A foundation model for generalizable disease detection from retinal images.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]. / Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI.

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.

A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI]. / Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the 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 impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-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 is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials ­ Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials ­ Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.

BACKGROUND: The 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. OBJECTIVE: In 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. METHODS: Ovid-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. RESULTS: The 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. CONCLUSIONS: Clinical 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 REGISTRATION: PROSPERO (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.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]. / Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI.

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.

A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]. / Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI.

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.

A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Angiotensin-converting enzyme inhibitors and risk of age-related macular degeneration in individuals with hypertension.

AIMS: Several observational studies have examined the potential protective effect of angiotensin-converting enzyme inhibitor (ACE-I) use on the risk of age-related macular degeneration (AMD) and have reported contradictory results owing to confounding and time-related biases. We aimed to assess the risk of AMD in a base cohort of patients aged 40 years and above with hypertension among new users of ACE-I compared to an active comparator cohort of new users of calcium channel blockers (CCB) using data obtained from IQVIA Medical Research Data, a primary care database in the UK. METHODS: In this study, 53 832 and 43 106 new users of ACE-I and CCB were included between 1995 and 2019, respectively. In an on-treatment analysis, patients were followed up from the time of index drug initiation to the date of AMD diagnosis, loss to follow-up, discontinuation or switch to the comparator drug. A comprehensive range of covariates were used to estimate propensity scores to weight and match new users of ACE-I and CCB. Standardized mortality ratio weighted Cox proportional hazards model was used to estimate hazard ratios of developing AMD. RESULTS: During a median follow-up of 2 years (interquartile range 1-5 years), the incidence rate of AMD was 2.4 (95% confidence interval 2.2-2.6) and 2.2 (2.0-2.4) per 1000 person-years among the weighted new users of ACE-I and CCB, respectively. There was no association of ACE-I use on the risk of AMD compared to CCB use in either the propensity score weighted or matched, on-treatment analysis (adjusted hazard ratio: 1.07 [95% confidence interval 0.90-1.27] and 0.87 [0.71-1.07], respectively). CONCLUSION: We found no evidence that the use of ACE-I is associated with risk of AMD in patients with hypertension.

Building trust in real-world data: lessons from INSIGHT, the UK's health data research hub for eye health and oculomics.

PURPOSE OF REVIEW: In this review, we consider the challenges of creating a trusted resource for real-world data in ophthalmology, based on our experience of establishing INSIGHT, the UK's Health Data Research Hub for Eye Health and Oculomics. RECENT FINDINGS: The INSIGHT Health Data Research Hub maximizes the benefits and impact of historical, patient-level UK National Health Service (NHS) electronic health record data, including images, through making it research-ready including curation and anonymisation. It is built around a shared 'north star' of enabling research for patient benefit. INSIGHT has worked to establish patient and public trust in the concept and delivery of INSIGHT, with efficient and robust governance processes that support safe and secure access to data for researchers. By linking to systemic data, there is an opportunity for discovery of novel ophthalmic biomarkers of systemic diseases ('oculomics'). Datasets that provide a representation of the whole population are an important tool to address the increasingly recognized threat of health data poverty. SUMMARY: Enabling efficient, safe access to routinely collected clinical data is a substantial undertaking, especially when this includes imaging modalities, but provides an exceptional resource for research. Research and innovation built on inclusive real-world data is an important tool in ensuring that discoveries and technologies of the future may not only favour selected groups, but also work for all patients.

Development of a Core Outcome Set for Clinical Trials in Non-infectious Uveitis of the Posterior Segment.

