Factors associated with opioid overdose during medication-assisted treatment: How can we identify individuals at risk?

Opioid agonist treatment outcomes among individuals with a history of nonfatal overdose: Findings from a pragmatic, pan-Canadian, randomized control trial

BACKGROUND AND OBJECTIVES

History of nonfatal overdose (NFO) is common among people who use opioids, but little is known about opioid agonist treatment (OAT) outcomes for this high-risk subpopulation. The objective of this study was to investigate the relative effectiveness of buprenorphine/naloxone and methadone on retention and suppression of opioid use among individuals with opioid use disorder (OUD) and history of NFO.

METHODS

Secondary analysis of a pan-Canadian pragmatic trial comparing flexible take-home buprenorphine/naloxone and supervised methadone for people with OUD and history of NFO. Logistic regression was used to examine the impact of OAT on retention in the assigned or in any OAT at 24 weeks and analysis of covariance was used to examine the mean difference in opioid use between treatment arms.

RESULTS

Of the 272 randomized participants, 155 (57%) reported at least one NFO at baseline. Retention rates in the assigned treatment were 17.7% in the buprenorphine/naloxone group and 18.4% in the methadone group (adjusted odds ratio [AOR] = 0.54, 95% CI: 0.17-1.54). Rates of retention in any OAT were 28% and 20% in the buprenorphine/naloxone and methadone arms, respectively (AOR = 1.55, 95% CI: 0.65-3.78). There was an 11.9% adjusted mean difference in opioid-free urine drug tests, favoring the buprenorphine/naloxone arm (95% CI: 3.5-20.3; p = .0057).

CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE

Among adults with OUD and a history of overdose, overall retention rates were low but improved when retention in any treatment was considered. These findings highlight the importance of flexibility and patient-centered care to improve retention and other treatment outcomes in this population.

"Naloxone? Not for me!" First cross-assessment by patients and healthcare professionals of the risk of opioid overdose

BACKGROUND

Opioid-related mortality is a rising public health concern in France, where opioids were in 2021 implicated in 75% of overdose deaths. Opioid substitution treatment (OST) was implicated in almost half of deaths related to substance and drug abuse. Although naloxone could prevent 80% of these deaths, there are a number of barriers to the distribution of take-home naloxone (THN) among opioid users in France. This study is the first one which compares patients' self-assessment of the risk of future opioid overdose with the hetero-assessment provided by healthcare professionals in a population of individuals eligible for naloxone.

METHODS

This was a multicenter descriptive observational study carried out in pharmacies across the Pays de la Loire region (France) during April and May 2022. All adult patients who visited a participating pharmacy for a prescription of OST and provided oral informed consent were enrolled in the study. Retrospective data were collected through cross-sectional interviews conducted by the pharmacist with the patient, utilizing an ad hoc questionnaire. The patient's self-assessment of overdose risk was evaluated using a Likert scale from 0 to 10. The pharmacist relied on the presence or absence of overdose risk situations defined by the French Health Authority (HAS). The need to hold THN was assessed using a composite criterion.

RESULTS

A total of 34 patients were interviewed; near one third were aware of the existence of THN and a minority had THN in their possession. Out of the 34 participants, 29 assessed their own risk of future opioid overdose: 65.5% reported having zero risk, while 6.9% believed they had a high risk. Nevertheless, at least one risk situation of opioid overdose was identified according to HAS criteria in 73.5% of the participants (n = 25). Consequently, 55% of the participants underestimated their risk of experiencing a future opioid overdose. Yet, dispensing THN has been judged necessary for 88.2% of the participants.

CONCLUSION

This study underscored the imperative need to inform not only healthcare professionals but also the patients and users themselves on the availability of THN and the risk situations of opioid overdose.

Outcomes Associated with Treatment with and Without Medications for Opioid Use Disorder

There is limited research on outcomes for patients who start treatment for opioid use disorder (OUD) with only psychosocial treatment compared to those who initiate treatment with either medications for OUD (MOUD) or the combination of psychosocial treatment and MOUD. Cox proportional hazards regression was used on a database of individuals with commercial health insurance or Medicare Advantage to estimate the associations of treatment type with opioid overdose and self-harm (separately). Logistic regression was used to estimate the association of treatment type with prescription opioid fill following treatment initiation. Relative to patients who initiated treatment with only psychosocial treatment, patients who also initiated treatment with MOUD had lower risk of having an overdose inpatient or emergency department (ED) encounter, a self-harm inpatient or ED encounter, and a prescription opioid filled following treatment initiation. Starting treatment with MOUD was associated with better patient outcomes than initiating treatment with only psychosocial treatment.

Urine Drug Test Results Among Adolescents and Young Adults in an Outpatient Office-Based Opioid Treatment Program

PURPOSE

Urine drug testing (UDT) is an important feature of outpatient treatment for opioid use disorder, but associations with patient characteristics among adolescent and young adult patients are unknown. This study assessed UDT results in office-based opioid treatment and characteristics associated with treatment compliance.

METHODS

This was a retrospective study of adolescent and young adult patients enrolled in office-based opioid treatment between January 1, 2009, and December 31, 2020. UDT results were described as positive results or expected and unexpected results. Expected results were negative UDTs for opioids, marijuana (THC [tetrahydrocannabinol]), or cocaine/methamphetamine, or a positive UDT for buprenorphine. Unexpected results were positive UDTs for opioids, THC, or cocaine/methamphetamine, or a negative UDT for buprenorphine. Treatment compliance was defined as ≥75% of UDTs provided being expected results. Counts and percentages described UDT results. Regressions evaluated associations between patient characteristics (retention time, age, sex, race/ethnicity, insurance, and comorbid mental health diagnoses) with treatment compliance, and assessed change of positivity rates for UDTs over time.

RESULTS

A total of 407 patients were included. Overall, 305 patients (74.9%) demonstrated treatment compliance. Rates of expected UDT results increased with longer retention time (p <.001), except for methamphetamine. Buprenorphine expected results ranged from 77.0% to 96.5%. Diagnosis of stimulant use disorder was associated with decreased compliance (p = .04), while diagnoses of depression, anxiety, nicotine use disorder, and post-traumatic stress disorder were associated with increased compliance (p ≤.04).

DISCUSSION

Proportion of expected UDT results increased with retention time. Diagnosis of specific mental health conditions affected treatment compliance. Further research regarding long-term health outcomes is needed.

Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media

Background

Medication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration-approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients' perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns.

Objective

A broad survey of patients' viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients' satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone.

Methods

We collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients' satisfaction. Lastly, we compared the prediction models' performance over different feature sets.

Results

Topics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9% to 90.8%. The Ridge classifier model, a regression-based method, outperformed the other models.

Conclusions

Assessment of patients' satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors' visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence.

Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder

Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. Methods: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. Results: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. Conclusions: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery.