Referable Retinopathy Research Articles

The scanning CONfoCal Ophthalmoscopy foR DIAbetic eye screening (CONCORDIA) study paper 2.

To determine if the Eidon white light 60-degree field Scanning Confocal Ophthalmoscope (SCO) camera was safe to use with staged mydriasis in a Diabetic Eye Screening Programme (DESP). The trial participants were recruited from people with diabetes attending appointments in DESP or Virtual Eye clinics for post-Covid delayed hospital appointments. Using staged mydriasis, the SCO images were taken before the pupils were dilated and compared to two-field 45 degrees mydriatic digital photography (the reference standard). Mydriatic SCO images were only compared to the reference standard if the non-mydriatic SCO images were unassessable. 1050 patients were recruited, 35 individuals were withdrawn, the majority (18) due to an imaging protocol deviation leaving 1015 individuals (2029 eyes). Using staged mydriasis, the sensitivity and specificity for any retinopathy was 97.5% (95% CI: 96.4-98.4%) and 82.3% (95% CI: 79.6-84.7%) respectively. The sensitivity and specificity for referable retinopathy was 92.7% (95% CI: 89.9-94.9%) and 85.4% (95% CI: 83.6-87.2%) respectively. The total number of eyes that were unassessable with the Eidon without mydriasis was 85/2029 (4.2%), and after mydriasis was 34/2029 (1.7%) and, with the reference standard, 34/2029 (1.7% - not always the same images) were unassessable. This study provides promising early results of the performance of the Eidon camera using staged mydriasis in a DESP which needs further evidence from a non-Caucasian population and from cost-effectiveness analyses.

The Scanning CONfoCal Ophthalmoscopy foR DIAbetic eye screening (CONCORDIA) study paper 1.

This project was to determine the performance of the Zeiss Clarus 700 (Clarus) and the Optos California (Optos) with staged mydriasis in a Diabetic Eye Screening Programme (DESP). Trial participants were recruited from people attending appointments in DESP or Virtual Eye clinics for delayed hospital appointments. Non-mydriatic photographs from the Clarus and Optos cameras were compared to 2-field 45 degrees mydriatic digital photography (the reference standard) and mydriatic photographs compared if the non-mydriatic photos were unassessable (staged mydriasis). 1573 patients were recruited. 76 individuals were withdrawn, leaving 1497 individuals (2993 eyes). For the Clarus and the Optos, the sensitivity for any retinopathy were 94.2% (95% CI: 92.9-95.3%) and 91.9% (95% CI: 90.5-93.2%) with specificities of 87.3% (95% CI: 85.4-89.0%) and 78.1% (95% CI: 75.7-80.3%) respectively. For referable DR the sensitivities for the Clarus and Optos were 86.0% (95% CI: 82.9-88.8%) and 77.6% (95% CI: 73.9-80.9%) with specificities of 92.8% (95% CI: 91.7-93.8%) and 95.4% (95% CI: 94.5-96.2%) respectively. The Clarus and Optos without mydriasis produced 100 (3.3%) and 152 (5.1%) unassessable eyes respectively, and after staged mydriasis 51 (1.7%) and 102 (3.4%) respectively with 52 (1.7%) reference standard images unassessable. This study reports the performance of the Clarus and the Optos using staged mydriasis in DR screening with wider fields detecting more referable retinopathy peripherally with some reduction in sensitivity centrally for macular lesions.

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.

Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process. This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative. This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05. The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant. The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended.

Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.V.) in a real-world setting. 45° non-mydriatic retinal images from 506 patients with diabetes were graded both by an ophthalmologist and by the AI algorithm, according to the International Clinical Diabetic Retinopathy severity scale. Less than moderate retinopathy (DR scores 0, 1) was defined as non-referable, while more severe stages were defined as referable retinopathy. The gradings were then compared both at eye-level and patient-level. Key metrics included sensitivity, specificity all measured with a 95% Confidence Interval. The percentage of ungradable eyes according to the AI was 2.58%. The performances of the AI algorithm for detecting referable DR were 97.18% sensitivity, 93.73% specificity at eye-level and 98.70% sensitivity and 91.06% specificity at patient-level. DRSplus paired with RetCAD represents a reliable DR screening solution in a real-world setting. The high sensitivity of the system ensures that almost all patients requiring medical attention for DR are referred to an ophthalmologist for further evaluation.

