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This webinar provided an interactive learning experience for attendees to explore adolescent substance use and new paths for research. Presenter Niranjan Karnik, MD, PhD, director of the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) Great Lakes Node, reviewed recent data trends related to the prevalence of substance use among teens, described current treatment approaches, and talked about the pros and cons of social media and digital mental health interventions.

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In this webinar co-hosted by the CTN Western States Node, Natania Crane, PhD, provides current information on research and treatment related to cannabis use and mental health, including recent changes in cannabis use, how the endocannabinoid system may regulate mental health symptoms, and what we currently know about cannabis use and mental health.

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In this session, Drs. Rossom and Hooker discussed the design and implementation of Opioid Wizard, a clinical decision support tool embedded in the EHR for primary care clinicians and developed as part of NIDA Clinical Trials Network protocol CTN-0095. The goal of the tool is to help clinicians identify, screen, diagnose and treat opioid use disorder (OUD).

They also discussed one of the supplements to CTN-0095, which tested a training to reduce stigma towards people with OUD among primary care clinicians.

Related protocols: CTN-0095

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In this session, Kathleen T. Brady, MD, PhD, of the Medical University of South Carolina, will discuss the NIDA Clinical Trials Network (CTN) study CTN-0108, “Transcranial Magnetic Stimulation for the Treatment of Methamphetamine/Cocaine Use Disorder,” a pilot study that aims to determine the feasibility, effectiveness, and safety for 20 sessions of repetitive transcranial magnetic stimulation (rTMS) versus sham in adults with a diagnosed methamphetamine or cocaine use disorder. Dr. Brady, co-Principal Investigator for CTN-0108, presented the rationale for the study, including the literature supporting the use of rTMS in stimulant use disorder, as well as the study methodology and preliminary results.

Related protocols: CTN-0108

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This presentation from Noel Vest, PhD, Assistant Professor at Boston University School of Public Health, explored findings from a mixed-methods national study examining the structure, implementation, and impact of Collegiate Recovery Programs (CRPs) in the U.S. and Canada.

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In this webinar, Erin Winstanley, PhD (University of Pittsburgh and West Virginia University, CTN Appalachian Node), described a NIDA Clinical Trials Network study, CTN-0135, examining clinician-reported challenges with initiating buprenorphine for people using fentanyl, including precipitated or prolonged withdrawal, patient reports that buprenorphine was ineffective, and patient preference for methadone. In response to these challenges, most clinicians surveyed reported modifying their standard induction protocols or patient counseling approaches. Clinicians treating larger patient volumes, seeing a high proportion of patients using fentanyl, or initiating treatment in non-inpatient settings were more likely to report difficulties starting patients on buprenorphine.

Related protocols: CTN-0135

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Retention on buprenorphine for opioid use disorder is known to be poor across health systems, despite established mortality benefits of treatment, and little is known about how to counsel patients who want to stop using buprenorphine or who have to stop due to barriers to ongoing care.

This webinar will consider what the evidence from a prescription opioid registry developed by NIDA Clinical Trials Network study CTN-0084 suggests for individuals who discontinue buprenorphine, including whether there was a length-of-treatment exposure associated with improved outcomes. Implications for patient education and shared decision-making will be considered. Understanding the optimal length of treatment after which individuals can safely discontinue buprenorphine could help patients better understand what treatment might look like at the outset, supporting informed decision-making and potentially improving both treatment retention and outcomes.

Related protocols: CTN-0084

Methadone is an essential tool for addressing opioid use disorder, especially with the prevalence of high-potency synthetic opioids in the drug supply. The current care delivery model in the United States with siloed methadone clinics has many limitations. Legislation has been proposed to expand access to methadone to office-based settings with pharmacy dispensing. Even with legal and regulatory changes, there are many practical barriers to implementation which include insurance coverage and patient cost, prior authorizations, and stocking of methadone.

Practical steps for clinicians and policymakers to take to overcome these barriers include ensuring insurance coverage for methadone, removing methadone from the algorithms that limit the amount of controlled substances pharmacies can order, and obtaining concentrated methadone formulations.

