Leading causes of certifiable visual loss in England and Wales during the year ending 31 March 2013

Quartilho, Ana; Simkiss, Philippa; Zekite, Antra; Xing, Wen; Wormald, Richard; Bunce, Catey

doi:10.1038/eye.2015.288

Cited by 118 publications

(92 citation statements)

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“…Thus, the model of having a central pool of ECLO services to cover multiple adjacent geographical areas should be considered in other UK countries and may improve the overall efficiency and consistency of the service. A vast majority (94%) of patients seen by ECLOs were of White British origin, bearing in mind that 87.1% of the UK population is of White ethnic background (national census 2011) and that 41.6% of referrals to an ECLO were for AMD, which is significantly more common in patients of White ethnic background, and this is consistent with a recent UK study . On the other hand, it may also be possible that cultural and language barriers may affect access to ECLO services, with a bias towards the White population.…”

Section: Discussionsupporting

confidence: 81%

“…Patients' data were collected using the AMD, which is significantly more common in patients of White ethnic background, and this is consistent with a recent UK study. 5 On the other hand, it may also be possible that cultural and language barriers may affect access to ECLO services, with a bias towards the White population. Furthermore, the level of home support may vary amongst patients of different ethnic backgrounds, which may in turn influence the need for ECLO support.…”

Section: Discussionmentioning

confidence: 99%

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Eye clinic liaison officers service in the United Kingdom

Papastefanou

Kang

Simkiss

et al. 2019

Health Planning & Management

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Summary Background To investigate the role of eye clinic liaison officers (ECLOs) in the United Kingdom and analyse patients' demographics and services provided. Methods This is a retrospective observational study. Data were collected from the Royal National Institute of Blind People for ECLOs in Wales, Scotland, Northern Ireland and England for the first quarter of 2015. Statistical analysis was performed using chi‐square and t test as appropriate. Results Trusts with ECLOs support vary greatly in the UK regions. Only one‐third of NHS trusts in England have an ECLO service. Over 4000 patients were assessed. The majority of patients were of White ethnic background (94%), lived alone (37%), had no carers (58%) and were in their 80s (29.5%). The principal ocular conditions causing sight loss and certification were age‐related macular degeneration (41.6%) and glaucoma (18.1%). Approximately 70% of patients are first seen at 13 to 18 months from diagnosis. Conclusions ECLO services vary in the UK regions. England has the lowest ECLO availability per trust and the majority of those assessed were of White British origin with AMD. There are significant delays from diagnosis to the first visit indicating the need for improved services. Further studies are necessary to develop the evidence base for the expansion and funding of ECLO services.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Eye clinic liaison officers service in the United Kingdom

Papastefanou

Kang

Simkiss

et al. 2019

Health Planning & Management

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“…2 Open-angle glaucoma (OAG) affects > 2% of those aged > 49 years and at least 5% of those aged > 80 years; its prevalence can be as high as 13% in black adults aged > 80 years. 3 OAG affects approximately 500,000 people in England and Wales.…”