PURPOSE: To develop an agreed upon set of outcomes known as a "core outcome set" (COS) for noninfectious uveitis of the posterior segment (NIU-PS) clinical trials. DESIGN: Mixed-methods study design comprising a systematic review and qualitative study followed by a 2-round Delphi exercise and face-to-face consensus meeting. PARTICIPANTS: Key stakeholders including patients diagnosed with NIU-PS, their caregivers, and healthcare professionals involved in decision-making for patients with NIU-PS, including ophthalmologists, nurse practitioners, and policymakers/commissioners. METHODS: A long list of outcomes was developed based on the results of (1) a systematic review of clinical trials of NIU-PS and (2) a qualitative study of key stakeholders including focus groups and interviews. The long list was used to generate a 2-round Delphi exercise of stakeholders rating the importance of outcomes on a 9-point Likert scale. The proportion of respondents rating each item was calculated, leading to recommendations of "include," "exclude," or "for discussion" that were taken to a face-to-face consensus meeting of key stakeholders at which they agreed on the final COS. MAIN OUTCOME MEASURE: Items recommended for inclusion in the COS for NIU-PS. RESULTS: A total of 57 outcomes grouped in 11 outcome domains were presented for evaluation in the Delphi exercise, resulting in 9 outcomes directly qualifying for inclusion and 15 outcomes being carried forward to the consensus meeting, of which 7 of 15 were agreed on for inclusion. The final COS contained 16 outcomes organized into 4 outcome domains comprising visual function, health-related quality of life, treatment side effects, and disease control. CONCLUSIONS: This study builds on international work across the clinical trials community and our qualitative research to construct the world's first COS for NIU-PS. The COS provides a list of outcomes that represent the priorities of key stakeholders and provides a minimum set of outcomes for use in all future NIU-PS clinical trials. Adoption of this COS can improve the value of future uveitis clinical trials and reduce noninformative research. Some of the outcomes identified do not yet have internationally agreed upon methods for measurement and should be the subject of future international consensus development.

Longitudinal Development of Peripapillary Hyper-Reflective Ovoid Masslike Structures Suggests a Novel Pathological Pathway in Multiple Sclerosis.

OBJECTIVE: Peripapillary hyper-reflective ovoid masslike structures (PHOMS) are a new spectral domain optical coherence tomography (OCT) finding. METHODS: This prospective, longitudinal study included patients (n = 212) with multiple sclerosis (MS; n = 418 eyes), 59 healthy controls (HCs; n = 117 eyes), and 267 non-MS disease controls (534 eyes). OCT and diffusion tensor imaging were used. RESULTS: There were no PHOMS in HC eyes (0/117, 0%). The prevalence of PHOMS was significantly higher in patients with MS (34/212, p = 0.001) and MS eyes (45/418, p = 0.0002) when compared to HCs (0/59, 0/117). The inter-rater agreement for PHOMS was 97.9% (kappa = 0.951). PHOMS were present in 16% of patients with relapsing-remitting, 16% of patients with progressive, and 12% of patients with secondary progressive disease course (2% of eyes). There was no relationship of PHOMS with age, disease duration, disease course, disability, or disease-modifying treatments. The fractional anisotropy of the optic radiations was lower in patients without PHOMS (0.814) when compared to patients with PHOMS (0.845, p = 0.03). The majority of PHOMS remained stable, but increase in size and de novo development of PHOMS were also observed. In non-MS disease controls, PHOMS were observed in intracranial hypertension (62%), optic disc drusen (47%), anomalous optic discs (44%), isolated optic neuritis (19%), and optic atrophy (12%). INTERPRETATION: These data suggest that PHOMS are a novel finding in MS pathology. Future research is needed to determine whether development of PHOMS in MS is due to intermittently raised intracranial pressure or an otherwise impaired "glymphatic" outflow from eye to brain. ANN NEUROL 2020;88:309-319.

Structural Endpoints and Outcome Measures in Uveitis.

Most uveitis entities are rare diseases but, taken together, are responsible for 5-10% of worldwide visual impairment which largely affects persons of working age. As with many rare diseases, there is a lack of high-level evidence regarding its clinical management, partly due to a dearth of reliable and objective quantitative endpoints for clinical trials. This review provides an overview of available structural outcome measures for uveitis disease activity and damage in an anatomical order from the anterior to the posterior segment of the eye. While there is a multitude of available structural outcome measures, not all might qualify as endpoints for clinical uveitis trials, and thorough testing of applicability is warranted. Furthermore, a consensus on endpoint definition, standardization, and "core outcomes" is required. As stipulated by regulatory agencies, endpoints should be precisely defined, clinically important, internally consistent, reliable, responsive to treatment, and relevant for the respective subtype of uveitis. Out of all modalities used for assessment of the reviewed structural outcome measures, optical coherence tomography, color fundus photography, fundus autofluorescence, and fluorescein/indocyanine green angiography represent current "core modalities" for reliable and objective quantification of uveitis outcome measures, based on their practical availability and the evidence provided so far.