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

Background/aimsNational guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on...

ASCEND-Eye: Effects of Omega-3 Fatty Acids on Diabetic Retinopathy

PurposePreclinical studies support a protective role for omega-3 fatty acids (FAs) on diabetic retinopathy (DR), but these observations have not been confirmed in randomized trials. We present randomized evidence for the effects of omega-3 FAs on DR outcomes. Design and ParticipantsA sub-study of the ASCEND (A Study of Cardiovascular Events iN Diabetes) double-blind, randomized, placebo-controlled trial of 1g omega-3 fatty acids (containing 460mg eicosapentaenoic acid and 380mg docosahexaenoic acid) daily for the primary prevention of serious cardiovascular events, in 15,480 UK adults at least 40 years of age, with diabetes. MethodsLinkage to electronic NHS Diabetic Eye Screening Programme records in England and Wales, and confirmation of participant-reported eye events via medical record review. Logrank and stratified logrank methods were used for intention-to-treat analyses of time until the main outcomes of interest. Main Outcome MeasuresThe primary efficacy endpoint was time to the first post-randomization recording of referable disease, a composite of referable retinopathy (R2 or R3a/s) or referable maculopathy (M1) based on the grading criteria defined by the UK National Screening Committee. Secondary and tertiary outcomes included the referable disease outcome stratified by the severity of DR at baseline, any progression in retinopathy grade, and incident diabetic maculopathy. ResultsLinkage data were obtained for 7360 participants (48% of those who were randomized in ASCEND). During their mean follow-up of 6.5 years, 548 [14.8%] had a referable disease event in the omega-3 FAs group, compared to 513 [13.9%] in the placebo group (rate ratio 1.07; 95% confidence interval: 0.95-1.20; P = 0.29). There were no statistically significant between-group differences in the proportion of events for either of the secondary or tertiary outcomes. DiscussionRepresenting the first large-scale, prospective test of its kind, these data exclude any clinically meaningful benefits of 1g daily omega-3 FAs on DR.

The risk of development and progression of diabetic retinopathy in a group of ethnically diverse pregnant women with diabetes attending three regional Diabetic Eye Screening Programs in the UK

Background/ObjectivesCurrently, all pregnant women with diabetes are asked to attend screening at least twice during pregnancy, even if no retinopathy is detected in early pregnancy. We hypothesise that for women with no diabetic retinopathy in early pregnancy, the frequency of retinal screening may be safely reduced.Subjects/MethodsIn this retrospective cohort study, data for 4718 pregnant women attending one of three UK Diabetic Eye Screening (DES) Programmes between July 2011 and October 2019 was extracted. The women’s UK DES grades at 13 weeks gestation (early pregnancy) and 28 weeks gestation (late pregnancy) were recorded. Descriptive statistics were used to report baseline data. Ordered logistic regression was used to control for covariates, such as age, ethnicity, diabetes duration, and diabetes type.ResultsOf the women with grades recorded for both early and late pregnancy, a total of 3085 (65.39%) women had no retinopathy in early pregnancy, and 2306 (74.7%) of these women did not develop any retinopathy by 28 weeks. The number of women without retinopathy in early pregnancy who developed referable retinopathy was 14 (0.45%), none of whom required treatment. Diabetic Retinopathy in early pregnancy remained a significant predictor of DES grade in late pregnancy when covariates of Age, Ethnicity, and Diabetes Type were controlled for (P < 0.001).ConclusionsIn summary, this study has demonstrated that the burden of managing diabetes for pregnant mothers may be safely reduced by limiting the number of diabetic eye screening appointments in women who have no retinal changes in early pregnancy. Screening of women with retinopathy in early pregnancy should continue in line with current UK guidance.

