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Abstract: Several large-scale, pragmatic clinical trials on opioid use disorder (OUD) have been completed in the National Drug Abuse Treatment Clinical Trials Network (CTN). However, the resulting data have not been harmonized between the studies to compare the patient characteristics. This paper provides lessons learned from a large-scale harmonization process that are critical for all biomedical researchers collecting new data and those tasked with combining datasets. Researchers harmonized data from multiple domains from CTN-0027 (N = 1269), which compared methadone and buprenorphine at federally licensed methadone treatment programs; CTN-0030 (N = 653), which recruited patients who used predominantly prescription opioids and were treated with buprenorphine; and CTN-0051 (N = 570), which compared buprenorphine and extended-release naltrexone (XR-NTX) and recruited from inpatient treatment facilities. Patient-level data were harmonized and a total of 23 database tables, with meticulous documentation, covering more than 110 variables, along with three tables with “meta-data” about the study design and treatment arms, were created. Domains included: social and demographic characteristics, medical and psychiatric history, self-reported drug use details and urine drug screening results, withdrawal, and treatment drug details. In this paper, the authors summarize the numerous issues with the organization and fidelity of the publicly available data which were noted and resolved, and present results on patient characteristics across the three trials and the harmonized domains, respectively. A systematic harmonization of OUD clinical trial data can be accomplished, despite heterogeneous data coding and classification procedures, by standardizing commonly assessed characteristics. Similar methods, embracing database normalization and/or “tidy” data, should be used for future datasets in other substance use disorder clinical trials. Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

Racial and ethnic disparities in access to treatment and quality of treatment for opioid use disorder (OUD) have been identified in usual care settings. In contrast, disparities in treatment quality within clinical trials are relatively unexamined. This study aimed to estimate racial and ethnic differences in the dose of opioid agonist treatment for OUD in the first 4 weeks of treatment in clinical trials.

This cohort study performed analysis of the methadone and buprenorphine treatment arms of 3 trials conducted by the National Institute on Drug Abuse Clinical Trials Network between May 2006, and January 31, 2017, at multiple Clinical Trials Network sites across the US (CTN-0027, START, CTN-0030, POATS, and CTN-0051, X:BOT). Trial participants who were randomized to and initiated buprenorphine or methadone treatment and who identified as Hispanic, non-Hispanic Black, or non-Hispanic White were included in the present study. Data were analyzed from November 1, 2023, to August 5, 2024. THe main outcomes and measures were the maximum daily dose of buprenorphine or methadone received in each week for the first 4 weeks of treatment. The mean dose and percentage of patients receiving a higher dose (buprenorphine =16 mg and methadone =60 mg) were also compared across race and ethnicity groups.

A total of 1748 patients (1263 who initiated buprenorphine and 485 who initiated methadone treatment) were included in the analysis (1168 [66.8%] male; median age, 33 [IQR, 26-45] years). Of these, 138 patients (7.9%) identified as Black, 273 (15.6%) as Hispanic, and 1337 (76.5%) as White. In week 4, Black patients received buprenorphine doses 2.5 (95% CI -4.6 to -0.5) mg lower and methadone doses 16.7 (95% CI, -30.7 to -2.7) mg lower compared with White patients, after standardizing by age and sex. In week 4, the percentage of patients receiving a higher dose of medication (buprenorphine =16 mg; methadone =60 mg) was 16.9 (95% CI, -31.9 to -1.9) points lower for Black patients compared with White patients. Hispanic and White patients received similar buprenorphine doses; Hispanic patients received lower methadone doses than White patients.

Conclusions: In this cohort study of data from 3 clinical trials, White patients generally received higher doses of medication than Black patients. Future research is needed to understand the mechanisms of and interventions to reduce disparities in OUD treatment quality and how such disparities impact generalizability of trial results.