Reporting guidelines for artificial intelligence in healthcare research.

Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of artificial intelligence (AI) reporting guidelines that address potential sources of bias specific to studies involving AI interventions has the potential to improve the quality of AI studies, through improvements in their design and delivery, and the completeness and transparency of their reporting. With a number of guidance documents relating to AI studies emerging from different specialist societies, this Review article provides researchers with some key principles for selecting the most appropriate reporting guidelines for a study involving an AI intervention. As the main determinants of a high-quality study are contained within the methodology of the study design rather than the intervention, researchers are recommended to use reporting guidelines that are specific to the study design, and then supplement them with AI-specific guidance contained within available AI reporting guidelines.

An informatics consult approach for generating clinical evidence for treatment decisions.

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.

Anti-tumour necrosis factor biological therapies for the treatment of uveitic macular oedema (UMO) for non-infectious uveitis.

BACKGROUND: Non-infectious uveitis describes a heterogenous group of ocular disorders characterised by intraocular inflammation in the absence of infection. Uveitis is a leading cause of visual loss, most commonly due to uveitic macular oedema (UMO). Treatment is aimed at reducing disease activity by suppression of the intraocular inflammatory response. In the case of macular oedema, the aim is to restore macular architecture as quickly as possible, in order to prevent irreversible photoreceptor damage in this area. Acute exacerbations are typically managed with corticosteroids, which may be administered topically, locally or systemically. Whilst these are often rapidly effective in achieving disease control, long-term use is associated with significant local and systemic side effects, and 'steroid sparing agents' are typically used to achieve prolonged control in severe or recalcitrant disease. Anti-tumour necrosis factor (TNF) drugs block a critical cytokine in the inflammatory signalling process, and have emerged as effective steroid-sparing immunomodulatory agents in a wide range of non-ocular conditions. There is mechanistic data to suggest that they may provide a more targeted approach to disease control in UMO than other agents, but to date, these agents have predominantly been used 'off label' as the majority are not licensed for ocular use. This review aims to summarise the available literature reporting the use of anti-TNF therapy in UMO, thus developing the evidence-base on which to make future treatment decisions and develop clinical guidelines in this area. OBJECTIVES: To assess the efficacy of anti-TNF therapy in treatment of UMO. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2018, Issue 2), which contains the Cochrane Eyes and Vision Trials Register; Ovid MEDLINE; Ovid Embase; LILACS; Web of Science Conference Proceedings Citation Index- Science (CPCI-S); System for Information on Grey Literature in Europe (OpenGrey); the ISRCTN registry; ClinicalTrials.gov and the WHO ICTRP. The date of the search was 29 March 2018. SELECTION CRITERIA: We planned to include all relevant randomised controlled trials assessing the use of anti-TNF agents in treatment of UMO. No limits were applied to participant age, gender or ethnicity. The primary comparisons of this review were: anti-TNF versus no treatment or placebo; anti-TNF versus another pharmacological agent; comparison of different anti-TNF drugs; comparison of different doses and routes of administration of the same anti-TNF drug. The primary outcome measure that we assessed for this review was best-corrected visual acuity (BCVA) in the treated eye. Secondary outcome measures were anatomical macular change, clinical estimation of vitreous haze and health-related quality of life. DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles and abstracts retrieved through the database searches. We retrieved full-text reports of studies categorised as 'unsure' or 'include' after we had reviewed the abstracts. Two review authors independently reviewed each full-text report for eligibility. We resolved discrepancies through discussion. MAIN RESULTS: We identified no completed or ongoing trial that was eligible for this Cochrane Review. AUTHORS' CONCLUSIONS: Our review did not identify any evidence from randomised controlled trials for or against the role of anti-TNF agents in the management of UMO. Although there are a number of high-quality randomised controlled trials that demonstrate the efficacy of anti-TNF agents in preventing recurrence of inflammation in uveitis, the reported study outcomes do not include changes in UMO. As a result, there were insufficient data to conclude whether there was a significant treatment effect specifically for UMO. Future trials should be designed to include quantitative measures of UMO as primary study outcomes, for example by reporting the presence or absence of UMO, or by measuring central macular thickness for study participants. Furthermore, whilst UMO is an important complication of uveitis, we acknowledge that uveitis is associated with many significant structural and functional complications. It is not possible to determine treatment efficacy based on a single outcome measure. We recommend that future reviews of therapeutic interventions in uveitis should use composite measures of treatment response comprising a range of potential complications of disease.

