The impact of psychiatric comorbidity on treatment discontinuation among individuals receiving medications for opioid use disorder

2020 ◽  
Vol 216 ◽  
pp. 108244
Author(s):  
Erik Loewen Friesen ◽  
Paul Kurdyak
Keyword(s):  
Psychiatric Comorbidity ◽  
Opioid Use Disorder ◽  
Treatment Discontinuation ◽  
Opioid Use ◽  
The Impact

scholarly journals Assessment of the impact of an opioid-specific education series on rates of medication-assisted treatment for opioid use disorder in veterans

2020 ◽  
Author(s):  
Taylor Kirby ◽  
Robert Connell ◽  
Travis Linneman
Keyword(s):  
Multivariate Analysis ◽  
Hospital Admission ◽  
Intervention Group ◽  
Opioid Use Disorder ◽  
Control Group ◽  
Opioid Use ◽  
Motivation To Change ◽  
Heroin Use ◽  
Ed Visits ◽  
The Impact

Abstract Purpose The impact of a focused inpatient educational intervention on rates of medication-assisted therapy (MAT) for veterans with opioid use disorder (OUD) was evaluated. Methods A retrospective cohort analysis compared rates of MAT, along with rates of OUD-related emergency department (ED) visits and/or hospital admission within 1 year, between veterans with a diagnosis of OUD who completed inpatient rehabilitation prior to implementation of a series of group sessions designed to engage intrinsic motivation to change behavior surrounding opioid abuse and provide education about MAT (the control group) and those who completed rehabilitation after implementation of the education program (the intervention group). A post hoc, multivariate analysis was performed to evaluate possible predictors of MAT use and ED and/or hospital readmission, including completion of the opioid series, gender, age (>45 years), race, and specific prior substance(s) of abuse. Results One hundred fifty-eight patients were included: 95 in the control group and 63 in the intervention group. Rates of MAT were 25% (24 of 95 veterans) and 75% (47 of 63 veterans) in control and intervention groups, respectively (P < 0.01). Gender, completion of the opioid series, prior heroin use, and marijuana use met prespecified significance criteria for inclusion in multivariate regression modeling of association with MAT utilization, with participation in the opioid series (odds ratio [OR], 9.56; 95% confidence interval [CI], 4.36-20.96) and prior heroin use (OR, 3.26; 95% CI, 1.18-9.01) found to be significant predictors of MAT utilization on multivariate analysis. Opioid series participation and MAT use were independently associated with decreased rates of OUD-related ED visits and/or hospital admission (hazard ratios of 0.16 [95% CI, 0.06-0.44] and 0.32 [95% CI, 0.14-0.77], respectively) within 1 year after rehabilitation completion. Conclusion Focused OUD-related education in a substance abuse program for veterans with OUD increased rates of MAT and was associated with a decrease in OUD-related ED visits and/or hospital admission within 1 year.

scholarly journals Conceptualizing the effects of the COVID-19 pandemic on people with opioid use disorder: an application of the social ecological model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Ethan Cowan ◽  
Maria R. Khan ◽  
Siri Shastry ◽  
E. Jennifer Edelman
Keyword(s):  
Structural Changes ◽  
Ecological Model ◽  
Disease Incidence ◽  
Opioid Use Disorder ◽  
Social Ecological Model ◽  
Physical And Mental Health ◽  
Opioid Use ◽  
Social Ecological ◽  
The Social ◽  
The Impact

AbstractThe COVID-19 pandemic has resulted in unparalleled societal disruption with wide ranging effects on individual liberties, the economy, and physical and mental health. While no social strata or population has been spared, the pandemic has posed unique and poorly characterized challenges for individuals with opioid use disorder (OUD). Given the pandemic’s broad effects, it is helpful to organize the risks posed to specific populations using theoretical models. These models can guide scientific inquiry, interventions, and public policy. Models also provide a visual image of the interplay of individual-, network-, community-, structural-, and pandemic-level factors that can lead to increased risks of infection and associated morbidity and mortality for individuals and populations. Such models are not unidirectional, in that actions of individuals, networks, communities and structural changes can also affect overall disease incidence and prevalence. In this commentary, we describe how the social ecological model (SEM) may be applied to describe the theoretical effects of the COVID-19 pandemic on individuals with opioid use disorder (OUD). This model can provide a necessary framework to systematically guide time-sensitive research and implementation of individual-, community-, and policy-level interventions to mitigate the impact of the COVID-19 pandemic on individuals with OUD.

