Development of a prediction model for advanced age-related macular degeneration

描述（由申请人提供）：视网膜相关性黄斑变性（AMD）是一种进行性疾病，其晚期形式占美国法律的失明的50%以上。由于晚期AMD导致的视力损害也显著降低了生活质量，并消耗了医疗保险预算的很大一部分。减少AMD进展的可改变的风险因素可能会带来显着的临床益处并节省医疗保健成本。然而，早期识别和密切随访处于发展晚期AMD的高风险的患者对于允许实施策略以延迟疾病进展到视力受损的阶段至关重要。最近，使用AMD相关眼病研究（AREDS）数据集PI（Chiu）开发了晚期AMD的预测模型（c-index = 0.877）。然而，在蓝山眼科研究（BMES）队列中对该AREDS模型的验证分析表明，有必要使用来自多个队列的数据来开发具有最大概括性的预测模型。我们的目标是利用患者病史和临床眼科检查中提供的危险因素信息，开发一种广泛适用的早期预测晚期AMD的工具。使用PI（Chiu）以前出版物的扩展方法和来自四个主要队列（包括AREDS队列）的15，000多人的汇总数据（基线时n = 4，757;将使用8年随访数据），比弗坝眼科研究（BDES）队列（基线时n = 4，926;将使用随访15年的数据），BMES队列（基线时n = 3，654;将使用10年随访的数据）和墨尔本视力障碍项目（VIP）队列（基线时n = 3，271;将使用5年随访数据），我们将使用逻辑回归，通过8个基线人口统计学（n = 5）和眼部（n = 3）预测因子，对进展为晚期AMD的结果特异性似然比进行建模。将使用独立模型准则（QIC）统计下的准似然确定最佳模型。接下来，将在四个队列中分别应用从该回归分析中得出的复合评分系统（C评分），以评价准确性，并通过Kaplan-Meier估计值和使用Andersen-Gill估计值的考克斯比例风险回归描述随访期间（长达15年）不同时间C评分-晚期AMD风险的关系。我们的C评分系统将增强我们延迟AMD从早期阶段进展为临床相关疾病的能力。它将有助于临床医生与患者沟通，并在疾病的早期阶段指导预防和治疗计划，从而大大提前视力受损的表现，有助于研究人员增加研究能力，同时降低成本，并有助于政策制定者分配医疗保险资源。 公共卫生相关性：本项目的目的是利用眼科诊所提供的信息，开发一个预测晚期AMD的实用评分系统。该系统将使眼科医生能够采取早期预防措施并启动治疗计划，以帮助他们的患者降低患上这种致盲疾病的风险。它还有助于研究人员提高研究能力，同时降低成本，并为决策者分配医疗保险资源。

DESCRIPTION (provided by applicant): Age-related macular degeneration (AMD) is a progressive disease, the advanced forms of which account for over 50% of legal blindness in the US. Vision impairment due to advanced AMD also significantly reduces quality of life and consumes a large portion of Medicare budget. Diminishing the modifiable risk factors for the progression of AMD could lead to significant clinical benefit and save health care cost. However, early identification and close follow-up of patients at high risk of developing advanced AMD are essential to allow for implementing strategies to delay progression of the disease to stages when vision is compromised. Recently, using the Age-Related Eye Disease Study (AREDS) dataset PI (Chiu) developed a prediction model for advanced AMD (c-index=0.877). However, validation analysis of this AREDS model in the Blue Mountains Eye Study (BMES) cohort indicated that it is necessary to use data from multiple cohorts to develop a prediction model with maximal generalizability. Our objective is to use risk factor information provided in the patient history and clinical eye examinations to develop a widely applicable tool for the early prediction of advanced AMD. Using methods extended from PI's (Chiu's) previous publications and pooled data of over 15,000 persons from four major cohorts, including the AREDS cohort (n=4,757 at baseline; data followed for 8 y will be used), the Beaver Dam Eye Study (BDES) cohort (n= 4,926 at baseline; data followed for 15 y will be used), the BMES cohort (n= 3,654 at baseline; data followed for 10 y will be used), and the Melbourne Visual Impairment Project (VIP) cohort (n= 3,271 at baseline; data followed for 5 y will be used), we will use logistic regression to model result-specific likelihood ratios of developing advanced AMD by 8 baseline demographic (n=5) and ocular (n=3) predictors. The quasi-likelihood under the independence model criterion (QIC) statistic will be used to determine the best model. Next, a composite scoring system (C score) derived from this regression analysis will be applied in the four cohorts individually to evaluate the accuracy and to depict the relationship of C score-advanced AMD risk at various times during follow-up (up to 15 y) by Kaplan-Meier estimators and Cox proportional-hazards regression using the Andersen-Gill estimators. Our C scoring system will enhance our ability to delay progress of AMD from early stages to clinically relevant diseases. It will be useful to clinicians for communicating with patients and guiding prevention and treatment plans at very early stages of disease well in advance of vision- compromising manifestations, to researchers for increasing study power while reducing cost, and to policymakers for allocating Medicare resources. PUBLIC HEALTH RELEVANCE: The objective of this project is to use accessible information from ophthalmic clinics to develop a practical scoring system for the prediction of advanced AMD. This system will enable eye doctors to take early prevention measures and initiate treatment plans to help their patients reduce risk of developing this blinding disease. It is also useful to researchers for increasing study power while reducing cost, and to policymakers for allocating Medicare resources.