You are here

  • Home
  • Archive
  • Volume 107, Issue 6
  • Relationship of macular ganglion cell complex thickness to choroidal microvasculature drop-out in primary open-angle glaucoma

Article Text

Article menu
Clinical science
Relationship of macular ganglion cell complex thickness to choroidal microvasculature drop-out in primary open-angle glaucoma
  1. Eleonora Micheletti1,
  2. Sasan Moghimi2,
  3. Nevin El-Nimri1,
  4. Takashi Nishida2,
  5. Min Hee Suh3,
  6. James A Proudfoot4,
  7. Alireza Kamalipour1,
  8. Linda M Zangwill5,
  9. Robert N Weinreb5
  1. 1 Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA
  2. 2 Viterbi Family Department of Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, USA
  3. 3 Ophthalmology, Inje University College of Medicine, Busan, Korea (the Republic of)
  4. 4 Hamilton Glaucoma Center, University of California San Diego, La Jolla, California, USA
  5. 5 Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA
  1. Correspondence to Dr Robert N Weinreb, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA; rweinreb{at}ucsd.edu

Abstract

Background/aims To investigate the rate of ganglion cell complex (GCC) thinning in primary open-angle glaucoma (POAG) patients with and without deep-layer microvasculature drop-out (MvD).

Methods POAG patients who had at least 1.5 years of follow-up and a minimum of three visits were included from the Diagnostic Innovations in Glaucoma Study. MvD was detected at baseline by optical coherence tomography angiography (OCT-A). Area and angular circumference of MvD were evaluated on en face choroidal vessel density images and horizontal B-scans. Rates of global and hemisphere GCC thinning were compared in MvD and non-MvD eyes using linear mixed-effects models.

Results Thirty-six eyes with MvD and 37 eyes without MvD of 63 patients were followed for a mean of 3.3 years. In 30 out of 36 eyes, MvD was localised in the inferotemporal region. While mean baseline visual field mean deviation was similar between the two groups (p=0.128), global GCC thinning was significantly faster in eyes with MvD than in those without MvD (mean differences: −0.50 (95% CI −0.83 to –0.17) µm/year; p=0.003)). Presence of MvD, area and angular circumference of MvD were independently associated with a faster rate of thinning (p=0.002, p=0.031 and p=0.013, respectively).

Conclusion In POAG eyes, GCC thinning is faster in eyes with MvD. Detection of MvD in OCT-A images can assist clinicians to identify patients who are at higher risk for central macula thinning and glaucomatous progression and may require more intensive management.

  • glaucoma

Data availability statement

Data are available on reasonable request.

http://dx.doi.org/10.1136/bjophthalmol-2021-320621

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    Glaucoma is a chronic optic neuropathy characterised by progressive loss of retinal ganglion cells (RGCs) associated with deterioration of retinal nerve fibre layer (RNFL) and optic nerve head (ONH).1 Among the multiple proposed mechanisms of optic nerve damage, microvascular changes of the ONH have been proposed as a potential factor in the development and progression of glaucoma.2 3 The choroidal (or deep-layer) microvasculature within the parapapillary area (PPA) may be of particular interest because it mainly receives its blood supply by the short posterior ciliary arteries that also perfuse deep ONH tissues.4

    Several studies using optical coherence tomography angiography (OCT-A) have demonstrated regional choroidal microvasculature drop-out (MvD) in patients with glaucoma,4 5 which corresponds to a perfusion defect identified with indocyanine green angiography.6 The presence of MvD in glaucoma patients has been associated with parameters of disease severity, such as a thinner RNFL and worse visual field (VF) mean deviation (MD).4

    MvDs have been frequently found in the inferotemporal (IT) region within the beta zone of parapapillary atrophy (β-PPA), the region consistent with the macula vulnerability zone (MVZ); this corresponds to the superior paracentral VF area.7 These findings suggested that the impaired choroidal perfusion in the form of MvD might be associated with initial parafoveal scotomas.8 To our knowledge, there are no previous studies that focused on the macular GGC layer to estimate its future changes in eyes with MvD. Early detection of such macular structural damage is relevant to glaucoma management as the loss of central vision can markedly impact patients’ quality of life (QoL).9 Prompt identification of these changes may help the clinician to intensify their management to preserve a patient’s QoL.9 10

    The purpose of this study was to compare the rates of macular ganglion cell complex (GCC) thinning in glaucoma eyes with and without MvD at baseline. We also examined whether area, angular circumference (AC) and location of MvD were associated with more rapid changes of macular GCC in glaucoma patients.

