Vision-threatening Diabetic Retinopathy Research Articles

Glycemic Control in Patients After Initiation of Treatment for Vision-Threatening Diabetic Retinopathy.

Purpose: To understand changes in glycemic control in patients being managed with vision-threatening diabetic retinopathy (DR). Methods: A retrospective cohort study was performed of patients with type 2 diabetes mellitus who were at least 18 years old. Patients who started treatment for vision-threatening DR were matched with controls based on age, sex, race/ethnicity, duration of diabetes, history of diabetes, and history of hypertension. The primary outcome was the difference in hemoglobin A1c (HbA1c) change after 12 months between cases and controls. Results: Four hundred fifty patients were included (225 cases paired with 225 controls); 58.7% of patients were men, and 33.8% were Hispanic. The mean (±SD) baseline HbA1c was 8.12% ± 1.57%. Patients receiving retinal interventions did not experience a significant change in HbA1c compared with controls 12 months after starting treatment (0.11% ± 1.51% vs -0.02% ± 1.52%; P = .31). In addition, there was not a significant difference in HbA1c change between cases and controls when considering the number of interventions: 2 or fewer interventions (+0.08% ± 1.30% vs -0.07% ± 1.15%; P = .46), 3 to 6 interventions (+0.41% ± 1.71% vs +0.01% ± 2.0%; P = .08), and 7 or more interventions (-0.17% ± 1.49% vs 0.0% ± 1.31%; P = .50). Conclusions: Patients who received treatment for vision-threatening DR did not experience a change in HbA1c. Increasing number of retinal interventions also did not appear to impact glycemic control. There appears to be a missed opportunity for improving diabetes management in patients with vision-threatening DR.

Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence.

Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, affecting people with diabetes. The timely diagnosis and treatment of DR are essential in preventing vision loss. Non-mydriatic fundus cameras and artificial intelligence (AI) software have been shown to improve DR screening efficiency. However, few studies have compared the diagnostic performance of different non-mydriatic cameras and AI software. This clinical study was conducted at the endocrinology clinic of Akdeniz University with 900 volunteer patients that were previously diagnosed with diabetes but not with diabetic retinopathy. Fundus images of each patient were taken using three non-mydriatic fundus cameras and EyeCheckup AI software was used to diagnose more than mild diabetic retinopathy, vision-threatening diabetic retinopathy, and clinically significant diabetic macular oedema using images from all three cameras. Then patients underwent dilation and 4 wide-field fundus photography. Three retina specialists graded the 4 wide-field fundus images according to the diabetic retinopathy treatment preferred practice patterns of the American Academy of Ophthalmology. The study was pre-registered on clinicaltrials.gov with the ClinicalTrials.gov Identifier: NCT04805541. The Canon CR2 AF AF camera had a sensitivity and specificity of 95.65% / 95.92% for diagnosing more than mild DR, the Topcon TRC-NW400 had 95.19% / 96.46%, and the Optomed Aurora had 90.48% / 97.21%. For vision threatening diabetic retinopathy, the Canon CR2 AF had a sensitivity and specificity of 96.00% / 96.34%, the Topcon TRC-NW400 had 98.52% / 95.93%, and the Optomed Aurora had 95.12% / 98.82%. For clinically significant diabetic macular oedema, the Canon CR2 AF had a sensitivity and specificity of 95.83% / 96.83%, the Topcon TRC-NW400 had 98.50% / 96.52%, and the Optomed Aurora had 94.93% / 98.95%. The study demonstrates the potential of using non-mydriatic fundus cameras combined with artificial intelligence software in detecting diabetic retinopathy. Several cameras were tested and, notably, each camera exhibited varying but adequate levels of sensitivity and specificity. The Canon CR2 AF emerged with the highest accuracy in identifying both more than mild diabetic retinopathy and vision-threatening cases, while the Topcon TRC-NW400 excelled in detecting clinically significant diabetic macular oedema. The findings from this study emphasize the importance of considering a non mydriatic camera and artificial intelligence software for diabetic retinopathy screening. However, further research is imperative to explore additional factors influencing the efficiency of diabetic retinopathy screening using AI and non mydriatic cameras such as costs involved and effects of screening using and on an ethnically diverse population.