Note: An invited commentary piece on this article was also published by JAMA Network Open (Schiff DM, Nidey N, Tiako MJN. Dosing inequities in opioid use disorder treatment trials. JAMA Network Open 2024;7(10):e2436582.)

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

This study aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. Data were obtained from six trials representing 1,777 individuals with OUD in the NIDA Clinical Trials Network (CTN-0001, -0002, -0009. -0030, -0049, and -0051). Researchers implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). They performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms.

There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively.
Conclusions: Researchers for this study observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

Related protocols: CTN-0001, CTN-0002, CTN-0009, CTN-0030, CTN-0049, CTN-0051

Abstract:

High levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design.

We reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage.

For trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00).

Conclusions: Psychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials.

Related protocols: CTN-0002, CTN-0003, CTN-0004, CTN-0006, CTN-0007, CTN-0009. CTN-0013, CTN-0014, CTN-0015, CTN-0017, CTN-0021, CTN-0029, CTN-0030, CTN-0031, CTN-0037, CTN-0044, CTN-0046, CTN-0048, CTN-0051, CTN-0053

Abstract:

Although buprenorphine is an effective treatment for opioid use disorder (OUD), much remains to be understood about treatment non-response and methods for improving treatment retention. The addition of behavioral therapies to buprenorphine has not yielded consistent benefits for opioid outcomes, on average. However, several studies suggest that certain subgroups may benefit from the combination of buprenorphine and behavioral therapy, highlighting the potential for personalized approaches to treatment. Furthermore, little is known about whether behavioral therapies improve buprenorphine retention or non-opioid (e.g., functional) outcomes.

The objective of this project is to harmonize four previously conducted clinical trials, including three independent trials and one NIDA Clinical Trials network multi-site trial (CTN-0030), testing the addition of behavioral therapy to buprenorphine maintenance for OUD and to use this larger dataset to answer critical clinical questions about the role of behavioral therapy in this population. Study aims include identifying potential moderators of the effect of the addition of behavioral therapy and quantifying the effect of behavioral therapy on buprenorphine retention and functional outcomes.

Analyses will consider outcomes of weeks of opioid use, weeks of retention in buprenorphine treatment, and functional outcomes as measured by the Addiction Severity Index. Analyses will include an indicator for each study to account for heterogeneity of samples and design.

Conclusions: Results will help to inform clinical and research efforts to optimize the use of behavioral therapies in the treatment of OUD.

Related protocols: CTN-0030

Abstract:

No existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder. The goal of this study was to develop an individual-level prediction tool for risk of return to use in opioid use disorder.

This decision analytical model (CTN-0094) used predictive modeling with individual-level data harmonized in June 1, 2019, to October 1, 2022, from 3 multicenter, pragmatic, randomized clinical trials of at least 12 weeks’ duration within the National Institute on Drug Abuse Clinical Trials Network (CTN) performed between 2006 and 2016 (CTN-0027 [START], CTN-0030 [POATS], and CTN-0051 [X:BOT]). The clinical trials covered a variety of treatment settings, including federally licensed treatment sites, physician practices, and inpatient treatment facilities. All 3 trials enrolled adult participants older than 18 years, with broad pragmatic inclusion and few exclusion criteria except for major medical and unstable psychiatric comorbidities.

All participants received 1 of 3 medications for opioid use disorder: methadone, buprenorphine, or extended-release naltrexone. Predictive models were developed for return to use, which was defined as 4 consecutive weeks of urine drug screen (UDS) results either missing or positive for nonprescribed opioids by week 12 of treatment.