    Materials and methods

    Participants

    This longitudinal study included primary open-angle glaucoma (POAG) patients enrolled in Diagnostic Innovations in Glaucoma Study (DIGS)11 12 who underwent OCT-A (Angiovue; Optovue, Fremont, California, USA). All participants from DIGS who met the inclusion criteria described below were included in this study. Informed consent was obtained from all study participants.

    Eyes were classified as glaucomatous if they had repeatable (at least two consecutive) abnormal VF test results and evidence of glaucomatous optic neuropathy—defined as excavation, the presence of focal thinning, notching of neuroretinal rim, or localised or diffuse atrophy of the RNFL on the basis of masked grading of optic disc photographs by two graders. An abnormal VF test was defined as a pattern spectral-domain (SD) outside of the 95% normal confidence limits or a glaucoma hemifield test result outside normal limits.

    Inclusion criteria also included (1) older than 18 years of age, (2) open angles on gonioscopy and (3) best-corrected visual acuity of 20/40 or better. Exclusion criteria included (1) history of trauma or intraocular surgery (except for uncomplicated cataract surgery or glaucoma surgery), (2) coexisting retinal disease, (3) uveitis, (4) non-glaucomatous optic neuropathy, (5) axial length of 26 mm or more and (6) VF MD ≤8 dB at baseline.

    Optical coherence tomography angiography

    All participants underwent OCT-A and SD-OCT imaging using AngioVue imaging system (OptoVue). This existing commercially available SD-OCT platform provides both thickness and vascular measurements. With the simultaneously acquired OCT and OCT-A volume of the AngioVue scan and automated segmentation by the AngioVue software, thickness and vascular analyses can be derived from the same image.

    Macula 3×3 mm2 scans (304 B-scans x 304 A-scans per B-scan) centred on the fovea and ONH 4.5×4.5 mm2 (304 B-scans x 304 A-scans per B-scan) centred on the ONH were acquired with the AngioVue OCT-A system (software V.2018.1.0.43). The retinal layers of each scan were segmented automatically by the AngioVue software.

    For this study, whole en face image vessel density and the en face choroidal vessel density map was derived from the entire 4.5×4.5 mm2 scan that was centred on the ONH. This en face choroidal vessel density map contains layers below the retinal pigment epithelium, including the choroid and sclera. The macula cube scanning protocol was used to assess GCC thickness. GCC thickness regions of the whole image (wiGCC) were analysed.

    OCT-A and SD-OCT image quality review was completed according to the Imaging Data Evaluation and Analysis Regarding Centre standard protocol on all scans processed with standard AngioVue software. Poor-quality images were excluded; these were defined as images with (1) low scan quality with quality index (QI) of less than 4; (2) poor clarity; (3) residual motion artefacts visible as irregular vessel pattern or disc boundary on the en face angiogram; (4) image cropping or local weak signal resulting from vitreous opacity or (5) segmentation errors that could not be corrected.

    Choroidal MvD detection

    Drop-out was required to be present in at least four consecutive horizontal B-scans and also to be >200 µm in diameter in at least one scan and to be in contact with the OCT disc boundary. The optic disc boundary was automatically detected by the Optovue software as the Bruch’s membrane/RPE complex opening. In case of errors in disc demarcation, one trained observer masked to the clinical information of the subjects corrected the disc boundary manually by searching for the positioning of Bruch’s membrane opening (BMO), as previously described.13

    Two observers (EM and NE-N), who were masked to the clinical characteristics of the participants, independently determined the presence or absence of MvD for each patient. Disagreements between the two observers about the presence MvD were resolved by a third adjudicator (SM).

    Measurement of MvD area, circumferential angle and location

    Optic disc and the PPA margins were detected by simultaneously viewing the stereoscopic optic disc photographs and the scanning laser ophthalmoscopic (SLO) images that were obtained along with the OCT-A images. MvD area was manually demarcated on en face choroidal vessel density maps using the line tool provided by ImageJ software (V.1.53; available at http://imagej.nih.gov/ij/download.html; National Institute of Health, Bethesda, Maryland, USA). Littmann’s formula was used to correct the ocular magnification in OCT-A.14 15 Details of the formula are provided elsewhere.15 The Avanti SD OCT has a default axial length of 23.95 mm and an anterior corneal curvature radius of 7.77 mm.

    MvD AC was measured as previously described.16 In brief, the two points at which the extreme borders of MvD area met the ONH border were identified and defined as angular circumferential margins. The AC was then determined by drawing two lines connecting the ONH centre to the AC margins of the MvD.

    Both area and AC of the MvD were independently assessed by two trained graders who were masked to the clinical data for each patient, including the GCC and RNFL thickness. Both MvD area and AC were defined as the mean of the measurements made by the two observers to minimise interobserver variation.

