Pegcetacoplan Treatment and Consensus Features of Geographic Atrophy Over 24 Months.

Importance: Despite widespread availability and consensus on its advantages for detailed imaging of geographic atrophy (GA), spectral-domain optical coherence tomography (SD-OCT) might benefit from automated quantitative OCT analyses in GA diagnosis, monitoring, and reporting of its landmark clinical trials. Objective: To analyze the association between pegcetacoplan and consensus GA SD-OCT end points. Design, Setting, and Participants: This was a post hoc analysis of 11 614 SD-OCT volumes from 936 of the 1258 participants in 2 parallel phase 3 studies, the Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (OAKS) and Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (DERBY). OAKS and DERBY were 24-month, multicenter, randomized, double-masked, sham-controlled studies conducted from August 2018 to July 2020 among adults with GA with total area 2.5 to 17.5 mm2 on fundus autofluorescence imaging (if multifocal, at least 1 lesion ≥1.25 mm2). This analysis was conducted from September to December 2023. Interventions: Study participants received pegcetacoplan, 15 mg per 0.1-mL intravitreal injection, monthly or every other month, or sham injection monthly or every other month. Main Outcomes and Measures: The primary end point was the least squares mean change from baseline in area of retinal pigment epithelium and outer retinal atrophy in each of the 3 treatment arms (pegcetacoplan monthly, pegcetacoplan every other month, and pooled sham [sham monthly and sham every other month]) at 24 months. Feature-specific area analysis was conducted by Early Treatment Diabetic Retinopathy Study (ETDRS) regions of interest (ie, foveal, parafoveal, and perifoveal). Results: Among 936 participants, the mean (SD) age was 78.5 (7.22) years, and 570 participants (60.9%) were female. Pegcetacoplan, but not sham treatment, was associated with reduced growth rates of SD-OCT biomarkers for GA for up to 24 months. Reductions vs sham in least squares mean (SE) change from baseline of retinal pigment epithelium and outer retinal atrophy area were detectable at every time point from 3 through 24 months (least squares mean difference vs pooled sham at month 24, pegcetacoplan monthly: -0.86 mm2; 95% CI, -1.15 to -0.57; P < .001; pegcetacoplan every other month: -0.69 mm2; 95% CI, -0.98 to -0.39; P < .001). This association was more pronounced with more frequent dosing (pegcetacoplan monthly vs pegcetacoplan every other month at month 24: -0.17 mm2; 95% CI, -0.43 to 0.08; P = .17). Stronger associations were observed in the parafoveal and perifoveal regions for both pegcetacoplan monthly and pegcetacoplan every other month. Conclusions and Relevance: These findings offer additional insight into the potential effects of pegcetacoplan on the development of GA, including potential effects on the retinal pigment epithelium and photoreceptors. Trial Registration: ClinicalTrials.gov Identifiers: NCT03525600 and NCT03525613.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3000 Inherited Retinal Disease Patients from the United Kingdom.

Purpose: To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Design: Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Participants: Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Methods: Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset. Main Outcome Measures: Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. Results: A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. Conclusions: We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Survival of medical treatment success in primary open-angle glaucoma and ocular hypertension.