Five-year outcomes of digital diabetic eye screening in individuals aged 80 and 85 years

ObjectiveTo assess the incidence of referable diabetic retinopathy (DR) in patients aged 80 and 85 years to determine whether screening interval can be extended safely in this age group.MethodsPatients who were aged 80 and 85 years when they attended digital screening during April 2014–March 2015 were included. Screening results at baseline and over the next four years were analysed.Results1880 patients aged 80 and 1105 patients aged 85 were included. Patients referred to hospital eye service (HES) for DR ranged from 0.7% to 1.4% in the 80-year-old cohort over 5 years. In this cohort a total of 76 (4%) were referred to HES for DR, of which 11 (0.6%) received treatment. Over the course of the follow up (FU), 403 (21%) died. In the 85-year-old cohort, referral to HES for DR each year ranged from 0.1% to 1.3%. In this cohort a total of 27 (2.4%) were referred to HES for DR, of which 4 (0.4%) received treatment. Over the course of follow-up 541(49%) died. All treated cases were for maculopathy in both cohorts and there were no cases of proliferative diabetic retinopathy requiring treatment.ConclusionThis study showed that the risk of progression of retinopathy is quite low in this age group and only a small proportion of patients developed referable retinopathy requiring treatment. This suggests relooking at the need for screening and ideal screening intervals in patients aged 80 years and over with no referable DR as they can be potentially classed as a group with low risk of sight loss.

Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm.

Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema. Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics. The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75-0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76). Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.

Quantification of risk factors for diabetic retinopathy progression.

To determine the importance of blood sugar control, blood pressure, and other key systemic factors on the risk of progression from no retinopathy to various stages of diabetic retinopathy. Restrospective cohort analysis of patients (N = 99, 280) in the Kaiser Permanente Northern California healthcare system with a baseline retina photographic screening showing no evidence of retinopathy and a minimum follow-up surveillance period of 3 years from 2008 to 2019. We gathered longitudinal data on diabetic retinopathy progression provided by subsequent screening fundus photographs and data captured in the electronic medical record over a mean surveillance of 7.3 ± 2.2 (mean ± SD) years. Progression from an initial state of no diabetic retinopathy to any of four outcomes was determined: (1) any incident retinopathy, (2) referable (moderate or worse) retinopathy, (3) diabetic macular edema, and (4) proliferative diabetic retinopathy.Multiple predictors, including age, race, gender, glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), cholesterol, chronic renal disease, and type of diabetes were investigated. Among modifiable risk factors, the average HbA1c had the strongest impact on the progression of diabetic retinopathy, followed by average SBP control and total cholesterol. Patients with an average HbA1c of 10.0% or greater (≥ 97mmol/mol) had a risk ratio of 5.72 (95% CI 5.44-6.02) for progression to any retinopathy, 18.84 (95% CI 17.25-20.57) for referable retinopathy, 22.85 (95% CI 18.87-27.68) for diabetic macular edema, and 25.96 (95% CI 18.75-36.93) for proliferative diabetic retinopathy compared to those with an average HbA1c of 7.0% (53mmol/mol) or less. Non-white patients generally had a higher risk of progression to all forms of diabetic retinopathy, while Asian patients were less likely to develop diabetic macular edema (HR 0.76, 95% CI 0.66-0.87). We confirm the critical importance of glucose control as measured by HbA1c on the risk of development of diabetic retinopathy.

Evaluation of a remote telemedicine platform using a novel handheld fundus camera: Physician and patient perceptions from real-world experience.

Although teleophthalmology has gained traction in recent years, it is at the center of the coronavirus disease pandemic. However, most hospitals are not ready owing to a severe lack of real-world experience. Furthermore, a limited number of studies have evaluated telemedicine applications on remote islands. This study aimed to evaluate real-world clinical and referral accuracy, image quality, physician-perceived diagnostic certainty, and patient satisfaction with telemedicine eye screening using a novel handheld fundus camera in a rural and medically underserved population. This prospective study included 176 eyes from a remote island. All participants underwent a comprehensive ophthalmic examination. Nonmydriatic retinal images obtained using a handheld fundus camera were reviewed by two retinal specialists to determine image quality, diagnosis, and need for referrals. The agreement of diagnosis between image-based assessments was compared with that of binocular indirect ophthalmoscopic assessments. Image quality of fundus photographs was considered acceptable or ideal in 97.7% and 95.5% of eyes assessed by two reviewers, respectively. There was considerable agreement in diagnosis between the indirect ophthalmoscopic assessment and image-based assessment by two reviewers (Cohen's kappa = 0.80 and 0.78, respectively). Likewise, substantial agreement was achieved in the referrals. The sensitivity for referable retinopathy from the two reviewers was 78% (95% confidence interval [CI], 57%-91%) and 78% (95% CI, 57%-91%), whereas specificity was 99% (95% CI, 95%-99%] and 98% (95% CI, 93%-99%), respectively. For physicians' perceived certainty of diagnosis, 93.8% and 90.3% were considered either certain or reliable. Overall, 97.4% of participants were satisfied with their experiences and greatly valued the telemedicine services. Novel fundus camera-based telemedicine screening demonstrated high accuracy in detecting clinically significant retinopathy in real-world settings. It achieved high patient satisfaction and physician-perceived certainty in diagnosis with reliable image quality, which may be scaled internationally to overcome geographical barriers under the global pandemic.

INCIDENCE AND UPDATES MANAGEMENT OF DIABETIC RETINOPATHY

One of the biggest causes of vision loss in the globe is diabetic retinopathy (DR), a consequence of diabetes. Despite significant attempts to lower the prevalence of vision impairment, DR continues to grow. There is damage to the vascular endothelial cells as the first pathophysiology of DRcells and a decrease in pericytes. As a result of the hypoxic reactions that follow, vascular endothelialvascular endothelial growth factor (VEGF) The most effective treatment available right nowControlling blood sugar levels is the key to preventing diabetic retinopathy and diabetic macular edema (DME). Superior in every way Laser therapy, anti-VEGF therapy, steroid therapy, and vitrectomy are all necessary treatments in these situations. Photocoagulation of the retinal pigment epithelium There is strong evidence that non-proliferative diabetic retinopathy (NPDR) is a viable treatment option.results that can be used to keep DR from progressing further. Furthermore, laser therapy has proven to be effective.For example, grid and subthreshold diode laser micropulse photocoagulation (SDM) for DME has been used.reported. In cases of vitreous hemorrhage or tractional retinal detachment in PDR patients, vitrectomy has been undertaken. There has also been significant interest in the ability of anti-VEGF medication to slow the progression of PDR from DME patients. Even with these therapies, many individuals with DR are still at risk of losing their eyesight and developing potentially dangerous side effects.. In the most advanced stages of DR, laser photocoagulation and vitrectomy are effective treatments for preventing severe vision loss. Both approaches, however, have their limitations. Evidence from preclinical and clinical studies shows that targeting renin-angiotensin system inhibition, vascular endothelial growth factor, and renin-angiotensin system blockade can reduce cardiovascular disease risk.the growth hormone, corticosteroids and protein kinase C.DR may be treated with these methodsMethodsThe study between January 2017 and November 2020 total number of patients 2968, Retrospective three year analysis.ResultsIncidence of referable retinopathy was independently associated with known duration of diabetes, age at diagnosis, and use of insulin treatment. For participants needing insulin treatment with a duration of diabetes of 10 years or more, cumulative incidence of referable retinopathy at one and three years was 9.61 and 30.99 per 1000 people, respectively.

48-OR: Barriers to Follow-Up Specialized Eye Care following Retinopathy Identification with Traditional vs. Autonomous Artificial Intelligence (AI) Camera

Point-of-care retinal imaging is effective at identifying diabetic retinopathy. However, appropriate follow-up, referral, and access to specialized eye care remains a barrier for the treatment of identified eye disease. We tested the hypothesis that rapid results and immediate referrals afforded by use of an automated diabetic retinopathy screening platform with AI technology (EyeArt) would result in greater patient follow up with ophthalmology specialists as compared to “traditional” tele-retinal screening (images acquired in clinic and transmitted electronically for interpretation by a vitreoretinal specialist) . All images were acquired during a patient’s regularly scheduled diabetes clinic visit. With traditional tele-retinal screening, the average turnaround time to patient notification of findings was days. Among the 357 patients that underwent traditional screening, 42 (12%) were found to have referable (more-than-mild) retinopathy, and of these, 32 (76%) followed up in ophthalmology clinic. In contrast, the fully autonomous diabetic retinopathy screening platform using AI technology provided an immediate assessment on the presence of retinopathy (within 60 seconds of photo acquisition) . Patients were given this point-of-care summary of their eye-exam findings. When indicated, an ophthalmology referral was ordered and patients were provided with contact information for the ophthalmology office. Of the 81 patients screened with AI technology, 22 (27%) had referable retinopathy detected, 15 (68%) of whom followed up in ophthalmology clinic. The higher detection rate with AI screening raises the question of “overcalling” the presence of disease. We conclude that although AI screening offers immediate results allowing for immediate ophthalmology referral, there remains a substantial barrier to ophthalmology follow up after a positive retinopathy screen identified by either screening method. Disclosure M.Wahl: None. R.Kiani: None. J.Neuberger: None. B.Wilson: None. M.Hartnett: None. S.Fisher: None.

