Experience of Anti-VEGF Treatment and Clinical Levels of Depression and Anxiety in Patients With Wet Age-Related Macular Degeneration

PURPOSE: To investigate detailed patient experiences specific to receiving vascular endothelial growth factor inhibitors (anti-VEGF) for wet age-related macular degeneration (wAMD), and to acquire a snapshot of the frequency of clinically significant levels of depression, anxiety, and posttraumatic stress among patients and levels of burden in patients’ carers. DESIGN: Observational cross-sectional mixed-methods study. METHODS: Three hundred patients with wAMD receiving anti-VEGF treatment and 100 patient carers were recruited. Qualitative data on patients’ experience of treatment were collected using a structured survey. Standardized validated questionnaires were used to quantify clinically significant levels of anxiety, depression, and posttraumatic stress, as well as cognitive function and carers’ burden. RESULTS: Qualitative data showed that 56% of patients (n =132) reported anxiety related to antiVEGF treatment. The main sources of anxiety were fear of going blind owing to intravitreal injections and concerns about treatment effectiveness, rather than around pain. From validated questionnaires, 17% of patients (n= 52) showed clinical levels of anxiety and 12% (n =36) showed clinical levels of depression. Depression levels, but not anxiety, were significantly higher in patients who received up to 3 injections compared with patients who received from 4 to 12 injections (analysis of variance [ANOVA] P = .027) and compared with patients who received more than 12 injections (ANOVA P = .001). CONCLUSIONS: Anti-VEGF treatment is often experienced with some anxiety related to treatment, regardless of the number of injections received. Clinical levels of depression seem to be more frequent in patients at early stages of anti-VEGF treatment. Strategies to improve patient experience of treatment and minimize morbidity are suggested

Macular telangiectasia type 2 - Visual acuity, disease endstage and the MacTel Area. MacTel Project Report No. 8

Purpose: To report the visual acuity measures from the MacTel registry study and to investigate and describe phenotypic findings in eyes with substantial vision loss due to MacTel type 2 Design: Cross-sectional multi-center study. Subjects: Participants of the Natural History Observation (and Registry) of MacTel Study. Methods: Best–corrected visual acuity (BCVA) data, retinal imaging data and clinical data were accessed from the MacTel study databases in May 2019. Main Outcome Measures: Frequency distribution of BCVA and its relation to age. Morphological changes in eyes with very late disease stages, defined by a BCVA ≤ 20/200. Average retinal thickness of ETDRS fields on OCT. Dimensions of the area affected by MacTel (MacTel area). Results: BCVA was ≤20/50 in 37.3% and ≤20/200 in 3.8% of 4449 eyes of 2248 patients. 18.4% and 0.7% of all patients had bilateral BCVA ≤20/50 and ≤20/200, respectively. There was an asymmetry between right and left eyes (median BCVA 71 versus 74 letters), a finding supported by more advanced morphological changes in right eyes. BCVA correlated with participant’s age, but the effect size was small. If a neovascularization or macular hole was present, bilateral occurrence was frequent (33% or 17%, respectively), and BCVA was >20/200 (79% or 78% respectively) or ≥20/50 (26% or 13%, respectively). Eyes with advanced disease (BCVA ≤20/200) showed the following characteristics: 1) Atrophy of the foveal photoreceptor layer with or without associated subretinal fibrosis; 2) an affected area, termed here the “MacTel area”, limited to a horizontal diameter not exceeding the distance between the temporal optic disc margin and foveal center, and the vertical diameter not exceeding approximately 0.85 times this distance. Exceptions were eyes with large active or inactive neovascular membranes; 3) reduced retinal thickness measures within the MacTel area; and 4) less frequent retinal greying and more frequent hyperpigmentations compared to eyes with better BCVA. Conclusions: Severe vision loss is rare in MacTel and is related to photoreceptor atrophy in most people. Results indicate disease asymmetry with slightly worse vision and more advanced disease manifestation in right eyes. MacTel-related neurodegeneration does not spread beyond the limits of the “MacTel area”

Progression of Retinopathy Secondary to Maternally Inherited Diabetes and Deafness – Evaluation of Predicting Parameters

