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Abstract The COTAT (Collaborative Opioid Taper After Trauma) Study was a randomized trial of an opioid taper support program using a physician assistant (PA) to provide pain and opioid treatment guidance to primary care providers assuming care for adult patients with moderate to severe trauma discharged from a Level I trauma center on opioid therapy. Patients were recruited, assessed, and randomized individually by a surgery research recruitment team one to two days prior to discharge to home. Participants randomized to the opioid taper support program were contacted by phone within a few days of discharge by the PA interventionist to confirm enrollment and their primary care provider (PCP). The intervention consisted of PA support as needed to the PCP concerning pain and opioid care at weeks 1, 2, 4, 8, 12, 16, and 20 after discharge or until the PCP office indicated they no longer needed support or the patient had tapered off opioids. The PA was supervised by a pain physician-psychiatrist, a family physician, and a trauma surgeon. Patients randomized to usual care received standard hospital discharge instructions and written information on managing opioid medications after discharge. Trial results were analyzed using repeated measures analysis. 37 participants were randomized to the intervention and 36 were randomized to usual care. The primary outcomes of the trial were pain, enjoyment, general activity (PEG score) and mean daily opioid dose at 3 and 6 months after hospital discharge. Treatment was unblinded but assessment was blinded. No significant differences in PEG or opioid outcomes were noted at either time point. Physical function at 3 and 6 months and pain interference at 6 months were significantly better in the usual care group. No significant harms of the intervention were noted. COVID-19 (corona virus 2019) limited recruitment of high-risk opioid tolerant subjects, and limited contact between the PA interventionist and the participants and the PCPs. Our opioid taper support program failed to improve opioid and pain outcomes, since both control and intervention groups tapered opioids and improved PEG scores after discharge. Future trials of post-trauma opioid taper support with populations at higher risk of persistent opioid use are needed. This trial is registered at clinicaltrials.gov under NCT04275258 19/02/2020. This trial was funded by a grant from the Centers for Disease Control and Prevention to the University of Washington Harborview Injury Prevention & Research Center (R49 CE003087, PI: Monica S. Vavilala, MD). The funder had no role in the analysis or interpretation of the data.

Abstract Objective Marijuana use is increasingly common among patients with chronic non-cancer pain (CNCP) and long-term opioid therapy (LTOT). We determined if lifetime recreational and medical marijuana use were associated with more frequent and higher dose prescription opioid use. Design Cross-sectional Subjects Eligible patients (n=1,037), who had a new period of prescription opioid use lasting 30-90 days, were recruited from two midwestern health care systems to a study of long-term prescription opioid use and mental health outcomes. The present cross-sectional analyses uses baseline data from this on-going cohort study. Methods Primary exposures were participant reported lifetime recreational and medical marijuana use versus no lifetime marijuana use. Prescription opioid characteristics included daily versus non-daily opioid use and ≥50 morphine milligram equivalent (MME) dose per day vs.

Abstract Background A valid opioid use disorder (OUD) identification algorithm for use in administrative medical record data would enhance investigators’ ability to study consequences of OUD, OUD treatment seeking and treatment outcomes. Main body Existing studies indicate ICD-9 and ICD-10 codes for opioid abuse and dependence do not accurately measure OUD. However, critical appraisal of existing literature suggests alternative validation methods would improve the validity of OUD identification algorithms in administrative data. Chart abstraction may not be sufficient to validate OUD, and primary data collection via structured diagnostic interviews might be an ideal gold standard. Conclusion and commentary Generating valid OUD identification algorithms is critical for OUD research and quality measurement in real world health care settings.