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: 10660029
• 负责人: Sally Liu Baxter
• 金额: $ 8.1万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10660029
• 关键词: Accounting; Address; Adherence; Administrative Supplement; All of Us Research Program; Award; Blindness; Blood Pressure; Chronic; Clinical; Data; Disease; Early Diagnosis; Early treatment; Electronic Health Record; Eye; Eye diseases; Functional disorder; Future; Genetic; Glaucoma; Home Blood Pressure Monitoring; Image; Individual; Intervention; Lead; Measures; Monitor; Outcome; Parents; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Quality of life; Research; Research Personnel; Risk; Symptoms; Testing; Therapeutic; United States National Institutes of Health; base; cohort; electronic sensor; experience; flexibility; health information technology; improved; innovation; machine learning prediction; medication compliance; multimodality; new therapeutic target; novel; personalized management; precision medicine; predictive modeling; programs; risk stratification; smart watch; social health determinants; substance use; treatment adherence

PROJECT SUMMARY/ABSTRACT Glaucoma is the world's leading cause of irreversible blindness. 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. These factors are important for providing a more comprehensive perspective of glaucoma management and for improving patient outcomes, yet they are relatively understudied. In the parent award, I am 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 (the NIH All of Us Research Program); (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. In this proposal for an administrative supplement, I intend to build upon my existing studies by continuing to analyze data from the NIH All of Us Researcher Workbench. I will leverage my extensive experience with All of Us and will conduct research to better understand the relationships between glaucoma and factors such as social determinants of health, substance use, wearable/activity data, and genetics. This will expand the impact of my existing research program, which aims to improve risk stratification and generate novel therapeutic targets for glaucoma patients.

项目摘要/摘要 青光眼是世界上不可逆失明的主要原因。早期检测和治疗 至关重要，因为症状通常不会出现，直到疾病疾病。数据驱动 需要精确医学方法来更好地识别有最大风险的个人 发展疾病，并且有最大的风险迅速发展为视力丧失。尽管 眼睛成像和测试取得了很大进展，以改善青光眼 监测，青光眼的精确管理是不完整的，而没有考虑患者 共存的系统疾病，同时进行的系统性药物和治疗以及 遵守规定的青光眼治疗。这些因素对于提供 青光眼管理和改善患者的更全面的视角 结果，但它们相对研究了。 在“父母奖”中，我正在申请健康信息技术的多模式进步 （IT）解决这些差距并实现以下特定目的：（1）开发机器 基于学习的预测模型，将有青光眼进展风险的患者分类 来自全国性患者队列的全身电子健康记录（EHR）数据（NIH 我们所有的研究计划）； （2）评估如何整合新颖的血压（BP）数据 基于智能手表的家庭BP监视增强了风险分层的预测模型 青光眼和（3）使用创新的柔性电子测量青光眼药物依从性 传感器以验证其用于未来干预措施的使用，以改善依从性和临床 青光眼的结果。 在此提案中，我打算以 继续分析美国国立卫生研究院所有研究人员工作台的数据。我会利用我的 与我们所有人的丰富经验，并将进行研究以更好地了解 青光眼之间的关系与健康，物质的社会决定因素等因素 使用，可穿戴/活动数据和遗传学。这将扩大我现有研究的影响 计划，旨在改善风险分层并为 青光眼患者。