National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART-India cross-sectional study

National estimates of the prevalence of vision impairment and blindness in people with diabetes are required to inform resource allocation. People with diabetes are more susceptible to conditions such as diabetic retinopathy that can impair vision; however, these are often missed in national studies. This study aims to determine the prevalence and risk factors of vision impairment and blindness in people with diabetes in India. Data from the SMART-India study, a cross-sectional survey with national coverage of 42 147 Indian adults aged 40 years and older, collected using a complex sampling design, were used to obtain nationally representative estimates for the prevalence of vision impairment and blindness in people with diabetes in India. Vulnerable adults (primarily those who did not have capacity to provide consent); pregnant and breastfeeding women; anyone deemed too ill to be screened; those who did not provide consent; and people with type 1 diabetes, gestational diabetes, or secondary diabetes were excluded from the study. Vision impairment was defined as presenting visual acuity of 0·4 logMAR or higher and blindness as presenting a visual acuity of 1·0 logMAR or higher in the better-seeing eye. Demographic, anthropometric, and laboratory data along with geographic distribution were analysed in all participants with available data. Non-mydriatic retinal images were used to grade diabetic retinopathy, and risk factors were also assessed. A total of 7910 people with diabetes were included in the analysis, of whom 5689 had known diabetes and 2221 were undiagnosed. 4387 (55·5%) of 7909 participants with available sex data were female and 3522 (44·5%) participants were male. The estimated national prevalence of vision impairment was 21·1% (95% CI 15·7-27·7) and blindness 2·4% (1·7-3·4). A higher prevalence of any vision impairment (29·2% vs 19·6%; p=0·016) and blindness (6·7% vs 1·6%; p<0·0001) was observed in those with ungradable images. In known diabetes, diabetic retinopathy (adjusted odds ratio [aOR] 3·06 [95% CI 1·25-7·51]), vision-threatening diabetic retinopathy (aOR 7·21 [3·52-14·75]), and diabetic macular oedema (aOR 5·41 [2·20-13·33]) were associated with blindness in adjusted analysis. Common sociodemographic risk factors for vision impairment and blindness include older age, lower educational attainment, and unemployment. Based on the estimated 101 million people with diabetes in 2021 and the interpretation of the data from this study, approximately 21 million people with diabetes have vision impairment in India, of whom 2·4 million are blind. Higher prevalence is observed in those from lower socio-economic strata and policy makers should focus on these groups to reduce inequalities in health care. Global Challenge Research Fund of United Kingdom Research and Innovation through the Medical Research Council.

Diabetic retinopathy screening using MII Ret Cam assisted smartphone-based fundus imaging

