Machine Learning for Opioid Addiction

Recruiting at 2 trial locations

Overseen By David Reeser

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: OpiAID

Must be taking: Methadone, Buprenorphine

Disqualifiers: Sleeve tattoo, Non-compliance, Others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

420 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

To train a machine learning model/algorithm for an evaluation of the use of biometric data captured at the wrist for the identification of acute opioid use events and the quantification of opioid withdrawal in opioid dependent individuals.

Who Is on the Research Team?

David MacQueen, PhD

Principal Investigator

OpiAID

Are You a Good Fit for This Trial?

Inclusion Criteria

I am 22 years old or older.

I have been diagnosed with opioid use disorder and can start treatment with methadone or buprenorphine.

Exclusion Criteria

I have trouble making decisions due to my health condition.

Subject not proficient with written and spoken English

Subject determined likely to be non-compliant by physician/HCP

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Induction and Monitoring

Participants are monitored using the OpiAID Strength Band Platform™ to detect MOUD events and quantify withdrawal levels

14 days

Continuous monitoring

Titration

Prescribing physician determines appropriate starting dose with expected titration over 2-6 weeks

2-6 weeks

Follow-up

Participants are monitored for safety and effectiveness after the monitoring period

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Strength Band Platform

How Is the Trial Designed?

Group I: Single arm 14 day monitoring period Experimental Treatment 1 Intervention

The goal of this real-world, multi-center, outpatient study is to train a machine learning model/algorithm utilizing patient-specific physiological parameters from the OpiAID Strength Band Platform™ can accurately detect MOUD events during the induction phase with a predefined classification success when comparing the True Positive Rate against the False Positive Rate as plotted on a Receiver Operator Curve. In addition to MOUD detection, machine learning will be used to quantify participant withdrawal level from physiological parameters. To demonstrate that withdrawal quantification performs as well or better than current measures used for this purpose the correlation between quantified withdrawal and time since last opioid dose (TSLD) will be computed and compared against the association between SOWS and TSLD in a non-inferiority analysis. Prescribing physician must determine appropriate starting dose (titration expected over 2-6 weeks)

Who Is Running the Clinical Trial?

OpiAID

Lead Sponsor

National Institute on Drug Abuse (NIDA)

Collaborator

Trials

2,658

Recruited

3,409,000+