Long-Term Impact of Diabetic Retinopathy on Response to Anti-VEGF Treatment in Neovascular AMD.

Purpose: To explore the long-term effect of diabetic retinopathy on response to anti-vascular endothelial growth factor (VEGF) treatment in age-related macular degeneration-associated type 1 macular neovascularization (MNV) using optical coherence tomography angiography (OCTA). Methods: A total of 45 eyes with exudative neovascular age-related macular degeneration (nAMD) with type 1 MNV were included in the analysis. Among them, 24 eyes of 24 patients had no history of diabetes mellitus (DM) in their anamnesis and were assigned to the Not Diabetic group; 21 eyes of 21 patients had mild diabetic retinopathy and were included in the Diabetic group. We considered the following outcome measures: (1) best-corrected visual acuity changes; (2) central macular thickness; (3) MNV lesion area; and (4) MNV flow area. The OCTA acquisitions were performed at the following time points: (1) baseline visit, which corresponded to the day before the first injection; (2) post-loading phase (LP), which was scheduled at 1 month after the last LP injection; and (3) 12-month follow-up visit. Results: All morphofunctional parameters showed a significant improvement after the LP and at the 12-month follow-up visit. Specifically, both the Diabetic group and the Not Diabetic group displayed a significant reduction of MNV lesion areas at both the post-LP assessment (P = 0.026 and P = 0.016, respectively) and the 12-month follow-up (P = 0.039 and P = 0.025, respectively). Similarly, the MNV flow area was significantly decreased in both the Diabetic group and the Not Diabetic group at the post-LP assessment (P < 0.001 and P = 0.012, respectively) and at the 12-month follow-up (P = 0.01 and P = 0.035, respectively) compared to baseline. A smaller reduction in the MNV lesion area was observed in the Diabetic group at both the post-LP evaluation (P = 0.015) and the 12-month follow-up (P = 0.032). No other significant differences were found between the groups for the other parameters (P > 0.05). Conclusions: Our results indicated that the Diabetic group exhibited a smaller reduction in MNV lesion area after 12 months of anti-VEGF treatment. This highlights the importance of considering diabetic retinopathy as a potential modifier of treatment outcomes in nAMD management, with DM serving as a crucial risk factor during anti-angiogenic treatment.

Provision of care for diabetic retinopathy in New Zealand: are there ethnic disparities?

AIMS: Ethnic disparities have been observed in treatment at first specialist appointments across various specialties within New Zealand. This study aimed to examine documentation and treatment decisions for diabetic retinopathy by ethnicity. METHODS: Retrospective audit of first specialist diabetic retinopathy clinic appointments for 388 patients at the Department of Ophthalmology, Te Whatu Ora Te Toka Tumai Auckland. Multiple domains of care were assessed, including comprehensiveness of history taking, examination, investigations and treatment decisions. RESULTS: Europeans comprised 42%, Maori only 9.5%, Pacific peoples 13.19%, Asian 32.7% and Middle Eastern/Latin American/African in 2%. Maori patients were eligible for a significantly greater number of treatments (p=0.001). The comprehensiveness of history taking (p=0.809), examination (p=0.513), investigations (p=0.623) and proportion of eligible treatments provided (p=0.788) was similar but did not reach the gold standard of care across all ethnicities. CONCLUSIONS: The standard of care provided in first specialist appointments for diabetic retinopathy appear to be similar across all ethnic groups, although Maori were underrepresented and had a higher disease burden at presentation. Our data highlights the need to reduce barriers faced by Maori in accessing GP, optometry and retinopathy screening referrals in Auckland, and improving local consultation and treatment guidelines.

Diabetic retinopathy: Screening, prevention, and treatment.

Internists are integral in the multidisciplinary approach to diabetic retinopathy, contributing significantly to the management of diabetes and diabetes-related complications. Effective screening processes, timely referrals, and strategic diabetes management are imperative to prevent and mitigate the consequences of diabetic retinopathy. The evolution of treatments for diabetic retinopathy has markedly improved vision outcomes and reduced the burden on patients. Despite these advances, a collaborative approach to care is essential to prevent the progression of vision impairment and manage associated complications.

One-Year Functional and Morphological Prognosis After Intravitreal Injection Treatments According to Different Morphological Patterns of Diabetic Macular Edema in Real-Life: MARMASIA Study Group Report No.13.

