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

开发晚期年龄相关性黄斑变性的预测模型

• 批准号: 8161990
• 负责人: Chung-Jung Chiu
• 金额: $ 39.5万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8161990
• 关键词: Accounting; Age related macular degeneration; Budgets; Castor; Characteristics; Clinic; Clinical; Cohort Studies; Data; Data Set; Development; Disease; Disease Progression; Early identification; Elderly; Epidemiology; Eye; Eye diseases; Figs - dietary; Florida; Gills; Guide prevention; Health Care Costs; Impairment; Individual; Lead; Legal Blindness; Logistic Regressions; Medicare; Meta-Analysis; Methodology; Methods; Modeling; Ophthalmic examination and evaluation; Ophthalmology; Patients; Performance; Persons; Probability; Procedures; Progressive Disease; Prophylactic treatment; Publications; Quality of life; ROC Curve; Recording of previous events; Regression Analysis; Research; Research Personnel; Resources; Risk; Risk Factors; Sensitivity and Specificity; Staging; Study models; System; Techniques; Time; Validation; Vision; Visual impairment; age related; base; clinical decision-making; clinically relevant; cohort; cost; follow-up; hazard; high risk; indexing; interest; meetings; modifiable risk; outcome forecast; statistics; tool; treatment planning

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.

描述（由申请人提供）：年龄相关性黄斑变性（AMD）是一种进行性疾病，其晚期形式占美国法定失明的50%以上。由于晚期AMD造成的视力损害也显著降低了生活质量，并消耗了很大一部分医疗保险预算。减少AMD进展的可改变的危险因素可以带来显著的临床益处并节省医疗保健费用。然而，早期识别和密切随访发展为晚期AMD的高风险患者是必不可少的，以允许实施策略，以延缓疾病进展到视力受损的阶段。最近，PI （Chiu）利用年龄相关眼病研究（AREDS）数据集建立了晚期AMD的预测模型（c-index=0.877）。然而，该AREDS模型在蓝山眼研究（Blue Mountains Eye Study， BMES）队列中的验证分析表明，需要使用多个队列的数据来建立具有最大泛化性的预测模型。我们的目标是利用患者病史和临床眼科检查提供的风险因素信息，开发一种广泛适用的工具，用于早期预测晚期AMD。采用PI （Chiu）先前出版物的扩展方法，并汇集了来自四个主要队列的15,000多人的数据，包括AREDS队列（基线时n=4,757人，随访8年的数据将被使用），Beaver Dam眼科研究（BDES）队列（基线时n= 4,926人，随访15年的数据将被使用），BMES队列（基线时n= 3,654人，随访10年的数据将被使用）和墨尔本视障项目（VIP）队列（基线时n= 3,271人，随访10年的数据将被使用）。我们将使用随访5年的数据)，我们将使用逻辑回归来通过8个基线人口统计学（n=5）和眼部（n=3）预测因子来模拟发生晚期AMD的结果特异性似然比。利用独立模型准则（QIC）统计量下的拟似然来确定最佳模型。接下来，从该回归分析中得出的复合评分系统（C分）将分别应用于四个队列，以评估准确性，并通过Kaplan-Meier估计和Cox比例风险回归来描述随访期间（最长15年）不同时间（最长15年）C分高级AMD风险的关系。我们的C评分系统将增强我们延缓AMD从早期发展到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，在视力损害表现出现之前，它将有助于研究人员在降低成本的同时增加研究能力，并有助于政策制定者分配医疗保险资源。