Clinically significant drug-drug interactions involving opioid analgesics used for pain treatment in patients with cancer: a systematic review

Opioid metabolism and drug-drug interaction in cancer

Concomitant use of multiple drugs in most patients with cancer may result in drug-drug interactions (DDIs), potentially causing serious adverse effects. These patients often experience unrelieved cancer-related pain (CRP) during and after cancer treatment, which can lead to a reduced quality of life. Opioids can be used as part of a multimodal pain management strategy when non-opioid analgesics are not providing adequate pain relief, not tolerated, or are contraindicated. However, due to their narrow therapeutic window, opioids are more susceptible to adverse events when a DDI occurs. Clinically relevant DDIs with opioids are usually pharmacokinetic, mainly occurring via metabolism by cytochrome P450 (CYP). This article aims to provide an overview of potential DDIs with opioids often used in the treatment of moderate-to-severe CRP and commonly used anticancer drugs such as chemotherapeutics, tyrosine kinase inhibitors (TKIs), or biologics. A DDI-checker tool was used to contextualize the tool-informed DDI assessment outcomes with clinical implications and practice. The findings were compared to observations from a literature search conducted in Embase and PubMed to identify clinical evidence for these potential DDIs. The limited results mainly included case studies and retrospective reviews. Some potential DDIs on the DDI-checker were aligned with literature findings, while others were contradictory. In conclusion, while DDI-checkers are useful tools in identifying potential DDIs, it is necessary to incorporate literature verification and comprehensive clinical assessment of the patient before implementing tool-informed decisions in clinical practice.

Conversion ratios: Why is it so challenging to construct opioid conversion tables?

Standardizing opioid management is challenging due to the absence of a ceiling dose, the unknown ideal therapeutic plasma level, and the lack of an clear relationship between dose and therapeutic response. Opioid rotation or conversion, which is switching from one opioid, route of administration, or both, to another, to improve therapeutic response and reduce toxicities, occurs in 20-40 percent of patients treated with opioids. Opioid conversion is often needed when there are adverse effects, toxicities, or inability to tolerate a certain opioid formulation. A majority of patients benefit from opioid conversion, leading to improved analgesia and less adverse effects. There are different published ways of converting opioids in the literature. This review of 20 years of literature is centered on opioid conversions and aims to discuss the complexity of converting opioids. We discuss study designs, outcomes and measures, pain phenotypes, patient characteristics, comparisons of equivalent doses between opioids, reconciling conversion ratios between opioids, routes, directional differences, half-lives and metabolites, interindividual variability, and comparison to package insert information. Palliative care specialists have not yet come to a consensus on the ideal opioid equianalgesic table; however, we discuss a recently updated table, based on retrospective evidence, that may serve as a gold standard for practical use in the palliative care population. More robust, well-designed studies are needed to validate and guide future opioid conversion data.

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

OBJECTIVE

This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models.

METHODS

The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin.

RESULTS

The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range.

CONCLUSION

The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

Morphine Versus Oxycodone for Cancer Pain Using a Catechol-O-methyltransferase Genotype Biomarker: A Multicenter, Randomized, Open-Label, Phase III Clinical Trial (RELIEF Study)

BACKGROUND

We hypothesized that the high-dose opioid requirement in patients carrying the rs4680-GG variant in the COMT gene encoding catechol-O-methyltransferase would be greater for patients taking morphine than for those taking oxycodone, thus providing a much-needed biomarker to inform opioid selection for cancer pain.

METHODS

A randomized, multicenter, open-label trial was conducted at a Japanese hospital's palliative care service. Patients with cancer pain treated with regular doses of nonsteroidal anti-inflammatory drugs or acetaminophen were enrolled and randomized (1:1) into morphine (group M) and oxycodone (group O) groups. The minimum standard dose of immediate-release (IR) oral opioids was repeatedly administered by palliative care physicians to achieve pain-reduction goals (Pain reduction ≥ 33% from baseline and up to ≤ 3 on a numerical rating scale). The primary endpoint was the proportion of subjects requiring high-dose opioids on day 0 with the GG genotype.

