Data driven public health approaches for diabetic retinopathy and age-related macular degeneration

数据驱动的糖尿病视网膜病变和年龄相关性黄斑变性的公共卫生方法

• 批准号: MR/S003770/1
• 负责人: David Wright
• 金额: $ 35.57万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS003770%2F1
• 关键词: Data; driven; public; health; approaches

This fellowship will consist of data-driven projects building on the research strengths of the Centre for Public Health, QUB, focusing on use of electronic health records (EHR) and retinal imaging data to improve population eye health. Developing novel outcome measures for chronic eye disease:Management of the most common sight-threatening eye diseases in the UK, age-related macular degeneration (AMD) and glaucoma, requires regular monitoring and rapid treatment if disease progression accelerates. Changes in either ocular structures or visual function can signal that progression is occurring. There have been rapid advances in 3D retinal imaging technology, centred on a technique known as optical coherence tomography (OCT), that can resolve ocular structures in unprecedented detail. However, analytical methods to make full use of this information are lacking, especially when attempting to link structural and functional changes in the retina. The fellow will develop novel statistical methods to integrate a large retinal imaging dataset of AMD patients with measurements of visual function drawn from EHRs. This work will be conducted with QUB ophthalmologists and OCT experts (groups led by Prof Tunde Peto and Dr Ruth Hogg) and statisticians at City, University of London. The aim is to develop meaningful outcome measures of AMD progression for use in clinical trials of new treatments. Optimising diabetic retinopathy screening:Diabetic retinopathy (DR), one of the most common causes of sight loss among working-age people, occurs when high blood sugar damages blood vessels in the retina. Those at risk of DR are screened with retinal photographs taken at regular intervals. Images are manually graded for the presence of specific changes to identify those in need of treatment. The aim of this project is to explore the potential of integrating automated image analysis into the Northern Ireland DR screening programme to target treatment more effectively and reduce costs. New analytical approaches will be developed to fully exploit information contained within the programme's substantial screening libraries. A key challenge is predicting which patients will progress to sight-threatening DR in the short term. Treatment could be targeted towards this group, rather than towards the many patients that remain stable in the intermediate stages of the disease across multiple screenings. Accurate prediction of progression could also inform risk-based screening with longer screening intervals for stable patients, reducing the overall number of screening visits for the population and the associated costs.DR progression is difficult to predict using current methods. There may be subtle patterns of retinal changes predicting DR progression detectable only using automated approaches that can analyse data from thousands of patients simultaneously. The latest generation of machine learning techniques (deep learning algorithms) can almost match the ability of human graders to detect DR in retinal images. The next step will be to determine whether these techniques can be applied to predict progression of DR, leveraging the full set of information within image sequences. These will be drawn from the Northern Ireland DR screening programme databank (clinical lead, Prof Peto), a unique repository of retinal images for approximately 87,000 patients that has recently been centralised and linked backed as far as 2002. Working with Prof Peto and mathematicians at King's College London, the fellow will develop and apply the latest machine learning techniques to a large set of screening images to detect novel features predictive of DR progression. Northern Ireland is an ideal for this study as there is little migration among older people so patient outcomes can be monitored more easily than in other parts of the UK. Performance of the automated methods will be assessed along with the potential for improvements to the screening programme.

该奖学金将包括基于 QUB 公共卫生中心研究优势的数据驱动项目，重点是使用电子健康记录 (EHR) 和视网膜成像数据来改善人群的眼睛健康。制定针对慢性眼病的新结果措施：治疗英国最常见的威胁视力的眼病，即年龄相关性黄斑变性（AMD）和青光眼，需要定期监测，如果疾病进展加速，则需要快速治疗。眼部结构或视觉功能的变化可以表明正在发生进展。以光学相干断层扫描 (OCT) 技术为中心的 3D 视网膜成像技术取得了快速发展，该技术可以以前所未有的细节解析眼部结构。然而，缺乏充分利用这些信息的分析方法，特别是在试图将视网膜的结构和功能变化联系起来时。该研究员将开发新颖的统计方法，将 AMD 患者的大型视网膜成像数据集与 EHR 中的视觉功能测量值整合起来。这项工作将由 QUB 眼科医生和 OCT 专家（由 Tunde Peto 教授和 Ruth Hogg 博士领导的小组）以及伦敦大学城市大学的统计学家共同进行。目的是开发有意义的 AMD 进展结果测量，用于新疗法的临床试验。优化糖尿病视网膜病变筛查：糖尿病视网膜病变 (DR) 是工作年龄人群视力丧失的最常见原因之一，当高血糖损害视网膜血管时就会发生。通过定期拍摄视网膜照片对有 DR 风险的人进行筛查。根据特定变化的存在对图像进行手动分级，以识别需要治疗的人。该项目的目的是探索将自动图像分析整合到北爱尔兰 DR 筛查计划中的潜力，以更有效地确定治疗目标并降低成本。将开发新的分析方法，以充分利用该计划的大量筛选库中包含的信息。一个关键的挑战是预测哪些患者会在短期内发展为威胁视力的 DR。治疗可以针对这一群体，而不是针对在多次筛查中仍处于疾病中期阶段的许多患者。准确预测进展还可以为基于风险的筛查提供信息，对稳定患者延长筛查间隔，减少人群的筛查就诊总数和相关成本。使用现有方法很难预测 DR 进展。可能存在预测 DR 进展的视网膜变化的微妙模式，只有使用可以同时分析数千名患者数据的自动化方法才能检测到。最新一代的机器学习技术（深度学习算法）几乎可以与人类分级人员检测视网膜图像中 DR 的能力相媲美。下一步将是确定这些技术是否可以利用图像序列中的全套信息来预测 DR 的进展。这些数据将从北爱尔兰 DR 筛查计划数据库（临床领导者，Peto 教授）中提取，该数据库是一个独特的视网膜图像存储库，包含大约 87,000 名患者，最近已于 2002 年进行集中化和链接支持。与 Peto 教授和数学家合作伦敦国王学院的研究员将开发最新的机器学习技术并将其应用于大量筛查图像，以检测预测 DR 进展的新特征。北爱尔兰是这项研究的理想选择，因为老年人很少迁移，因此比英国其他地区更容易监测患者的治疗结果。将评估自动化方法的性能以及改进筛查计划的潜力。

项目成果

IMPACT OF RETINAL ISCHEMIA ON FUNCTIONAL AND ANATOMICAL OUTCOMES AFTER ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR THERAPY IN PATIENTS WITH RETINAL VEIN OCCLUSION

视网膜缺血对视网膜静脉阻塞患者接受抗血管内皮生长因子治疗后功能和解剖结果的影响

• DOI: http://dx.10.1097/iae.0000000000002571
• 发表时间: 2020
• 期刊: Retina
• 作者: Khayat M

Stress and cataract surgery: A nationwide study evaluating surgeon burnout.

压力与白内障手术：一项评估外科医生职业倦怠的全国性研究。

• DOI: http://dx.10.1177/11206721231154611
• 发表时间: 2023
• 期刊: European journal of ophthalmology
• 影响因子: 1.7
• 作者: Ansari AS

Comparison of Goldmann applanation and Ocular Response Analyser tonometry: intraocular pressure agreement and patient preference.

Goldmann 压平法和眼反应分析仪眼压计的比较：眼压一致性和患者偏好。

• DOI: http://dx.10.1038/s41433-019-0556-2
• 发表时间: 2020
• 期刊: Eye (London, England)
• 作者: McCann P

Diagnostic Accuracy of Spectral-Domain OCT Circumpapillary, Optic Nerve Head, and Macular Parameters in the Detection of Perimetric Glaucoma.

谱域 OCT 环乳头、视神经乳头和黄斑参数在视野青光眼检测中的诊断准确性。

• DOI: http://dx.10.1016/j.ogla.2019.06.003
• 发表时间: 2019
• 期刊: Ophthalmology. Glaucoma
• 作者: McCann P

Quantitative Parameters from OCT Angiography in Patients with Diabetic Retinopathy and in Those with Only Peripheral Retinopathy Compared with Control Participants.

与对照参与者相比，糖尿病视网膜病变患者和仅周边视网膜病变患者的 OCT 血管造影定量参数。

• DOI: http://dx.10.1016/j.xops.2021.100030
• 发表时间: 2021
• 期刊: Ophthalmology science
• 作者: Hogg RE