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

项目摘要 背景年龄相关性黄斑变性(AMD)是一种进行性疾病，是一种进展性疾病。 这些人占美国法律盲人的50%以上。进展期AMD引起的视力障碍也 极大地降低了生活质量，并消耗了很大一部分医疗保险预算。逐渐减少 AMD进展的可改变的危险因素可能导致显著的临床益处和节省 医疗保健费用。然而，早期识别和密切随访高危患者 晚期AMD对于实施延缓疾病进展的策略是必不可少的 视力受损的阶段。最近，使用年龄相关眼病研究(AREDS) 数据集Pi(Chiu)建立了晚期AMD的预测模型(c-index=0.877)。但是，验证 在蓝山眼科研究(BMES)队列中对这个AREDS模型的分析表明，它是 有必要使用来自多个队列的数据来开发具有最大泛化能力的预测模型。 目的我们的目标是利用患者病史和临床眼睛中提供的危险因素信息。 旨在开发一种广泛适用的工具对晚期AMD进行早期预测。 方法使用从Pi(Chiu‘s)以前的文献中推广的方法和收集的数据 来自四个主要队列的15,000人，包括AREDS队列(基线时n=4,757人；数据如下 将使用8年)，海狸大坝眼科研究(BDES)队列(n=4,926人基线；数据跟踪 15年将使用)，BMES队列(n=3654，基线；将使用10年跟踪的数据)，以及 墨尔本视障项目(VIP)队列(n=3,271个基线；5年跟踪数据为 )，我们将使用Logistic回归对正在开发的晚期 AMD由8个基线人口学指标(n=5)和眼科指标(n=3)预测。模型下的拟似然 独立性模型准则(QIC)统计量将被用来确定最佳模型。接下来，一个复合体 由回归分析得出的评分系统(C分数)将应用于四个队列 单独评估准确性并描述C评分与晚期AMD风险的关系 由Kaplan-Meier估计者和Cox比例危险因素进行的不同时间的随访(最长15年) 使用Andersen-Gill估计量进行回归。 潜在影响我们的C评分系统将增强我们推迟AMD进展的能力 临床相关疾病的早期阶段。这将有助于临床医生与患者进行交流 并在疾病的早期阶段及早指导预防和治疗计划- 在降低成本的同时增加研究能力的研究人员，以及 政策制定者分配医疗保险资源。

PROJECT ABSTRACT Background 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. Objective 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. Methods 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. Potential implications 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.