Related protocols: CTN-0131

Background and aims: US regulatory changes allowed for additional methadone take-home doses following COVID-19 onset. How dispensing practices changed and which factors drove variation remains unexplored. We determined daily methadone dispensing trajectories over six months before and after regulatory changes due to COVID-19 using state sequence analysis and explored correlates.

Design: Retrospective chart review of electronic health records.

Settings: Nine opioid treatment programs (OTPs) across nine US states.

Participants: Adults initiating treatment in 2019 (n = 328) vs. initiating 1 month after the COVID-19 regulatory changes of March 2020 (n = 376).

Measurements: Type of daily methadone medication encounter (in-clinic, weekend/holiday take-home, take-home, missed dose, discontinued) based on OTP clinic; cohort (pre vs. post-COVID-19); and patient substance use, clinical and sociodemographic characteristics.

Findings: Following COVID-19 regulatory changes, allotted methadone take-home doses increased from 3.5% to 13.8% of total person-days in treatment within the first 6 months in care. Clinic site accounted for the greatest variation in methadone dispensing (6.2% and 9.5% of the variation of discrepancy between sequences pre- and post-COVID-19, respectively). People who co-use methamphetamine had a greater increase in take-homes than people who did not use methamphetamine (from 3.7% pre-pandemic to 21.2% post-pandemic vs. 3.5% to 12.5%) and higher discontinuation (average 3.6 vs. 4.7 months among people who did not use methamphetamine pre-COVID-19; average 3.3 vs. 4.6 months post-COVID-19). In the post-COVID-19 cohort, females had a higher proportion of missed doses (17.2% vs. 11.9%) than males. People experiencing houselessness had a higher proportion of missed doses (19% vs. 12.3%) and shorter stays (average 3.5 vs. 4.5 months) when compared with those with stable housing.

Conclusion: Daily methadone dispensing trajectories in the US both before and following COVID-19 regulatory changes appeared to depend more on the opioid treatment programs’ practices than individual patient characteristics or response to treatment.

Related protocols: CTN-0112

Aims: This study, supported by the CTN Ohio Valley Node, aimed to identify substance use disorder (SUD) patterns and their association with T2DM health outcomes among patients with type 2 diabetes and hypertension.

Methods: Researchers used latent class analysis on electronic health records from the MetroHealth System (Cleveland, Ohio) to obtain the target SUD groups: i) only tobacco (TUD), ii) tobacco and alcohol (TAUD), and iii) tobacco, alcohol, and at least one more substance (PSUD). A matching program with Mahalanobis distance within propensity score calipers created the matched control groups: no SUD (NSUD) for TUD and TUD for the other two SUD groups. The numbers of participants for the target-control groups were 8009 (TUD), 1672 (TAUD), and 642 (PSUD).

Results: TUD was significantly associated with T2DM complications. Compared to TUD, the TAUD group showed a significantly higher likelihood for all-cause mortality (adjusted odds ratio (aOR) = 1.46) but not for any of the T2DM complications. Compared to TUD, the PSUD group experienced a significantly higher risk for cerebrovascular accident (CVA) (aOR = 2.19), diabetic neuropathy (aOR = 1.76), myocardial infarction (MI) (aOR = 1.76), and all-cause mortality (aOR = 1.66).

Conclusions: The findings of increased risk associated with PSUDs may provide insights for better management of patients with T2DM and hypertension co-occurrence.

Aims: This study aimed to examine within and between effects of the relationship between depression, using the Beck Depression Inventory (BDI), and cocaine craving, using the visual analog craving scale (VAS), over time.

Methods: Data from the NIDA Clinical Trial Network Study Cocaine Use Reduction with Buprenorphine (CTN-0048) were used in a secondary analysis (CTN-0148). Random-effects regression modelling was used to examine relationships between participants’ depressive symptoms and their cocaine craving over time.

