Real-world outcomes of proliferative diabetic retinopathy

增殖性糖尿病视网膜病变的现实结果

• 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
• 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' 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

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真实结局 糖尿病视网膜病变导致的视力丧失仍然是工作年龄人群中可预防失明的主要原因。 成年人在美国（US）。晚期糖尿病视网膜病变被称为增殖性糖尿病 视网膜病变（PDR）。在许多患者中，与PDR相关的失明可以通过适当的治疗来预防， 及时诊断和治疗。不幸的是，一些PDR高危患者没有得到足够的治疗， 眼部护理需要更多关于真实世界环境中PDR结果的知识，以及 发表的研究结果和真实世界的有效性。电子健康记录（EHR）已在近 90%的门诊医生办公室，可以成为在现实世界中研究PDR的强大工具。的 本提案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）在EHR系统中使用自动化方法对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，临床笔记），（2）预测 非增殖性糖尿病视网膜病变进展为PDR的预测模型， 协变量，和（3）确定在全国范围内的大眼睛中治疗PDR的真实有效性 数据集。该研究将利用加州大学弗朗西斯科分校（UCSF）的数据去识别 临床数据仓库，一个去识别的EHR，拥有超过100万名患者，可进行眼科检查 信息，以及智能视力研究（IRIS）登记处，一个全国性的综合眼睛数据库 这包括来自美国超过15，000家眼科提供者的数据，其中有超过100万名PDR患者。的 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- 基于眼科临床研究。候选人Catherine Sun博士是一位眼科医生，长期- 长期目标是通过进行基于EHR的务实研究， 临床试验和使用大规模EHR数据。虽然她拥有基本技能，但额外的指导 数据分析、生物医学信息学、生物统计学和高级临床试验方面的培训和课程 设计和实施将帮助她实现目标。她的杰出导师团队 博士Nisha Acharya和共同导师Travis Porco博士和约书亚斯坦博士，以及 眼科和F.I.加州大学旧金山分校的普罗克特基金会将支持孙博士的发展 R 01资助的独立调查员