Search

Abstract Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which eight were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. RPS decouples traditional demographic variables from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score .

Abstract Optical coherence tomography angiography (OCTA) is widely used for non-invasive retinal vascular imaging, but the OCTA methods used to assess retinal perfusion vary. We evaluated the different methods used to assess retinal perfusion between OCTA studies. MEDLINE and Embase were searched from 2014 to August 2021. We included prospective studies including ≥ 50 participants using OCTA to assess retinal perfusion in either global retinal or systemic disorders. Risk of bias was assessed using the National Institute of Health quality assessment tool for observational cohort and cross-sectional studies. Heterogeneity of data was assessed by Q statistics, Chi-square test, and I2 index. Of the 5974 studies identified, 191 studies were included in this evaluation. The selected studies employed seven OCTA devices, six macula volume dimensions, four macula subregions, nine perfusion analyses, and five vessel layer definitions, totalling 197 distinct methods of assessing macula perfusion and over 7000 possible combinations. Meta-analysis was performed on 88 studies reporting vessel density and foveal avascular zone area, showing lower retinal perfusion in patients with diabetes mellitus than in healthy controls, but with high heterogeneity. Heterogeneity was lowest and reported vascular effects strongest in superficial capillary plexus assessments. Systematic review of OCTA studies revealed massive heterogeneity in the methods employed to assess retinal perfusion, supporting calls for standardisation of methodology.

La declaración SPIRIT 2013 tiene como objetivo mejorar la exhaustividad de los informes de los protocolos de los ensayos clínicos proporcionando recomendaciones basadas en la evidencia para el conjunto mínimo de elementos que deben abordarse. Esta guía ha sido fundamental para promover la evaluación transparente de nuevas intervenciones. Más recientemente, se ha reconocido cada vez más que las intervenciones con inteligencia artificial (IA) deben someterse a una evaluación rigurosa y prospectiva para demostrar su impacto en los resultados médicos. La extensión SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence, por sus siglas en inglés) es una nueva directriz para el reporte de los protocolos de ensayos clínicos que evalúan intervenciones con un componente de IA. Esta directriz se desarrolló en paralelo con su declaración complementaria para los informes de ensayos clínicos: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Ambas directrices se desarrollaron a través de un proceso de consenso por etapas que incluía la revisión de la literatura y la consulta a expertos para generar 26 ítems candidatos, que fueron consultados por un grupo internacional de múltiples partes interesadas en una encuesta Delphi de dos etapas (103 partes interesadas), acordados en una reunión de consenso (31 partes interesadas) y refinados a través de una lista de verificación piloto (34 participantes). La ampliación de SPIRIT-AI incluye 15 nuevos elementos que se consideraron suficientemente importantes para los protocolos de los ensayos clínicos con intervenciones de IA. Estos nuevos ítems deben ser reportados rutinariamente además de los ítems centrales de SPIRIT 2013. SPIRIT-AI recomienda que los investigadores proporcionen descripciones claras de la intervención de IA, incluyendo las instrucciones y las habilidades necesarias para su uso, el entorno en el que se integrará la intervención de IA, las consideraciones para el manejo de los datos de entrada y salida, la interacción entre el ser humano y la IA y el análisis de los casos de error. SPIRIT-AI ayudará a promover la transparencia y la exhaustividad de los protocolos de los ensayos clínicos de las intervenciones de IA. Su uso ayudará a los editores y revisores, así como a los lectores en general, a comprender, interpretar y valorar críticamente el diseño y el riesgo de sesgo de un futuro ensayo clínico.

La declaración CONSORT 2010 proporciona unas directrices mínimas para informar sobre los ensayos clínicos aleatorizados. Su uso generalizado ha sido fundamental para garantizar la transparencia en la evaluación de nuevas intervenciones. Más recientemente, se ha reconocido cada vez más que las intervenciones con inteligencia artificial (IA) deben someterse a una evaluación rigurosa y prospectiva para demostrar su impacto en la salud. La extensión CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) es una nueva pauta de información para los ensayos clínicos que evalúan intervenciones con un componente de IA, esta se desarrolló en paralelo con su declaración complementaria para los protocolos de ensayos clínicos: SPIRIT-AI (Standard Protocol Items – Artificial Intelligence: Recomendaciones para ensayos clínicos de intervención - Inteligencia Artificial). Ambas directrices se desarrollaron a través de un proceso de consenso por etapas que incluía la revisión de la literatura y la consulta a expertos para generar 29 elementos candidatos, que fueron evaluados por un grupo internacional de múltiples partes interesadas en una encuesta Delphi de dos etapas (103 partes interesadas congregados en una reunión de consenso de dos días (31 partes interesadas) y refinados a través de una lista de verificación piloto (34 participantes). La ampliación del CONSORT-AI incluye 14 nuevos elementos que se consideraron lo suficientemente importantes para las intervenciones de IA como para que se informen de forma rutinaria, además de los elementos básicos del CONSORT 2010. CONSORT-AI recomienda que los investigadores proporcionen descripciones claras de la intervención de IA, incluyendo las instrucciones y las habilidades requeridas para su uso, el entorno en el que se integra la intervención de IA, el manejo de los datos de entrada y los datos de salida de la intervención de IA, la interacción entre el ser humano y la IA y la provisión de un análisis de los casos de error. CONSORT-AI ayudará a promover la transparencia y la exhaustividad en los informes de los ensayos clínicos de las intervenciones de AI, también ayudará a los editores y revisores, así como a los lectores en general, a entender, interpretar y valorar críticamente la calidad del diseño del ensayo clínico y el riesgo de sesgo en los resultados comunicados.

