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: 10460760
• 负责人: Ravy Kuppalapalle Vajravelu
• 金额: $ 11.28万
• 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/10460760
• 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/antagonist; 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.

项目摘要 大型观察健康数据库（OHD）的开发扩展了可用于分析的数据 由药物电子学研究。这些研究的效率可以通过简单地提高 研究多种药物与感兴趣疾病的关联。不幸的是，先前的研究已经 证明在研究多个 简单的暴露，因此限制了从这些类型的研究中得出的结论，并代表了 该领域的主要差距。该提案的目的，这是实现申请人长期的第一步 使用从 OHD是评估和验证药物类富集分析（MCEA），这是一种基于集合的信号到基础的新型信号 申请人开发的噪声富集算法，以分析OHD的多次暴露 灵敏度和特异性。该提议的核心假设是MCEA具有同等的敏感性和 与逻辑回归相比，更特异性是OHD的最广泛使用的分析方法 确定药物与临床结果之间的真正关联。申请人将完成 遵循两个相互关联的特定目的来检验假设：目标1 - 计算灵敏度和 用于识别药物类富集分析（MCEA）和逻辑回归（LR）的特异性 与艰难梭菌感染（CDI）的药物关联和目标2 - 以计算灵敏度和 MCEA和LR的特异性用于鉴定与胃肠道出血（GIH）的药物关联。这 这些目标的理由是，通过复制已知的药物疾病疾病关联而没有假阳性， MCEA可用于确定未来与胃肠道疾病的新型药物关联 研究。拟议的研究的预期结果是，它将证明MCEA是一种有效的方法 对于药物电子研究研究，为多曝光OHD研究开辟了新的研究机会。 这些新的研究机会可能会导致对潜在的药理学原因的更快识别 新兴疾病和发现意外的有益药物效应，使此类药物能够 重新使用新的适应症。为了获得预期的结果，适用的 基于他临床流行病学科学硕士来学习计算生物学的课程工作， 机器学习和经济学技术。在这笔赠款及其机构的支持下，他也将 直接将这些技术应用于药物ePidemiology opperspress 精心挑选的教师团队，具有丰富的胃肠病学经验，药物电子学， 医学信息和心理知识的授予赠款接受者。通过这些活动，适用将 发展获得NIH R01级资金所需的技能，并成为发展小说的领导者 用于胃肠道疾病流行病学研究的技术。