BackgroundDiabetes Mellitus is a tremendous health implication and is proposed to be the seventh leading cause of death in 2030 by WHO. Diabetic retinopathy is one of the top complications of diabetes leading to preventable blindness. This investigation highlights the importance of timely screening and counseling using MII Ret cCam-assisted smartphone-based fundus imaging. Our study is a boon for bed-ridden and seriously ill patients who could not be evaluated on a fundus camera. MethodsThis is an observational prospective study conducted at the Ophthalmology unit, wards, and intensive care unit (ICU) of a single tertiary teaching hospital between 1 May to 31 November 2022. A total of 268 subjects with diabetes were included. The fundus of all the diabetic patients during the study period were examined by a single-trained ophthalmologist. Those diagnosed with diabetic retinopathy were taken for acquisition of fundus picture with MII Ret Cam. Images were captured using Apple iPhone 5s and 20D mounted in MII Ret Cam device. ResultsOut of 268 subjects (536 eyes) screened during the study period,86 patients (154 eyes) were found to have DR. Maleto Female ratio was 1.4:1. The mean age was 56.37 ± 15.26 years and the mean duration of diabetes was 10.60 ± 6.97 years. Grading of DR revealed proliferative diabetic retinopathy (PDR) to be the topmost (n=84 eyes, 54.5%), followed by mild non-proliferative diabetic retinopathy (NPDR) in 26 eyes (16%) and moderate NPDR in 24 eyes (15%) while severe NPDR was seen in 10 eyes (6.5%) and very severe NPDR was found in 8 eyes (5.2%) and diabetic papillopathy in 2 eyes. Grading of vitreous hemorrhage revealed Grade 1 in one eye (1.8%); Grade 2 in 19 eyes (34.54%); Grade 3 in 33 eyes (60%) and Grade 4 in 2 eyes (3.63%). Grading of macular edema according to the distribution of hard exudates as Grade 1(No Clinically significant macular edema) in 33 eyes (34.37%) and Grade 2 (Clinically significant macular edema) in 63 eyes (65.62%). The duration of diabetes had a direct association with the occurrence of DR, 13patients (15.1%) had a duration of diabetes less than five years; 31patients(36%) had a duration of diabetes between 6 years to 10 years; 28 patients (32.6%) had a duration between 11 years to 20 years. 14 patients (16.3%) had a duration of more than twenty years. The Mean duration of diabetes in Group I was 11.40+/- 7.09 years and was 9.10+/- 6.48 in Group II. The mean HbA1c in Group I is 7.44+/- 1.24 and 6.54 +/- 1.09 in Group II. The mean fasting blood sugar in Group I was 130.87 +/- 29.57 and 106.62+/- 29.85 in Group II. The mean of random blood sugar was 202.15 +/- 60.84 in Group I and 155.13 +/- 55.95 in Group II. Results are suggestive of the association of DR occurrence with fasting, random blood sugar, duration of diabetes, and HbA1c. ConclusionsDiabetic Retinopathy Screening using MII Ret Cam assisted Smartphone-based fundus imaging may be a useful device to screen for referable and vision-threatening diabetic retinopathy. MII Ret Cam is portable equipment and is easily accessible to counsel patients in resource-limited countries.

Association between visceral fat area and diabetic retinopathy among people with type 2 diabetes mellitus: a cross-sectional study in Ningbo, Zhejiang Province, China.

The objective of this study is to investigate the relationship between visceral fat area (VFA) and diabetic retinopathy (DR) in the context of type 2 diabetes mellitus (T2DM) within Ningbo, China. The data of a total of 3,707 subjects with T2DM treated at The First Affiliated Hospital of Ningbo University were enrolled. The existence and severity of diabetic retinopathy were assessed by employing the 45° two-field stereoscopic digital photography. Subjects were categorized into four distinct groups: those without DR (NDR), individuals with mild non-proliferative DR (mild NPDR), people with moderate non-proliferative DR (moderate NPDR), and those suffering from vision-threatening DR (VTDR). Bio-electrical impedance was employed to estimate the Visceral fat area (VFA). Multinomial logistic regression models were utilized to evaluate the association between VFA and DR. The mean VFA in patients without diabetic retinopathy (NDR) was notably lower compared to that of patients with diabetic retinopathy (DR) (85.21 ± 37.78 vs. 97.37 ± 44.58 cm2, p < 0.001). As the severity of DR increased, VFA increased gradually but insignificantly (94.41 ± 43.13 cm2, 96.75 ± 40.82 cm2, 100.84 ± 49.34 cm2, p = 0.294). After adjusting the confounding factors, there was an association identified between VFA and the occurrence of DR (OR = 1.020, 95% CI = 1.016-1.024). It showed that regardless of BMI, whether it's less than 25 kg/m2 or greater than or equal to 25 kg/m2, a higher VFA (≥100 cm2) level came with a higher prevalence of DR (p < 0.001). The outcomes of this research indicate a modest association between VFA and the incidence of DR among Chinese patients who have been diagnosed with T2DM in Ningbo.

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) – a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces “NIMEQ-SACNet,” a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.

Diabetic retinopathy screening guidelines for Physicians in India: position statement by the Research Society for the Study of Diabetes in India (RSSDI) and the Vitreoretinal Society of India (VRSI)-2023

Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults worldwide. India is the diabetes capital of the world and one in five adults is said to have diabetes in India. With the increase in diabetes, there is an increasing burden of diabetic retinopathy (DR). All patients with diabetes have a risk of losing vision due to DR. The prevalence of diabetic retinopathy is 12.5%; out of which, 4% are said to have vision-threatening diabetic retinopathy (VTDR) The early stages of DR are symptomless, necessitating a proactive screening for an early detection of DR in all people with diabetes before they develop VTDR. This is a position statement jointly developed by RSSDI (Research Society for the Study of Diabetes in India) and VRSI (Vitreo Retinal Society of India) to provide guidelines for Physicians on DR screening in India. These guidelines emphasize the need for regular DR screening of all people with diabetes. It is recommended that the Physicians establish an effective DR screening model in their clinics, eg., a non-mydriatic fundus camera utilizing artificial intelligence (AI) algorithms for fundus photography to identify referral or non-referral DR. This will facilitate early detection and timely referral to an ophthalmologist thereby preventing VTDR. The need to create public awareness regarding blindness due to DR and a collaboration between Physicians and ophthalmologists for the management of diabetes, opportunistic screening of DR, and timely management of DR may play a crucial role in decreasing the burden of blindness secondary to diabetes.

Efficacy of Smartphone-based Fundus Photo in Vision Threatening Diabetic Retinopathy Screening: Developing Country Perspective

Background Vision-threatening diabetic retinopathy (VTDR) is a microvascular retinal complication caused by diabetes mellitus, which may lead to blindness if left untreated. One of the most effective methods to prevent diabetic-related ocular complications is through diabetic retinopathy (DR) screening. The community rarely carries out diabetic retinopathy-related eye examinations because using non-portable fundus photographs as its gold standard is costly and impracticable. This study aimed to determine the accuracy of smartphone-based fundus photographs as a practical and affordable tool for VTDR screening in developing countries. Methods This cross-sectional study used a consecutive technique at Cicendo National Eye Hospital, Indonesia. Patients with diabetes mellitus aged ≥20 years were evaluated for two-field mydriatic fundus photos using a non-portable fundus photo and a smartphone- based fundus photo utilizing the i-Spot fundus adapter. Results were analyzed to determine diagnostic test parameters. Results Two hundred and nineteen two-field mydriatic fundus photos were obtained from 139 patients. Smartphone-based fundus photography demonstrated a sensitivity of 98.4% (CI 96.6–100%), a specificity of 87.1% (CI 75.3–98.9%), a positive predictive value of 97.9% (CI 95.9–99.9%), a negative predictive value of 90.0% (CI 79.3–100%), and an accuracy of 96.8% (CI 94.5–99.8%). Conclusion The use of smartphone-captured fundus images proves to be a reliable screening method for VTDR. This tool has the potential to effectively screen the population, helping prevent future visual loss attributed to the disease.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology.

DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Diabetic retinopathy in patients with newly diagnosed type 2 diabetes mellitus

Introduction: Diabetic retinopathy (DR) is the leading cause of visual impairment worldwide including Nepal and studies from populations have shown that up to 10% of diabetic patients have vision-threatening DR, with up to 5% requiring treatment at this initial visit. Within this context, this study aims to evaluate the presence of DR in newly diagnosed diabetic patients, presenting in the outpatient department of ophthalmology, at Patan Academy of Health Sciences. Method: It is a retrospective cross-sectional study carried out in referred cases of newly diagnosed type 2 Diabetes Mellitus (DM) from January 2021 to December 2022. Information regarding age, gender, Body Mass Index (BMI), blood pressure (BP), Triglyceride (TG), proteinuria, and findings of fundus evaluation were extracted from the record maintained in the Department of Ophthalmology. Result: Out of 102 patients with mean age of 49.1±13.6 y, 14(13.73%) had DR, 11(10.8%) had mild Non-proliferative Diabetic Retinopathy (NPDR) and three (2.9%) had moderate NPDR. There was female predominance 56(54.9%), 50(49%) patients were overweight or obese (BMI≥25 kg/m2), 15(14.7%) patients had proteinuria and 28(27.5%) had fasting hypertriglyceridemia and additional ocular anomaly was identified in 33(32.4%) patients. Conclusion: Diabetic retinopathy is already present in significant proportion of newly diagnosed type 2 DM patients. Routine screening for DR at the time of diagnosis can improve ocular outcomes in newly diagnosed diabetics with targeted early treatment. Future research should evaluate this in wider population to generate guidelines for need of mandatory eye evaluation at diagnosis.

Prevalence and predictors of developing vision-threatening diabetic retinopathy within the first three years of type 2 diabetes.