The overall sample included 2199 trial participants (mean [SD] age, 35.3 [10.7] years; 728 women [33.1%] and 1471 men [66.9%]). The final model based on 4 predictors at treatment entry (heroin use days, morphine- and cocaine-positive UDS results, and heroin injection in the past 30 days) yielded an area under the receiver operating characteristic curve (AUROC) of 0.67 (95% CI, 0.62-0.71). Adding UDS in the first 3 treatment weeks improved model performance (AUROC, 0.82; 95% CI, 0.78-0.85). A simplified score (CTN-0094 OUD Return-to-Use Risk Score) provided good clinical risk stratification wherein patients with weekly opioid-negative UDS results in the 3 weeks after treatment initiation had a 13% risk of return to use compared with 85% for those with 3 weeks of opioid-positive or missing UDS results (AUROC, 0.80; 95% CI, 0.76-0.84).

Conclusions: The prediction model described in this study may be a universal risk measure for return to opioid use by treatment week 3. Interventions to prevent return to regular use should focus on this critical early treatment period.

Related protocols: CTN-0027, CTN-0030, CTN-0051, CTN-0094

Abstract:

Patients in treatment with medications for opioid use disorder (MOUD) often report use of other substances in addition to opioids. Few studies exist that examine the relationship between use at treatment entry and early non-opioid use in opioid treatment outcome.

In this study, researchers combined and harmonized three randomized, controlled MOUD clinical trials from the National Institutes of Drug Abuse (NIDA) Clinical Trials Network (CTN) (N=2,197) and investigated the association of non-opioid substance use at treatment entry and during early treatment with a return to opioid use. The trials (CTN-0027 [START], CTN-0030 [POATS], and CTN-0051 [X:BOT]) compared MOUD treatment (buprenorphine, methadone, extended-release naltrexone) in populations with opioid use disorder (OUD). Non-opioid substances were identified through harmonizing self-reported use. The primary outcomes were markers of return to opioid use by 12 weeks.

When treatment cohorts were adjusted, no association between self-reported treatment entry use of non-opioid substances and week-12 opioid use was detected. During the first month of treatment, higher use of cocaine and amphetamine was found to be associated with higher likelihood of illicit opioid use by week 12. Exploratory analyses of potential treatment cohort-by-predictor interactions showed that those with heavier cocaine use had a lower rate of returning to opioid use in the extended-release naltrexone group than in the methadone group.

Conclusions: Substance use other than opioids at treatment entry is not associated with relapse. Use of cocaine or amphetamines during the first few weeks of MOUD treatment may signal a worse outcome, suggesting a need for additional interventions.

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

Overdose risk during a course of treatment with medication for opioid use disorder (MOUD) has not been clearly delineated. In this study, the authors sought to address this gap by leveraging a new data set from three large pragmatic clinical trials of MOUD: CTN-0027 (START), CTN-0030 (POATS), and CTN-0051 (X:BOT).

Adverse event logs, including overdose events, from the three trials (N=2,199) were harmonized, and the overall risk of having an overdose event in the 24 weeks after randomization was compared for each study arm (one methadone, one naltrexone, and three buprenorphine groups), using survival analysis with time-dependent Cox proportional hazard models.

Results found that by week 24, 39 participants had =1 overdose event. The observed frequency of having an overdose event was 15 (5.30%) among 283 patients assigned to naltrexone, eight (1.51%) among 529 patients assigned to methadone, and 16 (1.15%) among 1,387 patients assigned to buprenorphine. Notably, 27.9% of patients assigned to extended-release naltrexone never initiated the medication, and their overdose rate was 8.9% (7/79), compared with 3.9% (8/204) among those who initiated naltrexone.

Controlling for sociodemographic and time-varying medication adherence variables and baseline substance use, a proportional hazard model did not show a significant effect of naltrexone assignment. Significantly higher probabilities of experiencing an overdose event were observed among patients with baseline benzodiazepine use (hazard ratio=3.36, 95% CI=1.76, 6.42) and those who either were never inducted on their assigned study medication (hazard ratio=6.64, 95% CI=2.12, 19.54) or stopped their medication after initial induction (hazard ratio=4.04, 95% CI=1.54, 10.65).