PURPOSE: To evaluate the responses of different optical coherence tomography (OCT) patterns of diabetic macular edema (DME) to intravitreal injection therapy. METHODS: In this retrospective, comparative, and multicenter study, patients who had previously untreated DME, who received intravitreal ranibizumab (IVR) or aflibercept (IVA) and/or steroid treatment with the pro re nata (PRN) treatment regimen after a 3-month loading dose, and had a 12-month follow-up in the MARMASIA Study Group were included. Morphological patterns of DME were divided into four groups based on OCT features diffuse/spongious edema (Group 1), cystoid edema (Group 2), diffuse/spongious edema+subretinal fluid (SRF) (Group 3), and cystoid edema+SRF (Group 4). Changes in central macular thickness (CMT) and best-corrected visual acuity (BCVA) at months 3, 6, and 12, and the number of injections at month 12 were compared between the DME groups. RESULTS: 455 eyes of 299 patients were included in the study. The mean baseline BCVAs [Logarithm of the Minimum Angle of Resolution (logMAR)] in groups 1, 2, 3, and 4 were 0.54 ± 0.24, 0.52 ± 0.25, 0.55 ± 0.23, and 0.57 ± 0.27, respectively. There was no significant difference between the baseline mean BCVAs between the groups (p = .35). The mean BCVAs were significantly improved to 0,47 ± 0,33 in group 1, 0,42 ± 0,33 in group 2, 0,47 ± 0,31 in group 3, and 0,45 ± 0,43 at month 12. There was no significant difference between the groups in terms of BCVA change at month 12 (p = .71). The mean baseline CMTs in groups 1, 2, 3, and 4 were 387,19 ± 128,19, 447,02 ± 132,39, 449,12 ± 109,24, and 544,19 ± 178,61, respectively. At baseline, the mean CMT was significantly higher in Group 4 than in the other groups (p = .000). The mean CMTs were significantly decreased to 325,16 ± 97,55, 334,94 ± 115,99, 324,33 ± 79,20, and 332,08 ± 150,40 in four groups at month 12 respectively (p > .05). The groups had no significant difference in mean CMT at month 12 (p = .835). The change in CMT was significantly higher in Group 4 than in the other groups at month 12 (p = .000). The mean number of intravitreal anti-VEGF injections at month 12 was 4.51 ± 1.57 in Group 1, 4.63 ± 1.54 in Group 2, 4.88 ± 1.38 in Group 3, and 5.07 ± 1.49 in Group 4. The mean number of anti-VEGF injections in Group 1 and Group 2 was significantly lower than in Group 4 (p = 0,014 and p = 0,017). CONCLUSIONS: In real life, there was no significant difference between the DME groups in terms of visual improvement at month 12. However, better anatomical improvement was achieved in Group 4 than in the other DME groups.

Association of serum lipid profile and other systemic risk factors with retinal hard exudates in diabetic retinopathy.

PURPOSE: Diabetic macular edema is one of the leading causes of vision loss across the world. Hard exudates at the macula can lead to structural abnormalities in the retina leading to irreversible vision loss. Systemic dyslipidemia and other modifiable risk factors when identified and treated early may help prevent substantial vision loss. The purpose of this study was to study the association between serum lipid levels and other systemic risk factors like hemoglobin, HbA1c, and serum creatinine with hard exudates and macular edema in patients with diabetic retinopathy. METHODS: It is a prospective cross-sectional study conducted in a tertiary health care center in South India. 96 patients having diabetic retinopathy with hard exudates were included. Modified Airlie house classification was used to grade the hard exudates. Blood investigations including serum lipid profile, hemoglobin, HbA1c, and serum creatinine were carried out. Central subfield macular thickness was measured using optical coherence tomography. RESULTS: 96 patients of type II DM with diabetic retinopathy were divided into three groups of hard exudates. A statistically significant correlation was observed between the severity of hard exudates and total cholesterol (p = 0.00), triglycerides (p = 0.00), LDL (p = 0.00), and VLDL (p = 0.00). HbA1c levels showed a statistically significant correlation with the severity of hard exudates (p = 0.09), no significant correlation was noted between hard exudates and hemoglobin levels (p = 0.27) and with serum creatinine (p = 0.612). A statistically significant association between CSMT and hard exudates (p = 0.00) was noted. CONCLUSION: In our study, we concluded that the severity of hard exudates is significantly associated with increasing levels of serum total cholesterol, triglycerides, LDL, VLDL, and HbA1c levels in type II DM patients presenting with diabetic retinopathy. The increasing duration of diabetes is significantly associated with increasing severity of hard exudates. Central subfield macular thickness increases with increasing severity of hard exudates in diabetic retinopathy.

