# Real time relapse risk scoring for Opioid Use Disorder (OUD) from clinical trial datasets

> **NIH NIH R01** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2024 · $658,442

## Abstract

Real time relapse risk scoring for Opioid Use Disorder (OUD) from clinical trial datasets
Project Summary
Any clinician treating a patient with Opioid Use Disorder (OUD) would like to know whether this patient would
relapse in the next week or month. Such a score, analogous to a credit score in consumer finance, may be
similarly obtained from longitudinal data streams derived from patient behavior during SUD treatment. There
are several common data sources that every OUD patient in treatment produces: a binary, longitudinal data
survey of use patterns for a set of pre-determined substances of abuse, treatment session attendance records,
and medication records. In particular, urine drug screens (UDS) or alcohol and nicotine breathalyzers and
standard Timeline Follow Back (TLFB) questionnaires are universal surveys in every treatment delivery
context, including large pragmatic clinical trials. While these data streams are incomplete, of different lengths
and sampling frequencies, and correlate in complex ways, contemporary machine learning methods allow us to
overcome these challenges. We aim to build a toolbox that would allow for the following: 1) standardized
methods for risk scoring and visualization from UDS and TLFB datasets in existing large clinical trials; 2)
standard methods for inferences of risk scores: procedure for hypothesis testing whether an intervention made
a difference in the risk scores and their trajectories. 3) user-friendly software modules aimed toward
researchers and administrators for quality improvement projects and customized predictive modeling pipelines,
and interpretable web portal for clinicians, analogous to a credit report. This proposal will also incorporate
usability survey and evaluation for algorithmic bias. These applications will provide a computational framework
for future real time predictive modeling work for many other different substance use disorders.

## Key facts

- **NIH application ID:** 10892020
- **Project number:** 5R01DA055610-02
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** Raymond Balise
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $658,442
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10892020

## Citation

> US National Institutes of Health, RePORTER application 10892020, Real time relapse risk scoring for Opioid Use Disorder (OUD) from clinical trial datasets (5R01DA055610-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10892020. Licensed CC0.

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