Results: A total of 301 participants with past-year DSM-IV criteria for cocaine and opioid use disorder were analyzed (21.6% female, 10.3% Hispanic, 66.4% Black) being treated with placebo or buprenorphine+naloxone. Craving significantly decreased over time (B = −1.11, 95% CI [-1.21, −1.02], p < 0.001). Depression emerged as a significant within-person predictor of craving over time (B = 0.93, 95% CI [0.76, 1.09], p<0.001), indicating that when a person’s BDI score increased by one point from their own mean, their craving increased by 0.71 units. Between-person differences in average BDI did not have significant effects (p>0.05), indicating that depression scores across participants did not significantly predict differences in craving.

Conclusions: These findings highlight depression as a dynamic, time-varying clinical marker of heightened craving risk and suggest that monitoring and addressing increases in depressive symptoms during treatment may help mitigate craving spikes and potentially reduce vulnerability in returning to use.

Related protocols: CTN-0148

This is the primary outcomes paper for CTN-0101.

Background and aims:
Individuals who engage in illicit or nonmedical opioid use may have elevated risk of health and social consequences, including progression to opioid use disorder (OUD). Preventive interventions to reduce this risk are lacking. This trial tested the impact of a primary care-integrated collaborative care approach for reducing risky opioid use, defined as nonmedical use of prescription opioids or any use of illicit opioids.

Design: Cluster-randomized controlled trial randomized primary care providers (PCPs) and their patients into the Subthreshold Opioid Use Disorder Prevention (STOP) intervention or enhanced usual care (EUC).

Setting: Primary care clinics at 5 U.S. sites.

Participants:
PCPs and their patients were recruited January 2021–May 2023. A total of 119 PCP clusters (STOP=48, EUC=51) and 202 patients (STOP=88, EUC=114) enrolled. Eligible patients were adults (≥18 years) having current risky opioid use, without moderate–severe OUD. Patient participants were majority female (63.4%), white (70.8%) and non-Hispanic (96.5%), with a mean age of 55.7 [standard deviation (SD) = 12.7] years. At baseline, 63.4% of participants had moderate–severe pain (Brief Pain Inventory) and below average physical (79.2%) and mental (62.4%) health (SF-12).

Interventions: The STOP collaborative care intervention consisted of brief advice from the PCP about reducing risky opioid use, meetings with a clinic-embedded nurse care manager over 12 months and remote health coaching (2–6 sessions). Both groups received primary care treatment as usual and overdose risk reduction materials.

Measurements: The primary outcome was total days of risky opioid use, recorded from 6 monthly electronic surveys. A key secondary outcome was moderate–severe OUD at 6 and 12 months.

Findings: A total of 77 (87.5%) STOP and 107 (93.9%) EUC participants completed the 6-month assessment period. The primary outcome analysis used the Intention-to-Treat sample with multiple imputations of missing data. Mean days of risky opioid use at 180 days were lower in STOP than EUC [12.2 (SD = 27.73) vs. 15.5 (SD = 32.64)]; the difference between groups adjusted for baseline risky opioid use was not statistically significant (rate ratio 0.95, 95% confidence interval = 0.52–1.74). One STOP participant (1.1%) and 13 EUC participants (11.4%) developed moderate–severe OUD at 6 months, and 3 (3.4%) STOP and 6 (5.3%) EUC participants had moderate–severe OUD at 12 months (P<0.001).

Conclusions: This cluster-randomized controlled trial did not find evidence that the STOP intervention for reducing risky opioid use produced greater reductions over 6 months compared with enhanced usual care, though fewer intervention participants progressed to moderate–severe opioid use disorder. Patients had a high burden of pain and comorbidities that may present challenges to reducing opioid use.

Related protocols: CTN-0101

Substance use disorder (SUD) is a complex chronic condition requiring a multi-disciplinary approach to both research and treatment. Randomized controlled trials (RCTs) are gold standard methodologies for inferring causal relationships between an intervention and treatment outcomes but often face challenges in generalizability, scalability and real-world implementation. Target trial emulation (TTE) is a powerful methodological framework that uses observational or real-world data sources to emulate the methodology of these gold standard target trials to complement the learning from RCTs and enhance translation to real world evidence. An additional methodological innovation is the translational testing of clinical- and community-based digital health systems to provide new insights into SUD in the real world and provide scalable access to therapeutic resources.