Abstract Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Abstract The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

BackgroundThe reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals. ObjectiveThe systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance. MethodsPublicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance. ResultsScoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD. ConclusionsTo our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes. International Registered Report Identifier (IRRID)PRR1-10.2196/48156

BackgroundArtificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report. ObjectiveThis systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes. MethodsThis systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate. ResultsThe project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024. ConclusionsEvaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance. Trial RegistrationPROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747 International Registered Report Identifier (IRRID)PRR1-10.2196/51614

Introduction The diagnosis of neovascular age-related macular degeneration (nAMD), the leading cause of visual impairment in the developed world, relies on the interpretation of various imaging tests of the retina. These include invasive angiographic methods, such as Fundus Fluorescein Angiography (FFA) and, on occasion, Indocyanine-Green Angiography (ICGA). Newer, non-invasive imaging modalities, predominately Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), have drastically transformed the diagnostic approach to nAMD. The aim of this study is to undertake a comprehensive diagnostic accuracy assessment of the various imaging modalities used in clinical practice for the diagnosis of nAMD (OCT, OCTA, FFA and, when a variant of nAMD called Polypoidal Choroidal Vasculopathy is suspected, ICGA) both alone and in various combinations.Methods and analysis This is a non-inferiority, prospective, randomised diagnostic accuracy study of 1067 participants. Participants are patients with clinical features consistent with nAMD who present to a National Health Service secondary care ophthalmology unit in the UK. Patients will undergo OCT as per standard practice and those with suspicious features of nAMD on OCT will be approached for participation in the study. Patients who agree to take part will also undergo both OCTA and FFA (and ICGA if indicated). Interpretation of the imaging tests will be undertaken by clinicians at recruitment sites. A randomised design was selected to avoid bias from consecutive review of all imaging tests by the same clinician. The primary outcome of the study will be the difference in sensitivity and specificity between OCT+OCTA and OCT+FFA (±ICGA) for nAMD detection as interpreted by clinicians at recruitment sites.Ethics and dissemination The study has been approved by the South Central—Oxford B Research Ethics Committee with reference number 21/SC/0412.Dissemination of study results will involve peer-review publications, presentations at major national and international scientific conferences.Trial registration number ISRCTN18313457.

Abstract Background Electronic patient-reported outcome (ePRO) systems are increasingly used in clinical trials to provide evidence of efficacy and tolerability of treatment from the patient perspective. The aim of this study is twofold: (1) to describe how we developed an electronic platform for patients to report their symptoms, and (2) to develop and undertake usability testing of an ePRO solution for use in a study of cell therapy seeking to provide early evidence of efficacy and tolerability of treatment and test the feasibility of the system for use in later phase studies. Methods An ePRO system was designed to be used in a single arm, multi-centre, phase II basket trial investigating the safety and activity of the use of ORBCEL-C™ in the treatment of patients with inflammatory conditions. ORBCEL-C™ is an enriched Mesenchymal Stromal Cells product isolated from human umbilical cord tissue using CD362+ cell selection. Usability testing sessions were conducted using cognitive interviews and the ‘Think Aloud’ method with patient advisory group members and Research Nurses to assess the usability of the system. Results Nine patient partners and seven research nurses took part in one usability testing session. Measures of fatigue and health-related quality of life, the PRO-CTCAE™ and FACT-GP5 global tolerability question were included in the ePRO system. Alert notifications to the clinical team were triggered by PRO-CTCAE™ and FACT-GP5 scores. Patient participants liked the simplicity and responsiveness of the patient-facing app. Two patients were unable to complete the testing session, due to technical issues. Research Nurses suggested minor modifications to improve functionality and the layout of the clinician dashboard and the training materials. Conclusion By testing the effectiveness, efficiency, and satisfaction of our novel ePRO system (PROmicsR), we learnt that most people with an inflammatory condition found it easy to report their symptoms using an app on their own device. Their experiences using the PROmicsR ePRO system within a trial environment will be further explored in our upcoming feasibility testing. Research nurses were also positive and found the clinical dashboard easy-to-use. Using ePROs in early phase trials is important in order to provide evidence of therapeutic responses and tolerability, increase the evidence based, and inform methodology development. Trial registration: ISRCTN, ISRCTN80103507. Registered 01 April 2022, https://www.isrctn.com/ISRCTN80103507

BackgroundArtificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging. ObjectiveThis study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD. MethodsThe Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process. ResultsPreliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024. ConclusionsThis systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. International Registered Report Identifier (IRRID)DERR1-10.2196/52602

BackgroundDiabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. ObjectiveThis study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. MethodsThis work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. ResultsPhase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. ConclusionsThe multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. International Registered Report Identifier (IRRID)DERR1-10.2196/50568

Introduction Chimeric antigen receptor (CAR) T-cell therapies are novel, potentially curative therapies for haematological malignancies. CAR T-cell therapies are associated with severe toxicities, meaning patients require monitoring during acute and postacute treatment phases. Electronic patient-reported outcomes (ePROs), self-reports of health status provided via online questionnaires, can complement clinician observation with potential to improve patient outcomes. This study will develop and evaluate feasibility of a new ePRO system for CAR-T patients in routine care.Methods and analysis Multiphase, mixed-methods study involving multiple stakeholder groups (patients, family members, carers, clinicians, academics/researchers and policy-makers). The intervention development phase comprises a Delphi study to select PRO measures for the digital system, a codesign workshop and consensus meetings to establish thresholds for notifications to the clinical team if a patient reports severe symptoms or side effects. Usability testing will evaluate how users interact with the digital system and, lastly, we will evaluate ePRO system feasibility with 30 CAR-T patients (adults aged 18+ years) when used in addition to usual care. Feasibility study participants will use the ePRO system to submit self-reports of symptoms, treatment tolerability and quality of life at specific time points. The CAR-T clinical team will respond to system notifications triggered by patients’ submitted responses with actions in line with standard clinical practice. Feasibility measures will be collected at prespecified time points following CAR T-cell infusion. A qualitative substudy involving patients and clinical team members will explore acceptability of the ePRO system.Ethics and dissemination Favourable ethical opinion was granted by the Health and Social Care Research Ethics Committee B(HSC REC B) (ref: 23/NI/0104) on 28 September 2023. Findings will be submitted for publication in high-quality, peer-reviewed journals. Summaries of results, codeveloped with the Blood and Transplant Research Unit Patient and Public Involvement and Engagement group, will be disseminated to all interested groups.Trial registration number ISCTRN11232653.

Objective Despite significant advances in clinical care and understanding of the underlying pathophysiology, age-related macular degeneration (AMD)—a major cause of global blindness—lacks effective treatment to prevent the irreversible degeneration of photoreceptors leading to central vision loss. Limited studies suggest phosphodiesterase type 5 (PDE5) inhibitors, such as sildenafil, may prevent AMD by increasing retinal blood flow. This study explores the potential association between sildenafil use and AMD risk in men with erectile dysfunction using UK data.Methods and analysis Using the UK’s IQVIA Medical Research Data, the study analysed 31 575 men prescribed sildenafil for erectile dysfunction and no AMD history from 2007 to 2015, matched with a comparator group of 62 155 non-sildenafil users in a 1:2 ratio, over a median follow-up of approximately three years.Results The primary outcome was the incidence of AMD in the two groups. The study found no significant difference in AMD incidence between the sildenafil users and the non-users, with an adjusted hazard ratio (HR) of 0.99 (95% CI 0.84 to 1.16), after accounting for confounders such as age, ethnicity, Townsend deprivation quintile, body mass index category, and diagnosis of hypertension and type 2 diabetes.Conclusion The study results indicated no significant association between sildenafil use and AMD prevention in UK men with erectile dysfunction, suggesting sildenafil’s protective effect on AMD is likely insignificant.