Risk Factors for Progression to Referable Diabetic Eye Disease in People With Diabetes Mellitus in Auckland, New Zealand: A 12-Year Retrospective Cohort Analysis.

To evaluate the prevalence and risk factors for the development of any and referable diabetic eye disease in a multi-ethnic New Zealand population with diabetes mellitus attending a regional retinal screening service. Retrospective observational cohort study of people living with diabetes who attended the Auckland Regional Diabetic Retinal Screening Programme 2006-2018 inclusive (n = 41,786). Any retinopathy/maculopathy was present at first screening for 48.2% [95% confidence interval (CI): 45.8%-50.6%] / 37.8% (95% CI: 35.5%- 40.1%) of people with Type 1 and 25% (95% CI: 24.6%-25.4%) / 21.9% (95% CI: 21.5%-22.3%) with Type 2 diabetes. Referable retinopathy at baseline screening was 4.4% (95% CI: 3.6%-5.3%) and 1.6% (95% CI: 1.5%-1.7%) among people with Type 1 and Type 2 diabetes mellitus, respectively. After 4 years, cumulative incidence for referable retinopathy /referable maculopathy was 12/36 per 1000 people with Type 1 and 2.4/16 per 1000 people with Type 2 diabetes. Independent hazards for disease progression varied for the diabetes cohort types but baseline grade, duration of diabetes, and HbA1c were common to all. Referable diabetic eye disease at the first screening and after 4 years of follow-up is uncommon. Lengthening of the screening intervals for people with no or mild diabetic eye disease at first screening assessment could be considered.

Trends in diabetic retinopathy screening attendance and associations with vision impairment attributable to diabetes in a large nationwide cohort.

AimsTo investigate diabetic retinopathy screening attendance and trends in certified vision impairment caused by diabetic eye disease.MethodsThis was a retrospective study of attendance in three urban UK diabetic eye screening programmes in England. A survival analysis was performed to investigate time from diagnosis to first screen by age and sex. Logistic regression analysis of factors influencing screening attendance during a 15‐month reporting period was conducted, as well as analysis of new vision impairment certifications (Certificate of Vision Impairment) in England and Wales from 2009 to 2019.ResultsOf those newly registered in the Routine Digital Screening pathway (n = 97 048), 80% attended screening within the first 12 months and 88% by 36 months. Time from registration to first eye screening was longer for people aged 18–34 years, and 20% were unscreened after 3 years. Delay in first screen was associated with increased risk of referable retinopathy. Although 95% of participants (n = 291 296) attended during the 15‐month reporting period, uptake varied considerably. Younger age, social deprivation, ethnicity and duration of diabetes were independent predictors of non‐attendance and referable retinopathy. Although the last 10 years has seen an overall reduction in vision impairment certification attributable to diabetic eye disease, the incidence of vision impairment in those aged <35 years was unchanged.ConclusionsWhilst the majority of participants are screened in a timely manner, there is considerable variation in uptake. Young adults, have sub‐optimal attendance, and levels of vision impairment in this population have not changed over the last 10 years. There is an urgent need to explore barriers to/enablers of attendance in this group to inform policy initiatives and tailored interventions to address this issue.

Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients

Background/aimsHuman grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial...

Obstructive Sleep Apnea, a Risk Factor for Cardiovascular and Microvascular Disease in Patients With Type 2 Diabetes: Findings From a Population-Based Cohort Study.

