Home-Monitoring Vision Tests to Detect Active Neovascular Age-Related Macular Degeneration.

Importance: Most neovascular age-related macular degeneration (nAMD) treatments involve long-term follow-up of disease activity. Home-monitoring would reduce the burden on patients and their caregivers and release clinic capacity. Objective: To evaluate 3 vision home-monitoring tests for patients to use to detect active nAMD compared with diagnosing active nAMD at hospital follow-up during the after-treatment monitoring phase. Design, Setting, and Participants: This was a diagnostic test accuracy study wherein the reference standard was detection of active nAMD by an ophthalmologist at hospital follow-up. The 3 home-monitoring tests evaluated included the following: (1) the KeepSight Journal (KSJ [International Macular and Retinal Foundation]), which contains paper-based near-vision tests presented as word puzzles, (2) the MyVisionTrack (mVT [Genentech]) vision-monitoring mobile app, viewed on an Apple mobile operating system-based device, and (3) the MultiBit (MBT [Visumetrics]) app, viewed on an Apple mobile operating system-based device. Participants were asked to test weekly; mVT and MBT scores were transmitted automatically, and KSJ scores were returned to the research office every 6 months. Raw scores between hospital follow-ups were summarized as averages. Patients were recruited from 6 UK hospital eye clinics and were 50 years and older with at least 1 eye first treated for active nAMD for at least 6 months or longer to a maximum of 42 months before approach. Participants were stratified by time since starting treatment. Study data were analyzed from May to September 2021. Exposures: The KSJ, mVT, and MBT were compared with the reference standard (in-hospital ophthalmologist examination). Main Outcomes and Measures: Estimated area under receiver operating characteristic curve (AUROC). The study had 90% power to detect a difference of 0.06, or 80% power to detect a difference of 0.05, if the AUROC for 2 tests was 0.75. Results: A total of 297 patients (mean [SD] age, 74.9 [6.6] years; 174 female [58.6%]) were included in the study. At least 1 hospital follow-up was available for 312 study eyes in 259 participants (1549 complete visits). Median (IQR) home-monitoring testing frequency was 3 (1-4) times per month. Estimated AUROC was less than 0.6 for all home-monitoring tests, and only the KSJ summary score was associated with lesion activity (odds ratio, 3.48; 95% CI, 1.09-11.13; P = .04). Conclusions and Relevance: Results suggest that no home-monitoring vision test evaluated provided satisfactory diagnostic accuracy to identify active nAMD diagnosed in hospital eye service follow-up clinics. Implementing any of these evaluated tests, with ophthalmologists only reviewing test positives, would mean most active lesions were missed, risking unnecessary sight loss.

Anti-Vascular Endothelial Growth Factor Drugs Compared With Panretinal Photocoagulation for the Treatment of Proliferative Diabetic Retinopathy: A Cost-Effectiveness Analysis.

OBJECTIVES: This study aimed to evaluate the cost-effectiveness of anti-vascular endothelial growth factor drugs (anti-VEGFs) compared with panretinal photocoagulation (PRP) for treating proliferative diabetic retinopathy (PDR) in the United Kingdom. METHODS: A discrete event simulation model was developed, informed by individual participant data meta-analysis. The model captures treatment effects on best corrected visual acuity in both eyes, and the occurrence of diabetic macular edema and vitreous hemorrhage. The model also estimates the value of undertaking further research to resolve decision uncertainty. RESULTS: Anti-VEGFs are unlikely to generate clinically meaningful benefits over PRP. The model predicted anti-VEGFs be more costly and similarly effective as PRP, generating 0.029 fewer quality-adjusted life-years at an additional cost of £3688, with a net health benefit of -0.214 at a £20 000 willingness-to-pay threshold. Scenario analysis results suggest that only under very select conditions may anti-VEGFs offer potential for cost-effective treatment of PDR. The consequences of loss to follow-up were an important driver of model outcomes. CONCLUSIONS: Anti-VEGFs are unlikely to be a cost-effective treatment for early PDR compared with PRP. Anti-VEGFs are generally associated with higher costs and similar health outcomes across various scenarios. Although anti-VEGFs were associated with lower diabetic macular edema rates, the number of cases avoided is insufficient to offset the additional treatment costs. Key uncertainties relate to the long-term comparative effectiveness of anti-VEGFs, particularly considering the real-world rates and consequences of treatment nonadherence. Further research on long-term visual acuity and rates of vision-threatening complications may be beneficial in resolving uncertainties.

