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：
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
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/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

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

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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 Swarup S. Swaminathan，医学博士是Bascom Palmer Eye Institute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善评估的评估青光眼的进展和早期发现那些具有不可逆转视力丧失风险的患者。这该K23职业发展建议的主要目标是：1）比较当前可用的方法用于监测配备高阶贝叶斯预测模型的青光眼疾病进展带有来自电子健康记录（EHR）的数据，以及2）为学术青光眼专家提供指导了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关注的独立研究计划所需的关键技能应用数据科学原理来改善青光眼患者进展的临床评估。这拟议的K23应用将在生物统计学中提供有价值的指导和正式培训，分析大型包含纵向数据的数据库，医学科学中贝叶斯统计的应用以及人工智能和机器学习数据分析。 BASCOM可用的广泛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Swaminathan博士提供在过渡到独立的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，参加相关大学研讨会和讲习班，在全国会议上进行了正在进行的研究，并始终提交他的作品出版。这个建议将测试以EHR配备EHR模型的假设优于普通最小二乘（OLS）的假设准确性的回归及其更早检测进展的能力。在AIM 1中，贝叶斯模型配备了 EHR种群级成像和功能数据将构建以计算光学的变化速率单个患者的相干断层扫描和标准自动化指标。在AIM 2中，患者 - 特定的风险因素数据将纳入贝叶斯模型，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，假设贝叶斯模型将要优越。最后，在AIM 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。专业的临床医生面板将比较蒙面 OLS和贝叶斯从这些情况下进行了估计。拟议研究的结果将为 R01赠款的基金会，检查使用EHR数据以改善临床决策的使用青光眼患者的纵向护理。