Under-representation of key demographic groups in opioid use disorder trials

Comparative effectiveness of extended-release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients

BACKGROUND AND AIMS

Extended-release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) are both approved for opioid use disorder (OUD) treatment in any medical setting. We aimed to compare the real-world effectiveness of XR-NTX and SL-BUP.

DESIGN AND SETTING

This was an observational active comparator, new user cohort study of Medicaid claims records for patients in New Jersey and California, USA, 2016-19.

PARTICIPANTS/CASES

The participants were adult Medicaid patients aged 18-64 years who initiated XR-NTX or SL-BUP for maintenance treatment of OUD and did not use medications for OUD in the 90 days before initiation. Our cohort included 1755 XR-NTX and 9886 SL-BUP patients.

MEASUREMENTS

We examined two outcomes up to 180 days after medication initiation: (1) composite of medication discontinuation and death and (2) composite of overdose and death.

FINDINGS

In adjusted analyses, treatment with XR-NTX was more likely to result in discontinuation or death by the end of follow-up than treatment with SL-BUP: cumulative risk 75.9% [95% confidence interval (CI) = 73.9%, 77.9%] versus 62.2% (95% CI = 61.2%, 63.2%), respectively (risk difference = 13.7 percentage points, 95% CI = 11.4, 16.0). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.9% (95% CI = 3.0%, 4.8%) versus SL-BUP 3.3% (95% CI = 2.9%, 3.7%); risk difference = 0.5 percentage points, 95% CI = -0.4, 1.5. Results were consistent across sensitivity analyses.

CONCLUSIONS

Medicaid patients in California and New Jersey, USA, receiving treatment for opioid use disorder stayed in treatment longer on sublingual buprenorphine than on extended-release naltrexone, but the risk of overdose was similar. Most patients in this study discontinued medication within 6 months, regardless of which medication was initiated.

Sex disparities in outcome of medication-assisted therapy of opioid use disorder: Nationally representative study

Question

The opioid epidemic causes massive morbidity, and males have substantially greater overdose mortality rates than females. It is unclear whether there are sex-related disparities at different stages in the trajectory of opioid use disorders, in "real world" settings.

Goal

To determine sex disparities in non-medical opioid use (NMOU) at the end of outpatient medication-assisted treatment (MAT), using nationally representative data.

Design

Observational epidemiological study of publicly funded outpatient MAT programs in the national "Treatment episode data set-discharges" (TEDS-D) for 2019.

Participants

Persons aged ≥18 in their first treatment episode, in outpatient MAT for use of heroin or other opioids (N=11,549). The binary outcome was presence/absence of NMOU.

Results

In univariate analyses, males had significantly higher odds of NMOU, compared to females (odds ratio=1.27; Chi2 [df:1]=39.08; uncorrected p<0.0001; p=0.0041 after Bonferroni correction). A multivariable logistic regression detected a male>female odds ratio of 1.19 (95%CI=1.09-1.29; p<0.0001), adjusting for socio-demographic/clinical variables. Several specific conditions were revealed in which males had greater odds of NMOU compared to females (e.g., at ages 18-29 and 30-39; corrected p=0.012, or if they used opioids by inhalation; corrected p=0.0041).

Conclusions

This nationally representative study indicates that males have greater odds of NMOU in their first episode of MAT, indicating more unfavorable outcomes. The study reveals specific socio-demographic and clinical variables under which this sex disparity is most prominent.

Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention

OBJECTIVE

A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.

METHODS

This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.

RESULTS

The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score ( P < 0.001), used cocaine on more days over the prior 30 days than other quartiles ( P < 0.001), and had highest proportions with alcohol and cocaine use disorder ( P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference ( P = 0.02) and all experiencing homelessness ( P < 0.001).

CONCLUSIONS

Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.

Comparative effectiveness of extended release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients

Aims

To compare the real-world effectiveness of extended release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) for the treatment of opioid use disorder (OUD).

Design

An observational active comparator, new user cohort study.

Setting

Medicaid claims records for patients in New Jersey and California, 2016-2019.

Participants/Cases

Adult Medicaid patients aged 18-64 years who initiated XR-NTX or SL-BUP for maintenance treatment of OUD and did not use medications for OUD in the 90-days before initiation.

Comparators

New initiation with XR-NTX versus SL-BUP for the treatment of OUD.

Measurements

We examined two outcomes up to 180 days after medication initiation, 1) composite of medication discontinuation and death, and 2) composite of overdose and death.

Findings

Our cohort included 1,755 XR-NTX and 9,886 SL-BUP patients. In adjusted analyses, treatment with XR-NTX was more likely to result in discontinuation or death by the end of follow-up than treatment with SL-BUP: cumulative risk 76% (95% confidence interval [CI] 75%, 78%) versus 62% (95% CI 61%, 63%), respectively (risk difference 14 percentage points, 95% CI 13, 16). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.8% (95% CI 2.9%, 4.7%) versus SL-BUP 3.3% (95% 2.9%, 3.7%); risk difference 0.5 percentage points, 95%CI -0.5, 1.5. Results were consistent across sensitivity analyses.

Conclusions

Longer medication retention is important because risks of negative outcomes are elevated after discontinuation. Our results support selection of SL-BUP over XR-NTX. However, most patients discontinued medication by 6 months indicating that more effective tools are needed to improve medication retention, particularly after initiation with XR-NTX, and to identify which patients do best on which medication.

Virtual focus groups among individuals with use disorders: assessing feasibility and acceptability in an underserved clinical population

Objective

There are substantial barriers to conducting research among individuals with stigmatized and complicated health conditions like substance use disorders. These barriers slow progress when developing, refining, and assessing interventions to better treat underserved populations. Virtual focus groups are an innovative method for collecting data from individuals via a discreet and accessible platform which can inform novel as well as existing treatment approaches. This article reports on the feasibility and acceptability of virtual focus groups as a mechanism to recruit and engage geographically and demographically diverse samples of participants with substance use disorders that are otherwise logistically difficult to assess.

Method

Participants were assessed for eligibility for a virtual focus group study based on demographic features, drug use history, and psychiatric history via a remote, interview-based screening. Focus groups were completed anonymously without video or name-sharing. Discussion contributions, quantified with number of times speaking and total number of words spoken, were compared across gender, and treatment status. Participants provided quantitative and qualitative feedback on the focus group experience in a follow-up survey.

Results

Focus groups (N=26) based in geographical areas throughout the United States were conducted with 88 individuals with opioid use disorder or stimulant use disorder. Discussion contributions were comparable between genders and among individuals in treatment versus those seeking treatment. A follow-up survey (n=50, 57% of focus group participants) reflected high levels of enjoyment, comfort, and honesty during focus group discussions.

Discussion

Findings suggest virtual focus groups can be an effective and efficient tool for substance use research.

Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment

Importance

No existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder.

Objective

To develop an individual-level prediction tool for risk of return to use in opioid use disorder.

Design, Setting, and Participants

This decision analytical model 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. 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.

Intervention

All participants received 1 of 3 medications for opioid use disorder: methadone, buprenorphine, or extended-release naltrexone.

Main Outcomes and Measures

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.

Results

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 and Relevance

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.