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Background: In response to the opioid crisis in the United States, population-level prescribing of opioids has been decreasing; there are concerns, however, that dose reductions are related to potential adverse events., Objective: Examine associations between opioid dose reductions and risk of 1-month potential adverse events (emergency department (ED) visits, opioid overdose, benzodiazepine prescription fill, all-cause mortality)., Design: This observational cohort study used electronic health record and claims data from eight United States health systems in a prescription opioid registry (Clinical Trials Network-0084). All opioid fills (excluding buprenorphine) between 1/1/2012 and 12/31/2018 were used to identify baseline periods with mean morphine milligram equivalents daily dose of  ≥ 50 during six consecutive months., Patients: We identified 60,040 non-cancer patients with  ≥ one 2-month dose reduction period (600,234 unique dose reduction periods)., Main Measures: Analyses examined associations between dose reduction levels (1- < 15%, 15- < 30%, 30- < 100%, 100% over 2 months) and potential adverse events in the month following a dose reduction using logistic regression analysis, adjusting for patient characteristics., Key Results: Overall, dose reduction periods involved mean reductions of 18.7%. Compared to reductions of 1- < 15%, dose reductions of 30- < 100% were associated with higher odds of ED visits (OR 1.14, 95% CI 1.10, 1.17), opioid overdose (OR 1.41, 95% CI 1.09-1.81), and all-cause mortality (OR 1.39, 95% CI 1.16-1.67), but lower odds of a benzodiazepine fill (OR 0.83, 95% CI 0.81-0.85). Dose reductions of 15- < 30%, compared to 1- < 15%, were associated with higher odds of ED visits (OR 1.08, 95% CI 1.05-1.11) and lower odds of a benzodiazepine fill (OR 0.93, 95% CI 0.92-0.95), but were not associated with opioid overdose and all-cause mortality., Conclusions: Larger reductions for patients on opioid therapy may raise risk of potential adverse events in the month after reduction and should be carefully monitored., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge., Objective: The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization., Design: Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression., Patients: Hospitalized patients at an urban, safety net hospital., Main Measurements: Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others., Key Results: Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients., Conclusions: Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

The Veterans Health Administration (VA) Health Care for Reentry Veterans (HCRV) program links veterans exiting prison with treatment. Among veterans served by HCRV, national VA clinical data were used to describe contact with VA health care, and mental health and substance use disorder diagnoses and treatment use. Of veterans seen for an HCRV outreach visit, 56 % had contact with VA health care. Prevalence of mental health disorders was 57 %; of whom 77 % entered mental health treatment within a month of diagnosis. Prevalence of substance use disorders was 49 %; of whom 37 % entered substance use disorder treatment within a month of diagnosis. For veterans exiting prison, increasing access to VA health care, especially for rural veterans, and for substance use disorder treatment, are important quality improvement targets.

Background: Soft tissue infections (STIs) from injection drug use are a common cause of Emergency Department visits, hospitalizations, and operating room procedures, yet little is known about factors that may predict the need for these costly medical services., Objective: To describe a cohort of injection drug users seeking Emergency Department care for STIs and to identify risk factors associated with hospitalization. We hypothesized that participants who delayed seeking care would be hospitalized more often than those who did not., Design: Cohort study using in-person structured interviews and medical record review. Logistic regression assessed the association between hospital admission and delay in seeking care as well as other demographic, clinical, and psychosocial factors., Participants: Injection drug users who sought Emergency Department care for STIs from May 2001 to March 2002., Results: Of the 136 participants, 55 (40%) were admitted to the hospital. Delay in seeking care was not associated with hospital admission. Participants admitted for their infection were significantly more likely to be living in a shelter (P = .01) and to report being hospitalized 2 or more times in the past year (P < .01)., Conclusions: We identified a subpopulation of injection drug users, mostly living in shelters, who were hospitalized frequently in the past year and who were more likely to be hospitalized for their current infections compared to others. As members of this subpopulation can be easily identified and located, they may benefit from interventions to reduce the health care utilization resulting from these infections.