    MvD area that included large retinal vessels was included as part of the MvD area if the MvD extended beyond the vessels. In cases where the retinal vessels were located at the border of the MvD, the area covered by the vessels was excluded from the MvD area. Reflectance or shadowing of the large vessels on the horizontal and en face images were excluded from the quantitative analysis by the two independent observers masked to the patients’ baseline characteristics. In an eye showing more than one MvD, the area and the angular extent of each MvD were calculated separately and also added together to determine the total area and the total angular extension of MvD for the eye.

    The sectoral location of the drop-out was determined based on the eight separate sectors corresponding to those on the RNFL vessel density map of the OCTA. For each MvD, a line was drawn to equally bisect the angular circumferential margins of the MvD from the ONH centre, as previously reported,17 to define the location of the MvD. Disagreements between the two observers in determining the MvD location were adjudicated by the third experienced grader.

    Statistical analysis

    Patient and eye characteristics data were presented as mean (95% CI) for continuous variables and count (%) for categorical variables. Interobserver reproducibility for the presence of MvD and for the measured MvD area and AC were assessed using k statistics (ie, k value) and ICC, respectively. Categorical variables were compared using the chi-square test. Mixed-effects modelling was used to compare ocular parameters among groups. Mixed-effects modelling was used to compare ocular parameters among groups. Evaluation of the effect of microvascular drop-out (MvD) on the mean rates of change in wiGCC was performed using a linear mixed model with random intercepts and random slopes. In this model, the average values of the outcome variables were explored using a linear function of time, and random intercepts and random slopes were introduced with patient- and eye-specific deviations from this average value. This model can account for the fact that different eyes may have different rates of wiGCC thinning over time, while allowing for correlation between two eyes of the same individual.

    Factors contributing to the rate of wiGCC were explored using linear mixed models. Potential predictors which were associated with the rates of wiGCC thinning during the follow-up in univariable analysis (p<0.1) were included in the multivariable model. Statistical analyses were performed using Stata V.16.0 (StataCorp). Values of p<0.05 were considered statistically significant for all analyses.

    Results

    Eighty-two POAG eyes of 69 patients met the eligibility criteria. Of these, 9 eyes of 6 patients were excluded because of the poor quality of their OCT-A images, resulting in inclusion of 73 eyes of 63 (33 male and 30 female) POAG patients. Mean follow-up (95% CI) was 3.3 (3.1 to 3.5) years for both MvD and non-MvD eyes with an average of 4.2 (3.8 to 4.7) and 4.4 (4.0 to 4.7) OCT visits, respectively (p=0.664).

    Among the 73 eyes, MvD was observed in 36 (49.3%) eyes. In 16 (45%) of these 36 eyes, MvD was located in the inferior region, whereas 15 eyes (42%) showed MvD in both inferior and superior sectors. Interobserver agreement in detecting the presence of MvD (95% CI) was excellent (kappa=0.92 (0.83 to 1.00)). The intraclass correlation coefficient (ICC) for interobserver reproducibility in measuring the area and the AC of MvD (95% CI) was 0.98 (0.97 to 0.99) and 0.94 (0.90 to 0.96), respectively.

    Figure 1 shows a representative case of the relationship between MvD at baseline and GCC progressive thinning.

    Figure 1
    Figure 1

    OCT-A en face choroidal vessel density map showing superotemporal MvD (left) and subsequent hemisuperior GCC thinning (right) in an open-angle glaucoma eye. GCC, ganglion cell complex; MvD, deep-layer microvascular drop-out; OCT-A, NDB; normative database, optic coherence tomography-angiography; VD, vessel density.

    Table 1 compares the demographics and clinical characteristics of eyes with and without MvD. Mean age (95% CI) was 68.5 (65.1 to 71.9) and 70.5 (66.9 to 74.2) in the MvD group and non-MvD group, respectively (p=0.437). Mean baseline VF MD (95%) was −2.8 dB (−3.6 to –2.0) and −2.1 dB (−2.7 to –1.4) in the MvD eyes and non-MvD eyes, respectively (p=0.126). The groups were similar in age, gender, race, axial length, CCT, baseline IOP, glaucoma severity defined as VF MD and mean number of OCT follow-up visits. Disc haemorrhage (DH) was detected in 8 (22%) MvD eyes and 4 (10.5%) non-MvD eyes (p=0.156). Mean baseline GCC thickness (95% CI) was lower in the MvD group compared with the non-MvD group globally (89.0 µm (85.6 to 92.4) vs 94.1 µm (90.7 to 97.5), p=0.041) and in the inferior hemifield (p=0.003), but not in the superior hemifield (p=0.527).

