Precision Pharmacogenomic Perioperative Prediction

背景：VA手术质量改进计划（VASQIP）预测重要的风险

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.