Longitudinal Assessment of Intraretinal Microvascular Abnormalities in Diabetic Retinopathy Using Swept-Source Optical Coherence Tomography Angiography.

Purpose: To longitudinally investigate the changes in intraretinal microvascular abnormalities (IRMAs) over time, employing swept-source optical coherence tomography angiography in eyes with diabetic retinopathy. Methods: In this retrospective, longitudinal study, we evaluated 12 × 12-mm swept-source optical coherence tomography angiography centered on the macula at baseline and last available follow-up visit for (1) IRMA changes during follow-up, defined as (a) stable, (b) regressed, (c) obliterated, and (d) progressed; and the (2) development of new neovascularization (NV) and their origins. Competing-risk survival analysis was used to assess the factors associated with these changes. Results: In total, 195 eyes from 131 participants with diabetic retinopathy were included. Stable, regressed, obliterated, and progressed IRMA were observed in 65.1%, 12.8%, 11.3%, and 19% of eyes with diabetic retinopathy, respectively. Anti-VEGF injections during the follow-up periods and a slower increase of foveal avascular zone were associated with IRMA regression (P < 0.001 and P = 0.039). Obliterated IRMA were correlated with previous panretinal photocoagulation (P < 0.001) and a lower deep capillary plexus vessel density at baseline (P = 0.007), as well as with follow-up anti-VEGF injections (P = 0.025). A higher baseline ischemia index (ISI) and panretinal photocoagulation during the follow-up periods were associated with IRMA progression (P = 0.049 and P < 0.001). A faster increase in ISI predicted the development of NV elsewhere (NVE) from veins (P < 0.001). No significant factors were found to be associated with NVE originating from IRMA. Conclusions: Changes in IRMA closely correlated with the severity of retinal ischemia and treatment. Notably, our study confirmed the potential, yet relatively rare, development of NVE from IRMA in a large cohort; however, the risk factors associated with this transformation require further exploration.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.

Purpose: To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). Methods: This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. Results: The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). Conclusions: A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Translational Relevance: Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

ASSOCIATIONS BETWEEN CHOROIDAL ALTERATIONS AND EARLY NEURODEGENERATION IN DIABETES WITHOUT DIABETIC RETINOPATHY: Insights From Ultra-Widefield Optical Coherence Tomography Angiography Imaging.

PURPOSE: To identify associations between choroidal alterations and the reduction of peripapillary retinal nerve fiber layer (pRNFL) thickness in diabetes without diabetic retinopathy (nondiabetic retinopathy, NDR). METHODS: This retrospective cross-sectional study included 143 eyes from 83 patients with NDR and 124 eyes from 82 matched healthy controls. Ultra-widefield swept-source optical coherence tomography angiography was used to automatically measure retinal and choroidal thickness (ChT), retinal vascular density, and choroidal vascular metrics. Data were analyzed using Student's t-tests, generalized estimating equations, and generalized linear mixed models. RESULTS: Patients with NDR exhibited significant reductions in perifoveal ChT (e.g., perifoveal inferior region: 253.42 ± 86.59 µm vs. 281.01 ± 80.25 µm, P = 0.026 in GEE test) compared with the controls. The NDR group showed a significant decrease in the choroidal vascular index (P = 0.012 in GEE test), and an increase in the choroidal stromal index (P = 0.012 in GEE test). The average pRNFL thickness significantly decreased in patients with NDR (114.58 ± 11.88 µm vs. 120.25 ± 16.36 µm, P = 0.005 in GEE test). The thickness of the outer nuclear layer and total retina significantly decreased in patients with NDR (P < 0.05). In multivariate models, ChT was significantly correlated with pRNFL thickness (ß = 0.041, P = 0.001), even after adjusting by confounding factors (ß = 0.056, P = 0.001). CONCLUSION: In NDR, there were decreases in ChT, choroidal vascular index, pRNFL thickness, and outer nuclear layer thickness. The reduction in ChT was independently associated with the reduction in pRNFL thickness, suggesting that ChT could serve as a predictor of retinal neurodegeneration in NDR.