    View this table:
    Table 1

    Characteristics of eyes categorised by microvascular drop-out group

    Table 2 and figure 2 show the rate of thinning in global GCC and hemisectoral GCC in eyes with and without MvD. A significantly faster mean rate of GCC thinning in MvD eyes compared with non-MvD eyes was found in mean global, hemi superior, and hemi inferior area (mean difference (95% CI): –0.50 (–0.83 to –0.17) µm/year; p=0.003,–0.55 (−0.92 to –0.19) µm/year; p=0.003, and −0.48 (−0.84 to −0.11) µm/year; p=0.010, respectively). Similar results were found after adjusting for confounding factors age, baseline VF MD and mean IOP during follow-up. These findings were also similar when adjusted for GCC thickness instead of baseline 24–2 VF MD.

    View this table:
    Table 2

    Comparison of rates of whole image ganglion cell complex thinning between drop-out and non-drop-out eyes

    Figure 2
    Figure 2

    Rates of GCC thinning (µm/year) in eyes with and without deep-layer microvasculature drop-out. GCC, ganglion cell complex.

    Factors contributing to the rate of global GCC thinning during the follow-up are summarised in table 3. Multivariable analysis showed that presence of MvD, area and AC of MvD and mean IOP during follow-up were significantly associated with a faster rate of GCC thinning (p=0.002, p=0.038, p=0.013 and p=0.020, respectively) after adjusting for age, baseline VF MD and mean IOP during follow-up. These results were similar even after adjusting for baseline GCC thickness instead of baseline 24–2 VF MD. Similar results were found after including baseline IOP instead of mean IOP during follow-up (online supplemental table 1).

    View this table:
    Table 3

    Factors contributing to the rate of whole image ganglion cell complex thinning over time by univariable and multivariable mixed model analysis

    Discussion

    This study showed that, with more than 3 years of follow-up, the rates of progressive GCC thinning were significantly faster in eyes with MvD compared with non-MvD eyes, supporting the role of MvD as a predictor of glaucoma progression. Furthermore, larger areas and angular extensions of MvD showed faster rates of GCC loss when compared with eyes with smaller MvDs. These findings demonstrate a possible role for OCT-A choroidal vessel density assessment as a biomarker for predicting glaucomatous GCC thinning.

    Previous studies revealed a significant association between MvD and other predictors of glaucoma progression, such as lamina cribrosa defects18–21 and DH.22–25 Park et al reported that MvD was topographically related to DH, and was also associated RNFL thinning.26 Evidence of DH during the follow-up of glaucoma eyes has been reported as a relevant prognostic factor for accelerated central VF progression.27

    As MvD was measured in the choroidal layer, it largely represents a localised perfusion defect of the choriocapillaris and choroidal microvasculature in the β-PPA region, and may indicate impaired perfusion of deep-layer tissues of the prelaminar or laminar region of the ONH. Since IOP-independent factors, such as vascular insufficiency to the ONH, may play an important role in the prognosis of OAG,2 MvD may be associated with accelerated glaucoma progression. Indeed, higher rates of VF loss have been observed in MvD eyes compared with those without MvD, despite no significant differences in IOP between the two groups.28 A recent study also showed that the presence of MvD was one of the strongest predictive factors for faster progressive RNFL thinning over 2.5 years of follow-up.29

    In this study, eyes with MvD showed faster GCC thinning compared with non-MvD eyes over more than 3 years of follow-up. In agreement with these findings, previous investigators demonstrated a significant association between faster rate of central VF loss and MvD detected during follow-up, whereas the rates of VF progression in the peripheral VF region did not differ significantly between MvD and non-MvD groups.28 In this earlier study, the progression rate of VF was faster in the superior than in the inferior 10° zone, suggesting that the location of MvD in the inferior areas might be topographically related to poor prognosis during the course of the disease.

    Several hypotheses may explain the faster rate of central macula thinning in eyes with evidence of MvD. The large majority of the eyes showed MvD located in the IT area rather than in other regions, a location consistent with the MVZ in the retina,7 where most of the RGC axons from inferior macular region project. Moreover, the most common pattern of GCC thinning in glaucoma is the widening of an existing defect, followed by deepening.30 Because GCC progression reflects the expansion or deepening of a pre-existing initial GCC defect in eyes with MvD, the rate of GCC thinning is likely to be faster in eyes with MvD than without it. Another explanation is that parafoveal scotoma and central macula thickness involvement in patients with early glaucoma are often associated with risk factors closely related to vascular dysregulation in the ONH tissues, such as hypotension, migraine and DH.27 As a consequence, eyes with signs of hypoperfusion to the ONH, such as those with MvD, may show a faster rate of GCC thinning compared with eyes without MvD.

    Baseline macular GCC thickness of the inferior hemifield was shown to be predictive of central and peripheral VF progression in POAG.31–33 Of note, the involvement of the central 10° of the VF has been strongly correlated with vision-related QoL.9 Daily activities, such as walking, reading and driving, are more likely to be affected by initial parafoveal VF defects compared with initial arcuate defects in glaucoma patients. Given the substantial impact of central VF on QoL, meticulous assessment of the glaucomatous macular damage is recommended in imagining glaucoma patients with MvD.

    In this study, baseline inferior hemifield GCC was significantly thinner in the MvD eyes compared with non-MvD eyes, and MvD was mostly localised in the IT sector (7–8 o’clock). These relationships support the possibility of a common mechanism between the macular thinning and the choroidal vascular impairment. The rates of GCC thinning were significantly faster in the eyes with MvD in both superior and inferior hemifields compared with the non-MvD groups in this study. Similar results were shown by Lee et al, who reported faster rates of superior and inferior RNFL thinning in eyes with MvD in both superior and inferior hemispheres.34

    It remains unclear whether the extent (the area and circumference) of MvD is associated with the rate of glaucomatous damage. Whereas some retrospective studies17 35 found that the extent of MvD was positively associated with severity and rapid progression of VF loss, the area of MvD at baseline was not significantly associated with faster rate of RNFL thinning in the study by Kim et al.16 In this study, area and AC of MvD at baseline were significantly associated with the rate of future GCC thinning, suggesting that the extent of MvD may represent an indicator of high-risk glaucoma patients.

    Our study had several limitations. First, MvD size was measured on the en face choroidal vessel density map, which itself is subject to many limitations. To minimise subjectivity in the measurement of MvD. We defined MvD as a complete loss of the choroidal microvasculature with a size of 200 μm or greater in diameter, a method that has been validated in previous studies.19 In addition, large overlying retinal vessels or DHs may project onto en face choroidal vessel density images, and may induce projection artefacts or shadows or make it difficult to detect or define MvD boundaries. In order to reduce these potential false negatives, MvD was defined by three trained examiners using both en face and B-scan images and we found an excellent interobserver agreement (k=0.935) between graders. Second, subjects with advanced stages of glaucoma (ie, eyes with baseline MD ≤8 dB) were excluded. Thus, our results may not be generalisable to eyes with more advanced glaucoma. Third, MvD area and AC were measured on en face images using the automatic demarcation of the BMO, and this was not accurately demarcated in some eyes. To overcome this limitation, disc margin errors were manually corrected before the quantitative analysis of MvD by a trained observer who was masked to the clinical characteristic of the subjects. Fourth, the en face choroidal vessel density image was used to detect MvD. Although this slab includes both choroid and inner sclera, the choroid is not segmented specifically and one cannot assume that it represents only the choroidal layer. Finally, ocular magnification effects associated with axial length might have influenced MvD area as measured by OCT-A. However, eyes with axial length >26 mm were excluded in the current study. Moreover, Littmann’s formula was used to correct the magnification effect.

    In conclusion, MvD is an independent predictor for accelerated GCC loss in eyes with glaucoma, especially in early stages of the disease. The rate of GCC thinning was faster in eyes with evidence of MvD and thinner GCC at baseline. The rate of GCC thinning was significantly higher in both superior and inferior regions and the extent of MvD was also associated with the rate of GCC thinning in the future. Especially with early POAG, these findings suggest that assessment of MvD is useful for detection of patients at a high risk of rapid progression who require more intensive observation and treatment.

    Data availability statement

    Data are available on reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study received the institutional review board approval of the University of California, San Diego and the methodology adhered to the tenets of the Declaration of Helsinki. Participants gave informed consent to participate in the study before taking part.