RESULTS

Of 140 participants who developed cancer-related pain among 378 subjects registered and pre-screened for the genotype, 139 were evaluated in the current study. Among patients carrying a COMT rs4680-GG genotype, 48.3% required high-dose opioids in group M, compared with the 20.0% in group O (95% CI, 3.7%-50.8%; P = .029). Of those with the non-GG genotype, 41.5% treated with morphine and 23.1% with oxycodone required high-dose opioids (95% CI, 3.3%-38.3%; P = 0.098).

CONCLUSION

Using the COMT rs4680 genotype alone is not recommended for selecting between morphine and oxycodone for pain relief.

Less Well-Known Consequences of the Long-Term Use of Opioid Analgesics: A Comprehensive Literature Review

BACKGROUND

The adverse effects of short-term opioid analgesics are well known and acknowledged; however, the spectrum of the sequelae of long-term use seems less clear. Some effects may remain undetected but still have the potential to cause harm and reduce patients' quality of life.

OBJECTIVE

To review the literature on the adverse effects of long-term opioid therapy.

METHODS

We performed a quasi-systematic search, analyzing articles published in the MEDLINE database between January 2000 and March 2021 that identified adverse effects of opioids used for chronic pain treatment.

RESULTS

Growing evidence indicates that there are multiple serious adverse effects of opioid treatment. Long-term opioid use may have significant effects on the endocrine, immune, cardiovascular, respiratory, gastrointestinal, and neural systems. Studies show that long-term opioid treatment increases the risk of fractures, infections, cardiovascular complications, sleep-disordered breathing, bowel dysfunction, overdose, and mortality. Opioids may potentially affect cancer development. Most consequences of the long-term use of opioids have been identified in studies of patients with non-malignant pain.

CONCLUSION

Studies indicate that long-term use of opioids increases the risk of drug-related events in a significant number of patients. Clinicians should be aware of these complications associated with prescribing opioids, discuss them with patients, prevent complications, if possible, and diagnose them early and manage adequately. More human studies are needed to assess the risk, including trials with individual opioids, because they have different adverse effect profiles.

Safety of opioid prescribing among older cancer survivors

BACKGROUND

Cancer survivors receive more long-term opioid therapy (LTOT) than people without cancer, but the safety of LTOT prescribing is unknown.

METHODS

Opioid-naive adults aged ≥66 years who had been diagnosed in 2008-2015 with breast, lung, head and neck, or colorectal cancer were identified with data from Surveillance, Epidemiology, and End Results cancer registries linked with Medicare claims. Survivors with 1 or more LTOT episodes (≥90 consecutive days) occurring ≥1 year after their cancer diagnosis and before censoring at hospice entry, another cancer diagnosis, 6 months before death, or December 2016 were included. The safety of prescribing during the first 90 days of the first LTOT episode was measured during follow-up. As a positive safety indicator, the proportion of survivors with concurrent nonopioid pain management was measured. Indicators of less safe prescribing were the proportion of survivors with a high average daily opioid dose (≥90 morphine milligram equivalents) and the proportion of survivors with concurrent benzodiazepine dispensing. Multivariable logistic regression analyses were conducted to identify clinical predictors of each safety outcome.

RESULTS

In all, 3628 cancer survivors received LTOT during follow-up (median duration, 4.9 months; interquartile range, 3.5-8.0 months). Seventy-two percent of the survivors received multimodal pain management concurrently with LTOT. Eight percent of the survivors had high-dose opioid prescriptions; 25% of the survivors received benzodiazepines during LTOT. Multivariable analyses identified variations in safety measures by multiple clinical factors, although none were consistently significant across outcomes.

CONCLUSIONS

To improve safe LTOT prescribing for survivors, efforts should focus on increasing multimodal pain management and reducing inappropriate benzodiazepine prescribing. Different clinical predictors of each outcome suggest different drivers of safe prescribing.

The effect of different intensivist staffing patterns on the rate of potential drug-drug interactions in adult trauma intensive care units

BACKGROUND

Drug-drug interactions (DDIs) have created alarming challenges for public health, especially in those admitted to intensive care units (ICUs). Many studies have shown that involvement of intensivists in the ICUs improves the outcome and decreases the treatment costs. The effect of academic versus non-academic (therapeutic) intensivist as well as hours of coverage and attendance of intensivist on potential DDIs (pDDIs) was evaluated in six adult trauma ICUs of a level one trauma center.

