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

• 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
• 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

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 该项目的主要目标是整合运筹学和系统的原理和方法。 利用NEI资助的临床试验的数据进行工程设计，以开发个性化护理的创新方法 开角型青光眼(OAG)和高眼压(OHTN)患者预防可避免的失明 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)产生对 从OHTN进展到OAG和较轻到更严重的疾病状态的可能性，(B) 确定监测青光眼进展的特定诊断试验的最佳时间 患者，(C)确定OAG造成不可逆性视力丧失的最高风险者(即“快速进展者”)，以及 (D)生成推荐的眼压目标治疗目标。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)和单个患者过去和当前视野检查的测试结果， 眼压测量和光学相干断层扫描生成青光眼进展的个性化预测 动力学。我们小组之前的工作表明，我们开发的初步预测工具可以 准确识别OAG进展的实例更快57%(p=0.02)，效率更高29%(p&lt；0.0001)， 与目前许多患者每隔1年进行一次固定的患者评估和 测试。在这项提案中，我们希望在几个方面大大增强预测工具。在目标1中，我们将 使用来自OHTS试验的数据开发、参数化、校准和验证高级工具，以预测 OHTN患者会发展成OAG和进展到OAG的时间。虽然我们复杂的国家 空间卡尔曼滤波方法似乎在中到重度OAG患者中表现得非常好 为此，我们计划应用这一方法学来研究OHTN患者的疾病进展动力学。 使用来自OHTS的数据。在目标2中，我们计划将输入到预测工具的数据扩展到 眼压测量和视野测量现在也包括结构测试的数据，包括光学相干 体层摄影术。此外，这一目标建议扩展该工具的输出，以包括个性化预测 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，我们还计划预测并以图形显示患者可能的OAG进展轨迹 给出不同可能的眼压控制水平的菜单，帮助眼科护理提供者和患者选择 治疗应该有多积极。通过实现这项建议的目标，我们希望开发一种 高级预测工具，将为临床医生和患者提供个性化、动态更新、实时 每只眼睛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。

项目成果

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
• 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

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

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

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。

• DOI: 10.1136/bjophthalmol-2021-320283
• 发表时间: 2023-04
• 期刊: BRITISH JOURNAL OF OPHTHALMOLOGY
• 影响因子: 4.1
• 作者: Li, Tingyang;Reddy, Aparna;Stein, Joshua D.;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.