To investigate the prevalence of diabetic retinopathy (DR) and vision-threatening DR (VTDR) in patients with type 2 diabetes mellitus (T2DM) stratified by the duration of diabetes and to identify the clinical variations and risk factors for VTDR occurring at different stages of T2DM. This was a retrospective comparative study. Patients were divided into short- (≤3 years), intermediate- (3-7 years), and long-duration (>7 years) groups. All patients were followed-up for DR and VTDR development. Risk factors were explored using logistic regression analysis. A total of,2961 patients were included; among them, 1,036 (35.0%) patients developed DR, and 293 (9.9%) had VTDR. The frequency of VTDR in patients who developed DR in the short-duration group was significantly higher than that in the intermediate-duration group (25.7% vs. 15.0%; p = 0.019), but comparable with that of the long-duration group (25.7% vs. 31.8%; p = 0.138). Patients who developed VTDR within the first 3 years of T2DM were more likely to have a family history of diabetes (p = 0.024), had higher glycated hemoglobin (p = 0.025), were males (p = 0.042), and were notably older at the onset of diabetes (p <0.001) but younger when diagnosed with DR (p <0.001). Moreover, higher glycated hemoglobin (OR = 1.14; 95% CI: 1.00-1.29; p = 0.043) and diabetic nephropathy (DN) (OR = 2.31; 95% CI: 1.08-4.91; p = 0.030) were independent risk factors for developing VTDR during the first 3 years of T2DM. The risk of DR is not high in persons with ≤3 years' duration of T2DM, however, if afflicted, the risk of VTDR should never be neglected. More frequent retinal screening is warranted in patients with newly diagnosed T2DM.

Undiagnosed diabetic retinopathy in Northeast China: prevalence and determinants.

To report the prevalence and contributing factors of undiagnosed diabetic retinopathy (DR) in a population from Northeastern China. A total of 800 subjects from the Fushun Diabetic Retinopathy Cohort Study were enrolled. A questionnaire assessing incentives and barriers to diagnosis of DR was administered. Logistic regression was used to identify clinical and sociodemographic factors associated with undiagnosed DR. In a prespecified subgroup analysis, we divided patients into vision-threatening diabetic retinopathy (VTDR) and non-VTDR (NVTDR) subgroups. Among 800 participants with DR, 712 (89.0%) were undiagnosed. Among 601 with NVTDR, 566 (94.2%) were undiagnosed. Among 199 with VTDR, 146 (73.4%) were undiagnosed. The risk factors affecting the timely diagnosis of NVTDR and VTDR exhibit significant disparities. In multivariate models, factors associated with undiagnosed VTDR were age over 60 years (OR = 2.966; 95% CI = 1.205-7.299; P = 0.018), duration of diabetes over 10 years (OR = 0.299; 95% CI = 0.118-0753; P = 0.010), visual impairment or blindness (OR = 0.310; 95% CI = 0.117-0.820; P = 0.018), receiving a reminder to schedule an eye examination (OR = 0.380; 95% CI = 0.163-0.883; P = 0.025), and the belief that "people with diabetes are unlikely to develop an eye disease" (OR = 4.691; 95% CI = 1.116-19.724; P = 0.035). However, none of the factors were associated with undiagnosed NVTDR (all P ≥ 0.145). Our research has uncovered a disconcerting trend of underdiagnosis in cases of DR within our population. Addressing determinants of undiagnosed DR may facilitate early detection.

The Role of Obstructive Sleep Apnea in Vision-Threatening Diabetic Retinopathy-A National Register-Based Study.

we investigated the association between OSA and vision-threatening diabetic retinopathy (VTDR). we used three nationwide registers to identify subjects with and without OSA and patients with type 1 (T1DM) or type 2 diabetes mellitus (T2DM). The Danish Civil Registration System was used to link OSA with diabetes diagnosis. The primary outcome was the occurrence of VTDR in diabetic patients with and without OSA. The secondary outcome was the prevalence of diabetes mellitus in patients with and without OSA. we included 532,828 diabetic subjects comprising 13,279 patients with OSA (2.5%) and 519,549 without OSA (97.5%). Diabetic patients with OSA had a 57% lower risk of VTDR compared to diabetic patients without OSA (OR 0.43, 95% CI 0.38-0.50, p < 0.0001). our findings indicate that OSA is associated with a lower risk of VTDR. Since we did not adjust our outcomes for diabetes duration, hypertension control and hemoglobin A1c, future studies are needed to confirm our findings.