Abstract Background Intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections play a key role in treating a range of macular diseases. The effectiveness of these therapies is dependent on patients’ adherence (the extent to which a patient takes their medicines as per agreed recommendations from the healthcare provider) and persistence (continuation of the treatment for the prescribed duration) to their prescribed treatment regimens. The aim of this systematic review was to demonstrate the need for further investigation into the prevalence of, and factors contributing to, patient-led non-adherence and non-persistence, thus facilitating improved clinical outcomes. Methods Systematic searches were conducted in Google Scholar, Web of Science, PubMed, MEDLINE, and the Cochrane Library. Studies in English conducted before February 2023 that reported the level of, and/or barriers to, non-adherence or non-persistence to intravitreal anti-VEGF ocular disease therapy were included. Duplicate papers, literature reviews, expert opinion articles, case studies, and case series were excluded following screening by two independent authors. Results Data from a total of 409,215 patients across 52 studies were analysed. Treatment regimens included pro re nata, monthly and treat-and-extend protocols; study durations ranged from 4 months to 8 years. Of the 52 studies, 22 included a breakdown of reasons for patient non-adherence/non-persistence. Patient-led non-adherence varied between 17.5 and 35.0% depending on the definition used. Overall pooled prevalence of patient-led treatment non-persistence was 30.0% (P = 0.000). Reasons for non-adherence/non-persistence included dissatisfaction with treatment results (29.9%), financial burden (19%), older age/comorbidities (15.5%), difficulty booking appointments (8.5%), travel distance/social isolation (7.9%), lack of time (5.8%), satisfaction with the perceived improvement in their condition (4.4%), fear of injection (4.0%), loss of motivation (4.0%), apathy towards eyesight (2.5%), dissatisfaction with facilities 2.3%, and discomfort/pain (0.3%). Three studies found non-adherence rates between 51.6 and 68.8% during the COVID-19 pandemic, in part due to fear of exposure to COVID-19 and difficulties travelling during lockdown. Discussion Results suggest high levels of patient-led non-adherence/non-persistence to anti-VEGF therapy, mostly due to dissatisfaction with treatment results, a combination of comorbidities, loss of motivation and the burden of travel. This study provides key information on prevalence and factors contributing to non-adherence/non-persistence in anti-VEGF treatment for macular diseases, aiding identification of at-risk individuals to improve real-world visual outcomes. Improvements in the literature can be achieved by establishing uniform definitions and standard timescales for what constitutes non-adherence/non-persistence. Systematic review registration PROSPERO CRD42020216205.

BackgroundDiabetic retinopathy (DR) is the most frequent complication of type 2 diabetes and remains the leading cause of preventable blindness. Current clinical decisions regarding the administration of antidiabetic drugs do not sufficiently incorporate the risk of DR due to the inconclusive evidence from preceding meta-analyses. This umbrella review aimed to systematically evaluate the effects of antidiabetic drugs on DR in people with type 2 diabetes.MethodsA systematic literature search was undertaken in Medline, Embase, and the Cochrane Library (from inception till 17th May 2022) without language restrictions to identify systematic reviews and meta-analyses of randomized controlled trials or longitudinal studies that examined the association between antidiabetic drugs and DR in people with type 2 diabetes. Two authors independently extracted data and assessed the quality of included studies using the AMSTAR-2 (A MeaSurement Tool to Assess Systematic Reviews) checklist, and evidence assessment was performed using the GRADE (Grading of recommendations, Assessment, Development and Evaluation). Random-effects models were applied to calculate relative risk (RR) or odds ratios (OR) with 95% confidence intervals (CI). This study was registered with PROSPERO (CRD42022332052).ResultsWith trial evidence from 11 systematic reviews and meta-analyses, we found that the use of glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter-2 inhibitors (SGLT-2i), or dipeptidyl peptidase-4 inhibitors (DPP-4i) was not statistically associated with the risk of DR, compared to either placebo (RR: GLP-1 RA, 0.98, 0.89-1.08; SGLT-2i, 1.00, 95% CI 0.79-1.27; DPP-4i, 1.17, 0.99-1.39) or other antidiabetic drugs. Compared to other antidiabetic drugs, meglitinides (0.34, 0.01-8.25), SGLT-2i (0.73, 0.10-5.16), thiazolidinediones (0.92, 0.67-1.26), metformin (1.15, 0.81-1.63), sulphonylureas (1.24, 0.93-1.65), and acarbose (4.21, 0.44-40.43) were not statistically associated with the risk of DR. With evidence from longitudinal studies only, insulin was found to have a higher risk of DR than other antidiabetic drugs (OR: 2.47, 95% CI: 2.04-2.99).ConclusionOur results indicate that antidiabetic drugs are generally safe to prescribe regarding the risk of DR among people with type 2 diabetes. Further robust and large-scale trials investigating the effects of insulin, meglitinides, and acarbose on DR are warranted.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=332052, identifier CRD42022332052.

Introduction Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania.Methods and analysis We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2–4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening.Ethics and dissemination The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication.Trial registration number ISRCTN18317152.

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.

