scholarly journals Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256793
Author(s):  
Caroline A. King ◽  
Honora Englander ◽  
P. Todd Korthuis ◽  
Joshua A. Barocas ◽  
K. John McConnell ◽  
...  
Keyword(s):  
Markov Model ◽  
Opioid Use Disorder ◽  
Hospitalized Patients ◽  
Opioid Use ◽  
Post Discharge ◽  
Logistic Regression Models ◽  
Improve Patient Survival ◽  
Bayesian Logistic Regression ◽  
The Impact ◽  
Related Mortality

Introduction Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD. Methods We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards. Results There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity. Discussion Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.

scholarly journals Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality

2020 ◽  
Author(s):  
Caroline A. King ◽  
Honora Englander ◽  
P. Todd Korthuis ◽  
Joshua A. Barocas ◽  
K. John McConnell ◽  
...  
Keyword(s):  
Markov Model ◽  
Opioid Use Disorder ◽  
Hospitalized Patients ◽  
Opioid Use ◽  
Post Discharge ◽  
Logistic Regression Models ◽  
Improve Patient Survival ◽  
Bayesian Logistic Regression ◽  
The Impact ◽  
Related Mortality

AbstractIntroductionAddiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD.MethodsWe developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards.ResultsThere were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI= 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI= 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI= 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI= 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI =1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI= 5%, 13%).DiscussionOur novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.

An Acute Pain Service experience initiating methadone for opioid use disorder in hospitalized patients with acute pain

2019 ◽  
Vol 15 (4) ◽  
pp. 275-283
Author(s):  
Rebecca C. Dale, DO ◽  
Carol L. Metcalf, MN, ARNP ◽  
Dale J. Langford ◽  
Christina E. Bockman, PharmD ◽  
Debra B. Gordon, DNP, RN, FAAN ◽  
...  
Keyword(s):  
Acute Pain ◽  
Clinical Pathway ◽  
Retrospective Chart Review ◽  
Opioid Use Disorder ◽  
Hospitalized Patients ◽  
Acute Pain Service ◽  
Opioid Use ◽  
Post Discharge ◽  
Frequency Distributions ◽  
Average Maximum

Objective: Inform readers of the use of a clinical pathway that includes initiation of methadone in hospitalized patients with acute pain who have untreated opioid use disorder (OUD).Design: A retrospective chart review with frequency distributions and descriptive statistics calculated to describe demographic and clinical characteristics of the sample.Setting: Urban academic hospital.Patients: One hundred twenty consecutive patients with untreated OUD cared for by the Acute Pain Service (APS).Interventions: APS leadership spearheaded development of a clinical pathway to standardize pain management and optimize outcomes. The authors outline pathway development and describe 120 patients managed using this pathway, initiated on methadone for OUD.Results: The sample included patients, average age 40 years, predominantly non-Hispanic white (74.2 percent), male (61.7 percent), unemployed (88.2 percent), and on Medicaid (84.2 percent). 96.7 percent had a history of heroin use, and 52.1 percent had engaged in previous medication-assisted treatment (MAT). Methadone or other opioids were held for signs of intoxication/sedation in 10.9 percent or for prolonged corrected QT interval in 1.7 percent. The majority received at least one other analgesic agent. For those prescribed opioids upon discharge, the average maximum morphine equivalent dose was 68.2 mg/day for approximately 3 days. 68.3 percent agreed to schedule post-discharge MAT, and of these, 68 percent attended their intake appointment. A small percentage (4.7 percent) left the hospital against medical advice.Conclusion: This pathway provides an example of an effective and safe response to address the opioid epidemic and provide quality care to patients with OUD and pain.

Abstract 284: Association of Worsening Renal Function With Mortality and Heart Failure Readmissions in Patients Hospitalized With Acute Heart Failure

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Jacqueline B Palmer ◽  
Howard Friedman ◽  
Katherine Waltman Johnson ◽  
Prakash Navaratnam ◽  
Stephen S Gottlieb
Keyword(s):  
Heart Failure ◽  
Renal Function ◽  
Mortality Rates ◽  
Index Hospitalization ◽  
Post Discharge ◽  
Logistic Regression Models ◽  
Worsening Renal Function ◽  
Multivariable Analyses ◽  
The Impact ◽  
Related Mortality

