Real-time relapse risk scoring for Opioid Use Disorder from trial data
Official title: Real time relapse risk scoring for Opioid Use Disorder (OUD) from clinical trial datasets
Funded at New York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC · NIH-11374999 · Data updated 2026-11-12
A tool that uses data from opioid treatment trials to produce a real-time score predicting a patient’s risk of relapse in the near future.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | August 1, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $669,094 |
| NIH project number | 5R01DA055610-04 |
| Project ID | NIH-11374999 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with Opioid Use Disorder who are in treatment and have longitudinal data such as UD S, TLFB, attendance, and medication records from clinical trials or care settings.
✕Less likely to help
Patients without access to detailed longitudinal treatment data or who are not engaged in treatment may not directly benefit.
What this research studies
This project will develop methods to turn longitudinal treatment data, such as urine drug screens, TLFB questionnaires, and attendance and medication records, into a real-time relapse risk score. It will create standardized approaches for computing scores and visualizing risk from existing clinical trial data, and outline how to test whether interventions shift those risk scores over time. The effort will result in user-friendly software modules that clinicians can integrate into treatment workflows to support decision-making. By focusing on practical data streams collected during treatment, the work aims to provide actionable insights while addressing data incompleteness and varying sampling frequencies.
Why it matters
Potential benefit: If successful, clinicians could identify high-risk periods for relapse and tailor interventions to prevent relapse more effectively.
How similar studies have gone: Previous work shows that machine-learning approaches can extract predictive patterns from complex treatment data, though real-time clinical tools are still developing.
Where this research is happening
-
New York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC
New York, United States Active
Researchers
- Principal investigator: Luo, Sean Xiao — New York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC
- Study coordinator: Luo, Sean Xiao
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.