Purpose: Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Design: Dataset descriptor for routinely collected eye screening data. Participants: All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. Methods: The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)–led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom’s largest regional DR screening program. Main Outcome Measures: This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients’ diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. Results: At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. Conclusions: This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Purpose: Retinal vein occlusion (RVO) is the second leading cause of visual loss due to retinal disease. Retinal vein occlusion increases the risk of cardiovascular mortality and the risk of stroke. This article describes the data contained within the INSIGHT eye health data set for RVO and cardiovascular disease. Design: Data set descriptor for routinely collected eye and systemic disease data. Participants: All people who had suffered an RVO aged ≥ 18 years old, attending the Ophthalmology Clinic at Queen Elizabeth Hospital, University Hospitals Birmingham (UHB) National Health Service (NHS) Trust were included. Methods: The INSIGHT Health Data Research Hub for Eye Health is an NHS-led ophthalmic bioresource. It provides researchers with safe access to anonymized routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT UHB RVO and major adverse cardiovascular events data set, a data set of ophthalmology and systemic data derived from the United Kingdom’s largest acute care trust. Main Outcome Measures: This data set consists of routinely collected data from the hospital’s electronic patient records. The data set primarily includes structured data (relating to their hospital eye care and any cardiovascular data held for the individual) and OCT ocular images. Further details regarding the available data points are available in the supplementary information. Results: At the time point of this analysis (September 30, 2022) the data set was composed of clinical data from 1521 patients, from Medisoft records inception. The data set includes 2196 occurrences of RVO affecting 2026 eyes, longitudinal eye follow-up clinical parameters, over 6217 eye-related procedures, and 982 encountered complications. The data set contains information on 2534 major adverse cardiovascular event occurrences, their subtype, number experienced per patient, and chronological relation to RVO event. Longitudinal follow-up data including laboratory results, regular medications, and all-cause mortality are also available within the data set. Conclusions: This data set descriptor article summarizes the data set contents, the process of its curation, and potential uses. The data set is available through the structured application process that ensures research studies are for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Background: The glymphatic system removes neurodegenerative debris. The ocular glymphatic outflow is from the eye to the proximal optic nerve. In multiple sclerosis (MS), atrophy of the optic nerve increases the glymphatic outflow space. Here, we tested whether vitreous haze (VH) can provide novel insights into the relationship between neurodegeneration and the ocular glymphatic system in MS. Methods: This cross-sectional study comprised 315 persons with MS and 87 healthy controls (HCs). VH was quantified from optical coherence tomography (OCT) volume scans. Neurodegeneration was determined on three-dimensional T1 (3DT1) MRI, lesion detection on fluid-attenuated inversion (FLAIR), and layer thickness on OCT. Generalized estimating equations, corrected for age, were used to analyze associations between VH and metrics for neurodegeneration, demographics, and clinical scales. Group differences were determined between mild, moderate, and severe disability. Results: On the group level, VH scores were comparable between MS and control (p = 0.629). In MS, VH scores declined with disease duration (β = −0.009, p = 0.004) and age (β = −0.007, p = 0.001). There was no relation between VH scores and higher age in HCs. In MS patients, VH was related to normalized gray (NGMV, β = 0.001, p = 0.011) and white matter volume (NWMV, β = 0.001, p = 0.003), macular ganglion cell–inner plexiform layer thickness (mGCIPL, β = 0.006, p < 0.001), and peripapillary retinal nerve fiber layer thickness (pRNFL, β = 0.004, p = 0.008). VH was significantly lower in severe compared to mild disability (mean difference −28.86%, p = 0.058). Conclusions: There is a correlation between VH on OCT and disease duration, more severe disability and lower brain volumes in MS. Biologically, these relationships suggest accelerated glymphatic clearance with disease-related atrophy.

BackgroundThe rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. ObjectiveIn this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. MethodsOvid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. ResultsThe initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. ConclusionsClinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial RegistrationPROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005 International Registered Report Identifier (IRRID)RR2-10.2196/33145

BackgroundUveitis comprises a range of conditions that result in intraocular inflammation. Most sight-threatening uveitis falls into the broad category known as Non-infectious Posterior Segment-Involving Uveitis (PSIU). To evaluate treatments, trialists and clinicians must select outcome measures. The aim of this study was to understand healthcare professionals' perspectives on what outcomes are important to adult patients with PSIU and their carers.MethodsTwelve semi-structured telephone interviews were undertaken to understand the perspectives of healthcare professionals. Interviews were audio recorded, transcribed and thematically analysed. Findings were compared with the views of patients and carers and outcomes abstracted from a previously published systematic review.ResultsEleven core domains were identified as important to healthcare professionals: (1) visual function, (2) symptoms, (3) functional ability, (4) impact on relationships, (5) financial impact, (6) psychological morbidity and emotional well-being (7) psychosocial adjustment to uveitis, (8) doctor / patient / interprofessional relationships and access to health care, (9) treatment burden, (10) treatment side effects, (11) disease control. Healthcare professionals recognised a similar range of domains to patients and carers but placed more emphasis on certain outcomes, particularly in the disease control domain. In contrast the range of outcomes identified via the systematic review was limited.ConclusionHealthcare professionals recognise all of the published outcome domains as patients/carers in the previous publication but with subtly differing emphasis within some domains and with a priority for certain types of measures. Healthcare professionals discussed the disease control and side effects/complications to a greater degree than patients and carers in the focus groups.

