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To investigate the therapeutic effect of intravitreal pegcetacoplan on the inhibition of photoreceptor (PR) loss and thinning in geographic atrophy (GA) on conventional spectral-domain OCT (SD-OCT) imaging by deep learning-based automated PR quantification.Post hoc analysis of a prospective, multicenter, randomized, sham (SM)-controlled, masked phase II trial investigating the safety and efficacy of pegcetacoplan for the treatment of GA because of age-related macular degeneration.Study eyes of 246 patients, randomized 1:1:1 to monthly (AM), bimonthly (AEOM), and SM treatment.We performed fully automated, deep learning-based segmentation of retinal pigment epithelium (RPE) loss and PR thickness on SD-OCT volumes acquired at baseline and months 2, 6, and 12. The difference in the change of PR loss area was compared among the treatment arms. Change in PR thickness adjacent to the GA borders and the entire 20° scanning area was compared between treatment arms.Square-root transformed PR loss area in μm or mm, PR thickness in μm, and PR loss/RPE loss ratio.A total of 31 556 B-scans of 644 SD-OCT volumes of 161 study eyes (AM 52, AEOM 54, SM 56) were evaluated from baseline to month 12. Comparison of the mean change in PR loss area revealed statistically significantly less growth in the AM group at months 2, 6, and 12 than in the SM group (-41 μm ± 219 vs. 77 μm ± 126; P = 0.0004; -5 μm ± 221 vs. 156 μm ± 139; P0.0001; 106 μm ± 400 vs. 283 μm ± 226; P = 0.0014). Photoreceptor thinning was significantly reduced under AM treatment compared with SM within the GA junctional zone, as well as throughout the 20° area. A trend toward greater inhibition of PR loss than RPE loss was observed under therapy.Distinct and reliable quantification of PR loss using deep learning-based algorithms offers an essential tool to evaluate therapeutic efficacy in slowing disease progression. Photoreceptor loss and thinning are reduced by intravitreal complement C3 inhibition. Automated quantification of PR loss/maintenance based on OCT images is an ideal approach to reliably monitor disease activity and therapeutic efficacy in GA management in clinical routine and regulatory trials.

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

Preservation of photoreceptors beyond areas of retinal pigment epithelium atrophy is a critical treatment goal in eyes with geographic atrophy (GA) to prevent vision loss. Thus, we assessed the association of treatment with the complement C3 inhibitor pegcetacoplan with optical coherence tomography (OCT)-based photoreceptor laminae thicknesses in this post hoc analysis of the FILLY trial (NCT02503332).Retinal layers in OCT were segmented using a deep-learning-based pipeline and extracted along evenly spaced contour-lines surrounding areas of GA. The primary outcome measure was change from baseline in (standardized) outer nuclear layer (ONL) thickness at the 5.16°-contour-line at month 12.Participants treated with pegcetacoplan monthly had a thicker ONL along the 5.16° contour-line compared to the pooled sham arm (mean difference [95% CI] +0.29 z-score units [0.16, 0.42],PThese findings suggest that pegcetacoplan could slow GA progression and lead to a lesser thinning of photoreceptor layers beyond the GA boundary. Future trials in earlier disease stages, i.e., intermediate AMD, aiming to slow photoreceptor degeneration warrant consideration.

The delivery of integrated care requires the establishment of effective professional relationships that foster collaborative working across health systems. Evidence for how to prepare practitioners to work in those settings is limited. By exploring an innovative postgraduate Programme for Integrated Child Health (PICH) this article highlights the conditions by which effective collaboration can be encouraged. Our qualitative evaluation of PICH involved one-to-one semi-structured interviews with 23 postgraduate general practice and paediatric trainees and their mentors. We analysed the data using the concept of the ‘third space’, where multiple discourses between individuals with diverse professional backgrounds occur, enabling creative exploration of tensions inherent in new ways of working in order to identify enablers and barriers to collaboration. Our analysis identified three themes that enabled collaboration: effective communication, boundary work and educational spaces; and four themes that were barriers: traditional hierarchical professional identities, curriculum design, financial systems and workplace spaces. PICH demonstrated the value of educational spaces and their role in enabling collaborative practice, as participants explored their professional identities and those of other disciplines. Structural factors in the workplace which inhibit collaborative practice were also evident. We conclude by proposing a model for collaborative learning in third spaces based upon the recognition that, while educational programmes alone will not lead to change, they have the potential to inform the development of productive workplace spaces that will be required if collaborative practice in healthcare is to become a reality.

