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

项目总结 斯瓦鲁普·S·斯瓦米纳坦医学博士是巴斯科姆·帕尔默眼科研究所的眼科助理教授 他的职业目标是成为青光眼临床研究领域的独立临床医生和科学家。他的 总体研究重点是利用新的统计和数据科学方法来改进对 青光眼的进展和早期发现那些最有可能出现不可逆性视力丧失的患者。这个 这份K23职业发展提案的主要目标是：1)比较目前可用的方法 用于监测青光眼疾病进展的高阶贝叶斯预测模型 使用来自电子健康记录(EHR)的数据，以及2)为学术青光眼专家提供 有指导的研究经验和进行独立临床研究的正规培训。实现这些目标 目标将提供建立独立研究计划所需的关键技能，该计划侧重于 应用数据科学原理改进青光眼患者进展的临床评估。这个 拟议的K23应用程序将在生物统计、大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，贝叶斯统计学在医学科学中的应用，以及人工智能 智能和机器学习数据分析。Bascom提供的广泛的技术资源 帕尔默眼科研究所和迈阿密大学数据科学与计算研究所，导师和 他的咨询委员会的专业知识和专注的机构承诺将为斯瓦米纳坦博士提供 获得了转变为独立临床医生兼科学家所需的支持。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研讨会， 在国家会议上介绍正在进行的研究，并持续提交他的工作以供出版。这项建议 将检验配备EHR的贝叶斯模型优于普通最小二乘(OLS)的假设 准确率和更早发现进展的能力方面的倒退。在目标1中，贝叶斯模型配备了 将构建EHR种群水平成像和功能数据，以计算光学 个体患者的相干断层扫描和标准自动视野测量。在目标2中，病人- 具体的风险因素数据将被纳入贝叶斯模型，以进一步细化这些个性化 预测。这些模型将与OLS回归进行比较，假设贝叶斯模型将 要高人一等。最后，在目标3中，将开发一个交互式应用程序，以收集#年临床实践的数据 目的：验证贝叶斯模型在临床护理中的应用。一个临床专家小组将比对蒙面 来自这些案例的OL和贝叶斯估计。拟议研究的结果将提供 R01拨款基金会，审查EHR数据的使用，以改善 青光眼患者的纵向护理。

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.