Inequalities in Uptake and Use of Digital Applications for Home-Monitoring of Neovascular Age-Related Macular Degeneration in an Elderly Visually Impaired Population: The MONARCH Study.

Purpose: To describe inequalities in the Monitoring for Neovascular Age-related Macular Degeneration Reactivation at Home (MONARCH) diagnostic test accuracy study for: recruitment; participants' ability to self-test; and adherence to testing using digital applications during follow-up. Methods: Home-monitoring vision tests included two tests implemented as software applications (apps: MyVisionTrack and MultiBit) on an iPod Touch device. Patients were provided with all hardware required to participate (iPod and MIFI device) and trained to use the apps. Regression models estimated associations of age, sex, Index of Multiple Deprivation, strata of time since first diagnosis, and baseline visual acuity at study entry on outcomes of willingness to participate, ability to perform tests, and adherence to weekly testing. Results: A minority of patients who were approached were willing-in-principle to participate. Increasing age was associated with being unwilling-in-principle to participate. Patients from the most deprived areas had a 47% decrease in odds of being willing compared to those from the middle quintile deprived areas (odds ratio, 0.53; 95% confidence interval = 0.32, 0.88). Increasing age and worse deprivation were not consistently associated either with ability to self-monitor with the index tests, or adherence to weekly testing. Conclusions: Associations of increasing age and worse deprivation index were associated with unwillingness-in-principle to participate despite the provision of hardware' highlighting the potential for inequality with interventions of the kind evaluated. Translational Relevance: The clear evidence of inequalities in participation should prompt future research on ways to encourage adoption of mobile health technologies by underserved populations.

Descriptive study of the challenges when implementing an app for patients with neovascular age-related macular degeneration to monitor their vision at home.

OBJECTIVES: Remote monitoring of health has the potential to reduce the burden to patients of face-to-face appointments and make healthcare more efficient. Apps are available for patients to self-monitor vision at home, for example, to detect reactivation of age-related macular degeneration (AMD). Describing the challenges when implementing apps for self-monitoring of vision at home was an objective of the MONARCH study to evaluate two vision-monitoring apps on an iPod Touch (Multibit and MyVisionTrack). DESIGN: Diagnostic Test Accuracy study. SETTING: Six UK hospitals. METHODS: The study provides an example of the real-world implementation of such apps across health sectors in an older population. Challenges described include the following: (1) frequency and reason for incoming calls made to a helpline and outgoing calls made to participants; (2) frequency and duration of events responsible for the tests being unavailable; and (3) other technical and logistical challenges. RESULTS: Patients (n=297) in the study were familiar with technology; 252/296 (85%) had internet at home and 197/296 (67%) had used a smartphone. Nevertheless, 141 (46%) called the study helpline, more often than anticipated. Of 435 reasons for calling, all but 42 (10%) related to testing with the apps or hardware, which contributed to reduced adherence. The team made at least one call to 133 patients (44%) to investigate why data had not been transmitted. Multibit and MyVisionTrack apps were unavailable for 15 and 30 of 1318 testing days for reasons which were the responsibility of the app providers. Researchers also experienced technical challenges with a multiple device management system. Logistical challenges included regulations for transporting lithium-ion batteries and malfunctioning chargers. CONCLUSIONS: Implementation of similar technologies should incorporate a well-resourced helpline and build in additional training time for participants and troubleshooting time for staff. There should also be robust evidence that chosen technologies are fit for the intended purpose. TRIAL REGISTRATION NUMBER: ISRCTN79058224.

Diabetic Retinopathy and Quality of Life: A Systematic Review and Meta-Analysis.

