Deprescribing medications among patients with multiple prescribers: A socioecological model.

Deprescribing is the intentional dose reduction or discontinuation of a medication. The development of deprescribing interventions should take into consideration important organizational, interprofessional, and patient-specific barriers that can be further complicated by the presence of multiple prescribers involved in a patient's care. Patients who receive care from an increasing number of prescribers may experience disruptions in the timely transfer of relevant healthcare information, increasing the risk of exposure to drug-drug interactions and other medication-related problems. Furthermore, the fragmentation of healthcare information across health systems can contribute to the refilling of discontinued medications, reducing the effectiveness of deprescribing interventions. Thus, deprescribing interventions must carefully consider the unique characteristics of patients and their prescribers to ensure interventions are successfully implemented. In this special article, an international working group of physicians, pharmacists, nurses, epidemiologists, and researchers from the United States Deprescribing Research Network (USDeN) developed a socioecological model to understand how multiple prescribers may influence the implementation of a deprescribing intervention at the individual, interpersonal, organizational, and societal level. This manuscript also includes a description of the concept of multiple prescribers and outlines a research agenda for future investigations to consider. The information contained in this manuscript should be used as a framework for future deprescribing interventions to carefully consider how multiple prescribers can influence the successful implementation of the service and ensure the intervention is as effective as possible.

Channeling of New Neuropsychiatric Drugs-Impact on Safety and Effectiveness Studies.

This study aimed to examine differential prescribing due to channeling and propensity score non-overlap over time in new versus established treatments for common neurological conditions. We conducted cross-sectional analyses on a national sample of US commercially insured adults using 2005-2019 data. We compared new users of recently approved versus established medications for management of diabetic peripheral neuropathy (pregabalin versus gabapentin), Parkinson disease psychosis (pimavanserin versus quetiapine), and epilepsy (brivaracetam versus levetiracetam). Within these drug pairs, we compared demographic, clinical, and healthcare utilization characteristics of recipients of each drug. In addition, we fit yearly propensity score models for each condition and assessed propensity score non-overlap over time. For all three drug pairs, users of the more recently approved medications more frequently had prior treatment (pregabalin = 73.9%, gabapentin = 38.7%; pimavanserin = 41.1%, quetiapine = 14.0%; brivaracetam = 93.4%, levetiracetam = 32.1%). Propensity score non-overlap and its resulting sample loss after trimming were the greatest in the first year that the more recently approved medication was available (diabetic peripheral neuropathy, 12.4% non-overlap; Parkinson disease psychosis, 6.1%; epilepsy, 43.2%) and subsequently improved. Newer neuropsychiatric therapies appear to be channeled to individuals with refractory disease or intolerance to other treatments, leading to potential confounding and biased comparative effectiveness and safety study findings when compared to established treatments. Propensity score non-overlap should be reported in comparative studies that include newer medications. When studies comparing newer and established treatments are critically needed as soon as new treatments enter the market, investigators should recognize the potential for channeling bias and implement methodological approaches like those demonstrated in this study to understand and improve this issue in such studies.

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.