Personalized Forecasting of Disease Trajectory for Patients with Open Angle Glaucoma

开角型青光眼患者疾病轨迹的个性化预测

基本信息

批准号：
10004619
负责人：
Mariel Sofia Lavieri-Williams
金额：
$ 56.52万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10004619
关键词：
Address Affect American Blindness Brain Clinical Clinical Data Clinical Trials Data Decision Aid Development Diagnostic tests Disease Disease Progression Engineering Eye Foundations Funding Future Glaucoma Goals Government Intervention Studies Knowledge Mathematics Measurement Medical Methodology Methods Mission Modeling Monitor National Eye Institute Nature Nerve Fibers Ocular Hypertension Open-Angle Glaucoma Operations Research Operative Surgical Procedures Ophthalmologist Optic Nerve Optical Coherence Tomography Optometrist Outcome Output Patient Care Patients Patterns of Care Perimetry Physiologic Intraocular Pressure Prevalence Probability Process Provider Public Health Randomized Controlled Trials Recommendation Research Risk Science Severities Statistical Methods Structure Suggestion System Techniques Test Result Testing Therapeutic Time Tissues Update Vision Work advanced disease aggressive therapy base care providers data modeling experience high risk hypertension treatment improved individual patient innovation overtreatment patient subsets personalized care personalized predictions preservation prevent retinal nerve fiber layer structured data targeted treatment tonometry tool treatment trial unnecessary treatment

项目摘要

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.

项目摘要该项目的主要目的是整合操作研究和系统的原理和方法通过NEI资助的临床试验的数据进行工程技术，以开发一种创新的方法来个性化护理有开角青光眼（OAG）和眼高血压（OHTN）的患者以防止避免失明和视力丧失。结果感将通过（a）生产个性化森林来帮助临床医生从OHTN到OAG的概率以及更严重到更晚期疾病状态的概率，（B）确定特定诊断测试的最佳时机，以监视每种测试的青光眼进展（c）确定OAG不可逆视力丧失风险最高的人（即“快速进步者”）和（d）产生建议的眼内压（IOP）的目标治疗目标。为了实现这些目标，该项目集成了对NEI资助的临床试验的青光眼进展轨迹的理解（包括OHTS，CIGT和AGIS），具有个体患者的过去和当前测试结果， Tonometry和光学相干断层扫描，生成青光眼进展的个性化森林动力学。我们小组的先前工作表明，我们开发的初步预测工具准确地确定OAG进展的实例，效率提高57％（p = 0.02）和29％（p <0.0001），，与许多固定1年间隔的患者进行患者评估的患者相比测试。在此提案中，我们希望通过多种方式增强预测工具。在目标1中，我们将使用OHTS试验数据开发，参数化，校准和验证高级工具，以预测患有OHTN的患者将发展为OAG和进展到OAG的时间。而我们先进的状态太空卡尔曼过滤方法似乎在中度至重度OAG的患者中表现良好，我们计划将此方法应用于研究OHTN患者的疾病进展动力学使用来自OHTS的数据。在AIM 2中，我们计划将输入扩展到预测工具，超出数据分解和现在的周期还包括结构测试的数据，包括光学连贯性断层扫描。此外，该针对扩大工具输出的建议以包括个性化预测其中患者将成为快速的进步者，使临床医生能够在视力不可逆地丢失之前进行干预。在此目标中，我们还计划预测和图形显示患者的OAG进展轨迹给定一个可能的IOP控制级别的菜单，协助眼神提供者和患者选择治疗应该多么积极。通过履行该提议的目标，我们希望开发一个高级预测工具，将为临床医生和患者提供个性化的，动态的，真实的每只眼睛的OAG进展动力学的时间森林，这将极大地有助于减少可避免的 OAG的视力丧失和失明。

项目成果

期刊论文数量（42）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic.

DOI：
10.1016/j.ogla.2020.08.006
发表时间：
2021-01
期刊：
Ophthalmology. Glaucoma
影响因子：
0
作者：
Stagg BC;Stein JD;Medeiros FA;Wirostko B;Crandall A;Hartnett ME;Cummins M;Morris A;Hess R;Kawamoto K
通讯作者：
Kawamoto K

Mapping standard ophthalmic outcome sets to metrics currently reported in eight eye hospitals.

DOI：
10.1186/s12886-017-0667-0
发表时间：
2017-12-29
期刊：
BMC ophthalmology
影响因子：
2
作者：
Michelotti M;de Korne DF;Weizer JS;Lee PP;Flanagan D;Kelly SP;Odergren A;Sandhu SS;Wai C;Klazinga N;Haripriya A;Stein JD;Hingorani M
通讯作者：
Hingorani M

Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

Nallasamy 公式的评估：一种用于白内障手术屈光预测的堆叠集成机器学习方法。

DOI：
10.1136/bjophthalmol-2021-320599
发表时间：
2023-08
期刊：
BRITISH JOURNAL OF OPHTHALMOLOGY
影响因子：
4.1
作者：
Li, Tingyang;Stein, Joshua;Nallasamy, Nambi
通讯作者：
Nallasamy, Nambi

Reduced Mammography Screening for Breast Cancer among Women with Visual Impairment.

DOI：
10.1016/j.ophtha.2020.07.029
发表时间：
2021-02
期刊：
OPHTHALMOLOGY
影响因子：
13.7
作者：
Wu, Annie M.;Morse, Alan R.;Seiple, William H.;Talwar, Nidhi;Hansen, Sean O.;Lee, Paul P.;Stein, Joshua D.
通讯作者：
Stein, Joshua D.

Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.

通过人工智能驱动的术后晶状体位置预测提高了光线追踪人工晶状体计算性能。