126/scitranslmed.aat4544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Mano F, Sprehe N and Olsen TW. Association of drusen phenotype in age-related macular degeneration from human eye-bank eyes to disease stage and cause of death. Ophthalmol Retina 2021; 5: 743–749. [DOI] [PubMed] [Google Scholar]
- 25.Jaffe GJ, Ying GS, Toth CA, et al. Macular morphology and visual acuity in year five of the comparison of age-related macular degeneration treatments trials. Ophthalmology 2019; 126: 252–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sasaki M, Ito Y, Yamasaki T, et al. Association of choroidal thickness with intermediate age-related macular degeneration in a Japanese population. Ophthalmol Retina 2021; 5: 528–535. [DOI] [PubMed] [Google Scholar]
- 27.Lee JY, Lee DH, Lee JY, et al. Correlation between subfoveal choroidal thickness and the severity or progression of nonexudative age-related macular degeneration. Invest Ophthalmol Vis Sci 2013; 54: 7812–7818. [DOI] [PubMed] [Google Scholar]
- 28.Hutton D ASRS reports six cases of occlusive retinal vasculitis linked to pegcetacoplan injection. Ophthalmology Times 2023; 1. [Google Scholar]