Coprescription of Opioids With Other Medications and Risk of Opioid Overdose

Mechanism of gabapentinoid potentiation of opioid effects on cyclic AMP signaling in neuropathic pain

Over half of spinal cord injury (SCI) patients develop opioid-resistant chronic neuropathic pain. Safer alternatives to opioids for treatment of neuropathic pain are gabapentinoids (e.g., pregabalin and gabapentin). Clinically, gabapentinoids appear to amplify opioid effects, increasing analgesia and overdose-related adverse outcomes, but in vitro proof of this amplification and its mechanism are lacking. We previously showed that after SCI, sensitivity to opioids is reduced by fourfold to sixfold in rat sensory neurons. Here, we demonstrate that after injury, gabapentinoids restore normal sensitivity of opioid inhibition of cyclic AMP (cAMP) generation, while reducing nociceptor hyperexcitability by inhibiting voltage-gated calcium channels (VGCCs). Increasing intracellular Ca2+ or activation of L-type VGCCs (L-VGCCs) suffices to mimic SCI effects on opioid sensitivity, in a manner dependent on the activity of the Raf1 proto-oncogene, serine/threonine-protein kinase C-Raf, but independent of neuronal depolarization. Together, our results provide a mechanism for potentiation of opioid effects by gabapentinoids after injury, via reduction of calcium influx through L-VGCCs, and suggest that other inhibitors targeting these channels may similarly enhance opioid treatment of neuropathic pain.

Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations

The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.

Use and misuse of psychoactive medicines: a descriptive cross-sectional study in a densely populated region of Portugal

Introduction

Although psychoactive medicines (PMed) are needed in several psychiatric conditions, their use and misuse bear risks. We aimed at estimating the prevalence of PMed use and misuse.

Methods

Data on all PMed prescribed in 2017 and dispensed in community pharmacies of the Lisbon and Tagus Valley region of Portugal (ARSLVT) were extracted from ARSLVT medicines' dispensing database. For 21 PMed among prescription opioids, benzodiazepines and z-drugs (BZDR), antidepressants (AD) and anticonvulsants (AC), we estimated the number of users of each PMed, and assessed PMed misuse by a set of proxy indicators for studying this practice: chronic use (use of ≥180 DDD during the study period) of PMed intended for short-term treatments, concomitant use of several PMed, in particular if involving long-term (≥ 30 days) opioid analgesic (OA) use, and doctor shopping (patients consulting several physicians in order to have access to a quantity higher than intended by each prescriber). Data were analysed using descriptive statistics and hypothesis testing, and multivariate logistic regression was used to explore potential factors affecting long-term concomitant treatment of chronic OA with other PMed.

Results

PMed use prevalence was 21.7%: 6.6% for OA, 12.7% for benzodiazepines (BZD), 5.3% for AD and 2.8% for AC. BZDR were mainly prescribed in primary care and OA in hospital outpatients. Chronic use of PMed was observed in 25%, especially with sertraline and buprenorphine for opioid use disorder (long-term treatment), and lorazepam (short-term treatment). About 56.6% of OA chronic users were long-term concurrent users with other PMed, mainly BZDR. Risk of abuse was low for BZDR, whilst four opioids had meaningful doctor shopping indicators - fentanyl, opioid use disorder buprenorphine, morphine and hydromorphone.

Conclusions

BZD are the main PMed used in ARSLVT, often chronically, especially lorazepam. Prevalence of OA use is low, although with higher risk of misuse than BZDR. Concomitant use of several PMed is frequent.

Potentially Life-Threatening Interaction between Opioids and Intrathecal Baclofen in Individuals with a Childhood-Onset Neurological Disorder: A Case Series and Review of the Literature

BACKGROUND

 Spasticity and dystonia are movement impairments that can occur in childhood-onset neurological disorders. Severely affected individuals can be treated with intrathecal baclofen (ITB). Concomitant use of ITB and opioids has been associated with central nervous system (CNS) depression. This study aims to describe the clinical management of this interaction, based on a case series and review of literature.

