Predicting relapse risk for patients with 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-10892020 · Data updated 2026-11-12
This study is working on a new tool that helps doctors predict the chances of someone with Opioid Use Disorder relapsing, using information like their treatment habits and drug tests, so they can provide better support when it's needed most.
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 | 2024 |
| Award (this year) | $658,442 |
| NIH project number | 5R01DA055610-02 |
| Project ID | NIH-10892020 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals currently undergoing treatment for Opioid Use Disorder.
✕Less likely to help
Patients who are not currently in treatment for Opioid Use Disorder may not benefit from this research.
What this research studies
This research focuses on developing a real-time scoring system to predict the risk of relapse in patients with Opioid Use Disorder (OUD). By analyzing data from clinical trials, including patient behavior, treatment attendance, and drug screening results, the project aims to create a score similar to a credit score that can inform clinicians about a patient's likelihood of relapse in the near future. The use of advanced machine learning techniques will help to standardize and visualize these risk scores, making them accessible and actionable for healthcare providers. Ultimately, this tool aims to improve treatment outcomes by allowing for timely interventions based on individual risk assessments.
Why it matters
Potential benefit: If successful, this research could provide clinicians with valuable insights to better manage and support patients at risk of relapse.
How similar studies have gone: Other research has shown promise in using data analytics and machine learning to predict relapse in substance use disorders, indicating that this approach has potential for success.
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
Related research
Other NIH-funded projects on the same topics.
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Real-time relapse risk scoring for Opioid Use Disorder from trial data
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.
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Smartphone app helps people on opioid treatment stay abstinent
A smartphone app called OPTiMA supports people on medication-assisted treatment to stay in recovery by daily self-checks and personalized feedback.
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Predicting Relapse Risk for Opioid Use Disorder
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.
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Exploring how unrealistic optimism affects opioid use and relapse
This study is looking at how being overly optimistic might affect people with opioid use disorder and their chances of relapsing, so we can better understand their decision-making and help create more effective treatment plans.
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Combining buprenorphine with extended-release naltrexone to prevent opioid relapse
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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Using smartphone apps to help prevent relapse in people with opioid use disorder
This study is testing a smartphone app called OPTiMA to see if it can help people with opioid use disorder stay on track and avoid relapse, by giving them daily check-ins and personalized tips, while comparing it to a group that only gets regular monitoring.
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.