    References

    1. ↵
      2. Weinreb RN ,
      3. Aung T ,
      4. Medeiros FA
      . The pathophysiology and treatment of glaucoma: a review. JAMA 2014;311:1901–11.doi:10.1001/jama.2014.3192 pmid:http://www.ncbi.nlm.nih.gov/pubmed/24825645
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      2. Weinreb RN
      . Ocular blood flow in glaucoma. Can J Ophthalmol 2008;43:281–3.doi:10.3129/i08-058 pmid:http://www.ncbi.nlm.nih.gov/pubmed/18504463
      OpenUrlPubMed
    3. ↵
      2. Flammer J ,
      3. Orgül S ,
      4. Costa VP , et al
      . The impact of ocular blood flow in glaucoma. Prog Retin Eye Res 2002;21:359–93.doi:10.1016/S1350-9462(02)00008-3 pmid:http://www.ncbi.nlm.nih.gov/pubmed/12150988
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      2. Suh MH ,
      3. Na JH ,
      4. Zangwill LM , et al
      . Deep-layer microvasculature dropout in preperimetric glaucoma patients. J Glaucoma 2020;29:423–8.doi:10.1097/IJG.0000000000001489 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32205833
      OpenUrlCrossRefPubMed
    5. ↵
      2. Akagi T ,
      3. Iida Y ,
      4. Nakanishi H , et al
      . Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study. Am J Ophthalmol 2016;168:237–49.doi:10.1016/j.ajo.2016.06.009 pmid:http://www.ncbi.nlm.nih.gov/pubmed/27296492
      OpenUrlPubMed
    6. ↵
      2. Lee EJ ,
      3. Lee KM ,
      4. Lee SH , et al
      . Parapapillary choroidal microvasculature dropout in glaucoma: a comparison between optical coherence tomography angiography and indocyanine green angiography. Ophthalmology 2017;124:1209–17.doi:10.1016/j.ophtha.2017.03.039 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28433445
      OpenUrlPubMed
    7. ↵
      2. Hood DC ,
      3. Raza AS ,
      4. de Moraes CGV , et al
      . Glaucomatous damage of the macula. Prog Retin Eye Res 2013;32:1–21.doi:10.1016/j.preteyeres.2012.08.003 pmid:http://www.ncbi.nlm.nih.gov/pubmed/22995953
      OpenUrlCrossRefPubMed
    8. ↵
      2. Lee EJ ,
      3. Kim T-W ,
      4. Kim J-A , et al
      . Central visual field damage and parapapillary choroidal microvasculature dropout in primary open-angle glaucoma. Ophthalmology 2018;125:588–96.doi:10.1016/j.ophtha.2017.10.036 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29224927
      OpenUrlPubMed
    9. ↵
      2. Prager AJ ,
      3. Hood DC ,
      4. Liebmann JM , et al
      . Association of glaucoma-related, optical coherence tomography-measured macular damage with vision-related quality of life. JAMA Ophthalmol 2017;135:783–8.doi:10.1001/jamaophthalmol.2017.1659 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28594977
      OpenUrlPubMed
    10. ↵
      2. Kim JH ,
      3. Rabiolo A ,
      4. Morales E , et al
      . Risk factors for fast visual field progression in glaucoma. Am J Ophthalmol 2019;207:268–78.doi:10.1016/j.ajo.2019.06.019 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31238025
      OpenUrlCrossRefPubMed
    11. ↵
      2. Sample PA ,
      3. Girkin CA ,
      4. Zangwill LM , et al
      . The African descent and glaucoma evaluation study (ADAGES): design and baseline data. Arch Ophthalmol 2009;127:1136–45.doi:10.1001/archophthalmol.2009.187 pmid:http://www.ncbi.nlm.nih.gov/pubmed/19752422
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      2. Girkin CA ,
      3. Sample PA ,
      4. Liebmann JM , et al
      . African descent and glaucoma evaluation study (ADAGES): II. ancestry differences in optic disc, retinal nerve fiber layer, and macular structure in healthy subjects. Arch Ophthalmol 2010;128:541–50.doi:10.1001/archophthalmol.2010.49 pmid:http://www.ncbi.nlm.nih.gov/pubmed/20457974
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      2. Zang P ,
      3. Gao SS ,
      4. Hwang TS , et al
      . Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography. Biomed Opt Express 2017;8:1306–18.doi:10.1364/BOE.8.001306 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28663830
      OpenUrlPubMed
    14. ↵
      2. Aykut V ,
      3. Öner V ,
      4. Taş M , et al
      . Influence of axial length on peripapillary retinal nerve fiber layer thickness in children: a study by RTVue spectral-domain optical coherence tomography. Curr Eye Res 2013;38:1241–7.doi:10.3109/02713683.2013.820328 pmid:http://www.ncbi.nlm.nih.gov/pubmed/23972028
      OpenUrlCrossRefPubMedWeb of Science
    15. ↵
      2. Bennett AG ,
      3. Rudnicka AR ,
      4. Edgar DF
      . Improvements on littmann's method of determining the size of retinal features by fundus photography. Graefes Arch Clin Exp Ophthalmol 1994;232:361–7.doi:10.1007/BF00175988 pmid:http://www.ncbi.nlm.nih.gov/pubmed/8082844
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      2. Kim J-A ,
      3. Lee EJ ,
      4. Kim T-W
      . Evaluation of parapapillary choroidal microvasculature dropout and progressive retinal nerve fiber layer thinning in patients with glaucoma. JAMA Ophthalmol 2019;137:810–6.doi:10.1001/jamaophthalmol.2019.1212 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31120486
      OpenUrlPubMed
    17. ↵
      2. Lee JY ,
      3. Shin JW ,
      4. Song MK , et al
      . An increased choroidal microvasculature dropout size is associated with progressive visual field loss in open-angle glaucoma. Am J Ophthalmol 2021;223:205–19.doi:10.1016/j.ajo.2020.10.018 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33129811
      OpenUrlPubMed
    18. ↵
      2. Lee SH ,
      3. Kim T-W ,
      4. Lee EJ , et al