SUPERFICIAL AND DEEP VASCULAR COMPLEXES DAMAGED TO DIFFERENT EXTENTS IN ADVANCED STAGES OF DIABETIC RETINOPATHY.

PURPOSE: To identify the extent of damage to the superficial vascular complex and deep vascular complex as the stage of diabetic retinopathy (DR) increases. METHODS: Subjects were divided into four groups: patients with type 2 diabetes without DR (Group 1), those with mild-to-moderate nonproliferative DR (Group 2), those with severe-to-very severe nonproliferative DR (Group 3), and those with proliferative DR (Group 4). The vessel densities of the superficial vascular complex (superficial vessel density, SVD) and deep vascular complex (deep vessel density, DVD) and their ratios were compared. Linear regression analyses were used to identify factors associated with the SVD/DVD ratio. RESULTS: The SVDs were 25.5% ± 6.1%, 25.1% ± 7.0%, 24.5% ± 9.0%, and 21.6% ± 6.9% (P = 0.048); the DVDs 25.6% ± 5.3%, 23.0% ± 7.0%, 22.3% ± 8.8%, and 17.5% ± 5.0% (P < 0.001); and the SVD/DVD ratios 1.00 ± 0.16, 1.12 ± 0.20, 1.14 ± 0.33, and 1.24 ± 0.27 (P < 0.001) in Groups 1 to 4, respectively. In multivariate analysis, DR severity (B = 7.16, P < 0.001) and the HbA1c level (B = 1.57, P = 0.042) were significantly associated with the SVD/DVD ratio. CONCLUSION: Both the SVD and DVD tended to decrease in the advanced stages of DR, and the SVD/DVD ratio increased, indicating more severe damage to the deep vascular complex than the superficial vascular complex. The ratio was positively associated with the HbA1c level, indicating a significant relationship between that level and DVD rather than SVD damage.

A deep learning framework for the early detection of multi-retinal diseases.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

Development and external validation of a predictive model for type 2 diabetic retinopathy.

Diabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820-0.849) and 0.851 (95%CI 0.829-0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.

Comprehensive insights into a decade-long journey: The evolution, impact, and human factors of an asynchronous telemedicine program for diabetic retinopathy screening in Pennsylvania, United States.

Diabetic Retinopathy stands as a leading cause of irreversible blindness, necessitating frequent examinations, especially in the early stages where effective treatments are available. However, current examination rates vary widely, ranging from 25-60%. This study scrutinizes the Point-of-Care Diabetic Retinopathy Examination Program at the University of Pittsburgh/UPMC, delving into its composition, evolution, challenges, solutions, and improvement opportunities. Employing a narrative approach, insights are gathered from key stakeholders, including ophthalmologists and staff from primary care clinics. A quantitative analysis from 2008 to 2020 provides a comprehensive overview of program outcomes, covering 94 primary care offices with 51 retinal cameras. Program components feature automated non-mydriatic 45° retinal cameras, a dedicated coordinator, rigorous training, and standardized workflows. Over this period, the program conducted 21,960 exams in 16,458 unique individuals, revealing a diverse population with an average age of 58.5 and a balanced gender distribution. Average body mass index (33.96±8.02 kg/m2) and hemoglobin A1c (7.58%±1.88%) surpassed normal ranges, indicating prevalent risk factors for diabetes-related complications. Notably, 24.2% of patients underwent more than one exam, emphasizing program engagement. Findings indicated that 86.3% of exams were gradable, with 59.0% within normal limits, 12.1% showing some evidence of diabetic retinopathy, and 6.4% exhibiting vision-threatening diabetic retinopathy. Follow-up appointments with ophthalmologists were recommended in 31.5% of exams due to indeterminate results, positive diabetic retinopathy (≥moderate or macular exudate), or other findings like age-related macular degeneration or suspected glaucoma. The program demonstrated high reproducibility across diverse healthcare settings, featuring a sustainable model with minimal camera downtime, standardized workflows, and financial support from grants, health systems, and clinical revenues. Despite COVID-19 pandemic challenges, this research emphasizes the program's reproducibility, user-friendly evolution, and promising outcomes. Beyond technical contributions, it highlights human factors influencing program success. Future research could explore adherence to follow-up ophthalmological recommendations and its associated factors.