Conclusions: Patients undergoing MOUD treatment remain at risk of overdose events in the first 24 weeks after seeking treatment. The strongest message from these data is that patients who fail to initiate medication, or stop their medication, are at greater risk of experiencing an overdose event. The pharmacology of methadone, buprenorphine, and naltrexone is such that they all substantially lower overdose risk if taken as prescribed. Patients should be educated about overdose risk, the protective effect of MOUD, and the danger of discontinuing medication. Benzodiazepine use is also a signal of risk, and patients taking benzodiazepines should be evaluated and treated for mental health problems as part of an effort to wean them off benzodiazepines. The risk of overdose after discontinuing naltrexone may be greater than for other medications, although the present data are not definitive on this point, and the overall effect of naltrexone assignment on overdose was not statistically significant. Future large trials should implement more systematic assessments of overdose events based on a clear operationalization of overdose, querying actively rather than relying on spontaneous report, with detailed characterization of the event, including the substances involved and whether there was suicidal intent.

Related editorial: An editorial about this paper was published in the American Journal of Psychiatry in May 2023: Connery HS & Weiss RD. Drug overdose prevention: An exercise in optimism. American Journal of Psychiatry 2023;180:5. [doi: 10.1176/appi.ajp.20230170]

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

While polysubstance use has consistently been associated with higher rates of relapse, few studies have examined subgroups with specific combinations and time course of polysubstance use (i.e., polysubstance use patterns). This study aimed to classify and compare polysubstance use patterns and their associations with relapse to opioid use in 2637 participants in three large opioid use disorder (OUD) treatment trials in the NIDA Clinical Trials Network (CTN-0027, CTN-0030, and CTN-0051).

Researchers explored the daily patterns of self-reported substance use in the 28 days prior to treatment entry. Market basket analysis (MBA) and repeated measure latent class analysis (RMLCA) were used to examine the subgroups of polysubstance use patterns, and multiple logistic regression was used to examine associations between identified classes and relapse.

MBA and RMLCA identified 34 “associations rules” and 6 classes, respectively. Specific combinations of polysubstance use and time course (high baseline use and rapid decrease of use prior to initiation) predicts a worse relapse outcome. MBA showed individuals who co-used cocaine, heroin, prescription opioids, and cannabis had a higher risk for relapse (OR=2.82, 95%CI=1.13, 7.03). In RMLCA, higher risk of relapse was observed in individuals who presented with high baseline prescription opioid (OR = 1.9, 95% CI = 1.3, 2.76) or heroin use (OR = 3.54, 95%CI = 1.86, 6.72), although use decreased in both cases prior to treatment initiation.

Conclusions: Our analyses identified subgroups with distinct patterns of polysubstance use. Different patterns of polysubstance use differentially predict relapse outcomes. Interventions tailored to these individuals with specific polysubstance use patterns prior to treatment initiation may increase the effectiveness of relapse prevention.

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

Although there is consensus that having a “high-enough” dose of buprenorphine (BUP-NX) or methadone is important for reducing relapse to opioid use, there is debate about what this dose is and how it should be attained. We estimated the extent to which different dosing strategies would affect risk of relapse over 12 weeks of treatment, separately for BUP-NX and methadone.

This was a secondary analysis of three comparative effectiveness trials (CTN-0027, CTN-0030, and CTN-0051). We examined four dosing strategies: 1) increasing dose in response to participant-specific opioid use, 2) increasing dose weekly until some minimum dose (16 mg BUP, 100 mg methadone) was reached, 3) increasing dose weekly until some minimum and increasing dose in response to opioid use thereafter (referred to as the “hybrid strategy”), and 4) keeping dose constant after the first 2 weeks of treatment. We used a longitudinal sequentially doubly robust estimator to estimate contrasts between dosing strategies on risk of relapse.

