Patterns of Opioid and Benzodiazepine Use in Opioid-Naïve Patients with Newly Diagnosed Low Back and Lower Extremity Pain.

BACKGROUND: The morbidity and mortality associated with opioid and benzodiazepine co-prescription is a pressing national concern. Little is known about patterns of opioid and benzodiazepine use in patients with acute low back pain or lower extremity pain. OBJECTIVE: To characterize patterns of opioid and benzodiazepine prescribing among opioid-naïve, newly diagnosed low back pain (LBP) or lower extremity pain (LEP) patients and to investigate the relationship between benzodiazepine prescribing and long-term opioid use. DESIGN/SETTING: We performed a retrospective analysis of a commercial database containing claims for more than 75 million enrollees in the USA. PARTICIPANTS: Participants were adult patients newly diagnosed with LBP or LEP between 2008 and 2015 who did not have a red flag diagnosis, had not received an opioid prescription in the 6 months prior to diagnosis, and had 12 months of continuous enrollment after diagnosis. MAIN OUTCOMES AND MEASURES: Among patients receiving at least one opioid prescription within 12 months of diagnosis, we defined discrete patterns of benzodiazepine prescribing-continued use, new use, stopped use, and never use. We tested the association of these prescription patterns with long-term opioid use, defined as six or more fills within 12 months. RESULTS: We identified 2,497,653 opioid-naïve patients with newly diagnosed LBP or LEP. Between 2008 and 2015, 31.9% and 11.5% of these patients received opioid and benzodiazepine prescriptions, respectively, within 12 months of diagnosis. Rates of opioid prescription decreased from 34.8% in 2008 to 27.0% in 2015 (P < 0.001); however, prescribing of benzodiazepines only decreased from 11.6% in 2008 to 10.8% in 2015. Patients with continued or new benzodiazepine use consistently used more opioids than patients who never used or stopped using benzodiazepines during the study period (one-way ANOVA, P < 0.001). For patients with continued and new benzodiazepine use, the odds ratio of long-term opioid use compared with those never prescribed a benzodiazepine was 2.99 (95% CI, 2.89-3.08) and 2.68 (95% CI, 2.62-2.75), respectively. LIMITATIONS: This study used administrative claims analyses, which rely on accuracy and completeness of diagnostic, procedural, and prescription codes. CONCLUSION: Overall opioid prescribing for low back pain or lower extremity pain decreased substantially during the study period, indicating a shift in management within the medical community. Rates of benzodiazepine prescribing, however, remained at approximately 11%. Concurrent prescriptions of benzodiazepines and opioids after LBP or LEP diagnosis were associated with increased risk of long-term opioid use.

Qualitative Assessment of Clerkship Students' Perspectives of the Topics of Pain and Addiction in their Preclinical Curriculum.

OBJECTIVE: A majority of physicians feel poorly trained in the treatment of chronic pain and addiction. As such, it is critical that medical students receive appropriate education in both pain management and addiction. The purpose of this study was to assess the pre-clinical curriculum in pain medicine and addiction from the perspective of students after they had completed their pre-clinical training and to assess what they perceived as the strengths and weaknesses of their training. METHODS: The authors conducted focused interviews among clinical medical students who had completed at least 6 months of clerkships. The interviews targeted the students' retrospective opinions about the pre-clinical curriculum and their preparedness for clinical encounters with either pain or addiction-related issues during their rotations. Coders thematically analyzed the de-identified interview transcripts, with consensus reached through discussion and code modification. RESULTS: Themes that emerged through the focused interviews included: fragmented curricular structure (and insufficient time) for pain and addiction medicine, not enough specific treatment strategies for pain or addiction, especially for complex clinical scenarios, and lack of a trained work-force to provide guidance in the management of pain and addiction. CONCLUSION: This study demonstrated the feasibility of gathering student perspectives to inform changes to improve the pre-clinical curriculum in pain and addiction medicine. Students identified multiple areas for improvement at the pre-clerkship level, which have informed updates to the curriculum. More research is needed to determine if curricular changes based on student feedback lead to improved learning outcomes.

A call to include people with mental illness and substance use disorders alongside 'regular' smokers in smoking cessation research.

This commentary points out that smoking is increasingly concentrated among people with psychiatric problems and other substance use disorders (eg, alcohol use disorder), and argues that for clinical, ethical and efficiency reasons, such individuals should be routinely enrolled in smoking cessation research.

