# Precision Pharmacogenomic Perioperative Prediction

> **NIH VA I21** · PORTLAND VA MEDICAL CENTER · 2024 · —

## Abstract

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...

## Key facts

- **NIH application ID:** 10804667
- **Project number:** 5I21HX003714-02
- **Recipient organization:** PORTLAND VA MEDICAL CENTER
- **Principal Investigator:** Thomas William Barrett
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2023-02-01 → 2025-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10804667

## Citation

> US National Institutes of Health, RePORTER application 10804667, Precision Pharmacogenomic Perioperative Prediction (5I21HX003714-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10804667. Licensed CC0.

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