Purpose: To describe a novel optical coherence tomography (OCT) finding at the vitreomacular interface (VMI), and report its association with advanced choroidal neovascularisation (CNV). Observations: Optical coherence tomography (OCT) scans performed at three retinal imaging centres at Amanat Eye Hospital, Pakistan from May 2016 till May 2021 were reviewed. A specific change at the vitreomacular interface was noted consisting of abnormal hyper reflectivity at the point of attachment of the posterior hyaloid membrane to the foveal center which appears to ‘fill in’ the foveolar depression.Eight eyes of eight patients were identified. All affected eyes had advanced CNV and persistent vitreofoveolar adhesion. In all eyes, the foveal contour (concavity) was maintained and there was no inner retinal surface wrinkling which differentiates this OCT feature from vitreomacular traction or epiretinal membranes. The authors propose the term Central Posterior Hyaloidal Fibrosis (CPHF) for this specific OCT finding. Conclusions and Importance: Central Posterior Hyaloidal Fibrosis (CPHF) is a newly reported OCT finding associated with advanced CNV, which may represent a possible profibrotic influence of a choroidal neovascular membrane to the overlying posterior hyaloid adhesion.

Abstract Background An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis. Methods We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems. Results We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39–1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49–0.76) and ischaemic stroke (HR = 0.27, CI 0.08–0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results. Conclusion We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on ‘patients like me’. We identify the key challenges in offering such an Informatics Consult as a service.

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

Abstract Quantifying intraocular inflammation is crucial in managing uveitis patients. We assessed the minimum B-scan density for reliable automated vitreous intensity (VI) assessment, using a novel approach based on optical coherence tomography (OCT). OCT volume scans centered on the macula were retrospectively collected in patients with uveitis. Nine B-scans per volume scan at fixed locations were automatically analyzed. The following B-scan selections were compared against the average score of 9 B-scans per volume scan as a reference standard: 1/3/5/7 central scans (1c/3c/5c/7c), 3 widely distributed scans (3w). Image data of 49 patients (31 females) were included. The median VI was 0.029 (IQR: 0.032). The intra-class-correlation coefficient of the VI across the 9 B-scans was 0.923. The median difference from the reference standard ranged between 0.001 (7c) and 0.006 (1c). It was significantly lower for scan selection 3w than 5c, p(adjusted) = 0.022, and lower for selection 7c than 3w, p(adjusted) = 0.003. The scan selections 7c and 3w showed the two highest areas under the receiver operating curve (0.985 and 0.965, respectively). Three widely distributed B-scans are sufficient to quantify VI reliably. Highest reliability was achieved using 7 central B-scans. Automated quantification of VI in uveitis is reliable and requires only few OCT B-scans.

Objectives Ophthalmic conditions including anterior uveitis (AU), episcleritis and scleritis may occur in association with the inflammatory bowel diseases (IBD) as ophthalmic extraintestinal manifestations. The aim of this study was to assess the risk of a later IBD diagnosis in those presenting with IBD associated ocular inflammation (IAOI).Design Retrospective cohort study.Setting Primary care UK database.Participants 38 805 subjects with an IAOI were identified (median age 51 (38–65), 57% women) and matched to 153 018 subjects without IAOI.Measures The risk of a subsequent diagnosis of IBD in subjects with IAOIs compared with age/sex matched subjects without IAOI. HRs were adjusted for age, sex, body mass index, deprivation, comorbidity, smoking, baseline axial arthropathy, diarrhoea, loperamide prescription, anaemia, lower gastrointestinal bleeding and abdominal pain.Logistic regression was used to produce a prediction model for a diagnosis of IBD within 3 years of an AU diagnosis.Results 213 (0.6%) subsequent IBD diagnoses (102 ulcerative colitis (UC) and 111 Crohn’s disease (CD)) were recorded in those with IAOIs and 329 (0.2%) (215 UC and 114 CD) in those without. Median time to IBD diagnosis was 882 (IQR 365–2043) days in those with IAOI and 1403 (IQR 623–2516) in those without. The adjusted HR for a subsequent diagnosis of IBD was 2.25 (95% CI 1.89 to 2.68), p

