Development and Validation of Quantitative Anterior Segment OCT-based Methods to Evaluate Patients with Primary Angle Closure Disease

项目摘要 原发性闭角型青光眼（PACG）是原发性闭角型疾病（PACD）的最严重形式，是一种严重的青光眼。 全球永久性失明的主要原因。前房角镜检查，定性和主观角度评估 方法，是目前诊断PACD的临床标准，尽管其量化疾病严重程度的能力有限， 特别是在早期房角闭合的患者中。眼前节光学相干断层扫描（AS-OCT）是一种 定量和客观的成像为基础的方法来评估角度。但是，方法有限， 临床医生将AS-OCT应用于房角关闭患者的护理。K23职业生涯的主要目标 开发建议是：1）证明基于OCT的定量眼前节方法的益处 评估患有PACD的患者并基于AS-OCT测量开发PACD的分类模型; 以及2）为学术性青光眼专家提供培训和指导研究经验 进行独立的临床研究。实现这些目标将提供关键技能， 建立一个独立的研究计划，专注于应用AS-OCT成像， 提高对PACD患者的临床护理水平。拟议的K23应用程序将提供以下方面的额外培训 四个重要领域：1）慢性病的流行病学和机制; 2）疾病分类和预测 疾病风险; 3）临床研究的生物统计方法; 4）机器学习和自动化数据分析。 这项拟议中的研究将使用基于人口的数据，这些数据是NIH资助的美国华裔研究的一部分。 眼科研究（CHES），旨在比较AS-OCT和前房角镜对房角宽度的评估，并确定强度 这些评估与已知的PACD风险因素之间的关联。度之间的关系 通过AS-OCT测量的房角关闭和眼内压（IOP），房角关闭的后遗症， 青光眼的危险因素，将与CHES数据的特点，并与数据验证，通过使用一种新的 瞳孔控制系统对从USC Roski眼科研究所（USCREI）招募的PACD患者进行研究。分类 基于CHES的AS-OCT数据，将使用机器开发PACD分期和严重程度模型 学习算法，并用来自USCREI PACD患者的前瞻性数据进行验证。的结果 拟议的研究将为未来的纵向研究提供基础， 基于眼前节OCT的评估和分类模型，用于评估PACD患者并提供 对PACG风险较高的患者进行靶向治疗。我研究的最终目标是 基于定量成像的诊断和治疗方案，指导PACD的标准化护理 降低PACG及其相关眼部疾病的发病率。

PROJECT SUMMARY Primary angle closure glaucoma (PACG), the most severe form of primary angle closure disease (PACD), is a leading cause of permanent blindness worldwide. Gonioscopy, a qualitative and subjective angle assessment method, is the current clinical standard for diagnosing PACD despite its limited ability to quantify disease severity, especially in patients with early angle closure. Anterior segment optical coherence tomography (AS-OCT) is a quantitative and objective imaging-based method for assessing the angle. However, there are limited methods for clinicians to apply AS-OCT to the care of angle closure patients. The primary objectives of this K23 career development proposal are: 1) to demonstrate the benefit of a quantitative anterior segment OCT-based approach to evaluating patients with PACD and develop classification models for PACD based on AS-OCT measurements; and 2) to provide an academic glaucoma specialist with the training and mentored research experience necessary to conduct independent clinical research. Achieving these objectives will provide critical skills and experiences necessary to establish an independent research program focused on applying AS-OCT imaging to improve the clinical care of patients with PACD. The proposed K23 application will provide additional training in four vital areas: 1) epidemiology and mechanisms of chronic disease; 2) classification of disease and prediction of disease risk; 3) biostatistical methods for clinical research; 4) machine learning and automated data analysis. The proposed research will use population-based data collected as part of the NIH-funded Chinese American Eye Study (CHES) to compare AS-OCT and gonioscopic assessments of angle width and determine the strength of association between these assessments and known PACD risk factors. The relationship between the degree of angle closure, measured by AS-OCT, and intraocular pressure (IOP), a sequela of angle closure and strong risk factor for glaucoma, will be characterized with CHES data and validated with data obtained by using a novel pupil control system on PACD patients recruited from the USC Roski Eye Institute (USCREI). Classification models for PACD stage and severity based on AS-OCT data from CHES will be developed using machine learning algorithms and validated with prospective data from USCREI patients with PACD. The results of the proposed research will provide the foundation for a future longitudinal study examining the benefit of using anterior segment OCT-based assessments and classification models to evaluate PACD patients and deliver targeted treatment to patients with higher risk for PACG. The ultimate goal of my research is to develop quantitative imaging-based diagnostic and treatment protocols that guide the standardized care of PACD patients and decrease the incidence of PACG and its associated ocular morbidity.