Utility of Visual Evoked Potentials (VEPs) study in the evaluation of visual pathway dysfunction in diabetics without retinopathy: correlations with diabetic peripheral neuropathy and other clinical findings.

Aim and objectives: Visual dysfunction in diabetes mellitus (DM) is multifactorial and can be due to vascular disease, and metabolic abnormalities that can affect the retina, optic nerve, and visual pathways. Visual evoked potential (VEP) is an electrophysiological test that can quantify the functional integrity of the visual pathways from the retina via the optic nerves, and optic tracts to the visual cortices. In this study, we aimed to investigate the visual pathway dysfunction among diabetics without retinopathy compared with healthy controls and to look for any correlation with diabetic neuropathy, duration of diabetes, or HbA1c level. Methods: The study included 75 diabetic patients and 75 age and sex-matched controls. VEPs were recorded using the pattern reversal stimulation method on the Medtronic EMG EP machine, and P100 latency and N75-P100 amplitude were recorded in both diabetic patients and healthy controls. Results: Mean P100 latency was significantly prolonged and N75-P100 amplitude significantly reduced among diabetic cases compared to healthy controls (p < 0.001). Among diabetics with peripheral neuropathy, P100 latency was significantly prolonged and N75-P100 amplitude was significantly reduced compared to diabetics without peripheral neuropathy. A significant positive correlation of VEP P100 latency (p < 0.001) and a negative correlation with N75-P100 amplitude (p < 0.001) with duration of disease were also found. Conclusion: VEP changes are observed in diabetics before the development of retinopathy or peripheral neuropathy indicating optic pathway dysfunction, which precedes the development of these complications. Early preclinical visual pathway dysfunction can warrant taking the necessary measures to reduce diabetic complications. Abbreviations: DM = Diabetes Mellitus, VEP = Visual Evoked Potential, HbA1c = Hemoglobin A1 c, MRI = Magnetic Resonance Imaging, EEG = Electroencephalography, P100 = Positive wave peak at latency 100 ms (millisecond), N75 = Negative wave peak at latency 75 ms (millisecond), N145 = Negative wave peak at latency 145 ms (millisecond), OCT = Optical coherence tomography, PRVEP = Pattern Reversal Visual Evoked Potential, NCS = Nerve Conduction Study, SSR = Sympathetic Skin Response, IL1 = Interleukin-1, LIF = Leukemia inhibitory factor, CNTF = Ciliary neurotrophic factor, TNF alpha = Tumor necrosis factor-alpha, TGF-beta = Transforming growth factor-beta.

Unusual clinical features and histopathological findings in a case of Pseudo-endophthalmitis in neovascular glaucoma.

Objective: This case report aimed to describe the unusual clinical presentation and histopathological features of post-injection endophthalmitis. Methods: A 56-year-old male phakic patient with diabetic retinopathy received an intravitreal injection (Bevacizumab as per the patient) for neovascular glaucoma elsewhere and presented to our center one day after the dose with hypopyon. The eye was relatively white without pain or lid oedema. The patient was treated as a case of post-injection endophthalmitis with two doses of intravitreal antibiotics 48 hours apart. During the follow-up, he developed a Covid infection. After one week, when the media cleared, white exudates were seen in the vitreous cavity with a relatively healthy retina. He was taken up for pars plana vitrectomy and vitreous biopsy for histopathological study. Results: The microscopic examination of vitreous aspirate revealed crystalline deposits without any microorganisms. Two control slides, one with a mixture of intravitreal antibiotics, which were previously injected, and the other with fresh Triamcinolone were also examined. Although the findings of the drug mixture did not match the vitreous aspirate, they matched with triamcinolone, which established it as a case of pseudo endophthalmitis due to triamcinolone injected elsewhere. Discussion: Initially, it seemed like a straightforward case of post-injection endophthalmitis, but a further examination of the vitreous aspirate showed that it was pseudoendophthalmitis due to an intravitreal triamcinolone injection. Despite the patient being phakic, neovascularization or elevated intraocular pressure may have led to the disruption of the blood-ocular barrier and the migration of Triamcinolone into the anterior chamber. Conclusion: The case's uniqueness lies in being the first reported case of pseudo endophthalmitis in a phakic patient with an intact lens iris diaphragm. The case also highlighted the judicious use of available resources and out-of-the-box thinking to reach a diagnosis that may not always be obvious. Abbreviations: TA = Triamcinolone acetonide, AC = Anterior chamber, IVB = Intravitreal Bevacizumab, PL = Perception of light.