To determine the risk of cardiovascular disease (CVD), microvascular complications, and mortality in patients with type 2 diabetes who subsequently develop obstructive sleep apnea (OSA) compared with patients with type 2 diabetes without a diagnosis of OSA. This age-, sex-, BMI-, and diabetes duration-matched cohort study used data from a U.K. primary care database from 1 January 2005 to 17 January 2018. Participants aged ≥16 years with type 2 diabetes were included. Exposed participants were those who developed OSA after their diabetes diagnosis; unexposed participants were those without diagnosed OSA. Outcomes were composite CVD (ischemic heart disease [IHD], stroke/transient ischemic attack [TIA], heart failure [HF]), peripheral vascular disease (PVD), atrial fibrillation (AF), peripheral neuropathy (PN), diabetes-related foot disease (DFD), referable retinopathy, chronic kidney disease (CKD), and all-cause mortality. The same outcomes were explored in patients with preexisting OSA before a diagnosis of type 2 diabetes versus diabetes without diagnosed OSA. A total of 3,667 exposed participants and 10,450 matched control participants were included. Adjusted hazard ratios for the outcomes were as follows: composite CVD 1.54 (95% CI 1.32, 1.79), IHD 1.55 (1.26, 1.90), HF 1.67 (1.35, 2.06), stroke/TIA 1.57 (1.27, 1.94), PVD 1.10 (0.91, 1.32), AF 1.53 (1.28, 1.83), PN 1.32 (1.14, 1.51), DFD 1.42 (1.16, 1.74), referable retinopathy 0.99 (0.82, 1.21), CKD (stage 3-5) 1.18 (1.02, 1.36), albuminuria 1.11 (1.01, 1.22), and all-cause mortality 1.24 (1.10, 1.40). In the prevalent OSA cohort, the results were similar, but some associations were not observed. Patients with type 2 diabetes who develop OSA are at increased risk of CVD, AF, PN, DFD, CKD, and all-cause mortality compared with patients without diagnosed OSA. Patients with type 2 diabetes who develop OSA are a high-risk population, and strategies to detect OSA and prevent cardiovascular and microvascular complications should be implemented.

How is the risk of being diagnosed with referable diabetic retinopathy affected by failure to attend diabetes eye screening appointments?

This study examines the anecdotal impression that in diabetes eye screening there is a relationship between number of consecutive missed screening appointments and the incidence of referable retinopathy at the next screening appointment that is attended. A retrospective observational audit was conducted of data from 62,067 people who were due for annual diabetes eye screening in the North East London Diabetes Eye Screening Programme between January 2010 and January 2017, and who had missed at least one screening appointment within that time. Missing 5 consecutive screening appointments increased the incidence of referable retinopathy from a programme average of 4% up to 15%. The incidence of referable retinopathy in people missing 10 or more consecutive appointments was ~20%. There was an association between younger age, male gender, type I disease, and being of African ethnicity with increasing number of missed appointments. There was a strong association between the number of missed appointments and the proportion of patients showing referable retinopathy at the next visit. Approaches to reduce the number of missed appointments may help to reduce the incidence of referable retinopathy. These may be targeted at those showing the greatest non-attendance behaviour in the current study.

Risk factors associated with progression to referable retinopathy: a type 2 diabetes mellitus cohort study in the Republic of Ireland

To determine factors associated with progression to referable diabetic retinopathy in people with type 2 diabetes in the Republic ofIreland. The study was conducted in a dynamic cohort of 2770 people with type 2 diabetes, recruited between April 2005 and July 2013. Systemic factors (systolic and diastolic blood pressure, HbA1c , lipid levels, BMI) and baseline diabetic retinopathy grading results were evaluated at 4-monthly and yearly intervals, respectively. Associations between risk factors (most recently recorded value, and rate of change in value between pairs of consecutive systemic evaluations) and development of referable diabetic retinopathy were estimated using Cox proportional hazards models. There was a fourfold increased risk of progression to referral when retinopathy was present at baseline vs no retinopathy at baseline (hazard ratio 4.02, 95% CI 2.80-5.78; P<0.001). Higher current values of HbA1c (hazard ratio 1.22, 95% CI 1.11-1.34; P<0.001), systolic blood pressure (hazard ratio 1.29, 95% CI 1.15-1.45; P<0.001) and triglycerides (hazard ratio 1.10, 95% CI 1.03-1.18; P=0.004) were associated with increased risk of referral. Higher current BMI (hazard ratio 0.83, 95% CI 0.73-0.95; P=0.007) and diastolic blood pressure (hazard ratio 0.91, 95% CI 0.85-0.97; P=0.006) were associated with reduced risk of referral. Presence of retinopathy at baseline was strongly associated with increased risk of referral. Modest associations between systemic factors and risk of progression to referable retinopathy were detected.

Comparison of a Smartphone-Based Photography Method with Indirect Ophthalmoscopic Assessment in Referable Retinopathy of Prematurity: A Smart Retinopathy of Prematurity Model Pilot Study

Abstract This study sought to evaluate the agreement between smartphone-based device assisted remote screening and gold standard indirect ophthalmoscopic examination in cases of referable Retinopathy of Prematurity (ROP).