Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma

青光眼精准管理的多模式健康信息技术创新

• 批准号: 10260459
• 负责人: Sally Liu Baxter
• 金额: $ 39.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10260459
• 关键词: Accounting; Address; Adherence; Affect; African American; Aging; All of Us Research Program; Area; Award; Big Data; Blindness; Blood Pressure; Blood Vessels; Chronic; Chronic Disease; Clinical; Clinical Research; Counseling; Data; Data Science; Data Set; Department chair; Development; Devices; Disease; Disease Management; Disease Progression; Early Diagnosis; Early treatment; Electronic Health Record; Electronics; Ensure; Exhibits; Eye; Eye diseases; Eyedrops; Fellowship; Foundations; Functional disorder; Future; Glaucoma; Home; Home Blood Pressure Monitoring; Human Resources; Hypertension; Image; Individual; Informatics; Institutes; Institution; Intervention; Investigation; Latino; Lead; Leadership; Machine Learning; Measurement; Measures; Mentors; Methods; Modeling; Monitor; Morbidity - disease rate; Nerve Degeneration; Operative Surgical Procedures; Ophthalmologist; Ophthalmology; Optic Nerve; Outcome; Participant; Patient Care; Patient Self-Report; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Physical activity; Pilot Projects; Population; Predictive Analytics; Predictive Value; Public Health; Public Health Informatics; Quality of life; Research; Resources; Risk; Role; Sleep; Symptoms; Techniques; Technology; Testing; Therapeutic; Time; Track and Field; Training; United States National Institutes of Health; Vision; Visual Fields; Work; base; biomedical informatics; blood pressure regulation; circadian regulation; clinical phenotype; clinical practice; cohort; comorbidity; cost; data integration; early onset; electronic data; experience; faculty community; flexibility; health information technology; improved; innovation; medication compliance; multidisciplinary; multimodality; new therapeutic target; novel; novel therapeutic intervention; patient engagement; personalized management; precision medicine; predictive modeling; professor; programs; racial minority; risk stratification; sensor; sensor technology; smart watch; treatment adherence; wearable device

PROJECT SUMMARY/ABSTRACT Glaucoma is the world's leading cause of irreversible blindness and will affect >110 million people by 2040. Early detection and treatment are critical, as symptoms typically do not present until the disease is advanced. A data-driven precision medicine approach is needed to better identify individuals who are at greatest risk of developing the disease and who are at greatest risk of progressing quickly to vision loss. While there has been considerable progress in eye imaging and testing to improve glaucoma monitoring, precision management of glaucoma is incomplete without accounting for patients' co-existing systemic conditions, concurrent systemic medications and treatments, and adherence with prescribed glaucoma treatment. Understanding how systemic conditions, and specifically vascular conditions such as hypertension, impact glaucoma presents growing public health importance given the increasing co-morbidities facing aging populations. Preliminary studies have demonstrated the predictive value of systemic data, even without ophthalmic endpoints. Similarly, measuring medication adherence is important for guiding patient counseling and engagement and avoiding downstream interventions such as surgeries, which carry high cost and morbidity. These factors are important for providing a more comprehensive perspective of glaucoma management and for improving patient outcomes, yet they are relatively understudied. I propose applying multi-modal advancements in health information technology (IT) to address these gaps and achieve the following specific aims: (1) Develop machine learning-based predictive models classifying patients at risk for glaucoma progression using systemic electronic health record (EHR) data from a diverse nationwide patient cohort; (2) evaluate how integrating blood pressure (BP) data from novel smartwatch-based home BP monitors enhance predictive models for risk stratification in glaucoma, and (3) measure glaucoma medication adherence using innovative flexible electronic sensors to validate their use for future interventions aimed at improving adherence and clinical outcomes in glaucoma. These studies would leverage state- of-the-art methods in big-data predictive modeling as well as cutting-edge advancements in sensor technologies. This multi-faceted approach will build a foundation for a health IT framework geared toward improving risk stratification and generating novel therapeutic targets for glaucoma patients.

项目概要/摘要 青光眼是世界上导致不可逆转失明的首要原因，将影响超过 1.1 亿人 到 2040 年，早期发现和治疗至关重要，因为症状通常不会出现 直至病情进展。需要一种数据驱动的精准医学方法来更好地 确定最有可能罹患该疾病的人以及最有可能罹患该疾病的人 快速进展为视力丧失的风险。虽然眼科已经取得了长足的进步 成像和测试改善青光眼监测，青光眼的精准管理 如果不考虑患者共存的全身状况，则不完整，并发全身性疾病 药物和治疗，以及遵守规定的青光眼治疗。 了解全身状况，特别是血管状况，例如 鉴于高血压、影响青光眼的发病率不断增加，其对公共卫生的重要性日益增加 人口老龄化面临的共病。初步研究表明，预测 系统数据的价值，即使没有眼科终点。同样，测量药物 坚持对于指导患者咨询和参与以及避免 手术等下游干预措施的成本和发病率都很高。这些因素 对于提供更全面的青光眼治疗视角很重要 来改善患者的治疗效果，但它们的研究相对不足。 我建议应用健康信息技术 (IT) 的多模式进步来解决 弥补差距并实现以下具体目标：（1）开发基于机器学习的 使用系统电子对有青光眼进展风险的患者进行分类的预测模型 来自全国不同患者队列的健康记录 (EHR) 数据； (2) 评估如何整合 来自新型智能手表的家用血压监测仪的血压 (BP) 数据增强了预测能力 青光眼风险分层模型，以及（3）测量青光眼药物依从性 使用创新的柔性电子传感器来验证其在未来干预措施中的用途 改善青光眼的依从性和临床结果。这些研究将利用国家 大数据预测建模的最先进方法以及 传感器技术。这种多方面的方法将为健康 IT 奠定基础 旨在改善风险分层和产生新治疗目标的框架 对于青光眼患者。