METHODS

 Four individuals with childhood-onset CNS disorders (age 8-24) and CNS-depressant overdose symptoms after the concomitant use of ITB and opioids are described. The Drug Interaction Probability Scale (DIPS) was calculated to assess the cause-relationship (doubtful <2, possible 2-4, probable 5-8, and highly probable >8) of the potential drug-drug interaction. A literature review of similar previously reported cases and the possible pharmacological mechanisms of opioid-baclofen interaction is provided.

RESULTS

 After ITB and opioid co-administration, three out of four patients had decreased consciousness, and three developed respiratory depression. DIPS scores indicated a possible cause-relationship in one patient (DIPS: 4) and a probable cause-relationship in the others (DIPS: 6, 6, and 8). Discontinuation or adjusting ITB or opioid dosages resulted in clinical recovery. All patients recovered completely. In the literature, two articles describing nine unique cases were found.

CONCLUSION

 Although the opioid-ITB interaction is incompletely understood, concomitant use may enhance the risk of symptoms of CNS-depressant overdose, which are potentially life-threatening. If concomitant use is desirable, we strongly recommend to closely monitor these patients to detect interaction symptoms early. Awareness and monitoring of the potential opioid-ITB interaction is essential to reduce the risk of severe complications.

Association of Gabapentinoids With Opioid-Related Overdose in the Inpatient Setting: A Single Center Retrospective Case-Control Study

Objectives: Recent data suggest concomitant gabapentinoid use increases opioid-related overdose (ORO) risk; however, this association has not been well studied in the hospital setting. The primary objective of this study was to compare ORO risk, indicated by naloxone administration, in patients receiving opioids plus gabapentinoids versus opioids alone. Methods: In this retrospective case-control study of adults admitted to a large community hospital from 1/1/20 to 12/31/21, all cases (defined as patients who received naloxone more than 24 hours after admission) identified were matched 1:1 to randomly selected controls (defined as patients on opioids who did not receive naloxone). The primary outcome was the percentage of cases and controls with concomitant inpatient gabapentinoid use. Logistic regression was performed to determine the independent association between gabapentinoids and ORO (as evidenced by inpatient naloxone administration). Results: Baseline characteristics were similar between the 144 cases and 144 controls. Gabapentinoid exposure was greater for cases than controls (34.0%vs 20.8%, P = .0118). Median hospital length of stay (11vs 4 days, P < .0001) and mortality (19%vs 5%; P = .0018) were also higher for cases. In logistic regression analysis, ORO (adjusted OR 4.91; 95% CI 1.86-12.96) and serotonergic medication exposure (adjusted OR 4.31; 95% CI 1.50-12.38) were significantly associated with gabapentinoid use. Conclusions: Concomitant gabapentinoid use with opioids was associated with increased ORO risk in the inpatient setting. When considering prescribing gabapentinoids in conjunction with opioids in the hospital setting, potential benefits should be weighed against increased overdose risk.

Risk of fall or fracture with concomitant use of prescription opioids and other medications in osteoarthritis patients

BACKGROUND

Osteoarthritis (OA) patients taking prescription opioids for pain are at increased risk of fall or fracture, and the concomitant use of interacting drugs may further increase the risk of these events.

AIMS

To identify prescription opioid-related medication combinations associated with fall or fracture.

MATERIALS & METHODS

We conducted a case-crossover-based screening of two administrative claims databases spanning 2003 through 2021. OA patients were aged 40 years or older with at least 365 days of continuous enrollment and 90 days of continuous prescription opioid use before their first eligible fall or fracture event. The primary analysis quantified the odds ratio (OR) between fall and non-opioid medications dispensed in the 90 days before the fall date after adjustment for prescription opioid dosage and confounding using a case-time-control design. A secondary analogous analysis evaluated medications associated with fracture. The false discovery rate (FDR) was used to account for multiple testing.