Importance: The association between diabetic retinopathy (DR) and quality of life (QoL) has not been thoroughly investigated. Objective: To investigate the association between DR and both vision-related QoL (VRQoL) and general health-related QoL (HRQoL). Data Sources: MEDLINE, EBSCO, Embase, and Web of Science were searched from their inception to April 2022. Study Selection: Studies included adults with DR and a measure of QoL. Data Extraction and Synthesis: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Two assumption-free meta-analyses were conducted. Analysis 1 included studies with participants without DR as the referent group to which QoL scores of participants with DR, grouped according to DR severity, were compared. Analysis 2 included all studies with participants with DR and a measure of QoL. QoL scores were pooled within categories of DR severity, and comparisons were made between these categories. Main Outcome and Measures: QoL measured using HRQoL and VRQoL scales. Results: A total of 93 articles were included: 79 in the meta-analyses and 14 in the narrative results. VRQoL was recorded in 54 studies, HRQoL in 26, and both in 13 studies. The most commonly used scales were the National Eye Institute 25-item Visual Function Questionnaire (VFQ-25) (n = 49) for VRQoL and the Short Form (SF) Health Survey (n = 18) for HRQoL. Thirty-five studies reported VFQ-25 composite scores. Analysis 1 consisted of 8 studies including 1138 participants with DR and 347 participants without DR. Compared with participants without DR, the composite VFQ-25 score was 3.8 (95% CI, 1.0-6.7) points lower in those with non-vision-threatening DR (NVTDR), 12.5 (95% CI, 8.5-16.5) lower in those with any DR, and 25.1 (95% CI, 22.8-27.2) lower in VTDR (P < .001 for trend). Analysis 2 consisted of 35 studies including 6351 participants with DR. The pooled mean VFQ-25 composite score was 91.8 (95% CI, 91.0-92.7) for participants with NVTDR, 77.6 (95% CI, 76.9-78.3) for any DR, and 73.2 (95% CI, 72.6-73.7) for VTDR (P < .001 for trend). HRQoL scores had weak or no associations with NVTDR and strong associations with VTDR. Conclusions and Relevance: This study found that VRQoL declined with the presence and severity of DR. Interventions to reduce progression of DR at both early and more advanced stages could improve VRQoL.

Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images.

Importance: Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective: To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. Design, Setting and Participants: Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022. Exposure: Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development. Main Outcomes and Measures: Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy. Results: A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model's AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 9 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified. Conclusions and Relevance: This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

Importance: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. Objective: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. Design, Setting, and Participants: This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Exposures: Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). Main Outcomes and Measures: The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. Results: A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. Conclusions and Relevance: The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Accuracy of Integrated Artificial Intelligence Grading Using Handheld Retinal Imaging in a Community Diabetic Eye Screening Program.

Purpose: To evaluate mydriatic handheld retinal imaging performance assessed by point-of-care (POC) artificial intelligence (AI) as compared with retinal image graders at a centralized reading center (RC) in identifying diabetic retinopathy (DR) and diabetic macular edema (DME). Design: Prospective, comparative study. Subjects: Five thousand five hundred eighty-five eyes from 2793 adult patients with diabetes. Methods: Point-of-care AI assessment of disc and macular handheld retinal images was compared with RC evaluation of validated 5-field handheld retinal images (disc, macula, superior, inferior, and temporal) in identifying referable DR (refDR; defined as moderate nonproliferative DR [NPDR], or worse, or any level of DME) and vision-threatening DR (vtDR; defined as severe NPDR or worse, or any level of center-involving DME [ciDME]). Reading center evaluation of the 5-field images followed the international DR/DME classification. Sensitivity (SN) and specificity (SP) for ungradable images, refDR, and vtDR were calculated. Main Outcome Measures: Agreement for DR and DME; SN and SP for refDR, vtDR, and ungradable images. Results: Diabetic retinopathy severity by RC evaluation: no DR, 67.3%; mild NPDR, 9.7%; moderate NPDR, 8.6%; severe NPDR, 4.8%; proliferative DR, 3.8%; and ungradable, 5.8%. Diabetic macular edema severity by RC evaluation was as follows: no DME (80.4%), non-ciDME (7.7%), ciDME (4.4%), and ungradable (7.5%). Referable DR was present in 25.3% and vtDR was present in 17.5% of eyes. Images were ungradable for DR or DME in 7.5% by RC evaluation and 15.4% by AI. There was substantial agreement between AI and RC for refDR (κ = 0.66) and moderate agreement for vtDR (κ = 0.54). The SN/SP of AI grading compared with RC evaluation was 0.86/0.86 for refDR and 0.92/0.80 for vtDR. Conclusions: This study demonstrates that POC AI following a defined handheld retinal imaging protocol at the time of imaging has SN and SP for refDR that meets the current United States Food and Drug Administration thresholds of 85% and 82.5%, but not for vtDR. Integrating AI at the POC could substantially reduce centralized RC burden and speed information delivery to the patient, allowing more prompt eye care referral. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Macula vs periphery in diabetic retinopathy: OCT-angiography and ultrawide field fluorescein angiography imaging of retinal non perfusion.