PURPOSE: To investigate the prognostic value of demographic, functional, and imaging parameters on retinal pigment epithelium (RPE) atrophy progression secondary to Maternally Inherited Diabetes and Deafness (MIDD) and to evaluate the application of these factors in clinical trial design. DESIGN: Retrospective observational case series. METHODS: Thirty-five eyes of 20 patients (age range, 24.9-75.9 years) with genetically proven MIDD and demarcated RPE atrophy on serial fundus autofluorescence (AF) images were included. Lesion size and shape-descriptive parameters were longitudinally determined by two independent readers. A linear mixed effect model was used to predict the lesion enlargement rate based on baseline variables. Sample size calculations were performed to model the power in a simulated interventional study. RESULTS: The mean follow-up time was 4.27 years. The mean progression rate of RPE atrophy was 2.33 mm2/year revealing a dependence on baseline lesion size (+0.04 [0.02-0.07] mm2/year/mm2, p<0.001), which was absent after square root transformation. The fovea was preserved in the majority of patients during the observation time. In the case of foveal involvement, the loss of visual acuity lagged behind central RPE atrophy in AF images. Sex, age, and number of atrophic foci predicted future progression rates with a cross-validated mean absolute error of 0.13 mm/year and to reduce the required sample size for simulated interventional trials. CONCLUSIONS: Progressive RPE atrophy could be traced in all eyes using AF imaging. Shape-descriptive factors and patients' baseline characteristics had significant prognostic value, guiding appropriate subject selection and sample size in future interventional trial design

Evaluation of training, patient and practitioner perspectives on community‐based monitoring of patients with stable age‐related macular degeneration compared to hospital‐based care: The FENETRE study report no. 1

Purpose Describe the development, delivery, acceptability and evaluation of a modular training programme for community-based, non-medical practitioners monitoring patients with quiescent neovascular age related macular degeneration (QnAMD). Also, report on a qualitative process evaluation conducted during the pilot phase of a randomised control trial (the FENETRE Study) exploring patient and practitioner acceptability of community-based QnAMD care relative to hospital-based care. Methods Learning outcomes from The College of Optometrists’ Medical Retina higher qualifications and the Royal College of Ophthalmologists’ Common Clinical Competency Framework were used to develop a competency framework for QnAMD care. Training was delivered online, comprising six asynchronous lectures followed by two synchronous case-based discussion webinars, with an accredited assessment of 24 case vignettes. An anonymous evaluation survey was conducted with the first two FENETRE cohorts (n = 38). Separately, we undertook a qualitative process evaluation, sampling purposively in four hospitals and five community-based practices, interviewing nine patients and eight practitioners. Results Survey responses (n = 26) showed community optometrists were very satisfied (n = 12; 46%) or satisfied (n = 14; 54%) with the training; feedback reflected by qualitative process evaluation data. Overall, optometrists also felt either confident (n = 15; 58%) or very confident (n = 8; 31%) in conducting AMD monitoring appointments following training, a finding also corroborated by interview data from optometrists participating in the initial pilot phase roll-out. Optometrists identified patient convenience and alleviating pressures in hospital care as the primary reasons for acceptability of community pathways. Data from patients entering community practices suggested they largely found this at least as safe and convenient as hospital care, although some patients randomised to hospital care perceived that as safer. Conclusion This pilot study has shown the development and implementation of a collaborative community monitoring model is feasible, with satisfaction from community optometrists for training and accreditation, and broad acceptance for the pathway by both patients and practitioners

Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

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

Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10

Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity. Main Outcome Measures: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes. Results: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience

Deep learning has huge potential to transform healthcare. However, significant expertise is required to train such models and this is a significant blocker for their translation into clinical practice. In this study, we therefore sought to evaluate the use of automated deep learning software to develop medical image diagnostic classifiers by healthcare professionals with limited coding – and no deep learning – expertise. We used five publicly available open-source datasets: (i) retinal fundus images (MESSIDOR); (ii) optical coherence tomography (OCT) images (Guangzhou Medical University/Shiley Eye Institute, Version 3); (iii) images of skin lesions (Human against Machine (HAM)10000) and (iv) both paediatric and adult chest X-ray (CXR) images (Guangzhou Medical University/Shiley Eye Institute, Version 3 and the National Institute of Health (NIH)14 dataset respectively) to separately feed into a neural architecture search framework that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we performed external validation using the Edinburgh Dermofit Library dataset. Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (range: sensitivity of 73.3-97.0%, specificity of 67-100% and AUPRC of 0.87-1). In the multiple classification tasks, the diagnostic properties ranged from 38-100% for sensitivity and 67-100% for specificity. The discriminative performance in terms of AUPRC ranged from 0.57 to 1 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0.47, with a sensitivity of 49% and a positive predictive value of 52%. The quality of the open-access datasets used in this study (including the lack of information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitation of this study. All models, except for the automated deep learning model trained on the multi-label classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The availability of automated deep learning may become a cornerstone for the democratization of sophisticated algorithmic modelling in healthcare as it allows the derivation of classification models without requiring a deep understanding of the mathematical, statistical and programming principles. Future studies should compare several application programming interfaces on thoroughly curated datasets

Methods in Molecular Biology

Developmental processes are inherently dynamic and understanding them requires quantitative measurements of gene and protein expression levels in space and time. While live imaging is a powerful approach for obtaining such data, it is still a challenge to apply it over long periods of time to large tissues, such as the embryonic spinal cord in mouse and chick. Nevertheless, dynamics of gene expression and signaling activity patterns in this organ can be studied by collecting tissue sections at different developmental stages. In combination with immunohistochemistry, this allows for measuring the levels of multiple developmental regulators in a quantitative manner with high spatiotemporal resolution. The mean protein expression levels over time, as well as embryo-to-embryo variability can be analyzed. A key aspect of the approach is the ability to compare protein levels across different samples. This requires a number of considerations in sample preparation, imaging and data analysis. Here we present a protocol for obtaining time course data of dorsoventral expression patterns from mouse and chick neural tube in the first 3 days of neural tube development. The described workflow starts from embryo dissection and ends with a processed dataset. Software scripts for data analysis are included. The protocol is adaptable and instructions that allow the user to modify different steps are provided. Thus, the procedure can be altered for analysis of time-lapse images and applied to systems other than the neural tube

Association of ambient air pollution with age-related macular degeneration and retinal thickness in UK Biobank

AIM: To examine the associations of air pollution with both self-reported age-related macular degeneration (AMD), and in vivo measures of retinal sublayer thicknesses. METHODS: We included 115 954 UK Biobank participants aged 40-69 years old in this cross-sectional study. Ambient air pollution measures included particulate matter, nitrogen dioxide (NO2) and nitrogen oxides (NOx). Participants with self-reported ocular conditions, high refractive error ( +6 diopters) and poor spectral-domain optical coherence tomography (SD-OCT) image were excluded. Self-reported AMD was used to identify overt disease. SD-OCT imaging derived photoreceptor sublayer thickness and retinal pigment epithelium (RPE) layer thickness were used as structural biomarkers of AMD for 52 602 participants. We examined the associations of ambient air pollution with self-reported AMD and both photoreceptor sublayers and RPE layer thicknesses. RESULTS: After adjusting for covariates, people who were exposed to higher fine ambient particulate matter with an aerodynamic diameter <2.5 µm (PM2.5, per IQR increase) had higher odds of self-reported AMD (OR=1.08, p=0.036), thinner photoreceptor synaptic region (β=-0.16 µm, p=2.0 × 10-5), thicker photoreceptor inner segment layer (β=0.04 µm, p=0.001) and thinner RPE (β=-0.13 µm, p=0.002). Higher levels of PM2.5 absorbance and NO2 were associated with thicker photoreceptor inner and outer segment layers, and a thinner RPE layer. Higher levels of PM10 (PM with an aerodynamic diameter <10 µm) was associated with thicker photoreceptor outer segment and thinner RPE, while higher exposure to NOx was associated with thinner photoreceptor synaptic region. CONCLUSION: Greater exposure to PM2.5 was associated with self-reported AMD, while PM2.5, PM2.5 absorbance, PM10, NO2 and NOx were all associated with differences in retinal layer thickness