BACKGROUND/AIMS: Topical agents to lower intraocular pressure (IOP) are the most common initial therapeutic measure in glaucoma prevention. This study aims to assess treatment success duration among patients initiating or intensifying topical glaucoma medication. METHODS: Medical records (2013‒2018) for adults initiating/intensifying topical glaucoma medication were extracted from five secondary-care and tertiary-care UK ophthalmology centres. Main study outcomes were time from treatment initiation/intensification to treatment failure (<20% IOP reduction or IOP >21 mm Hg at consecutive clinic visits, or intensification of glaucoma treatment) and time from treatment change to subsequent treatment intensification. RESULTS: Study eyes (n=6587) underwent treatment intensification 0-to-1 glaucoma drop (5358 events), 1-to-2 drops (1469 events) and 2-to-3 drops (857 events) during the observation period. Median time to treatment failure was 1.60 (95% CI 1.57 to 1.65), 1.00 (95% CI 0.94 to 1.07) and 0.92 (95% CI 0.81 to 1.02) years following escalation 0-to-1, 1-to-2 and 2-to-3 drops, respectively. Median time to treatment intensification (non-IOP-based criterion) was 4.68 (95% CI 4.50 to 5.08) years for treatment initiators, 3.83 (95% CI 3.36 to 4.08) years on escalation 1-to-2 drops and 4.35 (95% CI 3.82 to 4.88) years on escalation 2-to-3 drops. On multivariable regression, significant risk factors for both treatment failure and intensification were lower baseline visual field mean deviation, primary open-angle glaucoma and lower eyedrop count in the fellow eye; lower baseline IOP was associated with treatment failure, higher baseline IOP with treatment intensification. CONCLUSION: Large-scale survival analyses provide the expected duration of treatment success from topical glaucoma medication.

Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.

OBJECTIVE: To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth. METHODS: Post hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area. RESULTS: Eyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively) CONCLUSION: The OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.

Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification: A Split-Person Study.

INTRODUCTION: To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification. METHODS: A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye. RESULTS: Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21). CONCLUSIONS: Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF. TRIAL REGISTRATION: Clinical Trials identifier: NCT02503332.

Visual and Anatomical Outcomes of a Single Intravitreal Dexamethasone in Diabetic Macular Edema: An 8 Year Real-World Study.

IMPORTANCE: Diabetic macular edema (DME) is a major cause of vision loss in patients with diabetes mellitus. Intravitreal dexamethasone is a treatment option for patients unsuitable for or non-responsive to anti-angiogenic agents. OBJECTIVE: To quantify visual and anatomical outcomes from an initial intravitreal dexamethasone injection over the expected 6-month period of dexamethasone release by the implant. Design and enrolment: This is a retrospective cohort study using electronic medical records of patients reviewed between 1 January 2012 and 1 April 2022. SETTING: A tertiary eye-care center in London, United Kingdom; Moorfields Eye Hospital National Healthcare System Foundation Trust. PARTICIPANTS: The cohort comprised 418 adult patients with DME who received an initial treatment of 700 µg intravitreal dexamethasone in the study period. Of these, 240 patients met the inclusion criteria of ≥2 hospital visits following initial injection (≥1 beyond 6 months) and no previous ocular corticosteroid treatment or missing assessment at baseline. EXPOSURE(S): Intravitreal dexamethasone implant (700 µg). MAIN OUTCOME(S) AND MEASURE(S): Probability of a positive visual outcome, defined as ≥5 or ≥10 Early Treatment Diabetic Retinopathy Study (ETDRS)-letter gain after treatment when compared to baseline (Kaplan-Meier models). RESULTS: From the initial intravitreal dexamethasone injection alone, we observed a >75% chance of gaining ≥5 ETDRS letters and >50% chance of gaining ≥10 ETDRS letters within 6 months. There was less than a 50% chance of sustaining either positive visual outcome beyond 4 months. CONCLUSIONS AND RELEVANCE: Most patients can be expected to have a positive visual outcome following an initial injection of dexamethasone implants that subsides within 4 months. Real-world re-treatment was observed to be delayed until after visual benefits were lost in half of the cohort. Further research will be needed to study the effects of delays in re-treatment.

Predicting the immediate impact of national lockdown on neovascular age-related macular degeneration and associated visual morbidity: an INSIGHT Health Data Research Hub for Eye Health report.