The Impact of Chronic Pain on Opioid Use Disorder Treatment Outcomes

2021 ◽  
Author(s):  
R. Ross MacLean ◽  
Suzanne Spinola ◽  
Gabriella Garcia-Vassallo ◽  
Mehmet Sofuoglu
Keyword(s):  
Chronic Pain ◽  
Treatment Outcomes ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Disorder Treatment ◽  
The Impact

scholarly journals A machine learning based two-stage clinical decision support system for predicting patients’ discontinuation from opioid use disorder treatment: retrospective observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Md Mahmudul Hasan ◽  
Gary J. Young ◽  
Jiesheng Shi ◽  
Prathamesh Mohite ◽  
Leonard D. Young ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Initiation ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Treatment Discontinuation ◽  
Discriminative Power ◽  
Opioid Use ◽  
Stage Models ◽  
C Statistic ◽  
One Year

Abstract Background Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. Objective To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. Methods For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. Results A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. Conclusion Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients’ long-term adherence to OUD treatment.

scholarly journals 213. A Comparison of Medication Assisted Therapy Treatment Strategies for Opioid Use Disorder in Persons who Inject Drugs and are Hospitalized with Serious Infections

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S126-S126
Author(s):  
Laura Marks ◽  
Evan Schwarz ◽  
David Liss ◽  
Munigala Satish ◽  
David K Warren ◽  
...  
Keyword(s):  
Fungal Infections ◽  
Hospital Readmissions ◽  
Treatment Strategies ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Readmission Rates ◽  
Persons Who Inject Drugs ◽  
Increased Risk ◽  
Hospital Patients ◽  
The Impact

Abstract Background Persons who inject drugs (PWID) with opioid use disorder (OUD) are at increased risk of invasive bacterial and fungal infections, which warrant prolonged, inpatient parenteral antimicrobial therapy. Such admissions are complicated by opioid cravings and withdrawal. Comparisons of medications for OUD during prolonged admissions for these patients have not been previously reported. The aim of this study was to evaluate the impact of different OUD treatment strategies in this population, and their impact on ED and hospital readmissions. Methods We retrospectively analyzed consecutive admissions for invasive bacterial or fungal infections in PWID, admitted between January 2016 and January 2019 at Barnes-Jewish Hospital. Patients in our cohort were required to receive an infectious diseases consult, and an anticipated antibiotic treatment duration of >2 weeks. We collected data on demographics, comorbidities, length of stay, microbiologic data, medications prescribed for OUD, mortality, and readmission rates. We compared 90-day readmission rates by OUD treatment strategies using Kaplan–Meier curves. Results In our cohort of 237 patients, treatment of OUD was buprenorphine (17.5%), methadone (25.3%), or none (56.2%). Among patients receiving OUD treatment, 30% had methadone tapers and/or methadone discontinued upon discharge. Patient demographics were similar for each OUD treatment strategy. Infection with HIV (2.8%), and hepatitis B (3%), and hepatitis C (67%) were similar between groups. Continuation of medications for OUD was associated with increased completion of parenteral antibiotics (odds ratio 2.11; 95% confidence interval 1.70–2.63). When comparing medications for OUD strategies, methadone had the lowest readmission rates, followed by buprenorphine, and no treatment (P = 0.0013) (figure). Discontinuation of methadone during the admission or upon discharge was associated with the highest readmission rates. Conclusion Continuation of OUD treatment without tapering, was associated with improved completion of parenteral antimicrobials in PWID with invasive bacterial or fungal infections lower readmission rates. Tapering OUD treatment during admission was associated with higher readmission rates. Disclosures All authors: No reported disclosures.

Sign in / Sign up

Export Citation Format

Share Document