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: 10396571
• 负责人: Catherine Qing Sun
• 金额: $ 25.26万
• 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/10396571
• 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; future implementation; 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.

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