Evaluation of multiple medication exposures concurrently using a novel algorithm

使用新算法同时评估多种药物暴露

• 批准号: 10363669
• 负责人: Ravy Kuppalapalle Vajravelu
• 金额: $ 15.58万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10363669
• 关键词: Address; Algorithms; Aminoglycosides; Antibiotics; Anticoagulants; Antiplatelet Drugs; Big Data to Knowledge; Biological; Carbapenems; Case-Control Studies; Cephalosporins; Characteristics; Charge; Clinical; Clinical Research; Clostridium difficile; Computational Biology; Computer software; Data; Databases; Development; Development Plans; Diagnosis; Digestive System Disorders; Disease; Electronic Health Record; Epidemiologic Methods; Evaluation; Faculty; Fluoroquinolones; Funding; Future; Gastroenterology; Gastrointestinal Diseases; Gastrointestinal Hemorrhage; Generations; Genomics; Goals; Grant; Health; Infection; Informatics; Inpatients; Institution; K-Series Research Career Programs; Lead; Learning; Logistic Regressions; Machine Learning; Master of Science; Medical; Medical Informatics; Mentors; Mentorship; Methods; National Institute of Diabetes and Digestive and Kidney Diseases; Noise; Non-Steroidal Anti-Inflammatory Agents; Outcome; Penicillins; Performance; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacology; Research; Research Design; Research Personnel; Sensitivity and Specificity; Signal Transduction; Specificity; Techniques; Testing; Time; United Kingdom; United States Department of Veterans Affairs; United States National Institutes of Health; Validation; analytical method; base; beta-Lactams; career development; clinical epidemiology; econometrics; epidemiology study; experience; improved; inhibitor; insight; interest; novel; research study; simulation; skills; usability

PROJECT SUMMARY The development of large observational health databases (OHD) has expanded the data available for analysis by pharmacoepidemiology research. The efficiency of these studies may be improved by simultaneously studying the association of multiple medications with a disease of interest. Unfortunately, prior research has demonstrated that it is difficult to distinguish true-positive from false-positive results when studying multiple exposures simultaneously, thus limiting the conclusions drawn from these types of studies and representing a major gap in the field. The objective of this proposal, which is the first step in achieving the applicant's long- term goal of improving the diagnosis and treatment of gastrointestinal diseases using insights derived from OHD, is to evaluate and validate medication class enrichment analysis (MCEA), a novel set-based signal-to- noise enrichment algorithm developed by the applicant to analyze multiple exposures from OHD with high sensitivity and specificity. The central hypothesis of this proposal is that MCEA has equal sensitivity and greater specificity compared to logistic regression, the most widely used analytic method for OHD, for identifying true associations between medications and clinical outcomes. The applicant will complete the following two interrelated specific aims to test the hypothesis: Aim 1 – to calculate the sensitivity and specificity of medication class enrichment analysis (MCEA) and logistic regression (LR) for identifying medication associations with Clostridium difficile infection (CDI) and Aim 2 – to calculate the sensitivity and specificity of MCEA and LR for identifying medication associations with gastrointestinal hemorrhage (GIH). The rationale for these aims is that by reproducing known medication-disease associations without false positives, MCEA can be used to identify novel pharmacologic associations with gastrointestinal diseases in future studies. The expected outcome for the proposed research is that it will demonstrate MCEA as a valid method for pharmacoepidemiology research, opening new research opportunities for the study of multi-exposure OHD. These new research opportunities may lead to more rapid identification of potential pharmacologic causes of emerging diseases and discovery of unanticipated beneficial medication effects, allowing such medications to be repurposed for new indications. To attain the expected outcome, the applicant will complete additional coursework that builds on his Master of Science in Clinical Epidemiology to learn computational biology, machine learning, and econometrics techniques. With the support of this grant and his institution, he will also directly apply these techniques to pharmacoepidemiology applications under the close mentorship of a carefully selected team of faculty with extensive experience in gastroenterology, pharmacoepidemiology, medical informatics, and mentoring prior K-award grant recipients. Through these activities, the applicant will develop the skills necessary to obtain NIH R01-level funding and become a leader in developing novel techniques for application to the epidemiologic study of gastrointestinal diseases.

项目概要 大型观察健康数据库（OHD）的发展扩大了可用于分析的数据 通过药物流行病学研究可以同时提高这些研究的效率。 不幸的是，先前的研究已经研究了多种药物与某种疾病的关联。 在研究多个结果时很难区分真阳性结果和假阳性结果 同时暴露，从而限制了从这些类型的研究中得出的结论并代表了 该提案的目标是实现申请人长期目标的第一步。 长期目标是利用来自以下领域的见解来改善胃肠道疾病的诊断和治疗 OHD 旨在评估和验证药物类别富集分析 (MCEA)，这是一种新颖的基于集合的信号到 申请人开发的噪声富集算法，用于分析 OHD 的多次曝光，具有高 该提案的中心假设是 MCEA 具有相同的敏感性和特异性。 与 OHD 最广泛使用的分析方法 Logistic 回归相比，具有更高的特异性 申请人将完成药物与临床结果之间的真实关联。 以下两个相互关联的具体目标来检验假设： 目标 1 – 计算灵敏度和 用于识别药物类别富集分析 (MCEA) 和逻辑回归 (LR) 的特异性 药物与艰难梭菌感染 (CDI) 的关联和目标 2 – 计算敏感性和 MCEA 和 LR 用于识别药物与胃肠道出血 (GIH) 相关性的特异性。 这些目标的基本原理是通过再现已知的药物与疾病的关联而不会出现误报， MCEA 未来可用于识别与胃肠道疾病的新药理学关联 拟议研究的预期结果是它将证明 MCEA 是一种有效的方法。 用于药物流行病学研究，为多次暴露 OHD 的研究开辟新的研究机会。 这些新的研究机会可能会导致更快速地识别潜在的药理学原因 新出现的疾病和发现意想不到的有益药物作用，使这些药物能够 为了达到预期的结果，申请人将完成额外的工作。 以他的临床流行病学理学硕士为基础学习计算生物学的课程， 在这笔赠款和他的机构的支持下，他还将学习机器学习和计量经济学技术。 在专家的密切指导下直接将这些技术应用于药物流行病学应用 精心挑选的团队由在胃肠病学、药物流行病学、 通过这些活动，申请人将： 培养获得 NIH R01 级资助所需的技能，并成为开发小说的领导者 胃肠道疾病流行病学研究的应用技术。