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Improving Individualized Assessments of Glaucoma Progression with Population-Based Electronic Health Record Data

利用基于人群的电子健康记录数据改进青光眼进展的个体化评估

基本信息

批准号：
10630915
负责人：
Swarup Sai Swaminathan
金额：
$ 22.47万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10630915
关键词：
Adoption Advisory Committees African African American African Caribbean Age Agreement Algorithms Applet Artificial Intelligence Bayesian Analysis Bayesian Modeling Bayesian Prediction Biometry Blindness Chronic Clinical Clinical Data Clinical Research Clinical assessments Communities Cornea Country Data Data Analyses Data Science Databases Dedications Disease Progression Early Diagnosis Early Intervention Educational workshop Elderly Electronic Health Record Eligibility Determination Ethnic Origin Exhibits Eye Florida Foundations Frequencies Future Glaucoma Goals Grant Image Individual Institution Latino Least-Squares Analysis Machine Learning Masks Measurement Medical Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methodology Methods Modeling Monitor Nature Ophthalmology Optical Coherence Tomography Outcome Patient Self-Report Patients Perimetry Physicians Physiologic Intraocular Pressure Population Positioning Attribute Progressive Disease Publications Race Research Research Personnel Resources Risk Risk Factors Science Scientist Self-Help Devices Specialist Statistical Methods Statistical Models Technology Testing Thick Thinness Time Training United States Universities Validation Visit Visual Visual Fields Work career career development clinical care clinical decision-making clinical encounter clinical practice demographics design disability electronic health data experience field study high risk improved indexing individual patient innovation longitudinal care meetings novel patient population personalized predictions population based predictive modeling prevent primary outcome professor programs rapid detection rate of change retinal nerve fiber layer sex skills

项目摘要

PROJECT SUMMARY Swarup S. Swaminathan, MD is an Assistant Professor of Ophthalmology at the Bascom Palmer Eye Institute with a career goal of becoming an independent clinician-scientist in the field of glaucoma clinical research. His overall research focus is to utilize novel statistical and data science methodologies to improve assessment of progression in glaucoma and early detection of those patients at greatest risk for irreversible vision loss. The primary objectives of this K23 career development proposal are: 1) to compare currently available methods used to monitor glaucomatous disease progression with higher-order Bayesian prediction models equipped with data from electronic health records (EHR), and 2) to provide an academic glaucoma specialist with the mentored research experience and formal training to conduct independent clinical research. Achieving these objectives will provide the critical skills required to establish an independent research program focused on applying data science principles to improve the clinical assessment of progression in glaucoma patients. The proposed K23 application will provide valuable mentorship and formal training in biostatistics, analysis of large databases containing longitudinal data, application of Bayesian statistics in the medical sciences, and artificial intelligence and machine learning data analysis. The extensive technical resources available at the Bascom Palmer Eye Institute and University of Miami Institute for Data Science & Computing, the mentorship and expertise of his advisory committee, and the dedicated institutional commitment will provide Dr. Swaminathan with the support needed to transition into an independent clinician-scientist. He will regularly meet with his mentors and advisors to discuss career development, attend pertinent university seminars and workshops, present ongoing research at national meetings, and consistently submit his work for publication. This proposal will test the hypothesis that EHR-equipped Bayesian models outperform ordinary least square (OLS) regression in accuracy and their ability to detect progression earlier. In Aim 1, Bayesian models equipped with EHR population-level imaging and functional data will be constructed to calculate the rate of change in optical coherence tomography and standard automated perimetry metrics of individual patients. In Aim 2, patient- specific risk factor data will be incorporated into Bayesian models to further refine these individualized predictions. These models will be compared to OLS regression, with the hypothesis that Bayesian models will be superior. Finally, in Aim 3, an interactive application will be developed to gather data from clinical practice in order to validate the use of Bayesian models in clinical care. An expert clinician panel will compare masked OLS and Bayesian estimates from these cases. The results of the proposed research will provide the foundation for an R01 grant examining the use of EHR data to improve clinical decision-making for the longitudinal care of glaucoma patients.

项目摘要斯瓦鲁普湾Swaminathan博士是巴斯科姆帕尔默眼科研究所的眼科助理教授其职业目标是成为青光眼临床研究领域的独立临床科学家。他总体研究重点是利用新的统计和数据科学方法，以改善评估青光眼的进展和早期发现那些最有可能发生不可逆视力丧失的患者。的本K23职业发展建议书的主要目标是：1）比较目前可用的方法用于监测昏迷性疾病进展，高阶贝叶斯预测模型配备从电子健康记录（EHR）的数据，和2）提供学术青光眼专家与有指导的研究经验和正规培训，以进行独立的临床研究。实现这些目标将提供建立独立研究计划所需的关键技能，重点是应用数据科学原理改善青光眼患者进展的临床评估。的拟议的K23应用程序将提供宝贵的指导和生物统计学，分析大型包含纵向数据的数据库，贝叶斯统计在医学科学中的应用，智能和机器学习数据分析。巴斯科姆拥有广泛的技术资源帕尔默眼研究所和迈阿密大学数据科学与计算研究所，导师和他的咨询委员会的专业知识，以及专门的机构承诺将为Swaminathan博士提供在转变为独立的临床科学家所需的支持下。他将定期与他的导师和顾问讨论职业发展，参加有关的大学研讨会和讲习班，在全国性会议上展示正在进行的研究，并不断提交他的工作以供出版。这项建议将检验装备EHR的贝叶斯模型优于普通最小二乘（OLS）的假设回归的准确性和早期检测进展的能力。在目标1中，贝叶斯模型配备了将构建EHR人群水平的成像和功能数据，以计算光学相干断层扫描和个体患者的标准自动视野测量。在目标2中，患者- 具体的风险因素数据将被纳入贝叶斯模型，以进一步完善这些个性化的预测。这些模型将与OLS回归进行比较，假设贝叶斯模型将成为上级。最后，在目标3中，将开发一个交互式应用程序，以收集来自临床实践的数据，为了验证贝叶斯模型在临床护理中的使用。专家临床小组将比较设盲的 OLS和贝叶斯估计从这些情况下。拟议研究的结果将提供 R 01基金会研究使用EHR数据来改善临床决策，青光眼患者的纵向护理。