OBJECTIVES: To investigate the association between peripheral non-perfusion index (NPI) on ultrawide-field fluorescein angiography (UWF-FA) and quantitative OCT-Angiography (OCT-A) metrics in the macula. METHODS: In total, 48 eyes with UWF-colour fundus photos (CFP), UWF-FA (California, Optos) and OCT-A (Spectralis, Heidelberg) were included. OCT-A (3 × 3 mm) was used to determine foveal avascular zone (FAZ) parameters and vessel density (VD), perfusion density (PD), fractal dimension (FD) on superficial capillary plexus (SCP). NPI's extent and distribution was determined on UWF-FA within fovea centred concentric rings corresponding to posterior pole (<10 mm), mid-periphery (10-15 mm), and far-periphery (>15 mm) and within the total retinal area, the central macular field (6×6 mm), ETDRS fields and within each extended ETDRS field (P3-P7). RESULTS: Macular PD was correlated to NPI in total area of retina (Spearman ρ = 0.69, p < 0.05), posterior pole (ρ = 0.48, p < 0.05), mid-periphery (ρ = 0.65, p < 0.05), far-periphery (ρ = 0.59, p < 0.05), P3-P7 (ρ = 0,55 at least, p < 0.05 for each), central macula (ρ = 0.47, p < 0.05), total area in ETDRS (ρ = 0.55, p < 0.05). Macular VD and FD were correlated to NPI of total area of the retina (ρ = 0.60 and 0.61, p < 0.05), the mid-periphery (ρ = 0.56, p < 0.05) and far-periphery (ρ = 0.60 and ρ = 0.61, p < 0.05), and in P3-P7 (p < 0.05). FAZ perimeter was significantly corelated to NPI at posterior pole and central macular area (ρ = 0.37 and 0.36, p < 0.05), and FAZ area to NPI in central macular area (ρ = 0.36, p < 0.05). CONCLUSIONS: Perfusion macular metrics on OCT-A correlated with UWF-FA's non-perfusion (NP), particularly in the retina's mid and far periphery, suggesting that OCT-A might be a useful non-invasive method to estimate peripheral retinal NP.

Improved Structure and Function in Early-Detected Second-Eye Neovascular Age-Related Macular Degeneration: FASBAT/Early Detection of Neovascular Age-Related Macular Degeneration Report 1.