RESULTS

We identified 41 693 OA patients who experienced a fall and 24 891 OA patients who experienced a fracture after at least 90 days of continuous opioid therapy. Top non-opioid medications by ascending p-value with OR > 1 for fall were meloxicam (OR 1.22, FDR = 0.08), metoprolol (OR 1.06, FDR >0.99), and celecoxib (OR 1.13, FDR > 0.99). Top non-opioid medications for fracture were losartan (OR 1.20, FDR = 0.80), alprazolam (OR 1.14, FDR > 0.99), and duloxetine (OR 1.12, FDR = 0.97).

CONCLUSION

Clinicians may seek to monitor patients who are co-prescribed drugs that act on the central nervous system, especially in individuals with OA.

Polypharmacy and Healthcare Service Use Among Prescription Opioid Poisoning Cases Age 50

Objectives: To examine relationships between polypharmacy and level of healthcare service use among prescription opioid poisoning cases age 50 and older. Methods: Data came from the American Association of Poison Control Center's National Poison Data System, 2015-2020. We used multinomial logistic regression to examine the study questions. Results: Of the 77 946 cases with prescription opioid exposures, 64.5% were managed at a healthcare facility (HCF). Of HCF-managed cases, 41.2% were treated/evaluated and released and 21.3% and 37.5% were admitted for noncritical care and critical care, respectively. Medications for cardiovascular disease, benzodiazepines, other types of sedatives/hypnotics, antipsychotics, muscle relaxants, acetaminophen, and gabapentin were associated with increased risk of admission to both noncritical and critical care compared to treatment/evaluation and release. Acetaminophen use had the highest relative risk ratios (RRRs) for noncritical care (1.70, 95% CI = 1.51-1.91) and critical care (1.56, 95% CI = 1.39-1.76). Each additional medication/substance used was associated with 1.14 (95% CI = 1.11-1.17) and 1.19 (95% CI = 1.16-1.22) greater risk of noncritical and critical care admissions, respectively. Conclusions: Among older-adult poison control center cases for prescription opioid exposures, co-use of several commonly prescribed/used medicines was associated with increased risk of admissions to both noncritical and critical care units. Careful monitoring of medication use among older adults who use prescription opioids may reduce the risk of unintentional and intentional opioid poisoning.

Non-fatal overdose risk associated with prescribing opioid agonists concurrently with other medication: Cohort study conducted using linked primary care, secondary care and mortality records

BACKGROUND AND AIMS

An apparently protective effect of opioid agonist treatment (OAT) on all-cause and cause-specific mortality risk has been widely reported. Non-fatal overdose (NFO) often precedes subsequent drug-poisoning deaths. We hypothesized that benzodiazepines, gabapentinoids, antipsychotics, antidepressants, Z-drugs or opioids increase the NFO risk when co-prescribed with OAT.

DESIGN

We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases. The cohort was linked to Hospital Episode Statistics admitted patient care data (HES-APC), neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records from the Office for National Statistics.

SETTING

Primary care in England.

PARTICIPANTS

We studied patients with opioid use disorder, aged 18-64 years, who were prescribed OAT (15155 methadone and 5743 buprenorphine recipients) between Jan 1, 1998, and Dec 31, 2017.

MEASUREMENTS

The main outcome examined was NFO risk during co-prescription of OAT with benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids. Overdose was defined according to International Classification of Diseases codes from the HES-APC data set. Negative binomial regression models were used to estimate weighted rate ratios (wRR) for NFO during co-prescription of OAT and benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids with periods of exclusive OAT usage.

