Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease

使用电子健康记录和登记数据检测和预测风湿病临床结果的计算方法

• 批准号: 9912723
• 负责人: Milena Anne Gianfrancesco
• 金额: $ 13.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-10 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912723
• 关键词: Address; Adverse event; Age; Algorithms; Antirheumatic Agents; Area; Autoimmune Process; Biological; Biological Response Modifier Therapy; California; Categories; Cessation of life; Characteristics; Clinic; Clinical; Clinical Sciences; Clinics and Hospitals; Code; Combination Medication; Computing Methodologies; Data; Data Set; Databases; Demographic Factors; Development Plans; Disease; Disease-Modifying Second-Line Drugs; Electronic Health Record; Environment; Epidemiologist; Epidemiology; Ethnic Origin; Event; Future; Goals; Gold; Hospitalization; Individual; Infection; Informatics; Institutes; Interdisciplinary Study; K-Series Research Career Programs; Lead; Marketing; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Modeling; Monitor; Morbidity - disease rate; NCI Scholars Program; Opportunistic Infections; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Pharmacotherapy; Population; Positioning Attribute; Probability; Quality of life; Race; Randomized Controlled Trials; Recommendation; Registries; Reporting; Research; Research Personnel; Rheumatism; Rheumatoid Arthritis; Rheumatology; Risk; Risk Assessment; Risk Factors; Safety; Sample Size; San Francisco; Sensitivity and Specificity; Serious Adverse Event; Severities; Structure; Subgroup; System; Systemic Lupus Erythematosus; Time; Training; Translational Research; Universities; University Hospitals; Validation; Work; adverse event risk; base; career development; comorbidity; data registry; disorder control; electronic structure; ethnic minority population; experience; high risk; improved; individual patient; individualized medicine; infection rate; infection risk; large datasets; medical schools; medical specialties; patient safety; patient stratification; personalized medicine; population based; predict clinical outcome; predictive modeling; programs; racial and ethnic; research and development; safety outcomes; sex; skills; sociodemographics; statistics; structured data; symposium; text searching; unstructured data

PROJECT SUMMARY / ABSTRACT This is a new application for a K01 award for Dr. Milena Gianfrancesco, an epidemiologist at the University of California, San Francisco (UCSF) School of Medicine, who plans a research program focusing on understanding risk factors as they relate to rheumatic disease patient outcomes, such as adverse events. Combined with a training plan focused on computational text mining methods and advanced causal inference statistics, the goal of the current study is to use large electronic health record and national registry data that reflects real-world prescribing patterns to examine the risk of infection attributed to biologic disease-modifying anti-rheumatic drugs in individuals with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). While biologic medications have improved disease control and are associated with significant gains in patients’ quality of life, several studies have demonstrated that biologic use is associated with an increased risk of serious adverse events, such as infection. How this risk differs based on a variety of patient factors, such as age, race, and ethnicity, is currently unknown, leaving clinicians with insufficient information to predict the probability of an adverse event occurring in a given patient who is prescribed a particular biologic. This proposal will utilize established local electronic health record and national registry data to examine over 80,000 individuals with RA and SLE to address three specific aims. In Aim 1, Dr. Gianfrancesco will apply and validate a text mining system to identify incident clinical and opportunistic infections from clinical notes. In Aim 2, Dr. Gianfrancesco will use the same databases to determine the longitudinal causal effect of biologics on risk of infection. In Aim 3, a risk-assessment model to predict risk of infection will be developed and validated in a rheumatology clinic. Findings from this study will further elucidate factors associated with infectious risk for individuals prescribed biologics, thereby improving their safety in the ambulatory settings. Dr. Gianfrancesco has assembled an exceptional mentorship team with expertise in computational text mining methods, advanced causal inference statistics, rheumatology and patient safety outcomes, as well as experience using national registry data to address these questions. She will have access to a rich research environment and provided support for career development through programs such as the UCSF Clinical and Translational Science Institute K-scholars program. Formal coursework and mentoring will also be supplemented with attendance at national conferences related to rheumatology, epidemiology, and informatics. Completing the proposed research and career development plan will allow Dr. Gianfrancesco to gain experience in state-of-the-art computational methods using large datasets to better understand important patient outcomes, such as serious adverse events. This mentored career development award will provide the skills, mentorship, and experience necessary to propel her to independence and enable her to lead an independent multidisciplinary research program.

项目摘要 /摘要 这是为K01奖的新申请Milena Gianfrancesco博士，他是大学的流行病学家 加利福尼亚州旧金山（UCSF）医学院，计划一项研究计划 了解与风湿病患者结局有关的风险因素，例如不良事件。 结合针对计算文本挖掘方法和高级因果推断的培训计划 统计数据，本研究的目的是使用大型电子健康记录和国家注册表数据 反映现实的处方模式，以检查归因于生物疾病改良的感染风险 类风湿关节炎（RA）和全身性红斑红血病（SLE）的个体中的抗流血药物。 虽然生物药物改善了疾病控制，并且与患者的显着增长有关 生活质量，几项研究表明，生物学使用与增加的风险有关 严重的不良事件，例如感染。这种风险差异如何基于各种患者因素，例如 年龄，种族和种族目前尚不清楚，使临床医生获得的信息不足以预测 处方特定生物学的给定患者发生不良事件的可能性。 该提案将利用已建立的当地电子健康记录和国家注册表数据进行检查 80,000名RA和SLE的人可以解决三个特定目标。在AIM 1中，Gianfrancesco博士将申请并 验证文本挖掘系统以识别临床注释中的临床和机会性感染。目标 2，Gianfrancesco博士将使用相同的数据库来确定生物制剂的纵向因果 感染的风险。在AIM 3中，将开发和验证一个预测感染风险的风险评估模型 风湿病诊所。这项研究的结果将进一步阐明与传染性风险相关的因素 个人开了生物制剂，从而提高了在门诊环境中的安全性。 Gianfrancesco博士已经组建了一个具有计算文本挖掘专业知识的杰出心态团队 方法，高级因果推理统计，风湿病学和患者安全结果以及 使用国家注册表数据来解决这些问题的经验。她将有丰富的研究 环境并通过UCSF临床和 翻译科学研究所K-Scholars计划。正式的课程和心理工作也将是 补充与风湿病，流行病学和信息有关的国家会议的出席。 完成拟议的研究和职业发展计划将使Gianfrancesco博士获得 使用大型数据集的最新计算方法经验，以更好地理解重要 患者的结果，例如严重的不良事件。这个重要的职业发展奖将为 促使她独立并使她能够领导她的技能，精神训练和经验 独立的多学科研究计划。