Predicting Relapse Risk for Opioid Use Disorder

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

NIH-funded research New York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC · NIH-11113878

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 typeR01 grant
Study typeNIH-funded research
Funding institutionNew York State Psychiatric Institute Dba Research Foundation for Mental Hygiene, INC NIH-funded
Lab location1 site (New York, United States)
Project IDNIH-11113878 on NIH RePORTER

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.

Who could benefit from this research

Good fit: This project analyzes existing data from patients who have participated in Opioid Use Disorder clinical trials and received treatment.

Not a fit: 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.

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 performed: 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

New York, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-09 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.