Section: Trial Registrationmentioning

confidence: 99%

Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study

Garway-Heath

Zhu

Qian

et al. 2018

Health Technol Assess

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Background Progressive optic nerve damage in glaucoma results in vision loss, quantifiable with visual field (VF) testing. VF measurements are, however, highly variable, making identification of worsening vision (‘progression’) challenging. Glaucomatous optic nerve damage can also be measured with imaging techniques such as optical coherence tomography (OCT). Objective To compare statistical methods that combine VF and OCT data with VF-only methods to establish whether or not these allow (1) more rapid identification of glaucoma progression and (2) shorter or smaller clinical trials. Design Method ‘hit rate’ (related to sensitivity) was evaluated in subsets of the United Kingdom Glaucoma Treatment Study (UKGTS) and specificity was evaluated in 72 stable glaucoma patients who had 11 VF and OCT tests within 3 months (the RAPID data set). The reference progression detection method was based on Guided Progression Analysis™ (GPA) Software (Carl Zeiss Meditec Inc., Dublin, CA, USA). Index methods were based on previously described approaches [Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement (ANSWERS), Permutation analyses Of Pointwise Linear Regression (PoPLR) and structure-guided ANSWERS (sANSWERS)] or newly developed methods based on Permutation Test (PERM), multivariate hierarchical models with multiple imputation for censored values (MaHMIC) and multivariate generalised estimating equations with multiple imputation for censored values (MaGIC). Setting Ten university and general ophthalmology units (UKGTS) and a single university ophthalmology unit (RAPID). Participants UKGTS participants were newly diagnosed glaucoma patients randomised to intraocular pressure-lowering drops or placebo. RAPID participants had glaucomatous VF loss, were on treatment and were clinically stable. Interventions 24-2 VF tests with the Humphrey Field Analyzer and optic nerve imaging with time-domain (TD) Stratus OCT™ (Carl Zeiss Meditec Inc., Dublin, CA, USA). Main outcome measures Criterion hit rate and specificity, time to progression, future VF prediction error, proportion progressing in UKGTS treatment groups, hazard ratios (HRs) and study sample size. Results Criterion specificity was 95% for all tests; the hit rate was 22.2% for GPA, 41.6% for PoPLR, 53.8% for ANSWERS and 61.3% for sANSWERS (all comparisons p ≤ 0.042). Mean survival time (weeks) was 93.6 for GPA, 82.5 for PoPLR, 72.0 for ANSWERS and 69.1 for sANSWERS. The median prediction errors (decibels) when the initial trend was used to predict the final VF were 3.8 (5th to 95th percentile 1.7 to 7.6) for PoPLR, 3.0 (5th to 95th percentile 1.5 to 5.7) for ANSWERS and 2.3 (5th to 95th percentile 1.3 to 4.5) for sANSWERS. HRs were 0.57 [95% confidence interval (CI) 0.34 to 0.90; p = 0.016] for GPA, 0.59 (95% CI 0.42 to 0.83; p = 0.002) for PoPLR, 0.76 (95% CI 0.56 to 1.02; p = 0.065) for ANSWERS and 0.70 (95% CI 0.53 to 0.93; p = 0.012) for sANSWERS. Sample size estimates were not reduced using methods including OCT data. PERM hit rates were between 8.3% and 17.4%. Treatment effects were non-significant in MaHMIC and MaGIC analyses; statistical significance was altered little by incorporating imaging. Limitations TD OCT is less precise than current imaging technology; current OCT technology would likely perform better. The size of the RAPID data set limited the precision of criterion specificity estimates. Conclusions The sANSWERS method combining VF and OCT data had a higher hit rate and identified progression more quickly than the reference and other VF-only methods, and produced more accurate estimates of the progression rate, but did not increase treatment effect statistical significance. Similar studies with current OCT technology need to be undertaken and the statistical methods need refinement. Trial registration Current Controlled Trials ISRCTN96423140. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 4. See the NIHR Journals Library website for further project information. Data analysed in the study were from the UKGTS. Funding for the UKGTS was provided through an unrestricted investigator-initiated research grant from Pfizer Inc. (New York, NY, USA), with supplementary funding from the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Imaging equipment loans were made by Heidelberg Engineering, Carl Zeiss Meditec and Optovue (Fremont, CA, USA). Pfizer, Heidelberg Engineering, Carl Zeiss Meditec and Optovue had no input into the design, conduct, analysis or reporting of any of the UKGTS findings or this work. The sponsor for both the UKGTS and RAPID data collection was Moorfields Eye Hospital NHS Foundation Trust. David F Garway-Heath, Tuan-Anh Ho and Haogang Zhu are partly funded by the NIHR Biomedical Research Centre based at Moorfields Eye Hospital and UCL Institute of Ophthalmology. David F Garway-Heath’s chair at University College London (UCL) is supported by funding from the International Glaucoma Association.

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“…Training strategies Three training strategies were investigated for model training ( Figure S4). (1) We created a 'Multi-layer perceptron' ('MLP') model by taking an pre-trained Inception-v3 model on ImageNet 55 (an image database of > 14 million natural images with corresponding labels; see Glossary), then training it on the AREDS dataset as a multi-layer perceptron model (with two densely-connected layers of 256 and 128 units, respectively).…”

Section: Appendixmentioning

confidence: 99%

DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs

et al. 2019

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Purpose: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score.Design: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP.Participants: DeepSeeNet was trained on 58,402 and tested on 900 images from the longitudinal follow-up of 4,549 participants from AREDS. Gold standard labels were obtained using reading center grades.Methods: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. Main Outcome Measures: Overall accuracy, specificity, sensitivity, Cohen's kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. Results: DeepSeeNet performed better on patient-based classification (accuracy = 0.671; kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). Conclusions: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/ DeepSeeNet.

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Leading causes of certifiable visual loss in England and Wales during the year ending 31 March 2013

Abstract: Purpose The last article on causes of sight impairment (SI) in England

Cited by 118 publications

References 5 publications

Eye clinic liaison officers service in the United Kingdom

Eye clinic liaison officers service in the United Kingdom

Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study

DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs

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