To explore these methodological innovations in SUD research, the National Institute on Drug Abuse Center for the Clinical Trials Network convened a variety of experts for a virtual workshop titled “Target Trial Emulation in Observational Research and Translational Testing of Advanced Digital Health Tools for Substance Use Disorder Prevention and Treatment.” This article summarizes the discourse of the workshop, focused on three thematic areas: TTE using real-world healthcare data, SUD evidence from nationwide data sources that may be useful in TTE analyses, and translational testing of clinical- and community-based digital health systems. The workshop also highlighted various exemplars of digital health systems that demonstrate success in translational research addressing SUDs, key methodological and translational challenges, importance of rigorous study design, robust data linkages and expanding use of common data elements, and the integration of digital health tools to enhance causal inference and clinical impact. Future research directions are outlined to refine these approaches, address barriers, and maximize the utility of real-world data in shaping effective SUD prevention and treatment strategies.

This presentation provided a brief summary of the CTN-0135 study, Buprenorphine Treatment Engagement and Induction Problems Among Individuals Using Fentanyl, which aimed to determine whether there are regional variations in the prevalence of fentanyl use and problems initiating buprenorphine/naloxone. A secondary purpose was to characterize the specific problems that clinicians are encountering when initiating BUP treatment in patients using fentanyl and to determine how standard clinical protocols are being modified to engage patients in treatment, which was carried out via a survey administered to 396 clinicians.

The study team wanted to explore other dissemination strategies besides publishing in peer-reviewed journals in order to better share the results with a broader audience. To that end, they decided to develop an online data dashboard, which they developed using Shiny Apps. A second survey was sent out to original participants who had opted in for future contact (n=364) to ask questions about the dashboard, including questions about general usability, basic demographics, and some open-ended items.

Responses to the dashboard were positive, with 84.4% reporting they were at least “somewhat satisfied” with the dashboard. 91.7% found the study results to be relevant to clinical practice, and 77.7% said they would be likely to use a data dashboard to learn about study results. Strengths of the dashboard noted in the open-ended questions included “simplicity and ease of use,” while opportunities for improvement included “color scheme, scrolling reduction, and font sizes.”

Overall, the data dashboard evaluation results suggest it is a feasible dissemination strategy for a clinical audience: easy to use, something clinicians would recommend to colleagues, and clinically relevant. Future work should integrate users into the development process and consider visualization literacy as well as computer skills.

Find the Trends in Drug Use Study Dashboard here.

Related protocols: CTN-0135

Background: While medication for opioid use disorder (MOUD) is effective for a significant proportion of patients, many return to using opioids during treatment. Understanding which factors lead to successful treatment informs the development of implementation approaches that can improve outcomes. This manuscript and its accompanying website provide an applied introduction to interpretable machine learning for clinical investigators interested in predicting treatment response for people using MOUD.

Methods: This study, which uses data from CTN-0094, applied machine learning (ML) algorithms (K-Nearest Neighbors (KNN), logistic regression with and without regularization, Multivariate Additive Regression Splines (MARS), Support Vector Machines, Classification and Regression Trees (CART), Random Forest, Bayesian Additive Regression Trees (BART), Boosted Trees, Neural Networks) to predict failure of treatment in a collection of 2478 individuals who had participated in the three largest pragmatic, clinical trials of MOUD.

Results: All models produced Receiver Operating Characteristic Area Under the Curve (ROC AUC) estimates in the range of 0.62 to 0.67 using cross-validation data and the optimal model, random forest, achieved 0.65 using testing data. The algorithms nearly universally identified predictive features such as age, intravenous drug use days, study medication, and study site. Most algorithms also identified various aspects of smoking. Only the algorithms that detect complex non-linear trends identified details from timeline follow-back. One algorithm, BART, performed well while devaluing all treatment-specific details.

Conclusions: After explaining how to apply, compare, and contrast various ML workflows, the results show that while overall modeling performance is similar across the models developed, the use of different algorithms identifies different sets of predictive features. Previous research has not recognized some features as important for predicting treatment outcomes. A companion website introduces clinical investigators to the concepts and implementations this study presents. That site also provides a detailed annotated blueprint to fully replicate, or even expand, this work.

Related protocols: CTN-0094