OBJECTIVE: Predicting the impact of neovascular age-related macular degeneration (nAMD) service disruption on visual outcomes following national lockdown in the UK to contain SARS-CoV-2. METHODS AND ANALYSIS: This retrospective cohort study includes deidentified data from 2229 UK patients from the INSIGHT Health Data Research digital hub. We forecasted the number of treatment-naïve nAMD patients requiring anti-vascular endothelial growth factor (anti-VEGF) initiation during UK lockdown (16 March 2020 through 31 July 2020) at Moorfields Eye Hospital (MEH) and University Hospitals Birmingham (UHB). Best-measured visual acuity (VA) changes without anti-VEGF therapy were predicted using post hoc analysis of Minimally Classic/Occult Trial of the Anti-VEGF Antibody Ranibizumab in the Treatment of Neovascular AMD trial sham-control arm data (n=238). RESULTS: At our centres, 376 patients were predicted to require anti-VEGF initiation during lockdown (MEH: 325; UHB: 51). Without treatment, mean VA was projected to decline after 12 months. The proportion of eyes in the MEH cohort predicted to maintain the key positive visual outcome of ≥70 ETDRS letters (Snellen equivalent 6/12) fell from 25.5% at baseline to 5.8% at 12 months (UHB: 9.8%-7.8%). Similarly, eyes with VA <25 ETDRS letters (6/96) were predicted to increase from 4.3% to 14.2% at MEH (UHB: 5.9%-7.8%) after 12 months without treatment. CONCLUSIONS: Here, we demonstrate how combining data from a recently founded national digital health data repository with historical industry-funded clinical trial data can enhance predictive modelling in nAMD. The demonstrated detrimental effects of prolonged treatment delay should incentivise healthcare providers to support nAMD patients accessing care in safe environments. TRIAL REGISTRATION NUMBER: NCT00056836.

Characterising treatment outcomes of patients achieving quarterly aflibercept dosing for neovascular age-related macular degeneration: real-world clinical outcomes from a large tertiary care centre.

BACKGROUND AND OBJECTIVE: To evaluate the proportion of patients achieving a 12-week (q12) aflibercept dosing interval in patients with neovascular age-related macular degeneration (nAMD). PATIENTS AND METHODS: Retrospective, comparative, non-randomised electronic medical record (EMR) database study of the Moorfields database of treatment-naïve nAMD eyes. Extraction criteria included at least 7 aflibercept injections in first year of treatment, AMD in the diagnosis field of EMR, and minimum of 1 year follow-up data. RESULTS: There were 2416 eyes of 2163 patients started on anti-vascular endothelial growth factor (anti-VEGF) between 01-11-2013 & 14-02-2020 who had received at least 7 aflibercept intravitreal injections (electronic database accessed March 2021). Of these, 1674 (68%) eyes of 1537 patients had at least one q12 dosing interval (>=84 and < =98 days between injections) during the first 2 years of treatment. This included 926 (61.8%) female patients and 856 (right eyes age at 1st injection), 936 (62.4%) Caucasian, and 32 (2.1%) Afro-Caribbean patients. The median time to the first q12 injection (95% confidence interval) was 1.76 years (1.70-1.86) with mean (±SD) of 11.8 (±6.0) injections. Visual acuity (ETDRS letters) of the eyes without q12 injection and eyes with a q12 injection was 57.9 ± 14.7 and 56.7 ± 14.8 respectively at baseline, 61.4 ± 18.1 and 63.0 ± 15.9 respectively at 12 months and 61.2 ± 20.1 and 61.1 ± 17.8 respectively at 24 months. CONCLUSION: 68% of eyes were able to achieve a q12 injection dose within the first 2 years of treatment. Eyes achieving a q12 injection in the first 2 years achieved a similar visual acuity outcome at both 1 and 2-year follow-up to those unable to do so, with a fewer number of total injections.

Burden of Glaucoma in the United Kingdom: A Multicenter Analysis of United Kingdom Glaucoma Services.

OBJECTIVE: To determine the spectrum of glaucoma-associated health care resource utilization among outpatients attending National Health Service (NHS) hospital glaucoma clinics and the costs of managing glaucoma in this setting. DESIGN: Retrospective observational cohort study using electronic medical record data. SUBJECTS: Patients aged ≥ 18 years attending 5 NHS glaucoma clinics in the United Kingdom (2013‒2018) with ≥ 12 months of continuous electronic medical record data. METHODS: Deidentified Medisoft Ophthalmology electronic medical record data (January 2013‒December 2018) from 43 742 eligible patients were categorized by year of clinic visit. Extracted information included patient demographics, glaucoma diagnoses, topical glaucoma medication prescription start/stop dates, types/numbers of glaucoma clinic visits, glaucoma investigations (visual acuity, intraocular pressure, visual field, and OCT), and glaucoma procedures received over 12 months after the first ("index") visit of the specified year. Direct glaucoma-related health care costs (clinic visits, investigations, procedures, and ongoing glaucoma medication initiated in the clinic) were estimated from event volumes and unit costs (UK national tariffs) and expressed from the direct-payer perspective. MAIN OUTCOME MEASURES: Glaucoma diagnoses and topical glaucoma medication use at the index clinic visit; numbers of glaucoma clinic visits, investigations and procedures; and glaucoma-related health care costs over 12 months postindex. RESULTS: For the 2016 cohort (n = 21 719), the estimated average total cost of NHS-provided glaucoma care over 12 months was £405 per patient (medical staff services £209, glaucoma investigations £126, glaucoma medication £40, glaucoma procedures £26). Among this cohort, 40.8% had ocular hypertension/suspected glaucoma, 70% had 0-to-mild visual field impairment, and 14% had undergone a glaucoma procedure. Over 12 months, patients received (mean) 2.0 glaucoma clinic visits and 1.5 visual field tests, and 7% underwent glaucoma procedure(s). Results were similar for the other years examined. CONCLUSIONS: Cost estimates for managing patients with glaucoma in the UK are required for effective service planning. Appreciable proportions of patients managed in NHS glaucoma clinics may be considered at low risk of blindness (glaucoma suspects and those with ocular hypertension with mild visual field loss) and may be more appropriately managed with alternative, more affordable models of care.

Reinforcement learning in ophthalmology: potential applications and challenges to implementation.

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them.

Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration.

PURPOSE: Neovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor (anti-VEGF) treatment is effective, response varies considerably between individuals. Thus, patients face substantial uncertainty regarding their future ability to perform daily tasks. In this study, we evaluate the performance of an automated machine learning (AutoML) model which predicts visual acuity (VA) outcomes in patients receiving treatment for nAMD, in comparison to a manually coded model built using the same dataset. Furthermore, we evaluate model performance across ethnic groups and analyse how the models reach their predictions. METHODS: Binary classification models were trained to predict whether patients' VA would be 'Above' or 'Below' a score of 70 one year after initiating treatment, measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The AutoML model was built using the Google Cloud Platform, whilst the bespoke model was trained using an XGBoost framework. Models were compared and analysed using the What-if Tool (WIT), a novel model-agnostic interpretability tool. RESULTS: Our study included 1631 eyes from patients attending Moorfields Eye Hospital. The AutoML model (area under the curve [AUC], 0.849) achieved a highly similar performance to the XGBoost model (AUC, 0.847). Using the WIT, we found that the models over-predicted negative outcomes in Asian patients and performed worse in those with an ethnic category of Other. Baseline VA, age and ethnicity were the most important determinants of model predictions. Partial dependence plot analysis revealed a sigmoidal relationship between baseline VA and the probability of an outcome of 'Above'. CONCLUSION: We have described and validated an AutoML-WIT pipeline which enables clinicians with minimal coding skills to match the performance of a state-of-the-art algorithm and obtain explainable predictions.

Enablers and Barriers to Deployment of Smartphone-Based Home Vision Monitoring in Clinical Practice Settings.