Prevalence and associated factors of diabetic retinopathy among people with diabetes screened using fundus photography at a community diabetic retinopathy screening program in Nepal

BackgroundThis study aimed to assess the prevalence and associated factors of diabetic retinopathy (DR) and vision threatening DR (VTDR) among people with diabetes screened using fundus photography in Nepal.MethodsThis is a retrospective study among people with diabetes presented for DR screening using fundus photography from 2013 to 2019. Detailed demographics, duration of diabetes, medical history, visual acuity, and grading of DR on fundus photography were analyzed. Fundus camera used in the study were;Topcon digital fundus camera 900 CXR and digital portable fundus cameras (Nidek-10 portable non-mydriatric fundus camera; Versacam & Trade & Alpha, France), and a Zeiss portable fundus camera (Zeiss Visucout 100). Macula centred and disc centred 45 degree two images were taken from each eye. Pupil were dilated in cases where there was media haze in un-dilated cases. DR was graded using early treatment diabetic retinopathy study criteria. The images were graded by fellowship trained retina specialist. DR prevalence included any DR changes in one or both eyes.ResultsTotal of 25,196 patients with diabetes were enrolled. Mean age was 54.2 years with Standard Deviation (S.D):12.9 years, ranging from 6 years to 97 years. Type 1 and type 2 diabetes comprised of 451 people (1.79%) and 24,747 (98.21%) respectively. Overall, 1.8% of the images were un-gradable. DR prevalence was 19.3% (95% Confidence Interval (CI): 18.8 − 19.7%). DR prevalence in type 1 and type 2 diabetes was 15.5% (95% CI: 12.5 − 18.6%) and 19.3% (CI: 18.8 − 19.8%) respectively. Clinically significant macular edema (CSME) was found in 5.9% (95% CI: 5.6-6.2%) and VTDR in 7.9% (95% CI: 7.7-8.3%). In multivariate analysis, our study revealed strong evidence to suggest that there is meaningful association between DR and VTDR with duration of diabetes, diabetic foot, diabetic neuropathy, agriculture occupation, those under oral hypoglycaemic agents or insulin or both as compared to those under diet only, and presenting visual acuity > 0.3LogMAR.ConclusionPrevalence and associated factors for DR and VTDR were similar to other DR screening programs in the region. Emphasis on wider coverage of DR screening could help for timely detection and treatment of STDR to avoid irreversible blindness.

SGLT2 inhibitors and diabetic retinopathy progression.

To evaluate whether sodium-glucose co-transporter 2 (SGLT2) inhibitors affect progression of non-proliferative diabetic retinopathy (NPDR) compared to standard of care. A retrospective cohort study compared subjects enrolled in a commercial and Medicare Advantage medical claims database who filled a prescription for a SGLT2 inhibitor between 2013 and 2020 to unexposed controls, matched up to a 1:3 ratio. Patients were excluded if they were enrolled for less than 2years in the plan, had no prior ophthalmologic exam, had no diagnosis of NPDR, had a diagnosis of diabetic macular edema (DME) or proliferative diabetic retinopathy (PDR), had received treatment for vision-threatening diabetic retinopathy (VTDR), or were younger than 18years. To balance covariates of interest between the cohorts, an inverse probability treatment weighting (IPTW) propensity score for SGLT2 inhibitor exposure was used. Multivariate Cox proportional hazard regression modeling was employed to assess the hazard ratio (HR) for VTDR, PDR, or DME relative to SGLT2 exposure. A total of 6065 patients who initiated an SGLT2 inhibitor were matched to 12,890 controls. There were 734 (12%), 657 (10.8%), and 72 (1.18%) cases of VTDR, DME, and PDR, respectively, in the SGLT2 inhibitor cohort. Conversely, there were 1479 (11.4%), 1331 (10.3%), and 128 (0.99%) cases of VTDR, DME, and PDR, respectively, among controls. After IPTW, Cox regression analysis showed no difference in hazard for VTDR, PDR, or DME in the SGLT2 inhibitor-exposed cohort relative to the unexposed group [HR = 1.04, 95% CI 0.94 to 1.15 for VTDR; HR = 1.03, 95% CI 0.93 to 1.14 for DME; HR = 1.22, 95% CI 0.89 to 1.67 for PDR]. Exposure to SGLT2 inhibitor therapy was not associated with progression of NPDR compared to patients receiving other diabetic therapies.

