Moorfields Eye Hospital

BACKGROUND: Contemporary data for causes of vision impairment and blindness form an important basis of recommendations in public health policies. Refreshment of the Global Vision Database with recently published data sources permitted modelling of cause of vision loss data from 1990 to 2015, further disaggregation by cause, and forecasts to 2020. METHODS: In this systematic review and meta-analysis, we analysed published and unpublished population-based data for the causes of vision impairment and blindness from 1980 to 2014. We identified population-based studies published before July 8, 2014, by searching online databases with no language restrictions (MEDLINE from Jan 1, 1946, and Embase from Jan 1, 1974, and the WHO Library Database). We fitted a series of regression models to estimate the proportion of moderate or severe vision impairment (defined as presenting visual acuity of <6/18 but ≥3/60 in the better eye) and blindness (presenting visual acuity of <3/60 in the better eye) by cause, age, region, and year. FINDINGS: We identified 288 studies of 3 983 541 participants contributing data from 98 countries. Among the global population with moderate or severe vision impairment in 2015 (216·6 million [80% uncertainty interval 98·5 million to 359·1 million]), the leading causes were uncorrected refractive error (116·3 million [49·4 million to 202·1 million]), cataract (52·6 million [18·2 million to 109·6 million]), age-related macular degeneration (8·4 million [0·9 million to 29·5 million]), glaucoma (4·0 million [0·6 million to 13·3 million]), and diabetic retinopathy (2·6 million [0·2 million to 9·9 million]). Among the global population who were blind in 2015 (36·0 million [12·9 million to 65·4 million]), the leading causes were cataract (12·6 million [3·4 million to 28·7 million]), uncorrected refractive error (7·4 million [2·4 million to 14·8 million]), and glaucoma (2·9 million [0·4 million to 9·9 million]). By 2020, among the global population with moderate or severe vision impairment (237·1 million [101·5 million to 399·0 million]), the number of people affected by uncorrected refractive error is anticipated to rise to 127·7 million (51·0 million to 225·3 million), by cataract to 57·1 million (17·9 million to 124·1 million), by age-related macular degeneration to 8·8 million (0·8 million to 32·1 million), by glaucoma to 4·5 million (0·5 million to 15·4 million), and by diabetic retinopathy to 3·2 million (0·2 million to 12·9 million). By 2020, among the global population who are blind (38·5 million [13·2 million to 70·9 million]), the number of patients blind because of cataract is anticipated to rise to 13·4 million (3·3 million to 31·6 million), because of uncorrected refractive error to 8·0 million (2·5 million to 16·3 million), and because of glaucoma to 3·2 million (0·4 million to 11·0 million). Cataract and uncorrected refractive error combined contributed to 55% of blindness and 77% of vision impairment in adults aged 50 years and older in 2015. World regions varied markedly in the causes of blindness and vision impairment in this age group, with a low prevalence of cataract (<22% for blindness and 14·1-15·9% for vision impairment) and a high prevalence of age-related macular degeneration (>14% of blindness) as causes in the high-income subregions. Blindness and vision impairment at all ages in 2015 due to diabetic retinopathy (odds ratio 2·52 [1·48-3·73]) and cataract (1·21 [1·17-1·25]) were more common among women than among men, whereas blindness and vision impairment due to glaucoma (0·71 [0·57-0·86]) and corneal opacity (0·54 [0·43-0·66]) were more common among men than among women, with no sex difference related to age-related macular degeneration (0·91 [0·70-1·14]). INTERPRETATION: The number of people affected by the common causes of vision loss has increased substantially as the population increases and ages. Preventable vision loss due to cataract (reversible with surgery) and refractive error (reversible with spectacle correction) continue to cause most cases of blindness and moderate or severe vision impairment in adults aged 50 years and older. A large scale-up of eye care provision to cope with the increasing numbers is needed to address avoidable vision loss. FUNDING: Brien Holden Vision Institute.

Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results: In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Early-onset, severe retinal dystrophy caused by mutations in the gene encoding retinal pigment epithelium-specific 65-kD protein (RPE65) is associated with poor vision at birth and complete loss of vision in early adulthood. We administered to three young adult patients subretinal injections of recombinant adeno-associated virus vector 2/2 expressing RPE65 complementary DNA (cDNA) under the control of a human RPE65 promoter. There were no serious adverse events. There was no clinically significant change in visual acuity or in peripheral visual fields on Goldmann perimetry in any of the three patients. We detected no change in retinal responses on electroretinography. One patient had significant improvement in visual function on microperimetry and on dark-adapted perimetry. This patient also showed improvement in a subjective test of visual mobility. These findings provide support for further clinical studies of this experimental approach in other patients with mutant RPE65. (ClinicalTrials.gov number, NCT00643747 [ClinicalTrials.gov].).

BACKGROUND: Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging. METHODS: In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176. FINDINGS: Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0-90·2) for deep learning models and 86·4% (79·9-91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1-96·4) for deep learning models and 90·5% (80·6-95·7) for health-care professionals. INTERPRETATION: Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology. FUNDING: None.

BACKGROUND: The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention. METHODOLOGY/PRINCIPAL FINDINGS: We review and summarise the evidence from a series of cohort studies that have assessed study publication bias and outcome reporting bias in randomised controlled trials. Sixteen studies were eligible of which only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Eleven of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies. CONCLUSIONS: Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

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.

BACKGROUND: Age-related macular degeneration is the most common cause of blindness in Western populations. Susceptibility is influenced by age and by genetic and environmental factors. Complement activation is implicated in the pathogenesis. METHODS: We tested for an association between age-related macular degeneration and 13 single-nucleotide polymorphisms (SNPs) spanning the complement genes C3 and C5 in case subjects and control subjects from the southeastern region of England. All subjects were examined by an ophthalmologist and had independent grading of fundus photographs to confirm their disease status. To test for replication of the most significant findings, we genotyped a set of Scottish cases and controls. RESULTS: The common functional polymorphism rs2230199 (Arg80Gly) in the C3 gene, corresponding to the electrophoretic variants C3S (slow) and C3F (fast), was strongly associated with age-related macular degeneration in both the English group (603 cases and 350 controls, P=5.9x10(-5)) and the Scottish group (244 cases and 351 controls, P=5.0x10(-5)). The odds ratio for age-related macular degeneration in C3 S/F heterozygotes as compared with S/S homozygotes was 1.7 (95% confidence interval [CI], 1.3 to 2.1); for F/F homozygotes, the odds ratio was 2.6 (95% CI, 1.6 to 4.1). The estimated population attributable risk for C3F was 22%. CONCLUSIONS: Complement C3 is important in the pathogenesis of age-related macular degeneration. This finding further underscores the influence of the complement pathway in the pathogenesis of this disease.

Abstract 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 disorders 1 . However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2 . 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.

This is the first European Crohn’s and Colitis Organisation [ECCO] consensus guideline that addresses extra-intestinal manifestations [EIMs] in inflammatory bowel disease [IBD]. It has been drafted by 21 ECCO members from 13 European countries. Although this is the first ECCO consensus guideline that primarily addresses EIMs, it is partly derived from, updates, and replaces previous ECCO consensus advice on EIMs, contained within the consensus guidelines for Crohn’s disease1 [CD] and ulcerative colitis2 [UC]. The strategy to define consensus was similar to that previously described in other ECCO consensus guidelines [available at www.ecco-ibd.eu]. Briefly, topics were selected by the ECCO guidelines committee [GuiCom]. ECCO members were selected to form working groups. Provisional ECCO Statements and supporting text were written following a comprehensive literature review, then refined following two voting rounds which included national representative participation by ECCO’s 35 member countries. The level of evidence was graded according to the Oxford Centre for Evidence-based Medicine [www.cebm.net]. The ECCO Statements were finalised by the authors at a meeting in Vienna in October 2014 and represent consensus with agreement of at least 80% of participants. Complete consensus [100% agreement] was reached for most statements. The supporting text was then finalised under the direction of each working group leader [VA, SV, FC, MH] before being integrated by the two consensus leaders [MH, FC]. This consensus guideline is pictorially represented within the freely available ECCO e-Guide [http://www.e-guide.ecco-ibd.eu/]. Up to 50% of patients with inflammatory bowel disease [IBD] experience at least one extra-intestinal manifestation [EIM], which can present before IBD is diagnosed.34,5,6 EIMs adversely impact upon patients’ quality of life and some, such as primary sclerosing cholangitis [PSC] or venous thromboembolism [VTE], can be life-threatening. The probability of developing EIMs increases with disease duration and in patients who already have one EIM.7 …

