Characterization of peer support services for substance use disorders in 11 US emergency departments in 2020: findings from a NIDA clinical trials network site selection process

INTRODUCTION

Emergency departments (ED) are incorporating Peer Support Specialists (PSSs) to help with patient care for substance use disorders (SUDs). Despite rapid growth in this area, little is published regarding workflow, expectations of the peer role, and core components of the PSS intervention. This study describes these elements in a national sample of ED-based peer support intervention programs.

METHODS

A survey was conducted to assess PSS site characteristics as part of site selection process for a National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) evaluating PSS effectiveness, Surveys were distributed to clinical sites affiliated with the 16 CTN nodes. Surveys were completed by a representative(s) of the site and collected data on the PSS role in the ED including details regarding funding and certification, services rendered, role in medications for opioid use disorder (MOUD) and naloxone distribution, and factors impacting implementation and maintenance of ED PSS programs. Quantitative data was summarized with descriptive statistics. Free-text fields were analyzed using qualitative content analysis.

RESULTS

A total of 11 surveys were completed, collected from 9 different states. ED PSS funding was from grants (55%), hospital funds (46%), peer recovery organizations (27%) or other (18%). Funding was anticipated to continue for a mean of 16 months (range 12 to 36 months). The majority of programs provided "general recovery support (81%) Screening, Brief Intervention, and Referral to Treatment (SBIRT) services (55%), and assisted with naloxone distribution to ED patients (64%). A minority assisted with ED-initiated buprenorphine (EDIB) programs (27%). Most (91%) provided services to patients after they were discharged from the ED. Barriers to implementation included lack of outpatient referral sources, barriers to initiating MOUD, stigma at the clinician and system level, and lack of ongoing PSS availability due to short-term grant funding.

CONCLUSIONS

The majority of ED-based PSSs were funded through time-limited grants, and short-term grant funding was identified as a barrier for ED PSS programs. There was consistency among sites in the involvement of PSSs in facilitation of transitions of SUD care, coordination of follow-up after ED discharge, and PSS involvement in naloxone distribution.

Implementing a pharmacist-integrated collaborative model of medication treatment for opioid use disorder in primary care: study design and methodological considerations

BACKGROUND

Pharmacists remain an underutilized resource in the treatment of opioid use disorder (OUD). Although studies have engaged pharmacists in dispensing medications for OUD (MOUD), few studies have evaluated collaborative care models in which pharmacists are an active, integrated part of a primary care team offering OUD care.

METHODS

This study seeks to implement a pharmacist integrated MOUD clinical model (called PrIMO) and evaluate its feasibility, acceptability, and impact across four diverse primary care sites. The Consolidated Framework for Implementation Research is used as an organizing framework for study development and interpretation of findings. Implementation Facilitation is used to support PrIMO adoption. We assess the primary outcome, the feasibility of implementing PrIMO, using the Stages of Implementation Completion (SIC). We evaluate the acceptability and impact of the PrIMO model at the sites using mixed-methods and combine survey and interview data from providers, pharmacists, pharmacy technicians, administrators, and patients receiving MOUD at the primary care sites with patient electronic health record data. We hypothesize that it is feasible to launch delivery of the PrIMO model (reach SIC Stage 6), and that it is acceptable, will positively impact patient outcomes 1 year post model launch (e.g., increased MOUD treatment retention, medication regimen adherence, service utilization for co-morbid conditions, and decreased substance use), and will increase each site's capacity to care for patients with MOUD (e.g., increased number of patients, number of prescribers, and rate of patients per prescriber).

DISCUSSION

This study will provide data on a pharmacist-integrated collaborative model of care for the treatment of OUD that may be feasible, acceptable to both site staff and patients and may favorably impact patients' access to MOUD and treatment outcomes.

TRIAL REGISTRATION

The study was registered on Clinicaltrials.gov (NCT05310786) on April 5, 2022, https://www.

CLINICALTRIALS

gov/study/NCT05310786?id=NCT05310786&rank=1.

Computational phenotypes for patients with opioid-related disorders presenting to the emergency department

OBJECTIVE

We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.

METHODS

This was a retrospective study of ED visits from 2013-2020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients ≥18 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.

RESULTS

In total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2-96.8%, rates of one-year ED returns from 9-34%, rates of one-year opioid event 10-17%, rates of medications for opioid use disorder from 17-43%, and a median Carlson comorbidity index of 2-8. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.

CONCLUSIONS

Our results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.

Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values

INTRODUCTION

The efficacy of treatments for substance use disorders (SUD) is tested in clinical trials in which participants typically provide urine samples to detect whether the person has used certain substances via urine drug screenings (UDS). UDS data form the foundation of treatment outcome assessment in the vast majority of SUD clinical trials. However, existing methods to calculate treatment outcomes are not standardized, impeding comparability between studies and prohibiting reproducibility of results.

METHODS

We extended the concept of a binary UDS variable to multiple categories: "+" [positive for substance(s) of interest], "-" [negative for substance(s)], "o" [patient failed to provide sample], "*" [inconclusive or mixed results], and "_" [no specimens required per study design]. This construct can be used to create a standardized and sufficient representation of UDS datastreams and sufficiently collapses longitudinal records into a single, compact "word", which preserves all information contained in the original data.

RESULTS

We developed the R software package CTNote (available on CRAN) as a tool to enable computers to parse these "words". The software package contains five groups of routines: detect a substance use pattern, account for a specific trial protocol, handle missing UDS data, measure the longest period of consecutive behavior, and count substance use events. Executing permutations of these routines result in algorithms which can define SUD clinical trial endpoints. As examples, we provide three algorithms to define primary endpoints from seminal SUD clinical trials.

DISCUSSION

Representing substance use patterns as a "word" allows researchers and clinicians an "at a glance" assessment of participants' responses to treatment over time. Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. We discuss some caveats when applying this data summarization technique in practice and areas of future study.

Estimating the impact of stimulant use on initiation of buprenorphine and extended-release naltrexone in two clinical trials and real-world populations

BACKGROUND

Co-use of stimulants and opioids is rapidly increasing. Randomized clinical trials (RCTs) have established the efficacy of medications for opioid use disorder (MOUD), but stimulant use may decrease the likelihood of initiating MOUD treatment. Furthermore, trial participants may not represent "real-world" populations who would benefit from treatment.

METHODS

We conducted a two-stage analysis. First, associations between stimulant use (time-varying urine drug screens for cocaine, methamphetamine, or amphetamines) and initiation of buprenorphine or extended-release naltrexone (XR-NTX) were estimated across two RCTs (CTN-0051 X:BOT and CTN-0067 CHOICES) using adjusted Cox regression models. Second, results were generalized to three target populations who would benefit from MOUD: Housed adults identifying the need for OUD treatment, as characterized by the National Survey on Drug Use and Health (NSDUH); adults entering OUD treatment, as characterized by Treatment Episodes Dataset (TEDS); and adults living in rural regions of the U.S. with high rates of injection drug use, as characterized by the Rural Opioids Initiative (ROI). Generalizability analyses adjusted for differences in demographic characteristics, substance use, housing status, and depression between RCT and target populations using inverse probability of selection weighting.

RESULTS

Analyses included 673 clinical trial participants, 139 NSDUH respondents (weighted to represent 661,650 people), 71,751 TEDS treatment episodes, and 1,933 ROI participants. The majority were aged 30-49 years, male, and non-Hispanic White. In RCTs, stimulant use reduced the likelihood of MOUD initiation by 32% (adjusted HR [aHR] = 0.68, 95% CI 0.49-0.94, p = 0.019). Stimulant use associations were slightly attenuated and non-significant among housed adults needing treatment (25% reduction, aHR = 0.75, 0.48-1.18, p = 0.215) and adults entering OUD treatment (28% reduction, aHR = 0.72, 0.51-1.01, p = 0.061). The association was more pronounced, but still non-significant among rural people injecting drugs (39% reduction, aHR = 0.61, 0.35-1.06, p = 0.081). Stimulant use had a larger negative impact on XR-NTX initiation compared to buprenorphine, especially in the rural population (76% reduction, aHR = 0.24, 0.08-0.69, p = 0.008).

CONCLUSIONS

Stimulant use is a barrier to buprenorphine or XR-NTX initiation in clinical trials and real-world populations that would benefit from OUD treatment. Interventions to address stimulant use among patients with OUD are urgently needed, especially among rural people injecting drugs, who already suffer from limited access to MOUD.

Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems

BACKGROUND

Pragmatic primary care trials aim to test interventions in "real world" health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial.

METHODS

This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ("baseline"). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD).

RESULTS

Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics' patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42).

CONCLUSIONS

trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials.

The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations

Introduction

Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes.

Methods

This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes.

Discussion

Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response.

Clinical Trial Registration

Identifier: NCT04535583.