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

• 批准号: 10428149
• 负责人: Swarup Sai Swaminathan
• 金额: $ 22.68万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 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/10428149
• 关键词: Adoption; Advisory Committees; African; African American; African Caribbean; Age; Algorithms; Applet; Artificial Intelligence; Bayesian Analysis; Bayesian Modeling; Bayesian Prediction; Biometry; Blindness; Caring; Chronic; Clinical; Clinical Data; Clinical Research; Clinical assessments; Communities; Cornea; Country; Data; Data Analyses; Data Science; Databases; Disease Progression; Early Diagnosis; Early Intervention; Educational workshop; Elderly; Electronic Health Record; Ethnic Origin; Exhibits; Eye; Florida; Foundations; Future; Glaucoma; Goals; Grant; Image; Individual; Institutes; Latino; Lead; 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; base; career; career development; clinical care; clinical decision-making; clinical encounter; clinical practice; demographics; design; disability; electronic data; experience; field study; high risk; improved; indexing; individual patient; innovation; meetings; novel; patient population; personalized predictions; population based; predictive modeling; prevent; primary outcome; professor; programs; rate of change; retinal nerve fiber layer; sex; skills

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.

项目概要 Swarup S. Swaminathan 医学博士是 Bascom Palmer 眼科研究所眼科助理教授 职业目标是成为青光眼临床研究领域的独立临床医生科学家。他的 总体研究重点是利用新颖的统计和数据科学方法来改进对 青光眼的进展以及早期发现不可逆视力丧失风险最大的患者。这 该 K23 职业发展提案的主要目标是： 1) 比较当前可用的方法 用于配备高阶贝叶斯预测模型来监测青光眼疾病进展 电子健康记录 (EHR) 中的数据，以及 2) 向学术青光眼专家提供 指导研究经验和正式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，重点关注 应用数据科学原理改进青光眼患者进展的临床评估。这 拟议的 K23 应用程序将在生物统计学、大型数据分析方面提供宝贵的指导和正式培训 包含纵向数据的数据库、贝叶斯统计在医学科学中的应用以及人工 情报和机器学习数据分析。 Bascom 拥有丰富的技术资源 帕尔默眼科研究所和迈阿密大学数据科学与计算研究所，指导和 他的咨询委员会的专业知识以及专门的机构承诺将为斯瓦米纳坦博士提供 获得转型为独立临床医生科学家所需的支持。他会定期与他的 导师和顾问讨论职业发展，参加相关的大学研讨会和讲习班， 在国家会议上展示正在进行的研究，并不断提交他的作品以供出版。这个提议 将测试配备 EHR 的贝叶斯模型优于普通最小二乘法 (OLS) 的假设 准确性和早期检测进展能力的回归。在目标 1 中，贝叶斯模型配备 将构建 EHR 人群水平成像和功能数据来计算光学变化率 个体患者的相干断层扫描和标准自动视野测量指标。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中，以进一步完善这些个性化的风险因素数据。 预测。这些模型将与 OLS 回归进行比较，假设贝叶斯模型将 变得优越。最后，在目标 3 中，将开发一个交互式应用程序，以从临床实践中收集数据 为了验证贝叶斯模型在临床护理中的使用。专家临床医生小组将比较蒙面 来自这些案例的 OLS 和贝叶斯估计。拟议研究的结果将提供 基金会 R01 拨款审查 EHR 数据的使用，以改善临床决策 青光眼患者的纵向护理。