OBJECTIVE: We aimed to develop consensus-based recommendations for streamlining medical communication among various health care professionals, to improve accuracy of diagnosis and treatment, and to facilitate future investigations for mucous membrane pemphigoid. PARTICIPANTS: Because of the highly specific nature of this group of diseases, the 26 invited participants included either international scholars in the field of mucous membrane pemphigoid or experts in cutaneous pharmacology representing the 3 medical disciplines ophthalmology, oral medicine, and dermatology. EVIDENCE: The first author (L.S.C.) conducted a literature search. Based on the information obtained, international experts who had contributed to the literature in the clinical care, diagnosis, and laboratory investigation for mucous membrane pemphigoid were invited to participate in a consensus meeting aimed at developing a consensus statement. CONSENSUS PROCESS: A consensus meeting was convened and conducted on May 10, 1999, in Chicago, Ill, to discuss the relevant issues. The first author drafted the statement based on the consensus developed at the meeting and the participants' written comments. The draft was submitted to all participants for 3 separate rounds of review, and disagreements were reconciled based on literature evidence. The third and final statement incorporated all relevant evidence obtained in the literature search and the consensus developed by the participants. The final statement was approved and endorsed by all 26 participants. CONCLUSIONS: Specific consensus-based recommendations were made regarding the definition, diagnostic criteria, pathogenic factors, medical treatment, and prognostic indicators for mucous membrane pemphigoid. A system of standard reporting for these patients was proposed to facilitate a uniform data collection.

BACKGROUND: Mutations in RPE65 cause Leber's congenital amaurosis, a progressive retinal degenerative disease that severely impairs sight in children. Gene therapy can result in modest improvements in night vision, but knowledge of its efficacy in humans is limited. METHODS: We performed a phase 1-2 open-label trial involving 12 participants to evaluate the safety and efficacy of gene therapy with a recombinant adeno-associated virus 2/2 (rAAV2/2) vector carrying the RPE65 complementary DNA, and measured visual function over the course of 3 years. Four participants were administered a lower dose of the vector, and 8 were administered a higher dose. In a parallel study in dogs, we investigated the relationship among vector dose, visual function, and electroretinography (ERG) findings. RESULTS: Improvements in retinal sensitivity were evident, to varying extents, in six participants for up to 3 years, peaking at 6 to 12 months after treatment and then declining. No associated improvement in retinal function was detected by means of ERG. Three participants had intraocular inflammation, and two had clinically significant deterioration of visual acuity. The reduction in central retinal thickness varied among participants. In dogs, RPE65 gene therapy with the same vector at lower doses improved vision-guided behavior, but only higher doses resulted in improvements in retinal function that were detectable with the use of ERG. CONCLUSIONS: Gene therapy with rAAV2/2 RPE65 vector improved retinal sensitivity, albeit modestly and temporarily. Comparison with the results obtained in the dog model indicates that there is a species difference in the amount of RPE65 required to drive the visual cycle and that the demand for RPE65 in affected persons was not met to the extent required for a durable, robust effect. (Funded by the National Institute for Health Research and others; ClinicalTrials.gov number, NCT00643747.).

BACKGROUND: Thyroid-associated ophthalmopathy, a condition commonly associated with Graves' disease, remains inadequately treated. Current medical therapies, which primarily consist of glucocorticoids, have limited efficacy and present safety concerns. Inhibition of the insulin-like growth factor I receptor (IGF-IR) is a new therapeutic strategy to attenuate the underlying autoimmune pathogenesis of ophthalmopathy. METHODS: We conducted a multicenter, double-masked, randomized, placebo-controlled trial to determine the efficacy and safety of teprotumumab, a human monoclonal antibody inhibitor of IGF-IR, in patients with active, moderate-to-severe ophthalmopathy. A total of 88 patients were randomly assigned to receive placebo or active drug administered intravenously once every 3 weeks for a total of eight infusions. The primary end point was the response in the study eye. This response was defined as a reduction of 2 points or more in the Clinical Activity Score (scores range from 0 to 7, with a score of ≥3 indicating active thyroid-associated ophthalmopathy) and a reduction of 2 mm or more in proptosis at week 24. Secondary end points, measured as continuous variables, included proptosis, the Clinical Activity Score, and results on the Graves' ophthalmopathy-specific quality-of-life questionnaire. Adverse events were assessed. RESULTS: In the intention-to-treat population, 29 of 42 patients who received teprotumumab (69%), as compared with 9 of 45 patients who received placebo (20%), had a response at week 24 (P<0.001). Therapeutic effects were rapid; at week 6, a total of 18 of 42 patients in the teprotumumab group (43%) and 2 of 45 patients in the placebo group (4%) had a response (P<0.001). Differences between the groups increased at subsequent time points. The only drug-related adverse event was hyperglycemia in patients with diabetes; this event was controlled by adjusting medication for diabetes. CONCLUSIONS: In patients with active ophthalmopathy, teprotumumab was more effective than placebo in reducing proptosis and the Clinical Activity Score. (Funded by River Vision Development and others; ClinicalTrials.gov number, NCT01868997 .).