PURPOSE: Visual acuity (VA) and structural biomarker assessment before and 24-months after early detection and routine treatment of second-eye involvement with neovascular age-related macular degeneration (nAMD) and additional comparison with the first eye affected. DESIGN: Prospective, 22-center observational study of participants with unilateral nAMD in the Early Detection of Neovascular AMD (EDNA) study, coenrolled into the Observing Fibrosis, Macular Atrophy and Subretinal Highly Reflective Material, Before and After Intervention with anti-VEGF Treatment (FASBAT) study for an additional 2-year follow-up. PARTICIPANTS: Older adults (> 50 years) with new onset nAMD in the first eye. METHODS: Assessment of both eyes with OCT, color fundus photography (CFP), clinic-measured VA, and quality of life (QoL). MAIN OUTCOME MEASURES: Prevalence of atrophy, subretinal hyperreflective material (SHRM), intraretinal fluid (IRF), subretinal fluid (SRF), and changes in VA over the study duration in both the first and second eyes affected with nAMD. Composite QoL scores over time. RESULTS: Of 431 participants recruited to the FASBAT study, the second eye converted to nAMD in 100 participants at a mean of 18.9 months. Visual acuity was 18 letters better at the time of early diagnosis in the second eye compared with conventional diagnosis in the first eye (72.9 vs. 55.6 letters). Visual acuity remained better in the second eye 24.9 months postconversion, at 69.5 letters compared with 59.7 letters at a similar matched time point in the first eye (18.9 months). A greater proportion of participants had vision > 70 letters in the second eye versus the first eye, 24.9 months postconversion (61 vs. 35). Prevalence of SHRM and IRF was lower in the second eye compared with the first eye 24.9 months postconversion. However, SRF prevalence was greater in the second eye 24.9 months postconversion. The development and progression of total area of atrophy appears similar in both eyes. Mean composite QoL scores increased over time, with a significant correlation between VA for the second eye only 24.9 months postconversion. CONCLUSION: This study has shown that early detection of exudative AMD in the second eye is associated with reduced prevalence of SHRM and IRF and greater VA, which is significantly correlated with maintained QoL. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Adalimumab vs placebo as add-on to Standard Therapy for autoimmune Uveitis: Tolerability, Effectiveness and cost-effectiveness-a protocol for a randomised controlled trial (ASTUTE trial).

INTRODUCTION: Adalimumab is an effective treatment for autoimmune non-infectious uveitis (ANIU), but it is currently only funded for a minority of patients with ANIU in the UK as it is restricted by the National Institute for Health and Care Excellence guidance. Ophthalmologists believe that adalimumab may be effective in a wider range of patients. The Adalimumab vs placebo as add-on to Standard Therapy for autoimmune Uveitis: Tolerability, Effectiveness and cost-effectiveness (ASTUTE) trial will recruit patients with ANIU who do and do not meet funding criteria and will evaluate the effectiveness and cost-effectiveness of adalimumab versus placebo as an add-on therapy to standard care. METHODS AND ANALYSIS: The ASTUTE trial is a multicentre, parallel-group, placebo-controlled, pragmatic randomised controlled trial with a 16-week treatment run-in (TRI). At the end of the TRI, only responders will be randomised (1:1) to 40 mg adalimumab or placebo (both are the study investigational medicinal product) self-administered fortnightly by subcutaneous injection. The target sample size is 174 randomised participants. The primary outcome is time to treatment failure (TF), a composite of signs indicative of active ANIU. Secondary outcomes include individual TF components, retinal morphology, adverse events, health-related quality of life, patient-reported side effects and visual function, best-corrected visual acuity, employment status and resource use. In the event of TF, open-label drug treatment will be restarted as per TRI for 16 weeks, and if a participant responds again, allocation will be switched without unmasking and treatment with investigational medicinal product restarted. ETHICS AND DISSEMINATION: The trial received Research Ethics Committee (REC) approval from South Central - Oxford B REC in June 2020. The findings will be presented at international meetings, by peer-reviewed publications and through patient organisations and newsletters to patients, where available. TRIAL REGISTRATION: ISRCTN31474800. Registered 14 April 2020.

Self-reported dual sensory impairment and related factors: a European population-based cross-sectional survey.

