Personalized Forecasting of Disease Trajectory for Patients with Open Angle Glaucoma

项目摘要 本计画的主要目标是整合运筹学与系统的原理与方法 利用NEI资助的临床试验数据，开发一种创新方法， 开角型青光眼（OAG）和高眼压症（OHTN）患者的治疗，以预防可避免的失明 和视力丧失。所寻求的结果将通过以下方式帮助临床医生：（a）产生个性化的预测， 从OHTN进展为OAG和从较不严重进展为更晚期疾病状态的概率，（B） 确定特定诊断测试的最佳时机，以监测每个患者的昏迷进展， 患者，（c）识别那些处于OAG不可逆视力丧失的最高风险的患者（即，“快速进展者”），以及 (d)产生眼内压（IOP）的推荐目标治疗目标。为了实现这些目标， 该项目整合了NEI资助的临床试验对青光眼进展轨迹的理解 （包括OHTS、CIGTS和AGIS）与个体患者过去和当前的视野检查结果， 眼压测量和光学相干断层扫描，以生成青光眼进展的个性化预测 动力学我们小组先前的工作表明，我们开发的初步预测工具可以 准确识别OAG进展的情况快57%（p=0.02），效率高29%（p<0.0001）， 与目前许多患者的固定1年患者评估间隔的实践相比， 试验.在这项提案中，我们希望从几个方面大大加强预测工具。在目标1中， 使用OHTS试验的数据开发、参数化、校准和验证先进工具，以预测 患有OHTN的患者将发展成OAG以及发展成OAG的时间。虽然我们先进的国家 空间卡尔曼滤波方法似乎对中度至重度OAG患者表现非常好， 为此，我们计划应用这种方法来研究OHTN患者的疾病进展动力学 使用OHTS的数据。在目标2中，我们计划将预测工具的输入扩展到来自 眼压测量和视野测量现在还包括来自结构测试的数据，包括光学相干性 断层扫描此外，该目标还建议扩大该工具的输出，以包括个性化的预测 其中患者将成为快速进展者，允许临床医生在视力不可逆转地丧失之前进行干预。 在这个目标中，我们还计划预测和图形显示患者可能的OAG进展轨迹 给出不同的可能的IOP控制水平的菜单，帮助眼科护理提供者和患者选择 治疗应该有多积极通过实现这一建议的目标，我们希望制定一个 先进的预测工具，将为临床医生和患者提供个性化的，动态更新的，真实的 每只眼睛的OAG进展动态的时间预测，这将大大有助于减少可避免的 OAG导致的视力丧失和失明。

Project Summary The primary objective of this project is to integrate principles and methods of operations research and systems engineering with data from NEI-funded clinical trials to develop an innovative approach to personalize the care of patients with open-angle glaucoma (OAG) and ocular hypertension (OHTN) to prevent avoidable blindness and vision loss. The outcomes sought will assist clinicians by (a) producing personalized forecasts of the probability of progressing from OHTN to OAG and less severe to more advanced disease states, (b) determining the optimal timing of specific diagnostic tests to monitor for glaucomatous progression for each patient, (c) identifying those at highest risk for irreversible vision loss from OAG (i.e., “fast progressors”), and (d) generating recommended target treatment goals of intraocular pressure (IOP). To achieve these objectives, this project integrates an understanding of glaucoma progression trajectory from NEI-funded clinical trials (including OHTS, CIGTS, and AGIS) with an individual patient's past and current test results from perimetry, tonometry and optical coherence tomography to generate personalized forecasts of glaucoma progression dynamics. Prior work by our group has shown that a preliminary forecasting tool we developed could accurately identify instances of OAG progression 57% sooner (p=0.02) and 29% more efficiently (p<0.0001), compared with the current practice for many patients of fixed 1-year intervals for patient assessment and testing. In this proposal, we look to greatly enhance the forecasting tool in several ways. In Aim 1 we will develop, parameterize, calibrate, and validate an advanced tool using data from the OHTS trial, to forecast if a patient with OHTN will develop OAG and the timing of progression to OAG. While our sophisticated state space Kalman filtering methodology appears to perform very well on patients with moderate to severe OAG, in this aim we plan to apply this methodology to study disease progression dynamics for patients with OHTN using data from OHTS. In Aim 2 we plan to extend the inputs to the forecasting tool beyond data from tonometry and perimetry to now also include data from structural testing including optical coherence tomography. In addition, this aim proposes to expand the output of the tool to include personalized predictions of which patients will become fast progressors, allowing clinicians to intervene before vision is irreversibly lost. In this aim, we also plan to forecast and graphically display the patient's likely OAG progression trajectory given a menu of different possible levels of IOP control, aiding the eye-care provider and patient in choosing how aggressive the treatment should be. By fulfilling the aims of this proposal, we hope to develop an advanced forecasting tool that will provide clinicians and patients with personalized, dynamically-updated, real time forecasts of OAG progression dynamics for each eye, which will greatly aid with decreasing avoidable vision loss and blindness from OAG.