Diabetic Retinopathy Screening at the Point of Care (DR SPOC): detecting undiagnosed and vision-threatening retinopathy by integrating portable technologies within existing services

IntroductionThe aim of this study was to determine the prevalence of diabetic retinopathy (DR) in a low socioeconomic region of a high-income country, as well as determine the diagnostic utility...

Trends in the Diagnosed Prevalence and Incidence of Major Eye Diseases in Medicare Part B Fee-for-Service Beneficiaries 68 Years of Age or Older

To study contemporary trends in the diagnosed prevalence and incidence of age-related eye diseases among Medicare Fee-for-Service (FFS) beneficiaries. Analysis of Medicare administrative claims data. Medicare FFS beneficiaries 68 years of age and older from 2005 through 2020 who were enrolled continuously in both Part A and Part B for 3 years, including the index year and a 2-year lookback period. Annual cross-sectional diagnosed prevalence and incidence rates were calculated. Age standardization was performed using the direct standardization method to account for changes in the age structure of the study population. Rates stratified by demographics (age, sex, race, and ethnicity) also were calculated. Annual prevalence and incidence of diagnosed age-related macular degeneration (AMD), diabetic retinopathy (DR) (among those with diabetes), and glaucoma. At baseline, in 2005, 60% of included beneficiaries were female, 20% were 85 years of age or older, 86% were non-Hispanic White, and one-quarter had a diagnosis of diabetes. From 2005 through 2019, the prevalence of a diagnosis of any of the conditions studied increased from 16.4% (n = 3 628 996) to 17.9% (n= 3 731 281). Diagnosed incidence decreased over this period from 4.9% (n = 954 878) in 2005 to 4.2% in 2019 (n=757 696). The diagnosed prevalence of AMD increased from 6.8% (n= 1 504 770) to 9.4% (n= 1 965 176); the diagnosed prevalence of any DR among those with diabetes decreased from 9.3% (n= 504 135) to 9.0% (n=532 859), although the diagnosed prevalence of vision-threatening DR increased from 2.0% to 3.4%; and the diagnosed prevalence of any diagnosed glaucoma decreased from 8.8% (n= 1 951 141) to 8.1% (n= 1 692 837). In 2020, the diagnosed prevalence and incidence of all diagnoses decreased. During the study period, we detected demographic differences in the prevalence and incidence of diagnosis of each condition. This study presents updated data on the prevalence and incidence of diagnosed major chronic, age-related eye diseases among Medicare FFS beneficiaries. Compared with older epidemiologic estimates, we found that the diagnosed prevalence of each condition studied was higher in more recent years. These findings may inform public health and policy planning and resource allocation to address the eye health of an increasingly older United States population. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China

The current epidemic status of diabetic retinopathy in China is unclear. A national prevalence survey of diabetic complications was conducted. 50,564 participants with gradable non-mydriatic fundus photographs were enrolled. The prevalence rates (95% confidence intervals) of diabetic retinopathy and vision-threatening diabetic retinopathy were 16.3% (15.3%–17.2%) and 3.2% (2.9%–3.5%), significantly higher in the northern than in the southern regions. The differences in prevalence between those who had not attained a given metabolic goal and those who had were more pronounced for Hemoglobin A1c than for blood pressure and low-density lipoprotein cholesterol. The participants with vision-threatening diabetic retinopathy had significantly higher proportions of visual impairment and blindness than those with non-vision-threatening diabetic retinopathy. The likelihoods of diabetic retinopathy and vision-threatening diabetic retinopathy were also associated with education levels, household income, and multiple dietary intakes. Here, we show multi-level factors associated with the presence and the severity of diabetic retinopathy.