For BUP-NX, increasing dose following the hybrid strategy resulted in the lowest risk of relapse. For methadone, holding dose constant resulted in greatest risk of relapse; the other three strategies performed similarly. For example, the hybrid strategy reduced week 12 relapse risk by 13 % (RR: 0.87, 95 %CI: 0.83–0.95) and by 20 % (RR: 0.80, 95 %CI: 0.71–0.90) for BUP-NX and methadone respectively, as compared to holding dose constant.

Conclusions: Doses should be targeted toward minimum thresholds and, in the case of BUP-NX, raised when patients continue to use opioids.

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

The extend to which clinical trials of medications for opioid use disorder (MOUD) are representative or not is unknown. Some patient characteristics modify MOUD effectiveness; if these same characteristics differ in distribution between the trial population and usual-care population, this could contribute to lack of generalizability — a discrepancy between trial and usual-care effectiveness. The objective of this study was to identify interpretable, multidimensional subgroups who were prescribed MOUD in substance use treatment programs in the US but who were not represented or under-represented by clinical trial participants.

This study was a secondary descriptive analysis of trial and real-world data. The trial data included 27 US opioid treatment programs in the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN-0027, CTN-0051, and CTN-0030), N=2,199 patients. The real-world data included US substance use treatment programs that receive public funding, N=740,015 patients (TEDS-A data). The authors characterized real-world patient populations who were non-represented and under-represented in the trial data in terms of sociodemographic and clinical characteristics that could modify MOUD effectiveness.

The authors found that 10.7% of MOUD patients in TEDS-A (real-world sample) were not represented in the three clinical trials. As expected, pregnant MOUD patients (n=19,490) were not represented. Excluding pregnancy, education, and marital status from the characteristics, 2.6% of MOUD patients were not represented. Patients aged 65 years and older (n=11,204) and those 50-64 years who identified as other (non-white, non-Black, and non-Hispanic) race/ethnicity or multi-racial (n=7,281) were under-represented.

Conclusions: Quantifying and characterizing non- or under-represented subgroups in trials can provide the data necessary to improve representation in future trials and address research-to-practice gaps.

Related protocols: CTN-0027, CTN-0030, CTN-0051

Abstract:

Side effects of medications for opioid use disorder (MOUD) such as weight gain contribute to their stigma. Substantial evidence suggests that women have a more severe side effect profile to MOUD than men, and concerns about weight gain during treatment are prevalent. However, the few studies reporting sex differences in weight gain during treatment show conflicting results and are restricted to methadone. In addition, little is known about possible sex differences in weight gain to buprenorphine, which is the most commonly prescribed MOUD in the United States.

To address these issues, the authors performed a systematic review and meta-analysis on the few studies reporting longitudinal data on sex differences in body mass index (BMI) gain during methadone treatment (Study 1). In a separate study, they also re-analyzed data from trial CTN-0030 of the National Institute on Drug Abuse Clinical Trial Network (NIDA CTN), which involved a 12-week buprenorphine treatment regimen (Study 2; n = 360; 209 Male, 151 Female).

For Study 1, across all papers reporting longitudinal data (k = 4, n = 362 OUD patients), there were BMI increases that ranged from 2.2 to 5.4 BMI after at least one year of methadone treatment, but there were no significant sex differences in BMI increases (Standardized Mean Difference, Female > Male = 0.352, SE =0.270; 95 % CI = [-0.18 0.88]; p = .193). Study 2 showed no significant differences in weight before and after 12 weeks of buprenorphine treatment nor did it show sex differences in weight change with treatment (ß = 2.34, p = .511).

Conclusions: These analyses corroborate evidence of weight gain with methadone treatment but did not observe a sex-based disparity in weight gain with methadone or buprenorphine treatment for OUD.