Objective: Predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in geographic atrophyDesign: Post-hoc analysis of data from a clinical trial and routine clinical care.Methods: Automated segmentation of OCT scans from 476 eyes (325 patients) with geographic atrophy. Machine learning modelling of resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under both standard luminance (VA) and low luminance (LLVA) conditions.Main Outcome Measure: Correlation coefficient (R2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters.Results: Best-corrected VA under both standard luminance (R2 0.46 MAE 10.2 ETDRS letters) and low-luminance conditions (R2 0.25 MAE 12.1) could be predicted. The foveal region contributed the most (46.5%) toward model performance, with retinal pigment epithelium loss and outer retinal atrophy contributing the most (31.1%). For LLVA, however, features in the non-foveal regions were most important (74.5%), led by photoreceptor degeneration (38.9%).Conclusions: Our method of automatic qOCT segmentation demonstrates functional significance for vision in geographic atrophy, including LLVA. LLVA is itself predictive of geographic atrophy progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.

Introduction: Clinical trials involving patients with Alzheimer’s disease (AD) continue to try to identify disease-modifying treatments. Although trials are designed to meet regulatory and registration requirements, many do not measure outcomes of the disease most relevant to key stakeholders. Methods: A systematic review sought research that elicited information from people with AD, their caregivers, and health-care professionals on which outcomes of the disease were important. Studies published in any language between 2008 and 2017 were included. Results: Participants in 34 studies described 32 outcomes of AD. These included clinical (memory, mental health), practical (ability to undertake activities of daily living, access to health information), and personal (desire for patient autonomy, maintenance of identity) outcomes of the disease. Discussion: Evidence elicited directly from the people most affected by AD reveals a range of disease outcomes that are relevant to them but are not commonly captured in clinical trials of new treatments.

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests.

Alzheimer's disease (AD) research, treatment, and prevention focus increasingly on developing personalized interventions based on personal genetic, biological, phenotypic data, for early intervention (EI) to limit harm. This approach has much to recommend it, but important ethical and philosophical challenges follow that should be considered, which we analyze here. We argue that advancing understanding of the causes of AD undermines the clarity of the distinction between primary and secondary prevention. This makes it increasingly unclear how primary and secondary categories can be appealed to as the basis for making judgements about what interventions are permissible, and for distinguishing between acceptably vs unacceptably early points in life to intervene. Timely efforts at prevention are vital for limiting harm from AD and given the logic of EI is that, in presence of risk, earlier is better, one might assume that earliest is best. This may or may not be the case; however, the permissibility of intervening in different ways at different stages of life is complex and turns on numerous contextual factors. We consider the particular ethical implications of intervening at different points in the life course, presenting a valuable resource for negotiating clinical and policy implications of EI in AD.

Embodiment is typically given insufficient weight in debates concerning the moral status of Novel Synthetic Beings (NSBs) such as sentient or sapient Artificial Intelligences (AIs). Discussion usually turns on whether AIs are conscious or self-aware, but this does not exhaust what is morally relevant. Since moral agency encompasses what a being wants to do, the means by which it enacts choices in the world is a feature of such agency. In determining the moral status of NSBs and our obligations to them, therefore, we must consider how their corporeality shapes their options, preferences, values, and is constitutive of their moral universe. Analysing AI embodiment and the coupling between cognition and world, the paper shows why determination of moral status is only sensible in terms of the whole being, rather than mental sophistication alone, and why failure to do this leads to an impoverished account of our obligations to such NSBs.