METHODS

In this 6-month cross-sectional study, 200 patients were included. The DDIs were classified into five groups, including type A, B, C, D, and X. pDDIs were defined as interactions belonged to C, D and X categories. Patients in six adult ICUs with three different patterns of intensivist staffing models including type A (once-daily therapeutic intensivist visit followed by 24 h on-call), B (twice-daily academic intensivist visit, 8 h of attendance in ICU and 16 h on-call) and C (all criteria just like ICU type B, except for the presence of therapeutic instead of academic intensivist) were screened for pDDIs.

RESULTS

In total, 3735 drug orders and 3869 drugs (193 different types) were screened and 1826 pDDIs were identified. Type C, D and X interactions accounted for 60.6%, 35.5%, and 3.9% of all pDDIs, respectively. The mean of pDDI per patient was significantly higher (p-value < 0.001) in the ICU type A than ICU types C and B. The frequency of pDDIs was the highest in the type A ICUs. A statistically significant relationship was observed between the number of prescribed drugs and ICU length of stay (p-value < 0.001 and p = 0.009, respectively).

CONCLUSION

Different patterns of intensivist staffing affect pDDIs to varying degrees. In the studied ICUs academic versus therapeutic intensivist, twice versus once-daily visit, and 8 h attendance with16 h on-call versus 24 h on-call were associated with more reductions in pDDIs.

PLAIN LANGUAGE SUMMARY

The impact of different intensivist staffing patterns in ICUs on the rate of potential drug-drug interactionsDrug-drug interactions (DDIs) have created alarming challenges for public health, especially in patients admitted to intensive care units (ICUs). Many studies have shown that involvement of intensivists in the ICUs improves the outcome and limits the costs. Considering the high incidence of potential DDIs (pDDIs) occurring for critically ill patients and the importance of ADRs caused by pDDIs in ICUs, the effect of the presence of an academic versus therapeutic intensivist, as well as the hour of coverage of intensivist on prevalence of pDDIs was evaluated in six adult trauma ICUs of a level one trauma center in Shiraz, Iran. We also determined the prevalence of pDDIs and their associated risk factors. To the best of our knowledge, this is the first study that has assessed the effect of various ICU physician staffing models on the incidence and pattern of pDDIs.

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

Medical Use of Long-term Extended-release Opioid Analgesics in Commercially Insured Adults in the United States

OBJECTIVES

We examined the proportion of patients initiating extended-release (ER) opioids who become long-term users and describe how pain-related diagnoses before initiation of opioid therapy vary between drugs and over time.

METHODS

Using MarketScan (2006-2015), a US national commercial insurance database, we examined pain-related diagnoses in the 182-day baseline period before initiation of ER opioid therapy to characterize indications for opioid initiation. We report the proportion who became long-term users, the median length of opioid therapy, and the proportion with cancer and other noncancer chronic pain, by active ingredient.

RESULTS

Among 1,077,566 adults initiating ER opioids, 31% became long-term users, with a median length of use of 209 days. The most common ER opioids prescribed were oxycodone (26%) and fentanyl (23%), and the most common noncancer pain diagnoses were back pain (65%) and arthritis (48%). Among all long-term users, 16% had a diagnosis of cancer. We found notable variation by drug. Eighteen percent of patients initiating drugs approved by the Food and Drug Administration >10 years ago had evidence of cancer during baseline compared with only 8% of patients who received newer drugs.

CONCLUSIONS

In a national sample of adults with private insurance, back pain was the most common diagnosis preceding initiation of opioid therapy. Opioids that have been approved within the last 10 years were more frequently associated with musculoskeletal pains and less frequently associated with cancer. Amid increasing concerns regarding long-term opioid therapy, our findings provide context regarding the conditions for which long-term opioid therapy is prescribed.

The impact of drug interactions on adverse effects of oral oxycodone in male geriatric patients

WHAT IS KNOWN AND OBJECTIVE

With increased opioid use, drug-drug interactions (DDIs) and associated adverse events are growing among geriatric patients. However, the clinical significance of potential metabolic DDIs associated with opioid use has not been fully evaluated among geriatric patients. Particularly, cytochrome (CYP) P450 enzymes are important in drug metabolism of oxycodone and a black box warning for oxycodone reveals serious risks associated with drug-oxycodone interactions. This study focused on the use of oxycodone in geriatric patients to evaluate its adverse drug reactions (ADRs) and DDIs associated with CYP P450 enzymes.

METHODS

A retrospective cohort study using patients treated at Korea Veterans Hospital was performed. Data from male patients aged 65 years and older who received oxycodone were analysed. Binomial variables describing patient-related characteristics, drug-related characteristics and CYP-mediating drugs were constructed. Associations between these variables and the frequency of ADRs were determined. The odds ratio (OR) and adjusted odds ratio (AOR) were calculated from univariable and multivariable analyses, respectively.

RESULTS AND DISCUSSION

Among 111 patients, 32.4% experienced at least one ADR. The most common ADR was gastrointestinal-related (n = 21), followed by dizziness and drowsiness (n = 8). Use of either CYP2D6 inhibitors or CYP3A4 inhibitors increased the rate of ADRs by 20.4 and 25.4 times, respectively. In the case of patients taking both inhibitors, the adjusted OR was 48.6, and the attributable risk was 97.9%.

WHAT IS NEW AND CONCLUSION

This study suggests that inappropriate combinations of oxycodone with CYP2D6 inhibitors and/or CYP3A4 inhibitors may warrant treatment modification to avoid ADRs in geriatric patients. Clinicians should monitor any signs of ADRs that may reflect DDIs while a geriatric patient is taking oxycodone.

Clinically Significant Drug-Drug Interactions Involving Medications Used for Symptom Control in Patients With Advanced Malignant Disease: A Systematic Review

CONTEXT

Most patients with advanced malignant disease need to take several drugs to control symptoms. This treatment raises risks of serious adverse effects and drug-drug interactions (DDIs).

OBJECTIVES

To identify studies reporting clinically significant DDIs involving medications used for symptom control, other than opioids used for pain management, in adult patients with advanced malignant disease.

METHODS

Systematic review with searches in Embase, MEDLINE, and the Cochrane Central Register of Controlled Trials, from the start of the databases (Embase from 1980) through June 21, 2018. In addition, reference lists of relevant full-text articles were hand-searched.

RESULTS

Of 9699 retrieved citations, 462 were considered potentially eligible. After full-text reading, 29 were included in the final analysis, together with 13 articles from reference lists. The 42 included publications were case reports, letters to the Editor, and one retrospective study. Drugs most often involved were antiepileptics, antidepressants, corticosteroids, and nonopioid analgesics. Clinical manifestations of identified DDIs included sedation, respiratory depression, serotonin syndrome, neuroleptic malignant syndrome, delirium, seizures, ataxia, liver and kidney failure, bleeding, cardiac arrhythmias, rhabdomyolysis, and others. The most common mechanisms eliciting DDIs were alteration of CYP450-dependent metabolism and overstimulation of serotonin receptors in the central nervous system.

CONCLUSION

Drugs used for symptom control in patients with advanced cancer may cause serious DDIs. Although there is limited evidence for the risk of clinically significant DDIs, physicians treating patients with cancer should try to limit polypharmacy, avoid drug combinations with a high risk of DDIs, and closely monitor patients for adverse drug reactions.

Trends in Opioid Use Among Older Survivors of Colorectal, Lung, and Breast Cancers

PURPOSE

Cancer survivors may be at increased risk for opioid-related harms. Trends in opioid use over time since diagnosis are unknown.

METHODS

Using data from SEER and Medicare, we conducted multilevel logistic regression analyses to compare chronic opioid use (≥ 90 consecutive days) among opioid-naïve survivors of colorectal, lung, and breast cancers diagnosed from 2008 to 2013 and matched with noncancer controls. Among cases and controls with chronic use, we compared rates of high-dose opioid use (average ≥ 90 morphine milligram equivalents daily).