Comparative Analysis of Retinal Structural and Angiographic Parameters in Diabetic Patients, Alzheimer's Disease Patients, and Healthy Controls: An OCT-Based Study.

Objective: This study aimed to investigate the potential connections between Alzheimer's Disease (AD) and diabetes. Methods: This is a cross-sectional study in which AD and diabetes patients sent by the Psychiatry and Diabetes Departments for ophthalmological screening were observed for inclusion/exclusion criteria. Patients were divided into two comparison groups. The first group (n=3) consisted of the age-matched normal and diabetic patient of the stage 3 AD disease participant. The second group (n=3) was for the stage 5 AD patient with diabetes and normal age-matched controls. Each patient underwent a full ophthalmological examination and SS-OCT (Swept Source-Ocular Computer Tomography) for retinal evaluation. Results: A total of 6 patients (12 eyes) were obtained, three men and three women. In the early AD group, the patient with diabetes showed lower macular thickness compared to other groups. In the nasal-inferior (NI) and temporal-superior (TS) ganglion cell layer (GCL), the AD patient showed statistically significant lower values compared to the other patients. In the moderately severe AD group, we found that the AD patient had lower retinal nerve fiber layer (RNFL) thickness on the temporal side compared to the rest of the patients and both the AD patient and diabetes patient showed lower RNFL thickness in the nasal-superior (NS) quadrant. Also, the foveal avascular zone (FAZ) area was statistically significantly lower for both the diabetes and AD patients compared to the healthy control. Conclusions: In conclusion, distinct retinal findings associated with AD and diabetes in young and elderly patients were revealed in our study. The clinical implications and potential interplay between these conditions need to be elucidated by further research. Abbreviations: AD = Alzheimer's Disease, SS-OCT = Swept Source - Ocular Computer Tomography, GCL = Ganglion cell layer, RNFL = Retinal nerve fiber layer, FAZ = foveal avascular zone.

Corneal parameters in diabetics versus non-diabetics and correlation with various blood sugar parameters.

Aim: To compare corneal parameters in diabetics versus age-group-matched non-diabetics; also, to correlate these parameters with the duration of diabetes, glycated haemoglobin (HbA1c) levels, and severity levels of diabetic retinopathy (DR). Materials and methods: A comparative study was conducted at a tertiary eye-care center from January 2020 to December 2020. Two-hundred patients (400 eyes) with type-2 diabetes (100) and age-sex-matched non-diabetics (100) were included. Corneal morphological parameters like central corneal thickness (CCT), endothelial cell density (ECD), coefficient of variance (CoV), hexagonality (6A), and average cell area were recorded by specular microscopy. These parameters were correlated with the duration of diabetes, severity of disease based upon fasting blood glucose levels, HbA1c, and grade of DR. Mean and standard deviation were calculated, and regular distribution of continuous data was tested using independent sample t-test and ANOVA. Results: Mean ECD (2447.32 ± 269.89/mm2), 6A (45.03 ± 6.71%), and IOP (15.47 ± 2.02 mmHg) changed in diabetic cases and were significantly low in diabetics, whereas, mean average cell area (413 ± 50.19 mm2), standard deviation (167.05 ± 77.91), CCT (525.81 ± 36.69) and CoV (39.84 ± 15.59%), were significantly high in diabetics. Mean CCT had insignificant variation. Subgroup analysis within diabetics showed a statistically significant reduction of ECD, cell count, and 6A with increased duration of diabetes, poor glycaemic control, and raised HbA1c. Discussion: The corneal endothelial analysis is vital in daily clinical practice and provides valuable evidence concerning the viability of corneal endothelium in various intraocular surgeries. Uncontrolled DM harms the cornea with 70% of diabetics resulting in complications like keratopathy. The study highlighted that the increased duration of diabetes raised HbA1c, and poor glycemic control negatively affected corneal morphology. Our study showed a definite reduction in ECD and 6A in diabetics compared to non-diabetics. Conclusion: A definite reduction in the corneal endothelial counts, cell density, and hexagonality was found in type-2 diabetics compared to non-diabetics. Abbreviations: DM = Diabetes Mellitus, CCT = central corneal thickness, ECC = endothelial cell counts, ECD = endothelial cell density, CoV = coefficient of variance, 6A = hexagonality, DR = Diabetic retinopathy, SD = Standard of deviation, IOP = Intraocular pressure.

