Publications

PURPOSE: Little is known about the longitudinal patterns of buprenorphine adherence among pregnant women with opioid use disorder, especially when late initiation, nonadherence, or early discontinuation of buprenorphine during pregnancy may increase the risk of adverse outcomes. We aimed to identify distinct trajectories of buprenorphine use during pregnancy, and factors associated with these trajectories in Medicaid-enrolled pregnant women.

METHODS: A retrospective cohort study included 2361 Pennsylvania Medicaid enrollees aged 15 to 46 having buprenorphine therapy during pregnancy and a live birth between 2008 and 2015. We used group-based trajectory models to identify buprenorphine use patterns in the 40 weeks prior to delivery and 12 weeks postdelivery. Multivariable multinomial logistic regression models were used to identify factors associated with specific trajectories.

RESULTS: Six distinct trajectories were identified. Four groups initiated buprenorphine during the first trimester of the pregnancy (early initiators): 31.6% with persistently high adherence, 15.1% with moderate-to-high adherence, 10.5% with declining adherence, and 16.7% with early discontinuation. Two groups did not initiate buprenorphine until midsecond or third trimester (late initiators): 13.5% had moderate-to-high adherence and 12.6% had low-to-moderate adherence. Factors significantly associated with late initiation and discontinuation were younger age, non-white race, residents of rural counties, fewer outpatient visits, more frequent emergency department visits and hospitalizations, and lower buprenorphine daily dose.

CONCLUSIONS: Six buprenorphine treatment trajectories during pregnancy were identified in this population-based Medicaid cohort, with 25% of women initiating buprenorphine late during pregnancy. Understanding trajectories of buprenorphine use and factors associated with discontinuation/nonadherence may guide integration of behavioral treatment with obstetrical/gynecological care to improve buprenorphine treatment during pregnancy.

BACKGROUND: Patterns of inpatient opioid use and their associations with postdischarge opioid use are poorly understood.

OBJECTIVE: To measure patterns in timing, duration, and setting of opioid administration in opioid-naive hospitalized patients and to examine associations with postdischarge use.

DESIGN: Retrospective cohort study using electronic health record data from 2010 to 2014.

SETTING: 12 community and academic hospitals in Pennsylvania.

PATIENTS: 148 068 opioid-naive patients (191 249 admissions) with at least 1 outpatient encounter within 12 months before and after admission.

MEASUREMENTS: Number of days and patterns of inpatient opioid use; any outpatient use (self-report and/or prescription orders) 90 and 365 days after discharge.

RESULTS: Opioids were administered in 48% of admissions. Patients were given opioids for a mean of 67.9% (SD, 25.0%) of their stay. Location of administration of first opioid on admission, timing of last opioid before discharge, and receipt of nonopioid analgesics varied substantially. After adjustment for potential confounders, 5.9% of inpatients receiving opioids had outpatient use at 90 days compared with 3.0% of those without inpatient use (difference, 3.0 percentage points [95% CI, 2.8 to 3.2 percentage points]). Opioid use at 90 days was higher in inpatients receiving opioids less than 12 hours before discharge than in those with at least 24 opioid-free hours before discharge (7.5% vs. 3.9%; difference, 3.6 percentage points [CI, 3.3 to 3.9 percentage points]). Differences based on proportion of the stay with opioid use were modest (opioid use at 90 days was 6.4% and 5.4%, respectively, for patients with opioid use for ≥75% vs. ≤25% of their stay; difference, 1.0 percentage point [CI, 0.4 to 1.5 percentage points]). Associations were similar for opioid use 365 days after discharge.

LIMITATION: Potential unmeasured confounders related to opioid use.

CONCLUSION: This study found high rates of opioid administration to opioid-naive inpatients and associations between specific patterns of inpatient use and risk for long-term use after discharge.

PRIMARY FUNDING SOURCE: UPMC Health System and University of Pittsburgh.

INTRODUCTION: Identifying Alzheimer's disease (AD) pharmacologic treatment options that effectively reduce the risk of mortality and hospitalization in real-world settings is critical.

METHODS: We compared donepezil, galantamine, memantine, oral rivastigmine, and transdermal rivastigmine with regard to all-cause mortality and all-cause hospitalization risk among fee-for-service Medicare beneficiaries with AD (aged ≥ 65 years) using a retrospective cohort study design. Our primary analysis was based on intention to treat (ITT), but we also present as-treated analysis.

RESULTS: In our final study sample (N = 21,558), significant difference in survival among index AD medication groups were observed with donepezil being associated with better survival than memantine, and oral and transdermal forms of rivastigmine for both ITT and as-treated analysis. Difference in hazards of all-cause hospitalization among index AD medication groups was observed in ITT analysis but not in as-treated analysis.

DISCUSSION: Significant differences exist in terms of mortality and hospitalization risk with different AD medication initiation in real-world setting.

IMPORTANCE: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.

OBJECTIVE: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.

DESIGN, SETTING, AND PARTICIPANTS: A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.

EXPOSURES: Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.

MAIN OUTCOMES AND MEASURES: Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity.

RESULTS: Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.

CONCLUSIONS AND RELEVANCE: Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.

