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: 8318584
• 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/8318584
• 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

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.

项目摘要 背景视网膜相关性黄斑变性（AMD）是一种进行性疾病， 这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 这大大降低了生活质量，并消耗了医疗保险预算的很大一部分。减少了具有 AMD进展的可改变的风险因素可能导致显著的临床获益和节省 保健费用。然而，对高风险患者的早期识别和密切随访 晚期AMD对于实施延缓疾病进展的策略至关重要， 视力受损的阶段。最近，使用眼动相关眼病研究（AREDS） 数据集PI（Chiu）开发了晚期AMD的预测模型（c-index=0.877）。但是，验证 在蓝山眼科研究（BMES）队列中对该AREDS模型的分析表明， 需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。 目的利用病史和临床资料中提供的危险因素信息， 检查开发一个广泛适用的工具，早期预测先进的AMD。 方法采用PI（Chiu）以前发表的方法， 来自四个主要队列的15，000人，包括AREDS队列（基线时n= 4，757;数据如下 将使用8年）、比弗坝眼科研究（BDES）队列（基线时n= 4，926;数据随访 15年）、BMES队列（基线时n= 3，654;将使用10年随访数据）和 墨尔本视力损害项目（VIP）队列（基线时n= 3，271;随访5年的数据将 使用），我们将使用逻辑回归模型的结果特定的似然比发展先进的 根据8个基线人口统计学（n=5）和眼部（n=3）预测因素确定AMD。下的拟似然 独立模型标准（QIC）统计将用于确定最佳模型。接下来，一个复合 将在四个队列中应用从该回归分析得出的评分系统（C评分） 分别评估准确性和描述C评分与晚期AMD风险的关系， 根据Kaplan-Meier估计值和考克斯比例风险，在随访期间的不同时间（长达15年） 回归使用Andersen-Gill估计。 潜在的影响我们的C评分系统将提高我们的能力，延缓AMD的进展， 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沟通 并在疾病的早期阶段指导预防和治疗计划， 妥协的表现，研究人员增加研究能力，同时降低成本， 政策制定者分配医疗资源。