FINDINGS

Among 20 898 patients observed over 83 856 person-years, we found an elevated overdose risk that resulted in hospital admission during co-prescription of OAT with benzodiazepines [wRR: 1.45; 95% confidence interval (CI) = 1.26-1.67], gabapentinoids (wRR = 2.22; 95% CI = 1.77-2.79), Z-drugs (wRR = 1.60; 95% CI = 1.31-1.96), antipsychotics (wRR = 1.85; 95% CI = 1.53-2.25) and opioids (wRR = 1.28; 95% CI = 1.02-1.60). The risk ratio for antidepressant co-prescriptions was below unity (wRR = 0.90; 95% CI = 0.79-1.02) but this result was not statistically significant.

CONCLUSION

Elevated risk of non-fatal overdose among opioid agonist treatment recipients is associated with concurrent use of medication prescribed for other reasons.

Spinal cord injury and prescribed opioids for pain: a scoping review

OBJECTIVE

Spinal cord injury (SCI) is a life-altering neurological condition affecting physical and psycho-social functioning and associated high rates of pain. Thus, individuals with SCI may be more likely to be exposed to prescription opioids. A scoping review was conducted to synthesize published research findings on post-acute SCI and prescription opioid use for pain, identify literature gaps, and propose recommendations for future research.

METHODS

We searched 6 electronic bibliographic databases (PubMed [MEDLINE], Ovid [MEDLINE], EMBASE, Cochrane Library, CINAHL, PsychNET) for articles published from 2014 through 2021. Terms for "spinal cord injury" and "prescription opioid use" were used. Included articles were in English and peer reviewed. Data were extracted using an electronic database by 2 independent reviewers. Opioid use risk factors for chronic SCI were identified and a gap analysis was performed.

RESULTS

Of the 16 articles included in the scoping review, a majority were conducted in the United States (n = 9). Most articles lacked information on income (87.5%), ethnicity (87.5%), and race (75%). Prescription opioid use ranged from 35% to 64% in articles reporting this information (n = 7 articles, n = 3675 participants). Identified risk factors for opioid use included middle age, lower income, osteoarthritis diagnosis, prior opioid use, and lower-level spinal injury. Limited reporting of diversity in study populations, absence of risk of polypharmacy, and limited high quality methodology were identified gaps.

CONCLUSIONS

Future research should report data on prescription opioid use in SCI populations, with additional demographics such as race, ethnicity, and income, given their importance to risk outcomes.

Evaluating the use of methods to mitigate bias from non-transient medications in the case-crossover design: A systematic review

PURPOSE

The case-crossover design is a self-controlled study design used to compare exposure immediately preceding an event occurrence with exposure in earlier control periods. The design is most suitable for transient exposures in order to avoid biases that can be problematic when using the case-crossover design for non-transient (i.e., chronic) exposures. Our goal was to conduct a systematic review of case-crossover studies and its variants (case-time-control and case-case-time-control) in order to compare design and analysis choices by medication type.

METHODS

We conducted a systematic search to identify recent case-crossover, case-time-control, and case-case-time-control studies focused on medication exposures. Articles indexed in MEDLINE and EMBASE using these study designs that were published between January 2015 and December 2021 in the English language were identified. Reviews, methodological studies, commentaries, articles without medications as the exposure of interest, and articles with no available full text were excluded. Study characteristics including study design, outcome, risk window, control window, reporting of discordant pairs, and inclusion of sensitivity analyses were summarized overall and by medication type. We further evaluated the implementation of recommended methods to account for biases introduced by non-transient exposures among articles that used the case-crossover design on a non-transient exposure.

RESULTS

Of the 2036 articles initially identified, 114 articles were included. The case-crossover was the most common study design (88%), followed by the case-time-control (17%), and case-case-time-control (3%). Fifty-three percent of the articles included only transient medications, 35% included only non-transient medications, and 12% included both. Across years, the proportion of case-crossover articles evaluating a non-transient medication ranged from 30% in 2018 to 69% in 2017. We found that 41% of the articles that evaluated a non-transient medication did not apply any of the recommended methods to account for biases and more than half of which were conducted by authors with no previous publication history of case-crossover studies.

CONCLUSION

Using the case-crossover design to evaluate a non-transient medication remains common in pharmacoepidemiology. Researchers should apply appropriate design and analysis choices when opting to use a case-crossover design with non-transient medication exposures.

Update of a Multivariable Opioid Overdose Risk Prediction Model to Enhance Clinical Care for Long-term Opioid Therapy Patients

BACKGROUND

Clinical opioid overdose risk prediction models can be useful tools to reduce the risk of overdose in patients prescribed long-term opioid therapy (LTOT). However, evolving overdose risk environments and clinical practices in addition to potential harmful model misapplications require careful assessment prior to widespread implementation into clinical care. Models may need to be tailored to meet local clinical operational needs and intended applications in practice.

OBJECTIVE

To update and validate an existing opioid overdose risk model, the Kaiser Permanente Colorado Opioid Overdose (KPCOOR) Model, in patients prescribed LTOT for implementation in clinical care.

DESIGN, SETTING, AND PARTICIPANTS

The retrospective cohort study consisted of 33, 625 patients prescribed LTOT between January 2015 and June 2019 at Kaiser Permanente Colorado, with follow-up through June 2021.

MAIN MEASURES

The outcome consisted of fatal opioid overdoses identified from vital records and non-fatal opioid overdoses from emergency department and inpatient settings. Predictors included demographics, medication dispensings, substance use disorder history, mental health history, and medical diagnoses. Cox proportional hazards regressions were used to model 2-year overdose risk.

KEY RESULTS

During follow-up, 65 incident opioid overdoses were observed (111.4 overdoses per 100,000 person-years) in the study cohort, of which 11 were fatal. The optimal risk model needed to risk-stratify patients and to be easily interpreted by clinicians. The original 5-variable model re-validated on the new study cohort had a bootstrap-corrected C-statistic of 0.73 (95% CI, 0.64-0.85) compared to a C-statistic of 0.80 (95% CI, 0.70-0.88) in the updated model and 0.77 (95% CI, 0.66-0.87) in the final adapted 7-variable model, which was also well-calibrated.

CONCLUSIONS

Updating and adapting predictors for opioid overdose in the KPCOOR Model with input from clinical partners resulted in a parsimonious and clinically relevant model that was poised for integration in clinical care.

Potentially clinically significant drug-drug interactions in older patients admitted to the hospital: A cross-sectional study

Background: An international consensus list of potentially clinically significant drug-drug interactions (DDIs) in older people has been recently validated. Our objective was to describe the prevalence and characteristics of drug combinations potentially causing clinically significant DDIs identified in the medication history of older patients admitted to the hospital and the prevalence and characteristics of manifest DDIs-DDIs involved in adverse drug events present at hospital admission, DDIs that contributed to ADE-related hospital admissions, and DDIs involved in drug-related laboratory deviations. Methods: The data were obtained from our previous study that examined the drug-relatedness of hospital admissions to University Hospital Hradec Králové via the department of emergency medicine in the Czech Republic. Patients ≥ 65 years old were included. Drug combinations potentially causing clinically significant DDIs were identified using the international consensus list of potentially clinically significant DDIs in older people. Results: Of the 812 older patients admitted to the hospital, 46% were exposed to drug combinations potentially causing clinically significant DDIs. A combination of medications that affect potassium concentrations accounted for 47% of all drug combinations potentially causing clinically significant DDIs. In 27 cases, potentially clinically significant DDIs were associated with drug-related hospital admissions. In 4 cases, potentially clinically significant DDIs were associated with ADEs that were present at admissions. In 4 cases, the potentially clinically significant DDIs were associated with laboratory deviations. Manifest DDIs that contributed to drug-related hospital admissions most frequently involved antithrombotic agents and central nervous system depressants. Conclusion: The results confirm the findings from the European OPERAM trial, which found that drug combinations potentially causing clinically significant DDIs are very common in older patients. Manifest DDIs were present in 4.3% of older patients admitted to the hospital. In 3.3%, manifest DDIs contributed to drug-related hospital admissions. The difference in the rates of potential and manifest DDIs suggests that if a computerized decision support system is used for alerting potentially clinically significant DDIs in older patients, it needs to be contextualized (e.g., take concomitant medications, doses of medications, laboratory values, and patients' comorbidities into account).

Identifying Clinically Relevant Drug-Drug Interactions With Methadone and Buprenorphine: A Translational Approach to Signal Detection

Methadone and buprenorphine have pharmacologic properties that are concerning for a high risk of drug-drug interactions (DDIs). We performed high-throughput screening for clinically relevant DDIs with methadone or buprenorphine by combining pharmacoepidemiologic and pharmacokinetic approaches. We conducted pharmacoepidemiologic screening via a series of self-controlled case series studies (SCCS) in Optum claims data from 2000 to 2019. We included persons 18 years or older who experienced an outcome of interest during target drug treatment. Exposures were all overlapping medications (i.e., the candidate precipitants) during target drug treatment. Outcomes were opioid overdose, non-overdose adverse effects, and cardiac arrest. We used conditional Poisson regression to calculate rate ratios, accounting for multiple comparisons with semi-Bayes shrinkage. We explored the impact of key study design choices in analyses that varied the exposure definitions of the target drugs and the candidate precipitant drugs. Pharmacokinetic screening was conducted by incorporating published data on CYP enzyme metabolism into an equation-based static model. In SCCS analysis, 1,432 events were included from 248,069 new users of methadone or buprenorphine. In the primary analysis, statistically significant DDIs included gabapentinoids with either methadone or buprenorphine; baclofen with methadone; and benzodiazepines with methadone. In sensitivity analysis, additional statistically significant DDIs included methocarbamol, quetiapine, or simvastatin with methadone. Pharmacokinetic screening identified two moderate-to-strong potential DDIs (clonidine and fluconazole with buprenorphine). The combination of clonidine and buprenorphine was also associated with a significantly increased risk of opioid overdose in pharmacoepidemiologic screening. These DDI signals may be the most important targets for future confirmation studies.

Benzodiazepines Remain Important Therapeutic Options in Psychiatric Practice

Benzodiazepines and medications acting on benzodiazepine receptors that do not have a benzodiazepine structure (z-drugs) have been viewed by some experts and regulatory bodies as having limited benefit and significant risks. Data presented in this article support the use of these medications as treatments of choice for acute situational anxiety, chronic anxiety disorders, insomnia, alcohol withdrawal syndromes, and catatonia. They may also be useful adjuncts in the treatment of anxious depression and mania, and for medically ill patients. Tolerance develops to sedation and possibly psychomotor impairment, but not to the anxiolytic effect of benzodiazepines. Sedation can impair cognitive function in some patients, but assertions that benzodiazepines increase the risk of dementia are not supported by recent data. Contrary to popular opinion, benzodiazepines are not frequently misused or conduits to misuse of other substances in patients without substance use disorders who are prescribed these medications for appropriate indications; most benzodiazepine misuse involves medications that are obtained from other people. Benzodiazepines are usually not lethal in overdose except when ingested with other substances, especially alcohol and opioids. Benzodiazepines comprise one of the few classes of psychotropic medication the mechanisms of action of which are clearly delineated, allowing for greater precision in their clinical use. These medications, therefore, belong in the therapeutic armamentarium of the knowledgeable clinician.

Comparative Risk of Opioid Overdose With Concomitant use of Prescription Opioids and Skeletal Muscle Relaxants

BACKGROUND AND OBJECTIVES

The concomitant use of prescription opioids and skeletal muscle relaxants has been associated with opioid overdose, but little data exist on the head-to-head safety of these drug combinations. The objective of this study was to compare the risk of opioid overdose among patients on long-term opioid therapy who concurrently initiate skeletal muscle relaxants.

METHODS

We conducted an active comparator cohort study spanning 2000 to 2019 using healthcare utilization data from four US commercial and public insurance databases. Individuals were required to have at least 180 days of continuous enrollment and at least 90 days of continuous prescription opioid use immediately before and on the date of skeletal muscle relaxant initiation. Exposures were the concomitant use of prescription opioids and skeletal muscle relaxants, and the main outcome was the hazard ratio (HR) and bootstrapped 95% confidence interval (CI) of opioid overdose resulting in an emergency visit or hospitalization. The primary analysis quantified opioid overdose risk across seven prescription opioid-skeletal muscle relaxant therapies and a negative control outcome (sepsis) to assess potential confounding by unmeasured illicit opioid use. Secondary analyses evaluated two- and five-group comparisons in patients with similar baseline characteristics; individuals without prior recorded substance abuse; and subgroups stratified by baseline opioid dosage, benzodiazepine co-dispensing, and oxycodone or hydrocodone use.

RESULTS

Weighted HR of opioid overdose relative to cyclobenzaprine was 2.52 (95% CI 1.29-4.90) for baclofen; 1.64 (95% CI 0.81-3.34) for carisoprodol; 1.14 (95% CI 0.53-2.46) for chlorzoxazone/orphenadrine; 0.46 (95% CI 0.17-1.24) for metaxalone; 1.00 (95% CI 0.45-2.20) for methocarbamol; and 1.07 (95% CI 0.49-2.33) for tizanidine in the 30-day intention-to-treat analysis. Findings were similar in the as-treated analysis, two- and five-group comparisons, and in patients without prior recorded substance abuse. None of the therapies relative to cyclobenzaprine were associated with sepsis, and no subgroups indicated increased risk of opioid overdose.

DISCUSSION

Concomitant use of prescription opioids and baclofen relative to cyclobenzaprine is associated with opioid overdose. Clinical interventions may focus on prescribing alternatives in the same drug class or providing access to opioid antagonists if treatment with both medications is necessary for pain management.

An algorithm to identify gabapentin misuse and/or abuse in administrative claims data

BACKGROUND

Despite gabapentin's misuse and abuse potential and associated adverse events, few algorithms are available to detect gabapentin misuse and/or abuse in claims data. This study aims to develop an algorithm to identify gabapentin misuse and/or abuse in administrative claims data.

METHODS

We developed an algorithm to identify gabapentin misuse and/or abuse over a 12-month period based on input from 21 clinical experts. We implemented the algorithm among 334,128 patients with at least one dispensed prescription of gabapentin between December 1, 2017 and December 1, 2018 in the IBM® MarketScan® Research Databases. We described the characteristics of patients who potentially misused and/or abused gabapentin and assessed factors associated with misuse and/or abuse using logistic regression.

RESULTS

The algorithm identified 17.6% of patients with gabapentin use who potentially misused and/or abused gabapentin. Factors associated with potential gabapentin misuse and/or abuse included men (adjusted odds ratio (aOR): 1.08; 95% confidence interval (CI): 1.06-1.10), comorbid conditions (e.g., drug and alcohol dependence (aOR: 1.31; 95% CI: 1.24-1.39); bipolar disorder (aOR: 1.34; 95% CI: 1.27-1.41)), and medication use (e.g., opioids (aOR: 1.23; 95% CI: 1.20-1.26), muscle relaxants (aOR: 1.24; 95% CI: 1.21-1.27), or serotonin-norepinephrine reuptake inhibitors (aOR: 1.33; 95% CI: 1.29-1.36)).

CONCLUSIONS

Approximately one in six patients with gabapentin use potentially misused and/or abused gabapentin in a large commercial claims database. Multiple comorbidities and drug use were associated with gabapentin misuse and/or abuse. Monitoring requirements and individualized safety measures should be put in place for patients at elevated risks of gabapentin misuse and/or abuse.

Oxycodone-Related Deaths: The Significance of Pharmacokinetic and Pharmacodynamic Drug Interactions

BACKGROUND AND OBJECTIVES

Oxycodone is frequently prescribed as well as detected in postmortem cases. Concurrent use of pharmacodynamically or pharmacokinetically interacting drugs can cause adverse effects or even fatal intoxication. The aims of this study were to investigate differences in prescriptions for and toxicological findings of pharmacodynamically and pharmacokinetically interacting drugs in fatal oxycodone-related intoxications and other causes of death. We also aimed to investigate the differences in prevalence of oxycodone prescriptions, and the detected postmortem oxycodone concentrations between fatal oxycodone-related intoxications and other causes of death.

METHODS

Forensic autopsy cases (2012-2018) where oxycodone was identified in femoral blood (n = 1236) were included. Medical history and prescription data were retrieved from national databases and linked to the forensic toxicology findings.

RESULTS

Oxycodone-related deaths were found to have higher blood concentrations of oxycodone (median 0.30 µg/g vs. 0.05 µg/g) and were less likely to have a prescription for oxycodone (OR 0.62) compared to nonintoxication deaths. Pharmacodynamically interacting drugs were prescribed in 79% and found in blood in 81% of the cases. Pharmacokinetically interacting drugs were rarely prescribed (1%). Oxycodone-related deaths were more likely to have prescriptions for a pharmacodynamically interacting drug (OR 1.7) and more often have co-findings of one or multiple pharmacodynamically interacting drugs (OR 5.6).

CONCLUSION

The results suggest that combined use of oxycodone and pharmacodynamically interacting drugs is associated with oxycodone-related death and that non-medical use of oxycodone is a potential risk factor for oxycodone-related intoxication.

Discharge locations after hospitalizations involving opioid use disorder among medicare beneficiaries

BACKGROUND

Hospitalizations involving opioid use disorder (OUD) have been increasing among Medicare beneficiaries of all ages. With rising OUD-related acute care use comes the need to understand where post-acute care is provided and the capacities for OUD treatment in those settings. Our objective was to describe hospitalized Medicare beneficiaries with OUD, their post-acute care locations, and all-cause mortality and readmissions stratified by post-acute care location.

METHODS

We conducted a retrospective cohort study of acute hospitalizations using 2016-2018 Medicare Provider Analysis and Review (MedPAR) files linked to Medicare enrollment data and the Residential History File (RHF) for 100% of Medicare fee-for-service beneficiaries. The RHF which provides a person-level chronological history of health service utilization and locations of care was used to identify hospital discharge locations. We used ICD-10 codes for opioid dependence or "abuse" to identify OUD diagnoses from the MedPAR file. We conducted logistic regression to identify factors associated with discharge to an institutional setting versus home adjusting for demographics, comorbidities, and hospital stay characteristics.

RESULTS

Our analysis included 459,763 hospitalized patients with OUD. Of these, patients aged < 65 years and those dually enrolled in Medicaid comprised the majority (59.1%). OUD and opioid overdose were primary diagnoses in 14.3% and 6.2% of analyzed hospitalizations, respectively. We found that 70.3% of hospitalized patients with OUD were discharged home, 15.8% to a skilled nursing facility (SNF), 9.6% to a non-SNF institutional facility, 2.5% home with home health services, and 1.8% died in-hospital. Within 30 days of hospital discharge, rates of readmissions and mortality were 29.7% and 3.9%; respectively, with wide variation across post-acute locations. Factors associated with greater odds of discharge to institutional settings were older age, female sex, non-Hispanic White race and ethnicity, dual enrollment, longer hospital stay, more comorbidities, intensive care use, surgery, and primary diagnoses including opioid or other drug overdoses, fractures, and septicemia.

CONCLUSIONS

More than one-quarter (25.8%) of hospitalized Medicare beneficiaries with OUD received post-acute care in a setting other than home. High rates and wide variation in all-cause readmissions and mortality within 30 days post-discharge emphasize the need for improved post-acute care for people with OUD.