BACKGROUND: Data on population-based self-reported dual vision and hearing impairment are sparse in Europe. We aimed to investigate self-reported dual sensory impairment (DSI) in European population. METHODS: A standardised questionnaire was used to collect medical and socio-economic data among individuals aged 15 years or more in 29 European countries. Individuals living in collective households or in institutions were excluded from the survey. RESULTS: Among 296 677 individuals, the survey included 153 866 respondents aged 50 years old or more. The crude prevalence of DSI was of 7.54% (7.36-7.72). Among individuals aged 60 or more, 9.23% of men and 10.94% of women had DSI. Eastern and southern countries had a higher prevalence of DSI. Multivariable analyses showed that social isolation and poor self-rated health status were associated with DSI with ORs of 2.01 (1.77-2.29) and 2.33 (2.15-2.52), while higher income was associated with lower risk of DSI (OR of 0.83 (0.78-0.89). Considering country-level socioeconomic factors, Human Development Index explained almost 38% of the variance of age-adjusted prevalence of DSI. CONCLUSION: There are important differences in terms of prevalence of DSI in Europe, depending on socioeconomic and medical factors. Prevention of DSI does represent an important challenge for maintaining quality of life in elderly population.

Retinal vascular structure independently predicts the initial treatment response in neovascular age-related macular degeneration.

PURPOSE: Prediction of the early treatment response is important in neovascular age-related macular degeneration (nAMD). Hence, we aimed to test if non-invasive measurements of the retinal vascular structure were able to predict a successful outcome of initial intravitreal treatment. METHODS: In 58 eyes of 58 patients with treatment-naïve nAMD, advanced markers of retinal vascular structure were measured by Singapore I Vessel Assessment prior to initial intravitreal treatment with three monthly injections of aflibercept with subsequently categorization of patients as full treatment responders (FTR) or non/partial treatment responders (N/PR), with the former defined as loosing fewer than five Early Treatment Diabetic Retinopathy Study letters and having no residual intra- or subretinal fluid or macular haemorrhage. RESULTS: Of 54 eyes attending follow-up, 44.4% were categorized as FTR. Patients with FTR were older (81.5 vs. 77 years, p = 0.04), and prior to treatment those eyes had a lower retinal arteriolar fractal dimension (Fd) (1.21 vs. 1.24 units, p = 0.02) and venular length-diameter ratio (LDR) (7.3 vs. 15.9 units, p = 0.006), but did not differ with respect to other retinal vascular parameters. In multiple logistic regression models, a lower chance of FTR was independently predicted by a higher retinal venular LDR (odds ratio [OR] 0.91, 95% CI 0.82-0.99, p = 0.03, for each 1 unit increment) and marginally by a higher retinal arteriolar Fd (OR 0.83, 95% CI 0.68-1.00, p = 0.05, for each 0.01 unit increment). CONCLUSION: Retinal venular LDR independently predicted the initial treatment response in nAMD. If confirmed by long-term, prospective studies, this might help to guide treatment.

Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading.

PURPOSE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). DESIGN: We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. PARTICIPANTS: A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. METHODS: We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. MAIN OUTCOME MEASURES: Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. RESULTS: The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. CONCLUSIONS: Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

CHOROIDAL VASCULARITY IN CHRONIC CENTRAL SEROUS CHORIORETINOPATHY AND ITS ASSOCIATION WITH RISK SINGLE-NUCLEOTIDE POLYMORPHISMS.

PURPOSE: To analyze the choroidal parameters of patients with chronic central serous chorioretinopathy (cCSC) and the association with central serous chorioretinopathy susceptibility genes. METHODS: The choroidal vascular index (CVI) was obtained by binarizing spectral domain optical coherence tomography enhanced depth images of patients with cCSC and healthy age-matched controls. Patients with cCSC were genotyped for three central serous chorioretinopathy susceptibility single-nucleotide polymorphisms: rs4844392 ( mir-29b-2/CD46 ), rs1329428 ( CFH ), and rs2379120 (upstream GATA5 ). RESULTS: One hundred three eyes with cCSC and 53 control eyes were included. There was a significant increase in the subfoveal choroidal area in both the affected (2.4 ± 0.6 mm 2 ) and fellow (2.2 ± 0.6 mm 2 ) eyes of patients with cCSC compared with controls (1.8 ± 0.5 mm 2 , P < 0.0001 and P < 0.0001). The CVI was reduced in patients with cCSC 63.5% ± 3.1% compared with controls 65.4% ± 2.3% ( P < 0.001) and also in the affected compared with the fellow eyes 64.6% ± 2.9% ( P < 0.01). There was a significant association between CVI in the cCSC group and presence of the risk single-nucleotide polymorphisms rs2379120 at GATA5 ( P < 0.01). CONCLUSION: The relative reduction of CVI in patients with cCSC may suggest a persistence of vessel hyperpermeability over dilation in chronic disease. GATA5 is associated with CVI in patients with cCSC and therefore may have a role in choroidal vascularity.