Treating Diabetic Macular Oedema (DMO): real world UK clinical outcomes for the 0.19mg Fluocinolone Acetonide intravitreal implant (Iluvien™) at 2 years.

BACKGROUND: To compare visual function and structural improvements in pseudophakic eyes with diabetic macular oedema (DMO) treated with the 0.19mg Fluocinolone Acetonide (FAc) intravitreal implant (IluvienTM) in a 'real world' setting. METHODS: A single centre retrospective evaluation of patients with DMO unresponsive to conventional treatment treated with the FAc implant according to UK guidelines. Primary efficacy endpoint was best corrected visual acuity (BCVA); secondary endpoints included optical coherence tomography evaluations of the macula (a) central retinal and (b) peak macular thickness collected at annual time points. Primary safety endpoint was new rise in IOP >27mmHg or glaucoma surgery. Patients with <1 year follow-up were excluded. RESULTS: Twenty-nine eyes were included, with mean(SD) follow up of 792(270) days. Improvement in BCVA and reduction in macular oedema was noted at all timepoints. Mean improvement in BCVA from baseline was 6 ETDRS letters at year 1(n=29), 6.5L at year 2(n=22) and 11L at year 3(n=6). Mean central retinal thickness at baseline was 451 microns, 337 microns at year 1, 342 microns at year 2 and 314 microns at year 3. Two eyes required IOP-lowering drops post implant. Supplementary treatment for persistence or recurrence of DMO was necessary in 18 eyes over the total study period of 3 years with mean time to supplementary treatment being 12 months. CONCLUSIONS: Our evaluation of the 0.19mg FAc implant delivered in a real-world setting, provides additional evidence that it is effective and safe in the treatment of patients with DMO, and can provide sustained benefit for patients with previously refractory disease.

Development and validation of a questionnaire assessing the quality of life impact of Colour Blindness (CBQoL).

BACKGROUND: Congenital colour vision deficiency (CVD), commonly called 'colour blindness', affects around 8% of men and 0.4% of women. Although many aspects of health (e.g. change in colour of urine) and healthcare (e.g. coloured medication, colour-coded diagnostic tests), and modern life depend upon colour coding (e.g. graphs, maps, signals), the impact of colour blindness on everyday life is not generally considered a topic of importance. This study is the first to create and validate a questionnaire measuring the quality of life (QoL) impact of being colour blind. METHODS: This study consisted of two phases. Firstly, the questionnaire design and development phase was led by an expert panel and piloted on a focus group. Secondly, an online sample of 128 men and 291 women filled in the questionnaire, and the psychometric properties of the questionnaire were analysed using principal components analysis (PCA). The scores of colour blind (CB) participants and normal-sighted controls, controlling for age and sex, were compared using matched t-tests. RESULTS: The PCA resulted in a questionnaire with three domains (or subscales): QoL for Health & Lifestyle, QoL for Work, and QoL for Emotions. Controlling for age, there was a significantly greater negative impact on QoL for CB people than normal-sighted controls in regards to confusion over colour in various aspects of their health (p = 5 × 10-7), work (p = 1.3 × 10-7), and emotional life (p = 6 × 10-5). CONCLUSION: Colour blindness can significantly impact quality of life for health, emotions, and especially careers. The tool developed here could be useful in future clinical studies to measure changes in CBQoL in response to therapy in conditions where colour vision is affected. We also discuss ways in which everyday problems related to colour vision might be reduced, for example, workplaces could avoid colour coding where a non-colour alternative is possible.

Uveitis: a sight-threatening disease which can impact all systems.