IMPORTANCE: Telemedicine is accelerating the remote detection and monitoring of medical conditions, such as vision-threatening diseases. Meaningful deployment of smartphone apps for home vision monitoring should consider the barriers to patient uptake and engagement and address issues around digital exclusion in vulnerable patient populations. OBJECTIVE: To quantify the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement. DESIGN, SETTING, AND PARTICIPANTS: In this cohort and survey study, consecutive adult patients attending Moorfields Eye Hospital receiving intravitreal injections for retinal disease between May 2020 and February 2021 were included. EXPOSURES: Patients were offered the Home Vision Monitor (HVM) smartphone app to self-test their vision. A patient survey was conducted to capture their experience. App data, demographic characteristics, survey results, and clinical data from the electronic health record were analyzed via regression and machine learning. MAIN OUTCOMES AND MEASURES: Associations of patient uptake, compliance, and use rate measured in odds ratios (ORs). RESULTS: Of 417 included patients, 236 (56.6%) were female, and the mean (SD) age was 72.8 (12.8) years. A total of 258 patients (61.9%) were active users. Uptake was negatively associated with age (OR, 0.98; 95% CI, 0.97-0.998; P = .02) and positively associated with both visual acuity in the better-seeing eye (OR, 1.02; 95% CI, 1.00-1.03; P = .01) and baseline number of intravitreal injections (OR, 1.01; 95% CI, 1.00-1.02; P = .02). Of 258 active patients, 166 (64.3%) fulfilled the definition of compliance. Compliance was associated with patients diagnosed with neovascular age-related macular degeneration (OR, 1.94; 95% CI, 1.07-3.53; P = .002), White British ethnicity (OR, 1.69; 95% CI, 0.96-3.01; P = .02), and visual acuity in the better-seeing eye at baseline (OR, 1.02; 95% CI, 1.01-1.04; P = .04). Use rate was higher with increasing levels of comfort with use of modern technologies (ß = 0.031; 95% CI, 0.007-0.055; P = .02). A total of 119 patients (98.4%) found the app either easy or very easy to use, while 96 (82.1%) experienced increased reassurance from using the app. CONCLUSIONS AND RELEVANCE: This evaluation of home vision monitoring for patients with common vision-threatening disease within a clinical practice setting revealed demographic, clinical, and patient-related factors associated with patient uptake and engagement. These insights inform targeted interventions to address risks of digital exclusion with smartphone-based medical devices.

Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.

BACKGROUND: Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable, and objective detection and quantification of geographic atrophy from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and to serve as clinical endpoints for therapy development. To this end, we aimed to develop and validate a fully automated method to detect and quantify geographic atrophy from OCT. METHODS: We did a deep-learning model development and external validation study on OCT retinal scans at Moorfields Eye Hospital Reading Centre and Clinical AI Hub (London, UK). A modified U-Net architecture was used to develop four distinct deep-learning models for segmentation of geographic atrophy and its constituent retinal features from OCT scans acquired with Heidelberg Spectralis. A manually segmented clinical dataset for model development comprised 5049 B-scans from 984 OCT volumes selected randomly from 399 eyes of 200 patients with geographic atrophy secondary to age-related macular degeneration, enrolled in a prospective, multicentre, phase 2 clinical trial for the treatment of geographic atrophy (FILLY study). Performance was externally validated on an independently recruited dataset from patients receiving routine care at Moorfields Eye Hospital (London, UK). The primary outcome was segmentation and classification agreement between deep-learning model geographic atrophy prediction and consensus of two independent expert graders on the external validation dataset. FINDINGS: The external validation cohort included 884 B-scans from 192 OCT volumes taken from 192 eyes of 110 patients as part of real-life clinical care at Moorfields Eye Hospital between Jan 1, 2016, and Dec, 31, 2019 (mean age 78·3 years [SD 11·1], 58 [53%] women). The resultant geographic atrophy deep-learning model produced predictions similar to consensus human specialist grading on the external validation dataset (median Dice similarity coefficient [DSC] 0·96 [IQR 0·10]; intraclass correlation coefficient [ICC] 0·93) and outperformed agreement between human graders (DSC 0·80 [0·28]; ICC 0·79). Similarly, the three independent feature-specific deep-learning models could accurately segment each of the three constituent features of geographic atrophy: retinal pigment epithelium loss (median DSC 0·95 [IQR 0·15]), overlying photoreceptor degeneration (0·96 [0·12]), and hypertransmission (0·97 [0·07]) in the external validation dataset versus consensus grading. INTERPRETATION: We present a fully developed and validated deep-learning composite model for segmentation of geographic atrophy and its subtypes that achieves performance at a similar level to manual specialist assessment. Fully automated analysis of retinal OCT from routine clinical practice could provide a promising horizon for diagnosis and prognosis in both research and real-life patient care, following further clinical validation FUNDING: Apellis Pharmaceuticals.

Big data simulations for capacity improvement in a general ophthalmology clinic.

PURPOSE: Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient's satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice. DESIGN AND METHODS: For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017. RESULTS: During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min. CONCLUSION: By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. DESIGN: Retrospective cohort study. PARTICIPANTS: Treatment-naive, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, United Kingdom single center) undergoing anti-VEGF therapy. METHODS: Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137 379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria, 926 eyes of 926 patients were analyzed. MAIN OUTCOME MEASURES: Correlation coefficients (R2 values) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual function, as well as the predictive value of these parameters for short-term visual change, that is, incremental visual acuity (VA) resulting from an individual injection, as well as VA at distant time points (up to 12 months after baseline). RESULTS: Visual acuity at distant time points could be predicted: R2 = 0.80 (MAE, 5.0 Early Treatment Diabetic Retinopathy Study [ETDRS] letters) and R2 = 0.7 (MAE, 7.2 ETDRS letters) after injection at 3 and at 12 months after baseline (P < 0.001 for both), respectively. Best performing models included both baseline qOCT parameters and treatment response. Furthermore, we present proof-of-principle evidence that the incremental change in VA from an injection can be predicted: R2 = 0.14 (MAE, 5.6 ETDRS letters) for injection 2 and R2 = 0.11 (MAE, 5.0 ETDRS letters) for injection 3 (P < 0.001 for both). CONCLUSIONS: Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine.

Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD) and make the raw segmentation output data openly available for further research. DESIGN: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. PARTICIPANTS: A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017. METHODS: A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first- and second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes. MAIN OUTCOME MEASURES: Volumes of segmented features (mm3) and central subfield thickness (CST) (µm). RESULTS: In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR, and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED, and SRF. Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups. Greater volumes of the majority of features were associated with worse VA. CONCLUSIONS: We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first- and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structure-function correlations. These data will be made publicly available for replication and future investigation by the AMD research community.

A virtual-clinic pathway for patients referred from a national diabetes eye screening programme reduces service demands whilst maintaining quality of care.

AIM: To evaluate the potential of an integrated virtual medical retina clinic in secondary care for diabetic patients screened and referred by the UK National Diabetic Eye Screening Program (DESP). METHODS: This retrospective cohort study included diabetic patients referred by the DESP to either a virtual or a traditional doctor's appointment (face-to-face, F2F) at the Moorfields Eye Hospital NHS Foundation Trust (London, UK) between January 2015 and December 2018. The primary outcome was the proportion of patients that qualified for a virtual-clinic appointment according to hospital guidance. Secondary outcomes included the rate of attendance, mean time from DESP referral to initial hospital appointment, mean time-to-discharge and -to-treatment of either panretinal photocoagulation or intravitreal injection of anti-vascular endothelial growth factor. RESULTS: We included 12,563 patients in this study. While 8833 patients (70.7%) would have qualified for a virtual appointment according to local triage guidance, only 2306 (18.4%) were referred to a virtual consultation due to capacity constraints. For routine referrals, mean time to the first hospital appointment was 66.9 days with a standard deviation of ±35.9 and 80.9 ± 44.4 days for a virtual and a F2F consultation, respectively. The mean time from referral to discharge to community was 71.7 ± 30.8 and 86.3 ± 37.0 days for a virtual and a F2F consultation, respectively. We did not observe a statistically significant difference in the mean time-to-treatment in the sub-cohort that required intravitreal therapy for maculopathy (virtual clinics: 220.7 ± 84.8; F2F: 178.0 days ± 80.7; p value > 0.05). Moreover, we observed a non-inferior attendance rate in virtual as compared to F2F clinics. CONCLUSION: A significant proportion of diabetic patients referred to a F2F clinic could initially be managed in a virtual clinic. Increasing the adoption of virtual clinics in the management of diabetic patients that do not need long-term management or monitoring in secondary services may help alleviate service demands without diminishing quality of clinical care. Collectively, our analyses suggest that virtual consultations are a faster and clinically appropriate alternative for a substantial proportion of diabetic patients.

Ten-year survival trends of neovascular age-related macular degeneration at first presentation.

BACKGROUND: To describe 10-year trends in visual outcomes, anatomical outcomes and treatment burden of patients receiving antivascular endothelial growth factor (anti-VEGF) therapy for neovascular age-related macular degeneration (nAMD). METHODS: Retrospective cohort study of treatment-naïve, first-affected eyes with nAMD started on ranibizumab before January 1, 2009. The primary outcome was time to best-corrected visual acuity (BCVA) falling ≤35 ETDRS letters after initiating anti-VEGF therapy. Secondary outcomes included time to BCVA reaching ≥70 letters, proportion of eyes with BCVA ≥70 and ≤35 letters in 10 years, mean trend of BCVA and central retinal thickness over 10 years, and mean number of injections. RESULTS: For our cohort of 103 patients, Kaplan-Meier analyses demonstrated median time to BCVA reaching ≤35 and ≥70 letters were 37.8 (95% CI 22.2 to 65.1) and 8.3 (95% CI 4.8 to 20.9) months after commencing anti-VEGF therapy, respectively. At the final follow-up, BCVA was ≤35 letters and ≥70 letters in 41.1% and 21%, respectively, in first-affected eyes, while this was the case for 5.4% and 48.2%, respectively, in a patient's better-seeing eye. Mean injection number was 37.0±24.2 per eye and 53.6±30.1 at patient level (63.1% of patients required injections in both eyes). CONCLUSIONS: The chronicity of nAMD disease and its management highlights the importance of long-term visual prognosis. Our analyses suggest that one in five patients will retain good vision (BCVA ≥70 ETDRS letters) in the first-affected eye at 10 years after starting anti-VEGF treatment; yet, one in two patients will have good vision in their better-seeing eye. Moreover, our data suggest that early treatment of nAMD is associated with better visual outcomes.

An open-source data set of anti-VEGF therapy in diabetic macular oedema patients over 4 years and their visual acuity outcomes.

OBJECTIVES: The objective of this paper is to evaluate visual acuity (VA) outcomes of intravitreal anti-vascular endothelial growth factor (VEGF) in diabetic macular oedema (DMO). METHODS: In this retrospective cohort study, electronic medical records for all patients undergoing intravitreal injections in a tertiary referral centre between March 2013 and October 2018 were analysed. Treatment response in terms of VA outcomes was reported for all eyes over a 4-year observation period. RESULTS: Our cohort includes 2614 DMO eyes of 1964 patients over 48 months. Cox proportional-hazards modelling identified injection number (hazard ratio (HR) = 1.18), male gender (HR = 1.13) and baseline VA (HR = 1.09) as independent predictors to reach a favourable visual outcome of more than 70 Early Treatment Diabetic Retinopathy Study letters. Half of our cohort reached 70 letters 1.9 months after starting anti-VEGF therapy. Of those that reached 70 letters, 50% fell below 70 letters by 14.7 months. CONCLUSION: To date, this is the largest single centre cohort study and over the longest observation period reporting on real-life outcomes of anti-VEGF in DMO. We have made an anonymised version of our data set available on an open-source data repository as a resource for clinical researchers globally.