Real-world outcomes of proliferative diabetic retinopathy

增殖性糖尿病视网膜病变的现实结果

基本信息

批准号：
10191673
负责人：
Catherine Qing Sun
金额：
$ 25.22万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10191673
关键词：
Adult Algorithms Anatomy Background Diabetic Retinopathy Biometry Blindness California Caring Classification Clinical Clinical Research Clinical Trials Design Clinical effectiveness Code Data Data Analytics Data Set Databases Development Diabetic Retinopathy Diagnosis Disease Effectiveness Electronic Health Record Environment Ethnic Origin Eye Eye diseases Foundational Skills Foundations Funding Future General Hospitals Goals Health Insurance Healthcare Hospitals ICD-9 Image Infrastructure Injections Intelligence Intervention Knowledge Light Coagulation Manuals Measures Mentors Mentorship Methods Modeling Natural Language Processing Observational Study Ophthalmic examination and evaluation Ophthalmologist Ophthalmology Outcome Outcome Study Outpatients Participant Patient Recruitments Patients Performance Pharmaceutical Preparations Physicians&apos Offices Pragmatic clinical trial Prospective Studies Provider Publishing Race Randomized Controlled Trials Registries Reporting Research Research Personnel Retrospective Studies Risk Factors San Francisco Structure System Techniques Testing Text The Sun Time Training Treatment Effectiveness United States Universities Vascular Endothelial Growth Factors Vision Visual aged base biomedical informatics clinical data warehouse comparison intervention cost effectiveness design diabetic follow-up health disparity high risk improved improved outcome innovation low socioeconomic status medication compliance novel predictive modeling prevent primary outcome proliferative diabetic retinopathy risk stratification routine care structured data tool treatment as usual unstructured data

项目摘要

PROJECT SUMMARY: Real-world outcomes of proliferative diabetic retinopathy Vision loss from diabetic retinopathy remains the leading cause of preventable blindness in working-aged adults in the United States (US). Advanced diabetic retinopathy is referred to as proliferative diabetic retinopathy (PDR). In many patients, blindness associated with PDR can be prevented with appropriate and timely diagnosis and treatment. Unfortunately, some patients at high risk for PDR are not receiving adequate eye care. More knowledge is needed about PDR outcomes in a real-world setting, and the differences between published study outcomes and real-world effectiveness. Electronic health records (EHRs) are used in nearly 90% of outpatient physician offices and can be a powerful tool for studying PDR in a real-world setting. The goal of this proposal is to develop and validate EHR-based methods to improve outcomes in PDR. The study aims are: (1) to classify patients with PDR in the EHR system using an automated method that incorporates structured (e.g., diagnosis code, medications, labs) and unstructured data (e.g., clinical notes), (2) to predict the progression of non-proliferative diabetic retinopathy to PDR using a forecasting model with time-varying covariates, and (3) to determine the real-world effectiveness of treatments for PDR in a large nationwide eye dataset. The study will utilize data from the University of California San Francisco’s (UCSF) De-Identified Clinical Data Warehouse, a de-identified EHR with over 1 million patients that has available eye exam information, and the Intelligent Research in Sight (IRIS) registry, a nationwide comprehensive eye database that includes data from over 15,000 eye providers in the US with over 1 million patients with PDR. The innovative methods and tools from this study can be applied to other eye conditions to facilitate future EHR- based clinical studies in ophthalmology. The candidate, Dr. Catherine Sun is an ophthalmologist whose long- term goal is to study real-world clinical outcomes in ophthalmology by conducting EHR-based pragmatic clinical trials and using large-scale EHR data. While she possesses the foundational skills, additional mentored training and coursework in data analytics, biomedical informatics, biostatistics, and advanced clinical trial design and implementation will help her reach her goals. Her outstanding mentorship team of primary mentor Dr. Nisha Acharya and co-mentors Dr. Travis Porco and Dr. Joshua Stein, and the exceptional environment of the Department of Ophthalmology and the F.I. Proctor Foundation at UCSF will support Dr. Sun’s development into an R01-funded independent investigator.

项目摘要：真实的糖尿病性视网膜病变的现实结果糖尿病性视网膜病变的视力丧失仍然是造成工人衰老的可预防失明的主要原因美国成年人（美国）。晚期糖尿病性视网膜病被称为增殖视网膜病（PDR）。在许多患者中，与PDR相关的失明可以通过适当的和及时的诊断和治疗。不幸的是，某些PDR高风险的患者没有得到足够的足够眼保健。在现实世界中，需要更多有关PDR结果的知识，以及已发表的研究结果和现实世界有效性。电子健康记录（EHR）几乎用于 90％的门诊物理办公室可以是在现实环境中研究PDR的强大工具。这该建议的目标是开发和验证基于EHR的方法以改善PDR的结果。研究目的是：（1）使用合并的自动化方法将EHR系统中的PDR分类结构化（例如，诊断代码，药物，实验室）和非结构化数据（例如临床注释），（2）预测随着时间变化协变量和（3）确定PDR治疗在全国范围内的现实有效性数据集。该研究将利用加利福尼亚大学旧金山大学（UCSF）的数据去识别临床数据仓库，一种已识别的EHR，拥有超过100万患者，可以进行眼科检查信息和智能研究（IRIS）注册表，这是一个全国性的全面眼睛数据库其中包括来自美国15,000多名眼部提供者的数据，其中有100万名PDR患者。这这项研究的创新方法和工具可以应用于其他眼睛条件，以促进未来的EHR- 基于眼科的临床研究。候选人凯瑟琳·桑博士（Catherine Sun）是一名眼科医生术语目标是通过进行基于EHR的务实研究在眼科中研究现实世界的临床结果临床试验并使用大规模的EHR数据。在拥有基础技能的同时，另外的指导数据分析，生物医学信息，生物统计学和高级临床试验的培训和课程设计和实施将有助于她实现目标。她杰出的主要精神精神团队 Nisha Acharya博士和联合官员Travis Porco博士和Joshua Stein博士，以及眼科和F.I. UCSF的Proctor基金会将支持Sun博士的发展进入R01资助的独立研究员。