Long-term Opioid Therapy For Chronic Pain Research Articles

Optimizing evidence-based practice implementation: a case study on simulated patient protocols in long-term opioid therapy

BackgroundSubstantial work has been done to update or create evidence-based practices (EBPs) in the changing health care landscape. However, the success of these EBPs is limited by low levels of clinician implementation.ObjectiveThe goal of this study is to describe the use of standardized/simulated patient/person (SP) methodology as a framework to develop implementation bundles to increase the effectiveness, sustainability, and reproducibility of EBPs across health care clinicians.DesignWe observed 12 clinicians’ first-time experiences with six unique decision-making algorithms, developed previously using rigorous Delphi methods, for use with patients exhibiting concerning behaviors associated with long-term opioid therapy (LTOT) for chronic pain. Clinicians were paired with two SPs trained to portray individuals with one of the concerning behaviors addressed by the algorithms in a telehealth environment. The SP evaluations were followed by individual interviews, guided by the Consolidated Framework for Implementation Research (CFIR), with each of the clinician participants.ParticipantsTwelve primary care clinicians and 24 SPs in Western Pennsylvania.Main measurementThe primary outcome was identifying likely facilitators for the successful implementation of the EBP using the SP methodology. Our secondary outcome was to assess the feasibility of using SPs to illuminate likely implementation barriers and facilitators.ResultsThe SP portrayal illuminated factors that were pertinent to address in the implementation bundle. SPs were realistic in their portrayal of patients with concerning behaviors associated with LTOT for chronic pain, but clinicians also noted that their patients in practice may have been more aggressive about their treatment plan.ConclusionsSP simulation provides unique opportunities for obtaining crucial feedback to identify best practices in the adoption of new EBPs for high-risk patients.SettingZoom simulated patient evaluations.

Simulated opioid choice linked to opioid use disorder severity among veterans with chronic pain: initial validation of a novel paradigm

ABSTRACT Background Modeling addictive behavior among individuals with, or at risk for, opioid use disorder (OUD) in a way that is accurate, ethical, and reproducible presents a pressing concern. OUD risk is elevated among people with chronic pain on long-term opioid therapy (LTOT). Objectives To provide initial validation of a novel opioid preference task as an index of OUD and its symptomatology among veterans prescribed opioids for chronic pain, a population at high risk for poor opioid-related outcomes. The relative ease by which such a paradigm can be implemented and performed in clinical contexts, including enabling investigation of opioid reinforcement and drug-seeking behavior while avoiding ethical pitfalls associated with direct drug administration, could make this task an attractive approach for potentially tracking OUD symptoms. Methods We studied 87 veterans (74 males, 13 females) on LTOT for chronic pain – 33 of whom had OUD diagnoses. Participants completed a picture-viewing choice task to assess preference for viewing opioid-related images in comparison with standardized pleasant, unpleasant, neutral, and blank images. Opioid-related choice, measured by vigor of button pressing, was tested for association with OUD severity (measured by symptom counts), as well as craving and anhedonia. Results Choice for opioid-related images was positively correlated with OUD severity (i.e., number of DSM-5 measured OUD symptoms) (r = 0.38, p < .001), particularly among those meeting diagnostic criteria for OUD (r = 0.47, p = .006). Neither craving nor anhedonia correlated with opioid-related choice. Conclusions Our results provide initial validation for a new opioid picture-choice paradigm in patients with chronic pain.

Study protocol for the targeting effective analgesia in clinics for HIV (TEACH) study – a cluster randomized controlled trial and parallel cohort to increase guideline concordant care for long-term opioid therapy among people living with HIV

Background: People living with HIV (PLWH) frequently experience chronic pain and receive long-term opioid therapy (LTOT). Adherence to opioid prescribing guidelines among their providers is suboptimal.Objective: This paper describes the protocol of a cluster randomized trial, targeting effective analgesia in clinics for HIV (TEACH), which tested a collaborative care intervention to increase guideline-concordant care for LTOT among PLWH.Methods: HIV physicians and advanced practice providers (n = 41) were recruited from September 2015 to December 2016 from two HIV clinics in Boston and Atlanta. Patients receiving LTOT from participating providers were enrolled through a waiver of informed consent (n = 187). After baseline assessment, providers were randomized to the control group or the year-long TEACH intervention involving: (1) a nurse care manager and electronic registry to assist with patient management; (2) opioid education and academic detailing; and (3) facilitated access to addiction specialists. Randomization was stratified by site and LTOT patient volume. Primary outcomes (≥2 urine drug tests, early refills, provider satisfaction) were collected at 12 months. In parallel, PLWH receiving LTOT (n = 170) were recruited into a longitudinal cohort at both clinics and underwent baseline and 12-month assessments. Secondary outcomes were obtained through patient self-report among participants enrolled in both the cohort and the RCT (n = 117).Conclusions: TEACH will report the effects of an intervention on opioid prescribing for chronic pain on both provider and patient-level outcomes. The results may inform delivery of care for PLWH on LTOT for chronic pain at a time when opioid practices are being questioned in the US.Trial registration: ClinicalTrials.gov identifier: NCT02564341.Trial registration: ClinicalTrials.gov identifier: NCT02525731.