Early intervention (EI) aims to identify children or families at risk of poor health, and take preventative measures at an early stage, when intervention is more likely to succeed. EI is concerned with the just distribution of "life chances," so that all children are given fair opportunity to realize their potential and lead a good life; EI policy design, therefore, invokes ethical questions about the balance of responsibilities between the state, society, and individuals in addressing inequalities. We analyze a corpus of EI policy guidance to investigate explicit and implicit ethical arguments about who should be held morally responsible for safeguarding child health and well-being. We examine the implications of these claims and explore what it would mean to put the proposed policies into practice. We conclude with some remarks about the useful role that philosophical analysis can play in EI policy development.

We thank the authors of the open peer commentaries (OPCs) for their thoughtful and illuminating contribution to the debate. There were lots of topics that we wanted to explore in our target article...

Objectives We present a national evaluation of the impact of independent verification visits (IVVs) performed by National Health Service (NHS) England as part of quality assuring medical revalidation. Organisational visits are central to NHS quality assurance. They are costly, yet little empirical research evidence exists concerning their impact, and what does exist is conflicting. Setting The focus was on healthcare providers in the NHS (in secondary care) and private sector across England, who were designated bodies (DBs). DBs are healthcare organisations that have a statutory responsibility, via the lead clinician, the responsible officer (RO), to implement medical revalidation. Participants All ROs who had undergone an IVV in England in 2014 and 2015 were invited to participate. 46 ROs were interviewed. Ethnographic data were gathered at 18 observations of the IVVs and 20 IVV post visit reports underwent documentary analysis. Primary and secondary outcome measures Primary outcomes were the findings pertaining to the effectiveness of the IVV system in supporting the revalidation processes at the DBs. Secondary outcomes were methodological, relating to the Model for Understanding Success in Quality (MUSIQ) and how its application to the IVV reveals the relevance of contextual factors described in the model. Results The impact of the IVVs varied by DB according to three major themes: the personal context of the RO; the organisational context of the DB; and the visit and its impact. ROs were largely satisfied with visits which raised the status of appraisal within their organisations. Inadequate or untimely feedback was associated with dissatisfaction. Conclusions Influencing teams whose prime responsibility is establishing processes and evaluating progress was crucial for internal quality improvement. Visits acted as a nudge, generating internal quality review, which was reinforced by visit teams with relevant expertise. Diverse team membership, knowledge transfer and timely feedback made visits more impactful.

Summary The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. Video Abstract

This paper shows how critical realism can be used to integrate empirical data and philosophical analysis within ‘empirical bioethics’. The term empirical bioethics, whilst appearing oxymoronic, simply refers to an interdisciplinary approach to the resolution of practical ethical issues within the biological and life sciences, integrating social scientific, empirical data with philosophical analysis. It seeks to achieve a balanced form of ethical deliberation that is both logically rigorous and sensitive to context, to generate normative conclusions that are practically applicable to the problem, challenge, or dilemma. Since it incorporates both philosophical and social scientific components, empirical bioethics is a field that is consistent with the use of critical realism as a research methodology. The integration of philosophical and social scientific approaches to ethics has been beset with difficulties, not least because of the irreducibly normative, rather than descriptive, nature of ethical analysis and the contested relation between fact and value. However, given that facts about states of affairs inform potential courses of action and their consequences, there is a need to overcome these difficulties and successfully integrate data with theory. Previous approaches have been formulated to overcome obstacles in combining philosophical and social scientific perspectives in bioethical analysis; however each has shortcomings. As a mature interdisciplinary approach critical realism is well suited to empirical bioethics, although it has hitherto not been widely used. Here I show how it can be applied to this kind of research and explain how it represents an improvement on previous approaches.

• For patients and families an accurate, timely diagnosis is key to understanding the situation in which they find themselves.