Predicting Relapse Risk for Opioid Use Disorder
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-11113878 · Data updated 2026-11-12
This project is creating a tool to help doctors understand a patient's risk of relapse for Opioid Use Disorder in real-time, using information from past treatment.
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 | 2025 |
| Award (this year) | $650,581 |
| NIH project number | 5R01DA055610-03 |
| Project ID | NIH-11113878 on NIH RePORTER |
Who could benefit from this research
✓Could help
This project analyzes existing data from patients who have participated in Opioid Use Disorder clinical trials and received treatment.
✕Less likely to help
Patients not currently receiving treatment for Opioid Use Disorder or those whose data is not part of existing clinical trial datasets may not directly benefit from this specific tool.
What this research studies
We are developing a new way to predict if someone with Opioid Use Disorder might relapse soon, similar to how a credit score works. This involves looking at information gathered during treatment, such as records of substance use, attendance at therapy sessions, and medication history. By using advanced computer methods, we can analyze these different types of patient data, even if they are incomplete or collected at different times. Our goal is to provide doctors with a clear, visual risk score and a way to see if treatments are making a positive difference in a patient's risk over time.
Why it matters
Potential benefit: If successful, this tool could help clinicians tailor treatment plans more effectively and intervene sooner to prevent relapse in patients with Opioid Use Disorder.
How similar studies have gone: While the concept of using longitudinal data for risk prediction is established in other fields, applying advanced machine learning to real-time OUD relapse risk scoring from diverse clinical trial datasets is a novel approach.
Where this research is happening
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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
Related research
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Real-time relapse risk scoring for Opioid Use Disorder from trial data
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This project checks whether adding daily buprenorphine to monthly extended‑release naltrexone helps people with opioid use disorder stay off opioids.
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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.