In December 2019, a novel coronavirus (CoV) epidemic, caused by the severe acute respiratory syndrome coronavirus – 2 (SARS-CoV-2) emerged from China. This virus causes the coronavirus disease 2019 (COVID-19). Since then, there have been anecdotal reports of ocular infection. The ocular implications of human CoV infections have not been widely studied. However, CoVs have been known to cause various ocular infections in animals. Clinical entities such as conjunctivitis, anterior uveitis, retinitis, and optic neuritis have been documented in feline and murine models. In this article, the current evidence suggesting possible human CoV infection of ocular tissue is reviewed. The review article will also highlight animal CoVs and their associated ocular infections. We hope that this article will serve as a start for further research into the ocular implications of human CoV infections.

OBJECTIVES: To report on the causes of blindness certifications in England and Wales in working age adults (16-64 years) in 2009-2010; and to compare these with figures from 1999 to 2000. DESIGN: Analysis of the national database of blindness certificates of vision impairment (CVIs) received by the Certifications Office. SETTING AND PARTICIPANTS: Working age (16-64 years) population of England and Wales. MAIN OUTCOME MEASURES: Number and cause of blindness certifications. RESULTS: The Certifications Office received 1756 CVIs for blindness from persons aged between 16 and 64 inclusive between 1 April 2009 and 31 March 2010. The main causes of blindness certifications were hereditary retinal disorders (354 certifications comprising 20.2% of the total), diabetic retinopathy/maculopathy (253 persons, 14.4%) and optic atrophy (248 persons, 14.1%). Together, these three leading causes accounted for almost 50% of all blindness certifications. Between 1 April 1999 and 31 March 2000, the leading causes of blindness certification were diabetic retinopathy/maculopathy (17.7%), hereditary retinal disorders (15.8%) and optic atrophy (10.1%). CONCLUSIONS: For the first time in at least five decades, diabetic retinopathy/maculopathy is no longer the leading cause of certifiable blindness among working age adults in England and Wales, having been overtaken by inherited retinal disorders. This change may be related to factors including the introduction of nationwide diabetic retinopathy screening programmes in England and Wales and improved glycaemic control. Inherited retinal disease, now representing the commonest cause of certification in the working age population, has clinical and research implications, including with respect to the provision of care/resources in the NHS and the allocation of research funding.

BACKGROUND: Patients with noninfectious uveitis are at risk for long-term complications of uncontrolled inflammation, as well as for the adverse effects of long-term glucocorticoid therapy. We conducted a trial to assess the efficacy and safety of adalimumab as a glucocorticoid-sparing agent for the treatment of noninfectious uveitis. METHODS: This multinational phase 3 trial involved adults who had active noninfectious intermediate uveitis, posterior uveitis, or panuveitis despite having received prednisone treatment for 2 or more weeks. Investigators and patients were unaware of the study-group assignments. Patients were randomly assigned in a 1:1 ratio to receive adalimumab (a loading dose of 80 mg followed by a dose of 40 mg every 2 weeks) or matched placebo. All patients received a mandatory prednisone burst followed by tapering of prednisone over the course of 15 weeks. The primary efficacy end point was the time to treatment failure occurring at or after week 6. Treatment failure was a multicomponent outcome that was based on assessment of new inflammatory lesions, best corrected visual acuity, anterior chamber cell grade, and vitreous haze grade. Nine ranked secondary efficacy end points were assessed, and adverse events were reported. RESULTS: The median time to treatment failure was 24 weeks in the adalimumab group and 13 weeks in the placebo group. Among the 217 patients in the intention-to-treat population, those receiving adalimumab were less likely than those in the placebo group to have treatment failure (hazard ratio, 0.50; 95% confidence interval, 0.36 to 0.70; P<0.001). Outcomes with regard to three secondary end points (change in anterior chamber cell grade, change in vitreous haze grade, and change in best corrected visual acuity) were significantly better in the adalimumab group than in the placebo group. Adverse events and serious adverse events were reported more frequently among patients who received adalimumab (1052.4 vs. 971.7 adverse events and 28.8 vs. 13.6 serious adverse events per 100 person-years). CONCLUSIONS: In our trial, adalimumab was found to be associated with a lower risk of uveitic flare or visual impairment and with more adverse events and serious adverse events than was placebo. (Funded by AbbVie; VISUAL I ClinicalTrials.gov number, NCT01138657 .).