Impact of a short online course on the accuracy of non-ophthalmic diabetic retinopathy graders in recognising glaucomatous optic nerves in Vietnam.

PURPOSE: To test an online training course for non-ophthalmic diabetic retinopathy (DR) graders for recognition of glaucomatous optic nerves in Vietnam. METHODS: This was an uncontrolled, experimental, before-and-after study in which 43 non-ophthalmic DR graders underwent baseline testing on a standard image set, completed a self-paced, online training course and were retested using the same photographs presented randomly. Twenty-nine local ophthalmologists completed the same test without the training course. DR graders then underwent additional one-to-one training by a glaucoma specialist and were retested. Test performance (% correct, compared with consensus grades from four fellowship-trained glaucoma experts), sensitivity, specificity, positive and negative predictive value, and area under the receiver operating (AUC) curve, were computed. RESULTS: Mean age of DR graders (32.6±5.5 years) did not differ from ophthalmologists (32.3±7.3 years, p=0.13). Online training required a mean of 297.9 (SD 144.6) minutes. Graders' mean baseline score (33.3%±14.3%) improved significantly after training (55.8%±12.6%, p<0.001), and post-training score did not differ from ophthalmologists (58.7±15.4%, p=0.384). Although grader sensitivity reduced before [85.5% (95% CI 83.5% to 87.3%)] versus after [80.4% (78.3% to 82.4%)] training, specificity improved significantly [47.8 (44.9 to 50.7) vs 79.8 (77.3 to 82.0), p<0.001]. Grader AUC also improved after training [66.6 (64.9 to 68.3)] to [80.1 (78.5 to 81.6), p<0.001]. Additional one-to-one grader training by a glaucoma specialist did not further improve grader scores. CONCLUSION: Non-ophthalmic DR graders can be trained to recognise glaucoma using a short online course in this setting, with no additional benefit from more expensive one-to-one training. After 5-hour online training in recognising glaucomatous optic nerve head, scores of non-ophthalmic DR graders doubled, and did not differ from local ophthalmologists. Intensive one-to-one training did not further improve performance.

Inhibition of bradykinin in SARS-CoV-2 infection: a randomised, double-blind trial of icatibant compared with placebo (ICASARS).

SARS-CoV-2 binds to ACE2 receptors and enters cells. The symptoms are cough, breathlessness, loss of taste/smell and X-ray evidence of infiltrates on chest imaging initially caused by oedema, and subsequently by a lymphocytic pneumonitis. Coagulopathy, thrombosis and hypotension occur. Worse disease occurs with age, obesity, ischaemic heart disease, hypertension and diabetes.These features may be due to abnormal activation of the contact system. This triggers coagulation and the kallikrein-kinin system, leading to accumulation of bradykinin and its derivatives, which act on receptors B1R and B2R. Receptor activation causes cough, hypotension, oedema and release of the cytokine interleukin-6 (IL-6) which recruits lymphocytes. These effects are core features seen in early SARS CoV-2 infection. METHODS AND ANALYSIS: In this study, hypoxic patients with COVID-19 with symptom onset ≤7 days will be randomised to either a bradykinin inhibitor (icatibant) or placebo. Patients and investigators will be blinded. The primary outcome will be blood oxygenation, measured by arterial blood sampling. The secondary outcome will be cardiovascular status. Retinal imaging will be performed to assess vessel size. Blood samples will be taken for measurement of inflammatory analyses including IL-6. As a separate substudy, we will also take comparator blood inflammatory samples from a COVID-19-negative cohort. ETHICS AND DISSEMINATION: The study has received the following approvals: West Midlands-Edgbaston Research Ethics Committee. Medicines and Healthcare products Regulatory Agency has issued a clinical trial authorisation. Belfast Health and Social Care Trust is the study sponsor. Results will be made available to participants upon request and findings will be presented and published. TRIAL REGISTRATION NUMBER: NCT05407597.