      . Focal lamina cribrosa defects are not associated with steep lamina cribrosa curvature but with choroidal microvascular dropout. Sci Rep 2020;10:6761.doi:10.1038/s41598-020-63681-6 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32317767
      OpenUrlPubMed
    19. ↵
      2. Suh MH ,
      3. Zangwill LM ,
      4. Manalastas PIC , et al
      . Deep retinal layer microvasculature dropout detected by the optical coherence tomography angiography in glaucoma. Ophthalmology 2016;123:2509–18.doi:10.1016/j.ophtha.2016.09.002 pmid:http://www.ncbi.nlm.nih.gov/pubmed/27769587
      OpenUrlPubMed
    20. ↵
      2. Suh MH ,
      3. Zangwill LM ,
      4. Manalastas PIC , et al
      . Deep-layer microvasculature dropout by optical coherence tomography angiography and microstructure of parapapillary atrophy. Invest Ophthalmol Vis Sci 2018;59:1996–2004.doi:10.1167/iovs.17-23046 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29677362
      OpenUrlPubMed
    21. ↵
      2. Han JC ,
      3. Choi JH ,
      4. Park DY , et al
      . Border tissue morphology is spatially associated with focal lamina cribrosa defect and deep-layer microvasculature dropout in open-angle glaucoma. Am J Ophthalmol 2019;203:89–102.doi:10.1016/j.ajo.2019.02.023 pmid:30825418
      OpenUrlPubMed
    22. ↵
      2. Rao HL ,
      3. Sreenivasaiah S ,
      4. Dixit S , et al
      . Choroidal microvascular dropout in primary open-angle glaucoma eyes with disc hemorrhage. J Glaucoma 2019;28:181–7.doi:10.1097/IJG.0000000000001173 pmid:http://www.ncbi.nlm.nih.gov/pubmed/30601223
      OpenUrlPubMed
    23. ↵
      2. Park H-YL ,
      3. Kim JW ,
      4. Park CK
      . Choroidal microvasculature dropout is associated with progressive retinal nerve fiber layer thinning in glaucoma with disc hemorrhage. Ophthalmology 2018;125:1003–13.doi:10.1016/j.ophtha.2018.01.016
      OpenUrlPubMed
    24. ↵
      2. Kim CY ,
      3. Lee EJ ,
      4. Kim J-A , et al
      . Progressive retinal nerve fibre layer thinning and choroidal microvasculature dropout at the location of disc haemorrhage in glaucoma. Br J Ophthalmol 2021;105:674–80.doi:10.1136/bjophthalmol-2020-316169 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32611606
      OpenUrlAbstract/FREE Full Text
    25. ↵
      2. Kwon JM ,
      3. Weinreb RN ,
      4. Zangwill LM , et al
      . Parapapillary deep-layer microvasculature dropout and visual field progression in glaucoma. Am J Ophthalmol 2019;200:65–75.doi:10.1016/j.ajo.2018.12.007 pmid:30578786
      OpenUrlPubMed
    26. ↵
      2. Park H-YL ,
      3. Kim JW ,
      4. Park CK
      . Choroidal microvasculature dropout is associated with progressive retinal nerve fiber layer thinning in glaucoma with disc hemorrhage. Ophthalmology 2018;125:1003–13.doi:10.1016/j.ophtha.2018.01.016 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29486903
      OpenUrlPubMed
    27. ↵
      2. David RCC ,
      3. Moghimi S ,
      4. Do JL , et al
      . Characteristics of central visual field progression in eyes with optic disc hemorrhage. Am J Ophthalmol 2021;231:109–19.doi:10.1016/j.ajo.2021.05.026 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34107310
      OpenUrlPubMed
    28. ↵
      2. Jo YH ,
      3. Kwon J ,
      4. Jeong D , et al
      . Rapid central visual field progression rate in eyes with open-angle glaucoma and choroidal microvasculature dropout. Sci Rep 2019;9:8525.doi:10.1038/s41598-019-44942-5 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31189960
      OpenUrlPubMed
    29. ↵
      2. Lee EJ ,
      3. Kim T-W ,
      4. Kim J-A , et al
      . Elucidation of the strongest factors influencing rapid retinal nerve fiber layer thinning in glaucoma. Invest Ophthalmol Vis Sci 2019;60:3343–51.doi:10.1167/iovs.18-26519 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31370062
      OpenUrlPubMed
    30. ↵
      2. Shin JW ,
      3. Sung KR ,
      4. Park S-W
      . Patterns of progressive ganglion cell-inner plexiform layer thinning in glaucoma detected by OCT. Ophthalmology 2018;125:1515–25.doi:10.1016/j.ophtha.2018.03.052 pmid:29705057
      OpenUrlCrossRefPubMed
    31. ↵
      2. Anraku A ,
      3. Enomoto N ,
      4. Takeyama A , et al
      . Baseline thickness of macular ganglion cell complex predicts progression of visual field loss. Graefes Arch Clin Exp Ophthalmol 2014;252:109–15.doi:10.1007/s00417-013-2527-9 pmid:http://www.ncbi.nlm.nih.gov/pubmed/24253499
      OpenUrlPubMed
    32. ↵
      2. Zhang X ,
      3. Dastiridou A ,
      4. Francis BA , et al
      . Baseline fourier-domain optical coherence tomography structural risk factors for visual field progression in the advanced imaging for glaucoma study. Am J Ophthalmol 2016;172:94–103.doi:10.1016/j.ajo.2016.09.015 pmid:27651070
      OpenUrlPubMed
    33. ↵
      2. Scuderi G ,
      3. Fragiotta S ,
      4. Scuderi L , et al
      . Ganglion cell complex analysis in glaucoma patients: what can it tell us? Eye Brain 2020;12:33–44.doi:10.2147/EB.S226319 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32099501
      OpenUrlPubMed
    34. ↵
      2. Lee EJ ,
      3. Kim J-A ,
      4. Kim T-W
      . Influence of choroidal microvasculature dropout on the rate of glaucomatous progression: a prospective study. Ophthalmol Glaucoma 2020;3:25–31.doi:10.1016/j.ogla.2019.10.001 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32672639
      OpenUrlPubMed
    35. ↵
      2. Kwon J ,
      3. Shin JW ,
      4. Lee J , et al
      . Choroidal microvasculature dropout is associated with parafoveal visual field defects in glaucoma. Am J Ophthalmol 2018;188:141–54.doi:10.1016/j.ajo.2018.01.035 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29428454
      OpenUrlPubMed