Background Glaucoma is a nonreversible optic neuropathy that may occur primarily or secondary to ocular pathology. Uveitis is a major etiological factor for secondary glaucoma, arising either owing to the inflammation itself or resulting from therapeutic steroid use. Many patients with uveitic glaucoma ultimately require a surgical intervention to maintain optimal intraocular pressures (IOP), often after treatment failure with multiple ocular antihypertensive agents. A leading surgical option for such patients is trabeculectomy augmented by intraoperative administration of mitomycin C (MMC). However, in the context of uveitic glaucoma, the relative merit of trabeculectomy with MMC compared with alternative surgical techniques, such as valve implantation, remains a subject of debate among ophthalmic surgeons. Study design This is a review of the literature to evaluate the efficacy and safety of trabeculectomy with MMC in uveitic glaucoma. Materials and methods A literature review of the use of trabeculectomy with MMC administration as an initial surgical management for uveitic glaucoma in adults was performed. Articles were identified by searches of PubMed and the Cochrane Library, using a strategy that defined the target condition (uveitis OR uveitic glaucoma OR inflammatory glaucoma) AND the intervention (trabeculectomy); limitation to MMC-augmented trabeculectomy was undertaken at the abstract and full-text stage. Data extracted included age of the patients, nature of the uveitis, preoperative status in terms of IOP and number of ocular hypotensive medications used, outcomes at annual time-points including IOP, number of ocular hypotensive medications, and surgical success (defined in terms of IOP) up to 5 years postoperatively. Adverse events were also recorded. Results The search strategy identified 498 papers, of which 32 were duplicates, 431 were excluded at abstract stage, and 28 were excluded at full-text stage; five of the 28 satisfied the inclusion criteria but were found to contain data that were entirely or largely contained within one of the other included papers. A total of seven studies were included, comprising 300 patients, with a weighted mean age of 45.8 years (range=30.0–56.9 years), and a range of uveitis syndromes including idiopathic (30%), Behcet’s disease (24.3%), Fuchs’ heterochromic uveitis (13.7%), and Vogt-Koyanagi-Harada syndrome (12.3%). Preoperative IOP and number of medications were reported in all studies. The mean (weighted) preoperative IOP was 34.2 mmHg (range=26.8–39.5 mmHg) and the mean (weighted) number of medications was 3.4 (range=2.3–4.5). Postoperative data were variably reported across the studies; the mean IOP at 1 year was reported in five studies (range=13.8–18.5 mmHg), the mean number of medications at 1 year was reported in four studies (range=0.3–2.6), and surgical success was variably defined preventing direct comparison or aggregation. Conclusion There is limited evidence to evaluate trabeculectomy with MMC as an initial surgical intervention for uveitic glaucoma, with the available evidence being derived entirely from retrospective studies, most of which do not have a parallel comparator group. Variation in reporting limits direct comparison and aggregation of these studies. Comparison between before and after intervention provides some limited evidence of effectiveness and safety. So, there is a need for well-designed prospective randomized controlled trials with long-term follow-up to compare interventions in this vulnerable group of patients.

Abstract Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.

Introduction Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4–12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies.Methods and analysis A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group.Ethics and dissemination Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers.Trial registration number 1567490.

Purpose Retinal signatures of systemic disease (‘oculomics’) are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach.Participants Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people.Findings to date Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358).Future plans AlzEye combines the world’s largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.

Summary: Background: We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods: Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings: Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation: This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenges. Funding: The Queen Elizabeth Diamond Jubilee Trust, Moorfields Eye Charity, National Institute for Health Research Moorfields Biomedical Research Centre, Wellcome Trust, Sightsavers, The Fred Hollows Foundation, The Seva Foundation, British Council for the Prevention of Blindness, and Christian Blind Mission. Translations: For the French, Spanish, Chinese, Portuguese, Arabic and Persian translations of the abstract see Supplementary Materials section.

Importance Poor access to existing care for diabetic retinopathy (DR) limits effectiveness of proven treatments. Objectives We examined whether outreach screening in rural China improves equity of access. Design, setting and participants We compared prevalence of female sex, age > = 65 years, primary education or below, and requiring referral care for DR between three cohorts with diabetes examined for DR in neighboring areas of Guangdong, China: passive case detection at secondary-level hospitals (n = 193); persons screened during primary-level DR outreach (n = 182); and individuals with newly- or previously-diagnosed diabetes in a population survey (n = 579). The latter reflected the “ideal” reach of a screening program. Results Compared to the population cohort, passive case detection reached fewer women (50·8% vs. 62·3%, p = 0·006), older adults (37·8% vs. 51·3%, p < 0·001), and less-educated persons (39·9% vs. 89·6%, p < 0·001). Outreach screening, compared to passive case detection, improved representation of the elderly (49·5% vs. 37·8%, p = 0·03) and less-educated (70·3% vs. 39·9%, p0.300) and persons aged > = 65 years (49.5% vs 51.3%, p = 0.723) in the outreach screening and population cohorts did not differ significantly. Prevalence of requiring referral care for DR was significantly higher in the outreach screening cohort (28·0%) than the population (14·0%) and passive case detection cohorts (7·3%, pConclusions and relevance Primary-level outreach screening improves access for the poorly-educated and elderly, and removes gender inequity in access to DR care in this setting, while also identifying more severely-affected patients than case finding in hospital.