RESULTS

We included 46,789 survivors and 138,136 noncancer controls. In the first year after the index date (survivor's diagnosis date), chronic use among colorectal and lung cancer survivors exceeded chronic use among controls (colorectal cancer: odds ratio, 1.34; 95% CI, 1.22 to 1.47; lung cancer: odds ratio, 2.55; 95% CI, 2.34 to 2.77). Differences in chronic use between survivors and controls declined each year after the index date. Chronic use among breast cancer survivors was less than that of controls each year after the index date. Survivors with chronic use were more likely to have a high daily dose than controls with chronic use in the first 3 to 5 years.

CONCLUSION

Among three large populations of older cancer survivors, chronic opioid use varied by cancer. However, by 6 years after diagnosis, survivors were no longer more likely to be chronic users than controls. Strategies for appropriate pain management during and after cancer treatment should take into account the risks associated with chronic high-dose opioid use.

Chinese herb cinobufagin-reduced cancer pain is associated with increased peripheral opioids by invaded CD3/4/8 lymphocytes

Objectives

To investigate the mechanism of cinobufagin-reduced cancer pain in mouse cancer pain model and in vitro cell co-culture system.

Methods

Female Kunming mice were randomly divided into 4 groups. One group of animals was set as normal control without any treatment. Other three groups of animals received H22 hepatoma cell inoculation in right hind paw. At day 9 after inoculation, mice in other three groups were injected intraperitoneally once a day for 8 days with the solvent, morphine or cinobufagin, respectively. The pain behavior was recorded daily. On the last day, all mice were sacrificed and xenograft tissues homogenate and plasma levels of β-endorphin (β-END), corticotropin-releasing factor (CRF) and interleukin-1β (IL-1β) were assessed by ELISA assay. Immunohistochemistry was performed to determine the expression of β-END, pro-opiomelanocortin (POMC) and the μ-opioid receptor (μ-OR) in the xenograft tissues. Immunofluorescence was used to localize lymphocytes with expression of CD3⁺, CD4⁺ and CD8⁺ in xenograft tumors and adjacent tissues. Mice splenic lymphocytes and H22 hepatoma carcinoma ascites cells were prepared for co-culture. β-END and CRF were detected in co-culture supernatants. The MTT assay and cytometry were used to assess cell proliferation. RT-PCR was conducted to determine the gene expression of POMC and Cathepsin L (CTSL). Chemotaxis was examined using a transwell-based migration assay.

Results

Compared to the model group, the thermal and mechanical pain thresholds were increased in mice after cinobufagin treatment. The expression of β-END and CRF in the plasma and tumor tissues of cinobufagin group were much higher than that of the model group mice, but the expression of IL-1β in the plasma and tumor tissues was much lower than that in the model group mice. Meanwhile, the expression of β-END, POMC and μ-OR proteins was significantly increased in the xenograft tissues from cinobufagin group. Lymphocyte population of CD3⁺, CD4⁺, CD8⁺ were also elevated in xenograft tumors and adjacent tissues. In the cell co-culture assays, the content of β-END in the supernatant was significantly increased by cinobufagin in a dose-dependent manner. Cinobufagin also largely increased the proliferation of immune cells and inhibited H22 hepatoma carcinoma cell proliferation in single or co-culture cell assays. Gene expression of POMC and CTSL in cinobufagin group was significantly up-regulated comparing to the control group. Finally, cinobufagin addition enhanced the migration of immune cells in transwell assay.

Conclusions

Cinobufagin-induced local analgesic effect might be associated with increased activity of POMC/β-END/μ-OR pathway released from invaded CD^3/4/8 lymphocytes in cancer tissues.

Opioid analgesics-related pharmacokinetic drug interactions: from the perspectives of evidence based on randomized controlled trials and clinical risk management

BACKGROUND

Multimorbidity results in complex polypharmacy which may bear a risk of drug interactions. A better understanding of opioid analgesics combination therapy used for pain management could help warrant medication safety, efficacy, and economic relevance. Until now there has been no review summarizing the opioid analgesics-related pharmacokinetic drug interactions from the perspective of evidence based on randomized controlled trials (RCTs).

METHOD

A literature search was performed using PubMed, MEDLINE, and the Cochrane Library, using a PRISMA flowchart.

RESULTS

Fifty-two RCTs were included for data interpretation. Forty-two RCTs (80.8%) were conducted in healthy volunteers, whereas 10 RCTs (19.2%) enrolled true patients. None of the opioid-drug/herb pairs was listed as contraindications of opioids involved in this review. Circumstances in which opioid is comedicated as a precipitant drug include morphine-P2Y12 inhibitors, morphine-gabapentin, and methadone-zidovudine. Circumstances in which opioid is comedicated as an object drug include rifampin-opioids (morphine, tramadol, oxycodone, methadone), quinidine-opioids (morphine, fentanyl, oxycodone, codeine, dihydrocodeine, methadone), antimycotics-opioids (buprenorphine, fentanyl, morphine, oxycodone, methadone, tilidine, tramadol), protease inhibitors-opioids (ritonavir, ritonavir/lopinavir-oxycodone, ritonavir-fentanyl, ritonavir-tilidine), grapefruit juice-opioids (oxycodone, fentanyl, methadone), antidepressants-opioids (paroxetine-tramadol, paroxetine-hydrocodone, paroxetine-oxycodone, escitalopram-tramadol), metoclopramide-morphine, amantadine-morphine, sumatriptan-butorphanol nasal sprays, ticlopidine-tramadol, St John's wort-oxycodone, macrolides/ketolides-oxycodone, and levomepromazine-codeine. RCTs investigating the same combination, almost unanimously, drew consistent conclusions, except two RCTs on amantadine-intravenous morphine combination where a different amantadine dose was used and two RCTs on morphine-ticagrelor combination where healthy volunteers and true patients were enrolled, respectively. RCTs investigating in true patients may reflect a realistic clinical scenario and overcome the limitation of RCTs performed in healthy volunteers under standardized conditions. Further research opportunities are also presented in this review.

CONCLUSION

Effective and safe combination therapy of opioids can be achieved by promoting the awareness of potential changes in therapeutic efficacy and toxicities, prescribing alternatives or changing administration strategy, tailoring dose, reviewing the appropriateness of orders, and paying attention to medication monitoring.

Psychoactive medications and disengagement from office based opioid treatment (obot) with buprenorphine

BACKGROUND

The prevalence of psychoactive medications (PAMs) use in patients enrolled in Office Based Opioid Treatment (OBOT) and its association with engagement in this care is largely unknown.

OBJECTIVE

To describe the use of PAMs, including those medications with emerging evidence of misuse ("emerging PAMs" - gabapentin, clonidine and promethazine) among patients on buprenorphine, and its association with disengagement from OBOT.

METHODS

This is a retrospective cohort study of adults on buprenorphine from January 2002 to February 2014. The association between use of PAMs and 6-month disengagement from OBOT was examined using multivariable logistic regression models. A secondary analysis exploring time-to-disengagement was conducted using Cox regression models.

RESULTS

At OBOT entry, 43% of patients (562/1308) were prescribed any PAM; including 17% (223/1308) on an emerging PAM. In separate adjusted analyses, neither the presence of any PAM (adjusted odds ratio [AOR] 1.07, 95% CI [0.78, 1.46]) nor an emerging PAM (AOR 1.28 [0.95, 1.74]) was significantly associated with 6-month disengagement. The results were similar for the Cox model (any PAM (adjusted hazard ratio [AHR] 1.16, 95% CI [1.00, 1.36]), emerging PAM (AHR 1.18 [0.98, 1.41])). Exploratory analyses suggested gabapentin (AHR 1.30 [1.05-1.62]) and clonidine (AHR 1.33 [1.01-1.73]) specifically, may be associated with an overall shorter time to disengagement.

CONCLUSIONS

Psychoactive medication use is common among patients in buprenorphine treatment. No significant association was found between the presence of any psychoactive medications, including medications with emerging evidence of misuse, and 6-month disengagement from buprenorphine treatment.