Background: The impact of worsening renal function (WRF) on heart failure (HF)-related readmissions (HFR) and HF-related mortality among hospitalized acute HF patients was examined. Methods: A patient’s first acute HF hospitalization event (index) was identified in Cerner Health Facts® database (Jan 2008–March 2011). Patients were categorized as WRF (serum creatinine ≥0.3 mg/dL and ≥25% increase from baseline) persisting at discharge (WRFp), not persisting at discharge (WRFt), or non WRF. Outcomes were compared for the index hospitalization and cumulatively at 30, 180, and 365 days post discharge. Generalized linear model (HFR count) and logistic regression models (mortality) were constructed. Results: The acute HF patients (77% [42,507 of 55,436] non WRF, 10% [5,563 of 55,436] WRFp, and 13% [7,366 of 55,436] WRFt) were 53% [29,442 of 55,436] female with a mean age of 72.4 (±14.3) years. WRFp had higher index mortality rates (23.6% [1,312 of 5,563] vs 5.7% [418 of 7,366] vs 3.9% [1,673 of 42,507], P<0.0001) than WRFt and non WRF patients, respectively. For mortality, 70% [3,403 of 4,883] of deaths occurred at the index hospitalization. WRFp and WRFt patients combined had higher 30-day HFR counts than non WRF patients (0.12 vs 0.09, P<0.0001), but there was no difference between WRFp and WRFt. These observations were consistent across all cumulative time points and confirmed by multivariable analyses. Conclusion: Acute HF patients with WRF were more likely to die or be readmitted than non WRF patients. WRFp patients experienced higher HF-related mortality rates than WRFt patients but there were no differences in HFR between WRFt and WRFp.

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 (&gt;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 &lt; 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 0259 Shiftworkers are at Increased Risk of Developing Chronic Pain and Opioid Use Disorders: A Study of 116,000 UK Biobank Participants Over a Decade

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A98-A99
Author(s):  
L Gao ◽  
P Li ◽  
L Cui ◽  
Y Luo ◽  
C Vetter ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Bone Fractures ◽  
Inflammatory Diseases ◽  
Opioid Use Disorder ◽  
Uk Biobank ◽  
Opioid Use ◽  
Logistic Regression Models ◽  
Diagnostic Coding ◽  
Increased Risk ◽  
Opioid Use Disorders

Abstract Introduction In the current epidemic of opioid-related deaths, and widespread use of opioids to treat chronic pain, there is a pressing need to understand the underlying risk factors that contribute to such devastating conditions. Shiftwork has been associated with adverse health outcomes. We tested whether shiftwork during middle age is linked to the development of chronic pain and opioid misuse. Methods We studied 116,474 participants in active employment between 2006–2010 (mean age 57±8; range 37–71) from the UK Biobank, who have been followed for up to 10 years until 2017. We included participants who were free from all forms of self-reported pain, and were not taking opioid medications at baseline. Chronic pain and opioid use disorder diagnoses were determined using hospitalization records and diagnostic coding from ICD-10. Multivariate logistic regression models were performed to examine the associations of shiftwork status (yes/no) and nightshift frequency (none/occasional/permanent) and with incident chronic pain and/or opioid use disorder during follow-up. Models were adjusted for demographics, education, Townsend deprivation index, major confounders (BMI, diabetes, bone fractures/injuries, operations, peripheral vascular disease, joint/inflammatory diseases, cancer, standing/manual labor at work) and covariates (smoking, alcohol, high cholesterol, depression/anxiety, and cardiovascular diseases). Results In total, 190 (1.6/1,000) developed chronic pain or opioid use disorders. Shiftworkers (n=17,673) saw a 1.5-fold increased risk (OR 1.56, 95% CI: 1.08–2.24, p=0.01) relative to day workers. Within shiftworkers, those who reported occasional nightshift work (n=3,966) were most vulnerable (OR 1.57, 95% CI: 1.06–2.34, p=0.02). Results remained similar after adjusting for baseline sleep duration, chronotype and insomnia. Conclusion Shiftwork, and in particular rotating nightshift work is associated with increased risk for developing chronic pain and opioid use disorders. Replication is required to confirm the findings and to examine underlying mechanisms. Support This work was supported by NIH grants T32GM007592, RF1AG064312, and RF1AG059867.

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