Weighing the Risks and Benefits of Chronic Opioid Therapy.

Evidence supports the use of opioids for treating acute pain. However, the evidence is limited for the use of chronic opioid therapy for chronic pain. Furthermore, the risks of chronic therapy are significant and may outweigh any potential benefits. When considering chronic opioid therapy, physicians should weigh the risks against any possible benefits throughout the therapy, including assessing for the risks of opioid misuse, opioid use disorder, and overdose. When initiating opioid therapy, physicians should consider buprenorphine for patients at risk of opioid misuse, opioid use disorder, and overdose. If and when opioid misuse is detected, opioids do not necessarily need to be discontinued, but misuse should be noted on the problem list and interventions should be performed to change the patient's behavior. If aberrant behavior continues, opioid use disorder should be diagnosed and treated accordingly. When patients are discontinuing opioid therapy, the dosage should be decreased slowly, especially in those who have intolerable withdrawal. It is not unreasonable for discontinuation of chronic opioid therapy to take many months. Benzodiazepines should not be coprescribed during chronic opioid therapy or when tapering, because some patients may develop cross-dependence. For patients at risk of overdose, naloxone should be offered to the patient and to others who may be in a position to witness and reverse opioid overdose.

Assessment of provider attitudes toward #naloxone on Twitter.

BACKGROUND: As opioid overdose rates continue to pose a major public health crisis, the need for naloxone treatment by emergency first responders is critical. Little is known about the views of those who administer naloxone. The current study examines attitudes of health professionals on the social media platform Twitter to better understand their perceptions of opioid users, the role of naloxone, and potential training needs. METHODS: Public comments on Twitter regarding naloxone were collected for a period of 3 consecutive months. The occupations of individuals who posted tweets were identified through Twitter profiles or hashtags. Categories of emergency service first responders and medical personnel were created. Qualitative analysis using a grounded theory approach was used to produce thematic content. The relationships between occupation and each theme were analyzed using Pearson chi-square statistics and post hoc analyses. RESULTS: A total of 368 individuals posted 467 naloxone-related tweets. Occupations consisted of professional first responders such as emergency medical technicians (EMTs), firefighters, and paramedics (n = 122); law enforcement officers (n = 70); nurses (n = 62); physicians (n = 48); other health professionals including pharmacists, pharmacy technicians, counselors, and social workers (n = 31); naloxone-trained individuals (n = 12); and students (n = 23). Primary themes included burnout, education and training, information seeking, news updates, optimism, policy and economics, stigma, and treatment. The highest levels of burnout, fatigue, and stigma regarding naloxone and opioid overdose were among nurses, EMTs, other health care providers, and physicians. In contrast, individuals who self-identified as "naloxone-trained" had the highest optimism and the lowest amount of burnout and stigma. CONCLUSIONS: Provider training and refinement of naloxone administration procedures are needed to improve treatment outcomes and reduce provider stigma. Social networking sites such as Twitter may have potential for offering psychoeducation to health care providers.

Trends in hallucinogen-associated emergency department visits and hospitalizations in California, USA, from 2016 to 2022.

BACKGROUND AND AIMS: Hallucinogens encompass a diverse range of compounds of increasing scientific and public interest. Risks associated with hallucinogen use are under-researched and poorly understood. We aimed to compare the trends in hallucinogen-associated health-care use with alcohol- and cannabis-associated health-care use. DESIGN, SETTING AND CASES: We conducted an ecological study with publicly available data on International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes associated with emergency department (ED) visits and hospitalizations from the California Department of Healthcare Access and Information (HCAI). HCAI includes primary and secondary ICD-10 codes reported with ED or hospital discharge from every non-federal health-care facility licensed in California, United States, from 2016 to 2022. MEASUREMENTS: ICD-10 codes were classified as hallucinogen-, cannabis- or alcohol-associated if they were from the corresponding category in the ICD-10 block 'mental and behavioral disorders due to psychoactive substance use'. FINDINGS: Observed hallucinogen-associated ED visits increased by 54% between 2016 and 2022, from 2260 visits to 3476 visits, compared with a 20% decrease in alcohol-associated ED visits and a 15% increase in cannabis-associated ED visits. The observed hallucinogen-associated hospitalizations increased by 55% during the same period, from 2556 to 3965 hospitalizations, compared with a 1% increase in alcohol-associated hospitalizations and a 1% increase in cannabis-associated hospitalizations. This rise in hallucinogenic ED visits was significantly different from the trend in cannabis-associated (P < 0.001) and alcohol-associated (P = 0.005) ED visits. The hallucinogen-associated hospitalizations trend also significantly differed when compared with cannabis- (P < 0.001) and alcohol-associated (P < 0.001) hospitalizations. CONCLUSIONS: Hallucinogen-associated emergency department visits and hospitalizations in California, USA, showed a large relative but small absolute increase between 2016 and 2022.

