# Cross-state validation of a novel prescription-based model to predict new long-term opioid use

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2023 · $84,988

## Abstract

PROJECT SUMMARY/ABSTRACT
Opioid use disorder and overdose remain significant public health concerns in the United States. The CDC
has identified reducing the number of previously opioid-naïve patients who transition to long-term (>90 days)
opioid use, an outcome associated with both overdose and opioid use disorder, as a key strategy for
preventing these opioid-related harms. Clinical tools to predict patient risk of transition to long-term use can
be an important component of this strategy. Prescription drug monitoring programs (PDMPs) are ideal
implementation platforms because they contain: a) statewide, population-level controlled substance
prescription data, b) web interfaces that clinicians in most states are required to check before prescribing
opioids, and c) basic support for clinical decision-making (e.g., identifying patients receiving prescriptions
from multiple prescribers). Key barriers to equipping PDMPs with these tools are that state populations differ
and PDMPs operate independently; building 50 different prediction models from scratch is inefficient and
impractical. States need accurate and generalizable PDMP-based prediction models that can be turned into
clinical tools to help clinicians avoid inappropriate transitions to long-term opioid use and, by extension,
prevent opioid-related harms. The goal of this project is to produce a scientifically transparent, generalizable,
and clinically useful prediction model that PDMP administrators can use as a foundation for future tool
development. Using 2016-2018 California PDMP data, we have developed and validated a novel risk
prediction model that predicts an opioid-naïve patients’ likelihood of transitioning to long-term opioid use with
high discrimination (concordance statistic = 0.91). Our proposed project has two objectives. First, to assess
how a California-based risk prediction model generalizes to Kentucky, a state with substantially different
demographics and higher rates of opioid prescribing and overdose than California, by updating our existing
2018 model to predict incident long-term use in Kentucky. Second, to compare accuracy of an updated form
of the existing California model versus new state-specific models developed using Kentucky PDMP data.
This project will provide PDMP administrators information about the trade-offs between using the product of
this proposal as a “foundational model” versus investing resources to develop their own state-specific
models. Study findings will set the stage for future implementation of prediction tools into the PDMPs of
Kentucky and California, and also provide critical information to PDMP agencies from other states interested
in implementing their own prediction tools. The proposed study is a necessary step in a research program to
build and implement PDMP-based tools that promote safe opioid prescribing and reduce the incidence of
opioid overdose, opioid use disorder, and other opioid-related harms in the United States.

## Key facts

- **NIH application ID:** 10654837
- **Project number:** 5R03DA054496-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Iraklis Erik Tseregounis
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $84,988
- **Award type:** 5
- **Project period:** 2022-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10654837

## Citation

> US National Institutes of Health, RePORTER application 10654837, Cross-state validation of a novel prescription-based model to predict new long-term opioid use (5R03DA054496-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10654837. Licensed CC0.

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