# Identifying Optimal Pain Management for Elders

> **NIH AHRQ R01** · STANFORD UNIVERSITY · 2021 · $393,732

## Abstract

ABSTRACT
Surgery is common and appropriate postoperative pain management is critical as poor management can impair
recovery and lead to adverse events, including prolonged opioid use and transition to chronic pain. Additional
specific risks in elder surgical patients include delirium and falls. Currently prejudice rather than evidence guides
the complex problem of elder perioperative pain management. Given the gravity of the US opioid epidemic,
policy makers are quickly establishing rules and regulations for opioid prescribing. These policies are blanket
regulations that neglect emerging evidence regarding the need for differential opioid prescriptions based on
clinical and patient factors, particularly in elders. Currently, there lacks tools to identify elders at high risk for
adverse pain outcomes. Such tools are needed to provide critical evidence on pain management to stakeholders
and move the field away from pain treatment for the ‘average’ elder patient to pain treatment for an individual. In
this grant, we propose an innovative approach to advance the systematic analysis of postoperative pain in elders.
Our approach will develop scalable, open source risk stratification tools for adverse pain outcomes in elders. We
will accomplish this work in three aims. First, we will develop clinical phenotypes to identify and extract key
discriminating features necessary to assess postoperative pain using EHRs. Next, we will develop pain risk
stratification models using machine learning, including deep learning, methods and tools based on phenotypes
developed in Aim 1. Finally, we will validate our models externally at the VA and disseminate our work through
open source libraries and public websites. This project will deliver validated risk-stratification tools derived from
real world evidence to identify elder patients at high risk for adverse pain outcomes following surgery, which can
potentially reduce prescribed opioids circulating in the community– a key to curbing the opioid epidemic.

## Key facts

- **NIH application ID:** 10212182
- **Project number:** 1R01HS027434-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Catherine Mills Curtin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $393,732
- **Award type:** 1
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212182

## Citation

> US National Institutes of Health, RePORTER application 10212182, Identifying Optimal Pain Management for Elders (1R01HS027434-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10212182. Licensed CC0.

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