Uveitis describes a group of conditions characterised by intraocular inflammation. The term uveitis technically describes inflammation of the uvea which comprises the iris, ciliary body and choroid, however now encompasses inflammation of adjacent intraocular structures such as the retina, vitreous and optic nerve. Uveitis is a significant cause of blindness worldwide, but its impact is generally underappreciated due to a lack of awareness and understanding of the condition among the public and most non-ophthalmic healthcare professionals. In this review, we provide an introduction to uveitis for the non-specialist, outlining the clinical presentations that should raise the suspicion of the disease, the signs that should be looked for and a framework in which to understand the condition. We show how a logical approach to classifying uveitis by aetiology and anatomical focus of disease provides the basis for treatment strategies (drug and route of administration) and clinical presentation and prognosis. We also show why understanding uveitis is helpful to clinicians working in almost every speciality due to the wide-ranging associations with systemic disease.

Birmingham Behçet's service: classification of disease and application of the 2014 International Criteria for Behçet's Disease (ICBD) to a UK cohort.

BACKGROUND: This study reports on the analysis of the application and diagnostic predictability of the revised 2014 ICBD criteria in an unselected cohort of UK patients, and the ensuing organ associations and patterns of disease. METHODS: A retrospective cohort study was conducted using a database of electronic medical records. Three categories were recognised: clinically defined BD, incomplete BD and rejected diagnoses of BD. We applied the ISG 1990 and ICBD 2014 classification criteria to these subgroups to validate diagnostic accuracy against the multidisciplinary assessment. RESULTS: Between 2012 and 2015, 281 patients underwent initial assessment at an urban tertiary care centre: 190 patients with a confirmed diagnosis of BD, 7 with an incomplete diagnosis, and 84 with a rejected diagnosis. ICBD 2014 demonstrated an estimated sensitivity of 97.89% (95% CI: 94.70 to 99.42) and positive likelihood ratio of 1.21 (1.10 to 1.28). The strongest independent predictors were: Central nervous lesions (OR = 10.57, 95% CI: 1.34 to 83.30); Genital ulceration (OR = 9.05, 95% CI: 3.35 to 24.47); Erythema nodosum (OR = 6.59, 95% CI: 2.35 to 18.51); Retinal vasculitis (OR = 6.25, 95% CI: 1.47 to 26.60); Anterior uveitis (OR = 6.16, 95% CI: 2.37 to 16.02); Posterior uveitis (OR = 4.82, 95% CI: 1.25 to 18.59). CONCLUSIONS: The ICBD 2014 criteria were more sensitive at picking up cases than ISG 1990 using the multidisciplinary assessment as the gold standard. ICBD may over-diagnose BD in a UK population. Patients who have an incomplete form of BD represent a distinct group that should not be given an early diagnostic label. Behçet's disease is a complex disease that is best diagnosed by multidisciplinary clinical assessment. Patients in the UK differ in their clinical presentation and genetic susceptibility from the original descriptions. This study also highlights an incomplete group of Behçet's patients that are less well defined by their clinical presentation.

Hydroxychloroquine-related retinal toxicity.

HCQ is widely used for the treatment of rheumatic diseases, particularly lupus and RA. It is generally well tolerated, but retinopathy is a concern. Retinopathy is rare, but is sight threatening, generally irreversible and may progress even after cessation of therapy. Damage may be subclinical. Although a number of risk factors have been proposed (such as duration of therapy and cumulative dose), the many exceptions (e.g. retinopathy on low-dose HCQ, or no retinopathy after a very large cumulative dose of HCQ) highlight our limited understanding of the disease process. Novel technologies such as optical coherence tomography (OCT), fundus autofluorescence (FAF) and multifocal electroretinogram (mfERG) may provide the earliest structural and functional evidence of toxicity in these stages. Along with the well-established technique of central visual field testing (10-2 visual fields), these modalities are increasingly being used as part of screening programmes. The ideal single test with high sensitivity and high specificity for HCQ retinopathy has still not been achieved. Screening for HCQ retinopathy remains an area of considerable debate, including issues of when, who and how to screen. Commonly accepted risk factors include receiving >6.5 mg/kg/day or a cumulative dose of >1000 g of HCQ, being on treatment for >5 years, having renal or liver dysfunction, having pre-existing retinopathy and being elderly. HCQ continues to be a valuable drug in treating rheumatic disease, but clinicians need to be aware of the associated risks and to have arrangements in place that would enable early detection of toxicity.