Which Social Media Platforms Provide the Most Informative Data for Monitoring the Opioid Crisis?

Background and Aims: Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have leveraged data from platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened their usability in opioid overdose surveillance systems. Here, we evaluate the potential of a broad set of platforms to detect emerging trends in the opioid crisis. Design: We identified 72 online platforms with a substantial global user base or prior citations in opioid-related research. We evaluated each platform's fit with our definition of social media, size of North American user base, and volume of opioid-related discourse. We created a shortlist of 11 platforms that met our criteria. We documented basic characteristics, volume and nature of opioid discussion, official policies regulating drug-related discussion, and data accessibility of shortlisted platforms. Setting: USA and Canada. Measurements: We quantified the volume of opioid discussion by number of platform-specific Google search hits for opioid terms. We captured informal language by including slang generated using a large language model. We report the number of opioid-related hits and proportion of opioid-related hits to hits for common nouns. Findings: We found that TikTok, YouTube, and Facebook have the most potential for use in opioid-related surveillance. TikTok and Facebook have the highest relative amount of drug-related discussions. Language on TikTok was predominantly informal. Many platforms offer data access tools for research, but changing company policies and user norms create instability. The demographics of users varies substantially across platforms. Conclusions: Social media data sources hold promise for detecting trends in opioid use, but researchers must consider the utility, accessibility, and stability of data on each platform. A strategy mixing several platforms may be required to cover all demographics suffering in the epidemic.

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

A telehealth inpatient addiction consult service is both feasible and effective in reducing readmission rates.

The COVID-19 pandemic compelled fast adaptation of telehealth to addiction treatment services. This study aims to examine the feasibility and effectiveness of transitioning an in-person hospital addiction consult service (ACS) to telehealth. The Stanford Hospital ACS adapted to the pandemic by transforming an in-person ACS to a telehealth ACS. We compared 30-day readmission rates in patients with and without an addiction medicine consult pre-pandemic (in-person ACS) and during the pandemic (telehealth ACS). The ACS completed 370 and 473 unique patient consults in the year preceding (in-person consults) and during the pandemic (telehealth consults) respectively. Patients seen by telehealth ACS had decreased 30-day readmission rates consistent with those seen before COVID-19. A telehealth ACS is feasible and effective in the in-patient setting. Telehealth ACS holds promise to extend the reach of substance use disorder evaluation and treatment in underserved areas.

Predicting premature discontinuation of medication for opioid use disorder from electronic medical records.

Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.

Time to abandon the self-medication hypothesis in patients with psychiatric disorders.

BACKGROUND: The Self-Medication Hypothesis (SMH) of addictive disorders as articulated by Edward Khantzian in his seminal 1985 paper postulates that individuals with psychiatric disorders use substances to relieve psychiatric symptoms and that this pattern of usage predisposes them to addiction. Khantzian's SMH also postulates that the preferred substance is not random, but is based on the unique pharmacological properties of the substance. For example, an individual with attention deficit disorder would prefer amphetamines to alcohol, due to its stimulating properties, whereas an individual with anxiety would prefer alcohol to amphetamines, due to its anxiolytic properties. Finally, Khantzian's SMH implies that treating the underlying psychiatric disorder will improve or resolve the problems of addiction. AIMS AND RESULTS: A review of the scientific literature demonstrates a striking lack of robust evidence in support of the SMH as put forth by Khantzian. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Nonetheless, the SMH has had a profound influence on medical and lay culture, as well as clinical care. Although originally formulated as a compassionate explanation for addiction in those with psychiatric disorders, the SMH does not provide, as originally intended, a "useful rationale" for guiding treatment and instead has led to under-recognition and under-treatment of substance use disorders.