# The Use of Novel Linked Databasesto Reduce Postoperative Opioid Use Among Patients Undergoing Inpatient Surgery

> **NIH NIH R61** · STANFORD UNIVERSITY · 2023 · $1,105,489

## Abstract

PROJECT SUMMARY AND ABSTRACT
Surgery places patients at increased for opioid use disorder and persistent postoperative opioid use (PPOU).
In addition to its direct impact on patient health, PPOU, which affects 5-6% of surgical patients, is associated
with an increased risk of opioid use disorder, opioid overdose, and surgical mortality/morbidity. This issue has
particular salience for older adults. Over half of all surgical procedures in the United States occur among older
adults, over half of older adults will require a surgery once in their lifetime, and the incidence of PPOU among
older adults can be as high as 10%. In this light, the long-term goal of this project is to characterize the
effectiveness of perioperative interventions in reducing the risk of long-term outcomes such as PPOU and
opioid use disorder among older adults undergoing inpatient surgery. While a wide variety of interventions has
been hypothesized to reduce the incidence of PPOU and other post-operative opioid outcomes, there remains
a lack of consensus about their effectiveness, in part due to data limitations. In particular, it is often challenging
to obtain detailed data on perioperative care (i.e., amount of opioid administered intraoperatively) and data on
long-term opioid outcomes. This study builds on a novel dataset that links two datasets: the Multicenter
Perioperative Outcomes Group (MPOG), a large, multicenter registry of surgical cases using data extracted
from electronic medical records and a healthcare claims data for Medicare fee-for-service patients. This novel
dataset unites the best aspects of both datasets: the ability to measure perioperative care and the ability to
follow patients in order to assess long-term opioid outcomes. We will accomplish the goals of the project
through four specific aims. First, we will augment the existing dataset by developing scalable and
generalizable tools to incorporate relevant data from the inpatient stay (i.e. opioid administration and the use of
non-opioid adjuncts). Second, we will demonstrate the feasibility of these methods at a single institution and
expand their use to five institutions. Third, we will use the augmented dataset to evaluate the association
between intraoperative interventions (i.e., opioid administration, use of nerve blocks) and long-term opioid
outcomes (i.e., PPOU and opioid use disorder). Finally, we will use the augmented dataset to evaluate the
association between inpatient stay interventions (i.e., reduced opioid utilization, reduced prescribing at
discharge) and long-term opioid outcomes. The findings of this project will be significant as they will help guide
crucial aspects of perioperative decision-making such as intraoperative and postoperative opioid
administration. The expected outcomes of this project are relevant to the goals of the HEAL Initiative as they
will enhance efforts to reduce the incidence of PPOU and other long-term opioid outcomes such as opioid use
disorder. Crucially,...

## Key facts

- **NIH application ID:** 10745607
- **Project number:** 1R61DA059168-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Douglas Alastair Colquhoun
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,105,489
- **Award type:** 1
- **Project period:** 2023-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10745607

## Citation

> US National Institutes of Health, RePORTER application 10745607, The Use of Novel Linked Databasesto Reduce Postoperative Opioid Use Among Patients Undergoing Inpatient Surgery (1R61DA059168-01). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10745607. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*