A retrospective study of the influence of the vitreomacular interface on macular oedema secondary to retinal vein occlusion

Vitreoretinal Interface Abnormalities in Patients With Retinal Vein Occlusion in a Tertiary Referral Center

PURPOSE

The purpose of this study is to investigate the prevalence of vitreoretinal interface (VRI) disorders in patients with retinal vein occlusion (RVO) and to evaluate the impact of VRI abnormalities on the treatment outcomes of macular edema secondary to RVO using intravitreal aflibercept.

METHODS

Participants in this prospective study were consecutive patients with macular edema secondary to RVO, who received intravitreal aflibercept injections. At baseline, best-corrected visual acuity (BCVA) was assessed, and spectral domain-optical coherence tomography (SD-OCT) was performed to measure central subfield thickness (CST) and to evaluate the presence of VRI disorders, namely, vitreoretinal adhesion (VMA), vitreoretinal traction (VMT), epiretinal membrane (ERM), lamellar macular hole (LMH), and full-thickness macular hole (FTMH). The primary outcomes were the prevalence of various VRI disorders in patients with RVO and the impact of VRI disorders on BCVA and CST after aflibercept treatment in such patients.

RESULTS

At baseline, 16.1% of patients had VMA, 3.2% VMT, 18.3% ERM, and 1.1% LMH. There were a statistically significant improvement in BCVA and a decrease in CST in RVO patients over time. There was no statistically significant difference regarding BCVA and CST at baseline and until month 24 after treatment between patients with VRI disorders and those without VRI disorders. However, the mean number of injections during the follow-up period was higher in the group with VRI disorders (9.4±2.1) compared to those without VRI disorders (8.1±0.7, p=0.0002).

CONCLUSIONS

The prevalence of VRI disorders in patients with RVO was 16.1% for VMA, 3.2% for VMT, 18.3% for ERM, and 1.1% for LMH. VRI disorders were not found to affect the anatomical and visual outcomes after intravitreal aflibercept treatment in patients with RVO, although more intravitreal injections were needed in patients with VRI disorders.

IMPLICATIONS OF COMPLETE POSTERIOR VITREOUS DETACHMENT IN EYES WITH CENTRAL RETINAL VEIN OCCLUSION

BACKGROUND/PURPOSE

To evaluate the status of the posterior vitreous hyaloid on presenting optical coherence tomography images of the macula and its relationship to clinical characteristics, treatment patterns, and outcomes in eyes with central retinal vein occlusion.

METHODS

This is a retrospective longitudinal cohort study of consecutive patients with acute, treatment-naive central retinal vein occlusion diagnosed between 2009 and 2021 who had at least 12 months of follow-up. Clinical characteristics, treatment patterns, and outcomes were analyzed between eyes stratified based on the presence or absence of a complete posterior vitreous detachment (PVD) on optical coherence tomography at presentation.

RESULTS

Of 102 acute, treatment-naive central retinal vein occlusions identified, 52 (51%) had complete PVD at presentation and 50 (49%) did not. Central subfield thickness was significantly lower in those with complete PVD (12 months: 284.9 ± 122.9 µ m vs. 426.8 ± 286.4 µ m, P < 0.001; last follow-up: 278 ± 127.9 vs. 372.8 ± 191.0 µ m, P = 0.022). One-year intravitreal injection burden was significantly less for those with a complete PVD than those without (5.1 ± 3.6 injections vs. 6.7 ± 3.3 injections, P = 0.013).

CONCLUSION

Central retinal vein occlusion with complete PVD on presentation had significantly lower central subfield thickness and 1-year injection burden. Assessment of the vitreomacular interface in central retinal vein occlusion may serve as a prognostic imaging biomarker.

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms

Objective

To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.

Design

Evaluation of a diagnostic test or technology.

Subjects

Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed.

Methods

We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset.

Main Outcome Measures

Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms.

Results

Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task.

Conclusions

Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

The prevalence of vitreomacular adhesion in eyes with macular oedema secondary to retinal vein occlusion selected for intravitreal injections

PURPOSE

To assess the prevalence of vitreomacular adhesion (VMA) in consecutive naïve eyes diagnosed with macular oedema (ME) secondary to retinal vein occlusion (RVO) and to longitudinally evaluate the incidence of vitreomacular interface changes over time and the influence on response to treatment.

DESIGN

Retrospective cross-sectional analysis and longitudinal cohort study conducted at two Italian tertiary referral centres.

METHODS

A total of 295 eyes, treated with intravitreal ranibizumab and/or dexamethasone for ME secondary to RVO between June 2008 and May 2018, were enrolled in the study. 280 fellow eyes met the inclusion criteria and were included as control group. The vitreomacular interface status was evaluated by spectral domain optical coherence tomography (OCT) and graded according to the OCT-based International Classification System developed by the International Vitreomacular Traction Study (IVTS) group.

RESULTS

At baseline, VMA was present in 130 (44.07%) RVO eyes and 142 (50.7%) control eyes (no statistically significant difference was found; p = 0.455). Mean follow-up (FU) was 35.98 months (min 6 - max 112). Throughout the FU, the incidence of spontaneous release of VMA (RVMA) in RVO eyes was significantly higher in comparison with that of the control group [59 (41.84%) RVO eyes versus 18 (12.33%) control eyes; p < 0.0001]. The number of injections in VMA+ eyes was significantly higher when compared with VMA- eyes. No significant difference was found between VMA+ and VMA- eyes regarding their mean best-corrected visual acuity (BCVA) at baseline and at each annual time point (p = 0.2). Differences in central macular thickness (CMT) were significant only at the baseline evaluation (p = 0.0303).

CONCLUSIONS

Vitreomacular adhesion (VMA) was not found to be more prevalent in eyes with RVO compared to healthy fellow eyes, and RVO, in turn, did not result in a higher persistence of VMA over time. This suggests that VMA and RVO might be two independent retinal phenomena, with no mutual pathogenetic influence. Vitreomacular adhesion (VMA) might have an impact on the response to treatment, since it was found to result in a more intensive treatment regimen; however, it did not affect visual and anatomic outcomes. These results do not support vitrectomy or PVD induction in the prevention, nor the treatment, of RVO.

Vitreomacular interface after anti-VEGF injections in diabetic macular edema

Background

The purpose of this study was to evaluate the incidence of vitreomacular adhesion (VMA) release after anti-VEGF therapy for the treatment of diabetic macular edema (DME) and to evaluate further changes in outcome.

Methods

This was a retrospective study that enrolled 66 eyes of 66 patients with DME who presented with VMA diagnosed by spectral-domain optical coherence tomography (OCT) at baseline. VMA was classified as focal (attachment: ≤ 1500 μm) or broad (attachment: > 1500 μm). All patients received at least three monthly intravitreal injections of an anti-VEGF agent. Follow-up visits were performed 1 month after each injection to evaluate the incidence of VMA release.

Results

The mean patient age was 61.4 years (range: 29 to 78 years), and 72.7 % were male. The mean best-corrected visual acuity was 0.62 logMAR, and the mean central retinal thickness (CRT) was 473 μm at baseline. The mean length of follow-up was 18.5 months, and the mean number of injections was 5.8. The intravitreal drugs used were aflibercept (40.9 %), ranibizumab (37.9 %) and bevacizumab (21.2 %). Forty-seven eyes had broad VMA, and 19 had focal VMA. Twenty-two eyes (33.3 %) developed VMA release following a mean of 5.7 injections (range: 3–13). Sixteen eyes (72.7 %) with focal VMA and 6 eyes (27.3 %) with broad VMA at baseline developed VMA release. Twenty-one eyes that developed VMA release showed an improvement in CRT following VMA release (mean: -106 μm; range: 22 to 289 μm).

Conclusions

VMA release occurs in approximately 1/3 of patients with DME following anti-VEGF therapy. Most of them show a short-term decrease in CRT.

Implantable anti-angiogenic scaffolds for treatment of neovascular ocular pathologies

The retinal physiology can accrue oxidative damage and inflammatory insults due to age and metabolic irregularities. Two notable diseases that involve retinal and choroidal neovascularization are proliferative diabetic retinopathy and wet age-related macular degeneration. Currently, these diseases are mainly treated with anti-VEGF drugs (VEGF = vascular endothelial growth factor), generally on a monthly dosage scheme. We discuss recent developments for the treatment of these diseases, including bioactive tissue-engineered materials, which may reduce frequency of dosage and propose a path forward for improving patient outcomes. Graphical abstract Development of materials for long-term intravitreal delivery for management of posterior segment diseases.

Accuracy of Spectral-Domain OCT of the Macula for Detection of Complete Posterior Vitreous Detachment

PURPOSE

To assess the accuracy of macular spectral-domain OCT in detecting complete posterior vitreous detachment (PVD).

DESIGN

Evaluation of diagnostic test or technology using a retrospective comparative study.

PARTICIPANTS

One hundred seventy-five eyes in 175 patients (111 women and 64 men; mean age, 65 years) with preoperative OCT within 90 days of vitrectomy.

METHODS

Posterior vitreous detachment status on preoperative macular OCT was compared with PVD determination during vitrectomy. Attached vitreous was identified on OCT by visualizing the posterior vitreous cortex or premacular bursa. Complete PVD was identified by the absence of these findings and considered a positive outcome for the purpose of analysis.

MAIN OUTCOME MEASURES

Sensitivity, specificity, positive predictive value, and negative predictive value of macular OCT for detection of complete PVD compared with findings at surgery.

RESULTS

Of the 38 eyes graded as showing complete PVD on OCT, 20 eyes were found to have pre-existing PVD at the time of surgery (true-positive results), and 18 eyes were found to have attached vitreous at the time of surgery (false-positive results). Of the 137 eyes graded as showing attached vitreous on OCT, 129 eyes had attached vitreous at the time of surgery (true-negative results), and 8 eyes had pre-existing PVD at the time of surgery (false-negative results). The sensitivity of OCT for detecting complete PVD was 71% and the specificity was 88%. In the study population, the positive predictive value of an OCT scan showing complete PVD was 53%, whereas the negative predictive value of an OCT scan showing attached vitreous was 94%.

CONCLUSIONS

If the premacular bursa or posterior vitreous cortex are visualized on macular OCT, an accurate determination of attached vitreous can be made. The diagnosis of complete PVD by macular OCT is less accurate and requires ultrasound.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning

Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is laborious and data on its impact on therapy of retinal vein occlusion (RVO) are limited. The aim of this study was to (1) develop a fully automated segmentation algorithm for the posterior vitreous boundary and (2) to study the effect of VMA on anti-VEGF therapy for RVO. A combined machine learning/graph cut segmentation algorithm for the posterior vitreous boundary was designed and evaluated. 391 patients with central/branch RVO under standardized ranibizumab treatment for 6/12 months were included in a systematic post-hoc analysis. VMA (70%) was automatically differentiated from non-VMA (30%) using the developed method combined with unsupervised clustering. In this proof-of-principle study, eyes with VMA showed larger BCVA gains than non-VMA eyes (BRVO: 15 ± 12 vs. 11 ± 11 letters, p = 0.02; CRVO: 18 ± 14 vs. 9 ± 13 letters, p < 0.01) and received a similar number of retreatments. However, this association diminished after adjustment for baseline BCVA, also when using more fine-grained VMA classes. Our study illustrates that machine learning represents a promising path to assess imaging biomarkers in OCT.