Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

OBJECTIVE: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication. METHODS: This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects. RESULTS: The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score (P < 0.001), used cocaine on more days over the prior 30 days than other quartiles (P < 0.001), and had highest proportions with alcohol and cocaine use disorder (P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference (P = 0.02) and all experiencing homelessness (P < 0.001). CONCLUSIONS: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.

Alcohol Withdrawal Severity Measures for Identifying Patients Requiring High-Intensity Care.

OBJECTIVES: Alcohol withdrawal syndrome (AWS) may progress to require high-intensity care. Approaches to identify hospitalized patients with AWS who received higher level of care have not been previously examined. This study aimed to examine the utility of Clinical Institute Withdrawal Assessment Alcohol Revised (CIWA-Ar) for alcohol scale scores and medication doses for alcohol withdrawal management in identifying patients who received high-intensity care. DESIGN: A multicenter observational cohort study of hospitalized adults with alcohol withdrawal. SETTING: University of Chicago Medical Center and University of Wisconsin Hospital. PATIENTS: Inpatient encounters between November 2008 and February 2022 with a CIWA-Ar score greater than 0 and benzodiazepine or barbiturate administered within the first 24 hours. The primary composite outcome was patients who progressed to high-intensity care (intermediate care or ICU). INTERVENTIONS: None. MAIN RESULTS: Among the 8742 patients included in the study, 37.5% (n = 3280) progressed to high-intensity care. The odds ratio for the composite outcome increased above 1.0 when the CIWA-Ar score was 24. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at this threshold were 0.12 (95% CI, 0.11-0.13), 0.95 (95% CI, 0.94-0.95), 0.58 (95% CI, 0.54-0.61), and 0.64 (95% CI, 0.63-0.65), respectively. The OR increased above 1.0 at a 24-hour lorazepam milligram equivalent dose cutoff of 15 mg. The sensitivity, specificity, PPV, and NPV at this threshold were 0.16 (95% CI, 0.14-0.17), 0.96 (95% CI, 0.95-0.96), 0.68 (95% CI, 0.65-0.72), and 0.65 (95% CI, 0.64-0.66), respectively. CONCLUSIONS: Neither CIWA-Ar scores nor medication dose cutoff points were effective measures for identifying patients with alcohol withdrawal who received high-intensity care. Research studies for examining outcomes in patients who deteriorate with AWS will require better methods for cohort identification.

Substance use disorder treatment and technology access among people who use drugs in rural areas of the United States: A cross-sectional survey.

PURPOSE: To evaluate how technology access affected substance use disorder (SUD) treatment prior to COVID-19 for people who use drugs in rural areas. METHODS: The Rural Opioid Initiative (January 2018-March 2020) was a cross-sectional study of people with prior 30-day injection drug or nonprescribed opioid use from rural areas of 10 states. Using multivariable mixed-effect regression models, we examined associations between participant technology access and SUD treatment. FINDINGS: Of 3,026 participants, 71% used heroin and 76% used methamphetamine. Thirty-five percent had no cell phone and 10% had no prior 30-day internet use. Having both a cell phone and the internet was associated with increased days of medication for opioid use disorder (MOUD) use (aIRR 1.29 [95% CI 1.11-1.52]) and a higher likelihood of SUD counseling in the prior 30 days (aOR 1.28 [95% CI 1.05-1.57]). Lack of cell phone was associated with decreased days of MOUD (aIRR 0.77 [95% CI 0.66-0.91]) and a lower likelihood of prior 30-day SUD counseling (aOR 0.77 [95% CI 0.62-0.94]). CONCLUSIONS: Expanding US rural SUD treatment engagement via telemedicine may require increased cell phone and mobile network access.

American College of Preventive Medicine: Addressing the Opioid Epidemic Through a Prevention Framework.

The opioid epidemic has resulted in significant morbidity and mortality in the U.S. Health systems, policymakers, payers, and public health have enacted numerous strategies to reduce the harms of opioids, including opioid use disorder (OUD). Much of this implementation has occurred before the development of OUD‒related comparative effectiveness evidence, which would enable an understanding of the benefits and harms of different approaches. This article from the American College of Preventive Medicine (ACPM) uses a prevention framework to identify the current approaches and make recommendations for addressing the opioid epidemic, encompassing strategies across a primordial, primary, secondary, and tertiary prevention approach. Key primordial prevention strategies include addressing social determinants of health and reducing adverse childhood events. Key primary prevention strategies include supporting the implementation of evidence-based prescribing guidelines, expanding school-based prevention programs, and improving access to behavioral health supports. Key secondary prevention strategies include expanding access to evidence-based medications for opioid use disorder, especially for high-risk populations, including pregnant women, hospitalized patients, and people transitioning out of carceral settings. Key tertiary prevention strategies include the expansion of harm reduction services, including expanding naloxone availability and syringe exchange programs. The ACPM Opioid Workgroup also identifies opportunities for de-implementation, in which historical and current practices may be ineffective or causing harm. De-implementation strategies include reducing inappropriate opioid prescribing; avoiding mandatory one-size-fits-all policies; eliminating barriers to medications for OUD, debunking the myth of detoxification as a primary solo treatment for opioid use disorder; and destigmatizing care practices and policies to better treat people with OUD.