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • EM and SM are joint first authors.

    • Contributors Concept design: EM, SM and RNW; acquisition and reviewing data: TN, SM, NE-N, MHS and AK; analysis or interpretation of data: EM, SM, TN, JAP, LMZ, RNW; drafting of the manuscript: EM, SM, NE-N and RNW; critical revision of the manuscript: all authors; obtained funding: SM, LMZ and RNW; supervision: SM, LMZ and RNW; guarantor: RNW.

    • Funding National Institutes of Health/National Eye Institute Grants R01EY029058, R01EY011008, R01EY026574, R01EY019869 and R01EY027510; Core Grant P30EY022589; by the donors of the National Glaucoma Research Program (no grant number); a programme of the BrightFocus Foundation Grant (G2017122); an Unrestricted Grant from Research to Prevent Blindness (New York, NY); UC Tobacco Related Disease Research Program (T31IP1511); and grants for participants’ glaucoma medications from Alcon, Allergan, Pfizer, Merck and Santen.

    • Disclaimer The sponsor or funding organisations had no role in the design or conduct of this research.

    • Competing interests Acknowledgment/financial support: National Institutes of Health/National Eye Institute Grants R01EY029058, R01EY011008, R01EY026574, R01EY019869 and R01EY027510; Core Grant P30EY022589; by the donors of the National Glaucoma Research Program (no grant number); a programme of the BrightFocus Foundation Grant (G2017122); an Unrestricted Grant from Research to Prevent Blindness (New York, NY); UC Tobacco Related Disease Research Program (T31IP1511); and grants for participants’ glaucoma medications from Alcon, Allergan, Pfizer, Merck, and Santen. Financial Disclosures: Eleonora Micheletti: none; SM: none; TN: none; NE-N: none; MHS: none ; JAP: none ; AK: none; LMZ: National Eye Institute (F), Carl Zeiss Meditec (F), Heidelberg Engineering(F), OptoVue (F, R), Topcon Medical Systems Inc. (F, R) Merck (C); Robert N. Weinreb: Allergan (C), Eyenovia (C), Topcon (C), Heidelberg Engineering (F), Carl Zeiss Meditec (F), Konan (F), OptoVue (F), Topcon (F), Centervue (F).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

    Linked Articles

    • Highlights from this issue
      At a glance
      Frank Larkin
      British Journal of Ophthalmology 2023; 107 i-ii Published Online First: 19 May 2023. doi: 10.1136/bjo-2023-323828