权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结/文摘