Examining the causal effect of sociodemographic and genetic factors on patient safety outcomes in individuals prescribed high-risk immunosuppressive medications

检查社会人口统计学和遗传因素对服用高风险免疫抑制药物的个体患者安全结果的因果影响

• 批准号: 9327592
• 负责人: Milena Anne Gianfrancesco
• 金额: $ 6.12万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9327592
• 关键词: Address; Adverse event; Ankylosing spondylitis; Autoimmune Diseases; California; Caring; Cessation of life; Characteristics; Clinical; Clinical Management; Comorbidity; Complex; Data; Data Set; Disease; Electronic Health Record; Environment; Ethnic Origin; Event; Face; General Hospitals; Genetic; Genomics; Healthcare Systems; Immune; Immunosuppressive Agents; Individual; Knowledge; Language; Major Histocompatibility Complex; Malignant Neoplasms; Mediating; Medical; Medical Genetics; Methodology; Monitor; Morbidity - disease rate; Outcome; Pathway Analysis; Patients; Pharmaceutical Preparations; Positioning Attribute; Preventive; Process; Race; Registries; Research; Rheumatoid Arthritis; Risk; Safety; San Francisco; Scleroderma; Socioeconomic Status; Statistical Methods; Subgroup; Systemic Lupus Erythematosus; Testing; Time; Universities; Variant; Vasculitis; adverse outcome; drug development; genetic information; genetic variant; health care quality; high risk; improved; insight; novel; novel strategies; patient safety; prevent; tool

PROJECT SUMMARY The objective of this project is to investigate the causal effect of sociodemographic, clinical, and genetic factors on patient safety outcomes in individuals on high-risk immunosuppressive medications. Patient safety events are a leading cause of morbidity and are a major health care quality problem, causing tens of thousands of deaths each year in the U.S. Although patient safety problems in the U.S. health care system are widely recognized, research to develop new approaches to improve safety is urgently needed. For example, individuals requiring immunosuppressive medications for high-risk conditions may face serious patient safety risks, including prescribing errors, monitoring errors, and preventable adverse events (AEs). However, research regarding which patients may be at a higher risk of experiencing an AE due to medication has not been extensively studied. The overall hypothesis of this project is that sociodemographic, clinical, and genetic factors will demonstrate a causal effect on patient safety outcomes in individuals prescribed high-risk immunosuppressive medications. This project will utilize established electronic health record (EHR)-enabled registries to examine over 35,000 individuals prescribed high-risk immunosuppressive medications from the University of California, San Francisco (UCSF, n=~23,000) and San Francisco General Hospital (SFGH, n=~12,000) to address three related hypothesis. First, factors such as socioeconomic status, race/ethnicity, number of medical comorbidities and preferred language status will demonstrate a causal effect on process errors or AEs in individuals prescribed high-risk immunosuppressive medications. State-of-the-art causal inference statistical methods, which account for missing data, time-varying confounding and censoring will be utilized to test for the presence of these effects. Second, genetic variants within the major histocompatibility complex (MHC) will demonstrate a causal effect on AEs in individuals prescribed high-risk immunosuppressive medications. Existing genomic information for patients with certain autoimmune diseases (n=~1,750) will be used for this analysis. Third, clustering and network approach analyses will identify significant sociodemographic characteristics, clinical features, and genetic variants associated with subgroups of individuals prescribed high-risk immunosuppressive medications. Using computational approaches, individuals will be classified to identify sociodemographic characteristics, clinical features, and genetic variants associated with adverse outcome risk (e.g., those who experience an AE vs. those who do not). The importance of contributing factors to process errors and AEs in individuals prescribed high-risk immunosuppressive medications will be demonstrated through this research and provide new insight into monitoring and improving safety in the ambulatory setting.

项目概要 该项目的目的是调查社会人口统计学、临床和遗传因素的因果影响 影响使用高风险免疫抑制药物的个体患者安全结果的因素。病人 安全事件是发病率的主要原因，也是一个重大的医疗保健质量问题，导致数十起事故 尽管美国医疗保健系统中的患者安全问题很严重，但美国每年仍有数千人死亡 众所周知，迫切需要研究开发新方法来提高安全性。例如， 因高危情况需要免疫抑制药物的个人可能面临严重的患者安全问题 风险，包括处方错误、监测错误和可预防的不良事件 (AE)。然而， 关于哪些患者可能因药物治疗而面临更高的 AE 风险的研究尚未 被广泛研究。 该项目的总体假设是社会人口统计学、临床和遗传因素将 证明对接受高风险处方的个体的患者安全结果有因果影响 免疫抑制药物。该项目将利用已建立的支持电子健康记录（EHR）的 登记处对 35,000 多名服用高风险免疫抑制药物的个人进行检查 加州大学旧金山分校 (UCSF, n=~23,000) 和旧金山总医院 (SFGH, n=~12,000）来解决三个相关的假设。首先，社会经济地位、种族/民族等因素， 医疗合并症的数量和首选语言状态将证明对过程的因果影响 个人处方高风险免疫抑制药物时出现的错误或不良事件。最先进的因果关系 推断统计方法，其中考虑了缺失数据、时变混杂和审查 用于测试这些影响的存在。二、主要组织相容性内的遗传变异 复合物 (MHC) 将证明对接受高风险免疫抑制治疗的个体的 AE 具有因果影响 药物。患有某些自身免疫性疾病的患者 (n=~1,750) 的现有基因组信息将是 用于此分析。第三，聚类和网络方法分析将识别重要的 与亚组相关的社会人口学特征、临床特征和遗传变异 个人服用高风险免疫抑制药物。使用计算方法，个人 将进行分类以确定社会人口特征、临床特征和相关的遗传变异 具有不良结果风险（例如，经历 AE 的人与未经历 AE 的人）。的重要性 导致接受高风险免疫抑制剂治疗的个体出现流程错误和不良事件的因素 药物将通过这项研究得到证明，并为监测和改善提供新的见解 流动环境中的安全。