Qualitative and quantitative comparison of two semi-manual retinal vascular density analyzing methods on optical coherence tomography angiography images of healthy individuals.

The aim of this study was to evaluate qualitative and quantitative differences in vascular density analysis of an established and a novel alternative for post-processing on optical coherence tomography angiography (OCTA) images in healthy individuals. OCTA examinations of 38 subjects were performed. After extracting the images, two semi-manual post-processing techniques, the already established Mexican hat filtering (MHF) and an alternative, the Shanbhag thresholding (ST) were applied. We assessed Vessel Density (VD), Skeleton Density (SkD) and Vessel Diameter Index (VDI). We analyzed the results in order to establish similarities or potentially relevant differences. Regarding SkD and VD, MHF generally gave higher values than ST. Simultaneously, mean values were also predominantly higher by MHF; however, standard deviations (SD) were higher by ST (range [mean ± SD]: 0.054 ± 0.038 to 0.134 ± 0.01 and 0.134 ± 0.095 to 0.362 ± 0.028 vs 0.012 ± 0.014 to 0.087 ± 0.03 and 0.039 ± 0.047 to 0.4 ± 0.095 for SkD and VD with MHF vs SkD and VD with ST, respectively). Values of VDI were considerably higher with ST than with MHF, while standard deviation was still significantly higher with ST (range [mean ± SD]: 2.459 ± 0.144 to 2.71 ± 0.084 and 2.983 ± 0.929 to 5.19 ± 1.064 for VDI with MHF and ST, respectively). The noise level reduction of the two methods were almost identical (noise levels: 65.8% with MHT and 65.24% with ST). Using MHF, the vascular network gets more fragmented by an average of 40% compared to ST. Both methods allow the segmentation of the vascular network and the examination of vascular density parameters, but they produce largely inconsistent results. To determine if these inconsistent results are clinically meaningful, and which method is more suitable for clinical use, our results provide further evidence that detailed understanding of the image analysis method is essential for reliable decision making for patients with retinal pathology. For longitudinal monitoring, use of the same image processing method is recommended.

Retinal main vessel calibers and systemic markers for long-term development of proliferative diabetic retinopathy.

PURPOSE: To evaluate if retinal vascular calibers and systemic risk factors in patients with no or minimal diabetic retinopathy (DR) can predict risk of long-term progression to proliferative diabetic retinopathy (PDR). METHODS: This was a matched case-control study of patients with diabetes having no or minimal DR at baseline with (cases) or without (controls) subsequent development of PDR. We collected six-field, 45-degree retinal images, demographic and clinical data from the Funen Diabetes Database. RESULTS: We included 52 eyes from 39 cases and 107 eyes from 89 controls matched on sex, age, type of diabetes, time from first to last screening episode and baseline DR level. Cases had higher HbA1c (73 vs. 55 mmoL/moL; p < 0.001), triglycerides (1.32 vs. 1.16 mmoL/L; p = 0.02) and longer duration of diabetes (19 vs. 14 years; p = 0.01), but the groups did not differ in calibers of retinal arterioles (229 vs. 227 µm; p = 0.49), venules (289 vs. 290 µm; p = 0.83) or the arterio-to-venule ratio (0.78 vs. 0.77; p = 0.86).In a multivariable logistic regression model with cluster robust standard error, HbA1c (OR 1.54 per 10 mmoL/moL; 95%-CI: 1.15-2.07; p = 0.004), triglyceride (OR 1.39 per 1 mmoL/L; 95%-CI: 1.03-1.86; p = 0.03) and duration of diabetes (OR 1.09 per year; 95%-CI: 1.03-1.16; p = 0.003) were independent risk factors for PDR. CONCLUSION: Retinal vascular calibers did not predict long-term development of PDR in contrast to well-established risk factors like HbA1c, triglyceride and duration of diabetes.

CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review.

Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.