The uveal tract consists of the iris, the ciliary body and the choroid; these three distinct tissues form a continuous layer within the eye. Uveitis refers to inflammation of any region of the uveal tract. Despite being grouped together anatomically, the iris, ciliary body and choroid are distinct functionally, and inflammatory diseases may affect only one part and not the others. Cellular structure of tissues direct their function, and understanding the cellular basis of the immune environment of a tissue in health, the “steady state” on which the perturbations of disease are superimposed, is vital to understanding the pathogenesis of those diseases. A contemporary understanding of the immune system accepts that haematopoietic and yolk sac derived leukocytes, though vital, are not the only players of importance. An array of stromal cells, connective tissue cells such as fibroblasts and endothelial cells, may also have a role in the inflammatory reaction seen in several immune-mediated diseases. In this review we summarise what is known about the cellular composition of the uveal tract and the roles these disparate cell types have to play in immune homeostasis. We also discuss some unanswered questions surrounding the constituents of the resident leukocyte population of the different uveal tissues, and we look ahead to the new understanding that modern investigative techniques such as single cell transcriptomics, multi-omic data integration and highly-multiplexed imaging techniques may bring to the study of the uvea and uveitis, as they already have to other immune mediated inflammatory diseases.

Abstract Background Inflammation in anterior uveitis is characterised by breakdown of the blood-ocular barrier, which allows leakage of blood constituents of higher molecular weight into the aqueous humour. In routine clinical care, increase in aqueous protein levels can be observed at the slit lamp as ‘flare’ and the severity can be graded using various clinical grading systems, of which the Standardization of Uveitis Nomenclature (SUN) grading system is most commonly used. Alternative instrument-based technologies are available, which can detect aqueous protein levels in an objective and quantifiable way. This review will identify instruments capable of measuring anterior chamber inflammation in this way, their level of reliability, and how well the measurements correlate with clinical grading and/or actual aqueous protein concentration. Methods Standard systematic review methodology will be used to identify, select and extract data from studies that report the use of any instrument-based technology in the assessment of aqueous protein levels. Searches will be conducted through bibliographic databases (MEDLINE, EMBASE and Cochrane Library), clinical trial registries and the grey literature. No restrictions will be placed on language or year of publication. The outcomes of interest are the level of correlation between identified instrument-based test measurements, clinical grading and/or actual aqueous protein concentration, as well as the reliability of each index test identified. Study quality assessment will be based on QUADAS2. Correlation and reliability outcomes will be pooled and meta-analysed if appropriate. Discussion The assessment of inflammation in anterior chamber protein levels currently relies on crude and subjective clinical examination. The findings of this review will identify non-invasive technologies which show good correlation with actual protein concentration, which could be used in routine clinical practice for objective monitoring of AC inflammation. Systematic review registration PROSPERO CRD42017084167. Study screening stage has just been completed.

Abstract Background Childhood uveitis is a collection of chronic rare inflammatory eye disorders which result in visual loss in at least one eye of one fifth of affected children. Despite the introduction of novel systemic immunochemotherapies, it remains a blinding disease. We have undertaken a systematic review of outcome measures used in interventional trials of children with, or at risk of uveitis, in order to investigate metric quality and heterogeneity, as possible barriers to the translation of clinical research into improved outcomes. Methods Systematic review of trials registered within databases approved by the International Committee of Medical Journal Editors (ICMJE). Eligible trials for were those which involved participants aged under 18 years with or at risk of non-infectious uveitis. Data on date of study commencement, uveitis site, inclusion age criteria, and outcome measure characteristics including type, dimension and quality were extracted independently by two authors. Quality was determined using the reproducibility, validity and age-appropriateness of the metric. Results Of 917 identified trials, 57 were eligible for inclusion. Twenty different domains across five dimensions were used as primary outcome measures. The structure most commonly used was multiple separate outcome measures. In a quarter of studies, outcomes were assessed less than 3 months following the intervention. Disease activity was the most commonly assessed dimension, with only 30 studies (60%) using reproducible methodologies to assess activity. Only 2/12 (18%) studies on intermediate or posterior uveitis used reproducible activity grading schemes. Of 18 studies involving children aged under 6 years old which used outcome measures related to visual function, only 8/18 (44%) described the use of age-appropriate acuity assessment measures. None of the studies used a vision related quality of life metrics which had been validated for use in childhood. Conclusion This review of outcome measures in childhood uveitis interventional trials has identified under-utilisation of reproducible or child appropriate measures, and considerable heterogeneity in metric type, and structure. Clinicians and researchers interested in improving outcomes for affected children must identify a patient and family centred core outcome set, and work to validate both objective and patient (or proxy) reported disease age appropriate outcome measures.

Introduction Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysis The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and dissemination Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.

Summary: The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial.