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Detection of Disease Progression in Advanced Glaucoma

晚期青光眼疾病进展的检测

基本信息

批准号：
9888147
负责人：
Kouros Nouri-Mahdavi
金额：
$ 40.15万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/9888147
关键词：
Affect Age Algorithm Design Algorithmic Software Axon Bayesian Analysis Bayesian Modeling Blindness Clinical Complex Computer software Data Decision Making Detection Deterioration Development Diagnostic Disease Disease Progression Early Diagnosis Enrollment Ethnic Origin Eye Floor Future Gender Glaucoma Goals Image Joints Lead Linear Regressions Measurement Measures Methods Modeling Monitor Noise Optic Disk Optical Coherence Tomography Patients Pattern Performance Positioning Attribute Probability Quality of life Reproducibility Residual state Retina Retinal Ganglion Cells Retinal macula Severities Severity of illness Signal Transduction Software Design Structure Structure-Activity Relationship Testing Thick Time Vision Visit Visual Visual Fields advanced disease age related base central visual field clinical application clinical encounter cohort disability experience follow-up functional loss functional outcomes high risk improved inclusion criteria longitudinal analysis macula meetings nonlinear regression novel patient population predictive modeling prospective rate of change retinal nerve fiber layer software development time use tool

项目摘要

A pressing unmet need in the field of glaucoma diagnostics is to find methods for objective detection of disease worsening or prediction of visual field (VF) progression in eyes with advanced disease. Eyes with advanced glaucoma are at high risk of losing the remaining vision and blindness. Retinal nerve fiber layer (RNFL) and optic nerve head measures reach their measurement floor as glaucoma progresses beyond the early stages. Hence, functional assessment of the central VF is currently the main tool for monitoring advanced glaucoma. Our central hypothesis is that assessment of the macular retinal ganglion cell (RGC)/axonal complex can lead to improved detection or prediction of disease progression since the last RGCs to disappear in glaucoma reside in the central retina (the macula). We will test this hypothesis in a cohort of glaucoma subjects just reaching 5 years of follow-up and validate our methods in separate cohorts of glaucoma and normal subjects. Aim 1. Are macular thickness measures able to detect change earlier and with a stronger signal compared to RNFL measures in advanced glaucoma? We will measure progression rates for global and local macular and RNFL measures within a Bayesian hierarchical framework. We will compare progression rates and the proportion of progressing eyes/regions/sectors for macular and RNFL measures to normal eyes and account for differing scales, age-related decay, and treatment. Aim 2A. Can macular OCT thickness changes confirm and predict changes in central VFs for advanced glaucoma? We will estimate longitudinal/temporal structure-function relationships with Bayesian joint hierarchical longitudinal modeling of macular OCT and central 10° VF measures. These models will determine whether there is a contemporaneous or lagged deterioration of OCT and VF. We will assess the influence of baseline disease severity, treatment and other covariates on these joint longitudinal models. We will also compare the joint macular/central VF models to joint models of RNFL and 24° VFs and develop functional prediction models from 1 to 4 years ahead. Aim 2B. To validate the performance of prediction models, we will initiate a second prospectively enrolled cohort of patients meeting similar inclusion criteria and matched to the original cohort by age, gender, ethnicity and baseline glaucoma severity. We will compare VF point predictions (e.g., one- or two-visit step ahead) to the observed VF data. Aim 3. Develop software for combining macular structural and functional data in real time as a clinical tool for detection or prediction of progression. It will provide clinicians with structural/functional rates of change and structural ‘step’ changes from baseline, and the probability and distribution of predicted functional changes The information provided by the application can be used during a clinical encounter to make decisions regarding ongoing management of glaucoma. Widespread real-time use of our software will result in significant improvements in disease monitoring and timely treatment of progressive glaucoma through advanced stages and will help reduce visual disability from glaucoma.

在青光眼诊断领域，一个亟待满足的需求是为青光眼的客观检测找到新的方法。进展期视力障碍(VF)和进展性眼部疾病的病情恶化或严重程度的预测指标。晚期青光眼患者面临失去剩余视力和失明的高风险。视网膜神经纤维层。随着青光眼的进展，美国国家橄榄球联盟(RNFL)和视神经中心的措施将达到他们的最低测量标准。早期阶段。因此，欧洲中央银行VF的功能评估体系目前仍是全球先进银行监管的主要工具。青光眼。我们的中心神经假说是对黄斑和视网膜中央神经节细胞(RGC)/轴突的评估。自上一批RGC似乎消失以来，复杂的技术可能会导致对疾病进展的更好的检测和预测。在青光眼中，我们不会在青光眼的一个队列中检验这一假说。受试者刚刚达到5年的随访期，并将在青光眼和青光眼的不同队列中验证我们的方法。正常受试者。他们的目标是：1.黄斑厚度的测量方法能够更早地发现黄斑厚度的变化，并与更强的黄斑厚度保持一致。与RNFL在晚期青光眼中所采取的措施相比，信号如何？我们将不会衡量全球癌症和青光眼的进展和比率。局部黄斑部和RNFL的测量是在贝叶斯和分层的医疗框架内进行的。我们将不会比较它们的进展。黄斑部和RNFL的眼球/区域/扇区进展的比率和措施有助于恢复正常的眼球。并考虑到不同的黄斑分级，以及与年龄相关的黄斑腐烂、疼痛和治疗。他们的目标是2A.它可以改变黄斑的10月厚度。变化是否能证实和预测VFS治疗晚期青光眼的中央动脉系统的变化？我们将不会估计。纵向/时态结构-功能关系与模型的贝叶斯-贝叶斯-联合-分层-纵向建模模型黄斑中心10°VF和中央10°VF的测量。这两个模型将不能决定是否有一个新的同期。或者滞后于10月1日和11月的VF恶化速度。我们将不会评估基线、疾病严重程度和治疗的影响。和其他协变量依赖于这些联合的和纵向的VF模型。我们也将不会比较两个联合的黄斑/中央VF。模型包括RNFL和VFS的联合模型，并将开发从1岁到4岁的功能预测模型。未来。我们的目标是2B。为了更好地验证各种预测模型的最佳性能，我们还将前瞻性地启动第二次评估。登记了符合相似纳入标准的患者的队列，并按年龄、性别和年龄匹配的患者加入了第一个原始队列。种族和青光眼严重程度的基线。我们将不会比较VF和点预测(例如，一次或两次)的步骤。 3.开发将黄斑结构数据和功能数据相结合的软件平台。在未来，它将作为一种重要的临床辅助工具，用于肿瘤的检测和进展的预测。它还将为临床医生提供更好的治疗方案。结构/功能变化率和结构变化率是从基线开始的，结构/功能变化率是从基准开始变化的，结构/功能变化率是变动率的最大概率。应用程序提供的信息将在一次测试期间无法正常使用，因此应用程序提供的信息可能会发生功能变化。临床医生需要就青光眼正在进行的药物治疗做出新的决定。广泛和实时的药物使用。我们的医疗软件系统将使我们的疾病监测系统和进展性疾病的及时治疗系统得到显著改善。青光眼经历了两个进展期，这将有助于减少青光眼导致的视力障碍。