Related protocols: CTN-0030

Abstract:

Opioid use disorder (OUD) and chronic pain frequently co-occur. Little is known about change in pain during buprenorphine/naloxone (BUP/NX) maintenance and whether outcomes vary by pain levels. The present study examined changes in pain intensity and pain interference over 12 weeks of BUP/NX maintenance among participants with OUD and chronic pain (N=194). Differences in outcomes were assessed during BUP/NX maintenance (Week 12) and 2 months following a BUP/NX taper (Week 24). Data from Phase 2 of the CTN Prescription Opioid Addiction Treatment Study (POATS, CTN-0030) were used. Two latent transition models were conducted to characterize profiles and transitions between profiles of pain intensity or pain interference (estimated separately). Each model identified a high and low profile. In the pain interference model, the majority were classified in the low profile at baseline. In the pain intensity model, the majority were classified in the high profile at baseline. In both models, patients were more likely to remain in or transition to the low profiles by Week 12. Worse depression was associated with membership in the high pain interference profile at both timepoints. Women were more likely to be in the high pain intensity profile at baseline. Those in the high intensity and high pain interference profiles at Week 12 reported worse mental health quality of life (MH-QOL) at Week 12, as well as high pain intensity and high pain interference at Week 24.

Conclusions: For a subgroup of patients, high pain intensity and high pain interference remains unchanged during BUP/NX maintenance treatment.

Related protocols: CTN-0030

Abstract:

In the multi-site NIDA Clinical Trials Network Prescription Opioid Addiction Treatment Study (POATS), the best predictor of successful opioid use outcome was lifetime diagnosis of major depressive disorder. The primary aim of this secondary analysis of data from POATS was to empirically assess two explanation for this counterintuitive finding.

The POATS study was a national, 10-site randomized controlled trial (N=360 enrolled in the 12-week buprenorphine-naloxone maintenance treatment phase) sponsored by the NIDA CTN. This study evaluated how the presence of a history of depression influences opioid use outcome (negative urine drug assays). Using adjusted logistic regression models, researchers tested the hypothesis that 1) a reduction in depressive symptoms and 2) greater motivation and engagement in treatment account for the association between depression history and good treatment outcome.

Analysis found that although depressive symptoms decreased significantly throughout treatment, this improvement was not associated with opioid outcomes. Reporting a goal of opioid abstinence at treatment entry was also not associated with outcomes, however, mutual-help group participation was associated with good treatment outcomes. In each of these models, lifetime major depressive disorder remained associated with good outcomes.

Conclusions: Findings are consistent with the premise that greater engagement in treatment is associated with good opioid outcomes. Nevertheless, depression history continues to be associated with good opioid outcomes in adjusted models. More research is needed to understand how these factors could improve treatment outcomes for those with opioid use disorder.

Abstract:

Relapse is common in treatment for opioid use disorders (OUD). Pain and depression often co-occur during OUD treatment, but little is known about how they influence relapse among patients with a primary diagnosis of prescription opioid use disorder (POUD). Advanced statistical analyses that can simultaneously model these two conditions may lead to targeted clinical interventions.

The objective of this study was to utilize a discrete survival analysis with a growth mixture model to test time to prescription opioid relapse, predicted by parallel growth trajectories of depression and pain, in a clinical sample of patients in buprenorphine/naloxone treatment. The latent class analysis characterized heterogeneity with data collected from the NIDA Clinical Trials Network protocol CTN-0030 (Prescription Opioid Addiction Treatment Study, which compared buprenorphine/naloxone with standard medical management).

Results suggested a 4-class solution was the most conservative based on global fit indices and clinical relevance. The 4 classes identified were: 1) low relapse, 2) high depression and moderate pain, 3) high pain, and 4) high relapse. Odds ratios for time-to-first use indicated no statistically significant differences in time to relapse between the high pain and the high depression classes, but all other classes differed significantly.

Conclusions: This is the first longitudinal study to characterize the influence of pain, depression, and relapse in patients receiving buprenorphine and naloxone treatment. These results emphasize the need to monitor the influence of pain and depression during stabilization on buprenorphine and naloxone. Future work may identify appropriate interventions that can be introduced to extend time-to-first prescription opioid use among patients.

Related protocols: CTN-0030