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：
10018290
负责人：
Sally Liu Baxter
金额：
$ 39.4万
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/10018290
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 Blood Pressure Monitoring Home environment 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 Risk stratification 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 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.

项目摘要/摘要青光眼是世界上不可逆失明的主要原因，将影响> 1.1亿到2040年，人们。早期发现和治疗至关重要，因为症状通常不存在直到疾病疾病。需要采用数据驱动的精确医学方法来改善识别患有最大风险患疾病并且最大风险的人迅速发展视力丧失的风险。虽然眼睛取得了很大进展成像和测试以改善青光眼监测，青光眼的精确管理是不完整的情况下，不对患者共存的全身状况，同时进行全身状况药物和治疗，并遵守处方青光眼治疗。了解系统状况，特别是血管状况（例如高血压，影响青光眼呈现出越来越重要的公共健康重要性面临老龄化人口的合并症。初步研究证明了预测性即使没有眼科端点，系统数据的价值也是如此。同样，测量药物依从性对于指导患者咨询和参与很重要，避免下游干预措施，例如手术，这些干预措施具有高成本和发病率。这些因素对于提供青光眼管理和更全面的观点很重要为了改善患者的预后，他们相对研究了。我建议在健康信息技术（IT）中应用多模式的进步来解决这些差距并达到以下特定目的：（1）开发基于机器学习的使用全身电子的预测模型将有青光眼进展的患者分类来自全国各地患者队列的健康记录（EHR）数据；（2）评估如何整合来自新型基于智能手表的家庭BP监视器的血压（BP）数据增强了预测青光眼风险分层的模型，（3）测量青光眼药物依从性使用创新的灵活电子传感器来验证其用于未来干预措施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 - 大数据预测建模中的艺术方法以及最先进的进步传感器技术。这种多方面的方法将为健康建立基础旨在改善风险分层和产生新型治疗目标的框架对于青光眼患者。