Precision Pharmacogenomic Perioperative Prediction

精准药物基因组围手术期预测

• 批准号: 10643419
• 负责人: Thomas William Barrett
• 金额: --
• 依托单位: PORTLAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643419
• 关键词: Adrenergic beta-Antagonists; Adverse drug event; Affect; Anaphylaxis; Anesthetics; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Antibiotics; Anticoagulants; Antidotes; Antiemetics; Artificial Intelligence; Aspirin; Bacteremia; Beds; Blood Vessels; Cardiac; Cardiac Surgery procedures; Cardiovascular system; Caring; Cessation of life; Characteristics; Clinical; Collaborations; Cox Proportional Hazards Models; Cytochrome P450; Data; Databases; Decision Making; Delirium; Drug Targeting; Drug Transport; Drug usage; Environment; Event; Exposure to; Frequencies; Future; Genes; Goals; Heart failure; Hemorrhage; Heparin; Home; Hospitals; Immune response; Immunotherapeutic agent; Individual; Informatics; Infrastructure; Injury to Kidney; Intervention; Intervention Studies; Label; Learning; Length; Length of Stay; Life; Linear Regressions; Liver Dysfunction; Machine Learning; Malignant hyperpyrexia due to anesthesia; Medicine; Metabolism; Methodology; Mission; Modeling; Myocardial Infarction; Naloxone; Non-Steroidal Anti-Inflammatory Agents; Operative Surgical Procedures; Opioid; Oral; Outcome; Patient Care; Patients; Perioperative; Persons; Pharmaceutical Preparations; Pharmacogenomics; Pneumonia; Postoperative Pain; Postoperative Period; Private Sector; Procedures; Reaction; Research; Resources; Retrospective cohort study; Risk; Safety; Sepsis; Single Nucleotide Polymorphism; Stroke; Subgroup; Testing; Time; Treatment Effectiveness; Update; Urinary tract infection; Variant; Venous; Veterans; Vitamin K; Warfarin; Wound Infection; artificial neural network; classification trees; clinical care; clinical decision support; clinical practice; clopidogrel; cost; cost effectiveness; data warehouse; design; drug metabolism; functional status; high risk; hospital readmission; improved; improved outcome; innovation; kidney dysfunction; liver injury; machine learning method; novel therapeutics; opioid exposure; pain score; patient oriented; patient subsets; performance site; phenomics; phenotypic data; process improvement; programs; prospective; regression trees; risk prediction; surgery outcome; surgical risk; thrombotic; treatment response

Background: The VA Surgical Quality Improvement Program (VASQIP) predicts risk for important postoperative outcomes and shares process improvements from high performance sites with lower performance sites to continuously improve surgical outcomes. The VASQIP was so successful it was implemented in the private sector and continues today. The proposed research will add pharmacogenomic data from the Million Veterans Program (MVP) to the VASQIP. In addition, the VASQIP is collaborating with the VA National Artificial Intelligence Institute (NAII) to add more phenotype data from other VA databases including VASQIP, Centralized Interactive Phenomics Resource (CIPHER), VA Informatics and Computing Infrastructure (VINCI), and the Corporate Data Warehouse. This phenotype data will also be added to the VASQIP and machine learning/ artificial intelligence will be used to update the VASQIP in a separate project that will be done in parallel. Significance: Pharmacogenomics examines an individual person’s genes that affect drug metabolism, drug target, drug transport, or drug immune response and the impact on adverse drug events and treatment effectiveness. Pharmacogenomics can explain the variation in treatment response that is commonly seen in clinical practice. Pharmacogenomics has been associated with both worse and improved outcomes and cost effectiveness in a number of clinical settings. Pharmacogenomic data is included on 499 FDA drug labels. Despite this acknowledgement of the benefits of Pharmacogenomic testing, such testing is not routinely completed within the VA in general, and not for surgery specifically. Innovation & Impact: There are several innovative approaches to the proposed research. Applying pharmacogenomic data to surgical outcomes, using machine learning and artificial intelligence to add phenotypic data to the VASQIP program with the goal of rapidly implementing the results into patient care to optimize patient centered decision making and outcomes are all innovative. Specific Aims: 1) Identify pharmacogenomic risk associations with outcomes among individuals receiving vascular surgery and cardiac surgery the past 10 years for established (tier 1 and 2) drug/ gene sets. 2) Identify pharmacogenomic risk associations with outcomes among individuals receiving vascular surgery and cardiac surgery the past 10 years for non-established (tier 3) drug/ gene sets. 3) Assess frequency of study drug usage and presence of pharmacogenomic genes for power modeling future studies. 4) Identify high-risk subgroups that may benefit from pharmacogenomic testing. Methodology: This is a retrospective cohort study that will use the standard VASQIP variables and outcomes. Baseline analysis will use linear regression or Cox’s proportional hazards model and will control for patient baseline characteristics and surgical factors using propensity scores with matching or inverse weighting. Machine learning methods such as artificial neural networks, classification and regression trees, or ensemble learning will be used to improve predictions and account for nonlinear relationships and interactions among the potentially large set of pharmacogenomic features. Next Steps/Implementation: The results of the proposed research will be used to update all VASQIP surgeries, then field implementation can occur for real time clinical decision support. High risk patient subgroups will be identified that would benefit from preoperative pharmacogenomic testing and further intervention studies.

背景：VA 手术质量改进计划 (VASQIP) 预测重要手术的风险 术后结果并分享高性能站点的流程改进 VASQIP 非常成功。 拟议的研究将在私营部门实施，并在今天继续进行。 此外，VASQIP 还与百万退伍军人计划 (MVP) 合作。 VA 国家人工智能研究所 (NAII) 从其他 VA 数据库添加更多表型数据 包括 VASQIP、集中式交互式表型组学资源 (CIPHER)、VA 信息学和计算 基础设施 (VINCI) 和企业数据仓库 此表型数据也将添加到 VASQIP 和机器学习/人工智能将用于在单独的项目中更新 VASQIP 这将同时进行。 意义：药物基因组学检查个体影响药物代谢、药物 靶点、药物转运或药物免疫反应以及对药物不良事件和治疗的影响 有效性。药物基因组学可以解释常见的治疗反应变化。 临床实践中，药物基因组学与较差和改善的结果和成本相关。 499 个 FDA 药物标签中包含药物基因组学数据。 尽管承认药物基因组测试的好处，但此类测试并不常规进行 一般在 VA 内完成，而不是专门用于手术。 创新和影响：所提出的研究有几种创新方法。 使用机器学习和人工智能将药物基因组数据添加到手术结果中 将表型数据提供给 VASQIP 计划，目标是将结果快速应用到患者护理中 优化以患者为中心的决策和结果都是创新的。 具体目标： 1) 确定药物基因组风险与接受治疗的个体的结果之间的关联 过去 10 年针对已建立的（第 1 级和第 2 级）药物/基因组进行的血管外科和心脏外科手术 2)。 确定药物基因组风险与接受血管手术的个体的结果之间的关联 过去 10 年针对未确定的（第 3 级）药物/基因组进行过心脏手术 3) 评估研究频率。 药物使用和药物基因组基因的存在，用于未来研究的功率建模 4) 识别高风险。 可能受益于药物基因组学测试的亚组。 方法：这是一项回顾性队列研究，将使用标准 VASQIP 变量和结果。 基线分析将使用线性回归或 Cox 比例风险模型，并将控制患者 使用倾向评分和匹配或反向加权来确定基线特征和手术因素。 机器学习方法，例如人工神经网络、分类和回归树或集成 学习将用于改进预测并解释非线性关系和相互作用 潜在的大量药物基因组特征。 后续步骤/实施：拟议研究的结果将用于更新所有 VASQIP 手术，然后可以进行现场实施，为高风险患者提供实时临床决策支持。 将确定受益于术前药物基因组学测试的亚组，并进一步 干预研究。