BACKGROUND/AIMS: Real-life anti-vascular endothelial growth factor (VEGF) therapy use in patients with wet age-related macular degeneration (wAMD) was assessed in a retrospective, observational study in Canada, France, Germany, Ireland, Italy, the Netherlands, UK and Venezuela. METHODS: Medical records of patients with wAMD, who started ranibizumab treatment between 1 January 2009 and 31 August 2009, were evaluated. Data were collected until the end of treatment and/or monitoring or until 31 August 2011. RESULTS: 2227 patients who received ≥1 anti-VEGF injection with a baseline visual acuity assessment and ≥1 postbaseline visual acuity assessment for the treated eye were evaluated. Visual acuity improved until about day 120; thereafter, visual acuity gains were not maintained. Mean change in visual acuity score from baseline to years 1 and 2 was +2.4 and +0.6 letters, respectively. Patients received a mean of 5.0 and 2.2 injections in the first and second year, respectively. There were substantial differences in visual outcomes and injection frequency between countries. More frequent visits and injections were associated with greater improvements in visual acuity. CONCLUSIONS: In clinical practice, fewer injections are administered than in clinical trials. Anti-VEGF treatment resulted in an initial improvement in visual acuity; however, this was not maintained over time. TRIAL REGISTRATION NUMBER: NCT01447043.

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.

The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating 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 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. Both guidelines were developed through a staged consensus process, involving a 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 on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The CONSORT-AI extension includes 14 new items, which 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 providing 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.

BACKGROUND: Primary angle-closure glaucoma is a leading cause of irreversible blindness worldwide. In early-stage disease, intraocular pressure is raised without visual loss. Because the crystalline lens has a major mechanistic role, lens extraction might be a useful initial treatment. METHODS: From Jan 8, 2009, to Dec 28, 2011, we enrolled patients from 30 hospital eye services in five countries. Randomisation was done by a web-based application. Patients were assigned to undergo clear-lens extraction or receive standard care with laser peripheral iridotomy and topical medical treatment. Eligible patients were aged 50 years or older, did not have cataracts, and had newly diagnosed primary angle closure with intraocular pressure 30 mm Hg or greater or primary angle-closure glaucoma. The co-primary endpoints were patient-reported health status, intraocular pressure, and incremental cost-effectiveness ratio per quality-adjusted life-year gained 36 months after treatment. Analysis was by intention to treat. This study is registered, number ISRCTN44464607. FINDINGS: Of 419 participants enrolled, 155 had primary angle closure and 263 primary angle-closure glaucoma. 208 were assigned to clear-lens extraction and 211 to standard care, of whom 351 (84%) had complete data on health status and 366 (87%) on intraocular pressure. The mean health status score (0·87 [SD 0·12]), assessed with the European Quality of Life-5 Dimensions questionnaire, was 0·052 higher (95% CI 0·015-0·088, p=0·005) and mean intraocular pressure (16·6 [SD 3·5] mm Hg) 1·18 mm Hg lower (95% CI -1·99 to -0·38, p=0·004) after clear-lens extraction than after standard care. The incremental cost-effectiveness ratio was £14 284 for initial lens extraction versus standard care. Irreversible loss of vision occurred in one participant who underwent clear-lens extraction and three who received standard care. No patients had serious adverse events. INTERPRETATION: Clear-lens extraction showed greater efficacy and was more cost-effective than laser peripheral iridotomy, and should be considered as an option for first-line treatment. FUNDING: Medical Research Council.