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.

Trends and Barriers in Diabetic Retinopathy Screening: Korea National Health and Nutritional Examination Survey 2016-2021.

BACKGROUND: The prevalence of diabetes is increasing globally, highlighting the importance of preventive healthcare. This study aimed to identify the diabetic retinopathy (DR) screening rates and risk factors linked to DR screening nonadherence in the Korean population through a nationally representative sample survey. METHODS: Among the Korea National Health and Nutrition Examination Survey database from 2016 to 2021, participants aged ≥ 40 years with diabetes were included. The weighted estimate for nonadherence to DR screening within a year was calculated. Risk factor analyses were conducted using univariate and multivariate logistic regression. RESULTS: Among the 3,717 participants, 1,109 (29.5%) underwent DR screening within the past year, and this national estimate exhibited no statistically significant difference from 2016 to 2021 (P = 0.809). Nonadherence to annual DR screening was associated with residing in rural areas, age ≥ 80 years, low educational level, self-reported good health, absence of ocular disease, current smoking, lack of exercise and dietary diabetes treatment, and no activity limitation (all P < 0.05). CONCLUSION: The recent DR screening rate in Korea was relatively low. Factors associated with apathy and complacency towards personal health were associated with the nonadherence to DR screening. Educational interventions have the potential to enhance the annual screening rate for diabetic patients.

Transcriptome analysis combined with Mendelian randomization screening for biomarkers causally associated with diabetic retinopathy.

Background: Diabetic retinopathy (DR) is considered one of the most severe complications of diabetes mellitus, but its pathogenesis is still unclear. We hypothesize that certain genes exert a pivotal influence on the progression of DR. This study explored biomarkers for the diagnosis and treatment of DR through bioinformatics analysis. Methods: Within the GSE221521 and GSE189005 datasets, candidate genes were acquired from intersections of genes obtained using WGCNA and DESeq2 packages. Mendelian randomization (MR) analysis selected candidate biomarkers exhibiting causal relationships with DR. Receiver Operating Characteristic (ROC) analysis determined the diagnostic efficacy of biomarkers, the expression levels of biomarkers were verified in the GSE221521 and GSE189005 datasets, and a nomogram for diagnosing DR was constructed. Enrichment analysis delineated the roles and pathways associated with the biomarkers. Immune infiltration analysis analyzed the differences in immune cells between DR and control groups. The miRNet and networkanalyst databases were then used to predict the transcription factors (TFs) and miRNAs, respectively, of biomarkers. Finally, RT-qPCR was used to verify the expression of the biomarkers in vitro. Results: MR analysis identified 13 candidate biomarkers that had causal relationships with DR. The ROC curve demonstrated favorable diagnostic performance of three biomarkers (OSER1, HIPK2, and DDRGK1) for DR, and their expression trends were consistent across GSE221521 and GSE189005 datasets. The calibration curves and ROC curves indicated good predictive performance of the nomogram. The biomarkers were enriched in pathways of immune, cancer, amino acid metabolism, and oxidative phosphorylation. Ten immune cell lines showed notable disparities between the DR and control groups. Among them, effector memory CD8+ T cells, plasmacytoid dendritic cells, and activated CD4+ T cells exhibited good correlation with biomarker expression. The TF-mRNA-miRNA network suggested that hsa-mir-92a-3p, GATA2, and RELA play important roles in biomarker targeting for DR. RT-qPCR results also demonstrated a notably high expression of HIPK2 in patients with DR, whereas notably low expression of OSER1. Conclusion: OSER1, HIPK2, and DDRGK1 were identified as biomarkers for DR. The study findings provide novel insights into the pathogenesis of DR.