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.

项目概要：增殖性糖尿病视网膜病变的现实结果糖尿病视网膜病变导致的视力丧失仍然是工作年龄组可预防性失明的主要原因在美国，晚期糖尿病视网膜病变被称为增殖性糖尿病。在许多患者中，通过适当和适当的治疗可以预防与 PDR 相关的失明。不幸的是，一些 PDR 高风险患者没有得到足够的诊断和治疗。需要更多关于现实环境中的 PDR 结果以及之间的差异的知识。已发表的研究结果和现实世界的有效性几乎被使用。 90% 的门诊医生办公室，可以成为在现实环境中研究 PDR 的强大工具。该提案的目标是开发和验证基于 EHR 的方法，以改善 PDR 的结果。目标是：(1) 使用自动化方法对 EHR 系统中的 PDR 患者进行分类结构化（例如诊断代码、药物、实验室）和非结构化数据（例如临床记录），(2) 进行预测使用时变预测模型研究非增殖性糖尿病视网膜病变进展为 PDR 协变量，以及 (3) 确定 PDR 治疗在全国范围内的真实有效性该研究将利用来自加州大学旧金山分校 (UCSF) 的去识别数据。临床数据仓库，一个去识别化的 EHR，拥有超过 100 万患者，可以进行眼科检查信息，以及智能视觉研究 (IRIS) 注册中心，这是一个全国性的综合眼科数据库其中包括来自美国 15,000 多家眼科服务提供者的数据，其中包括超过 100 万 PDR 患者。这项研究的创新方法和工具可以应用于其他眼部疾病，以促进未来的 EHR- 候选人 Catherine Sun 博士是一位眼科医生，长期从事眼科临床研究。术语目标是通过进行基于 EHR 的实用性研究来研究眼科的真实临床结果虽然她拥有基础技能，但她还接受了额外的指导。数据分析、生物医学信息学、生物统计学和高级临床试验的培训和课程设计和实施将帮助她实现她的目标。 Nisha Acharya 博士和联合导师 Travis Porco 博士和 Joshua Stein 博士，以及卓越的环境加州大学旧金山分校眼科和 F.I. Proctor 基金会将支持孙博士的发展成为 R01 资助的独立调查员。