Increasing gabapentinoid use has raised concerns of misuse and abuse in the United States (US). Little is known about the characteristics of gabapentinoid use in general clinical practice over time. This cross-sectional study used data from the National Ambulatory Medical Care Survey. We examined the trends of patient and prescriber characteristics and the diagnoses associated with US ambulatory care visits involving gabapentinoids for adult visits from 2003 to 2016. Using multivariable logistic regression, we estimated the adjusted proportion of gabapentinoid-involved visits among all visits and tested for trend significance. Among the weighted estimate of 260.1 million gabapentinoid-involved visits (aged 18-64 years: 61.8%; female: 61.9%; white: 85.5%), the adjusted annual proportion of gabapentinoid-involved visits nearly quadrupled from 2003 to 2016 (9.1 to 34.9 per 1000 visits; Ptrend < 0.0001), driven mainly by gabapentin. Nearly half had concurrent use with opioids (32.9%) or benzodiazepines (15.3%). Primary care physicians (45.8%), neurologists (8.2%), surgeons (6.2%), and psychiatrists (4.8%) prescribed two-thirds of the gabapentinoids. Most (96.6%) of the gabapentinoid visits did not have an approved indication for gabapentinoids among the first three diagnoses. Among US ambulatory care visits from 2003 to 2016, gabapentinoid use increased substantially, commonly prescribed by primary care physicians.

BACKGROUND: With increasing efforts to scrutinize and reduce opioid prescribing, limited data exist on the recent trend in receipt of prescription pain medications before diagnosis of opioid use disorder (OUD) or opioid-related overdose (OD).

METHODS: Using 2005-2016 Truven MarketScan Commercial Claims databases, we assessed trends in annual 1) incidence of OUD or OD and 2) prevalence of receipt of prescription opioids or four commonly-prescribed adjuvant analgesics among patients newly diagnosed with OUD/OD. Trends were examined in the overall sample and by 3 age groups, including youths (≤18 years), adults (19-64 years), and older adults (≥65 years).

RESULTS: The incidence of diagnosed OUD or OD increased more than 3-fold from 4.99 to 23.81 per 10,000 persons from 2006 to 2016, with the highest increase (14.18-fold) seen in older adults, followed by adults (3.53-fold), and youths (0.16-fold). Between 2006 and 2016, the proportion of patients with incident OUD/OD who received anticonvulsant adjuvant analgesics in the year before diagnosis increased (from 23.4% to 34.3% [P-trend = .005]) whereas the proportion receiving high-dose prescriptions opioids decreased (from 45.5% to 34.8% [P-trend =< .001]). A decreasing trend was observed in general for tricyclic antidepressants and serotonin and norepinephrine reuptake inhibitors.

DISCUSSION: In US commercially insured patients newly diagnosed with OUD/OD, receipt of high-dose opioid prescriptions preceding the diagnosis decreased over time, paralleled by increased use of anticonvulsants commonly prescribed for pain conditions. Further investigations are warranted to understand how prescribed and anticonvulsants contribute to the development of OUD/OD.

OBJECTIVE: To quantify the extent and identify predictors of potentially inappropriate antidepressant use among older adults with dementia and newly diagnosed major depressive disorders (MDD).

METHODS: This retrospective cohort study included older adults (aged ≥65 years) with dementia and newly diagnosed MDD using Medicare 5% sample claims data (2012-2013). Based on Healthcare Effectiveness Data and Information Set guidelines, intake period for new antidepressant medication use was from May 1, 2012, through April 30, 2013. Index prescription start date was the first date of antidepressant prescription claim during the intake period. Dependent variable of this study was potentially inappropriate antidepressant use as defined by the Beers Criteria and the Screening Tool of Older Persons' potentially inappropriate Prescriptions criteria. The authors conducted multiple logistic regression analysis to identify individual-level predictors of potentially inappropriate antidepressant use.

RESULTS: The authors' final study sample consisted of 7,625 older adults with dementia and newly diagnosed MDD, among which 7.59% (N = 579) initiated treatment with a potentially inappropriate antidepressant. Paroxetine (N = 394) was the most commonly initiated potentially inappropriate antidepressant followed by amitriptyline (N = 104), nortriptyline (N = 35), and doxepin (N = 32). Initiation of a potentially inappropriate antidepressant was associated with age and baseline use of anxiolytic medications.

CONCLUSION: More than 7% of older adults in the study sample initiated a potentially inappropriate antidepressant, and the authors identified a few individual-level factors significantly associated with it. Appropriately tailored interventions to address modifiable and nonmodifiable factors significantly associated with potentially inappropriate antidepressant prescribing are required to minimize risks in this vulnerable population.

MHC class I-specific reagents such as fluorescently-labeled multimers (e.g., tetramers) have greatly advanced the understanding of CD8+ T cells under normal and diseased states. However, recombinant MHC class I components (comprising MHC class I heavy chain and β2 microglobulin) are usually produced in bacteria following a lengthy purification protocol that requires additional non-covalent folding steps with exogenous peptide for complete molecular assembly. We have provided an alternative and rapid approach to generating soluble and fully-folded MHC class I molecules in eukaryotic cell lines (such as CHO cells) using a Sleeping Beauty transposon system. Importantly, this method culminates in generating stable cell lines that reliably secrete epitope-defined MHC class I molecules into the tissue media for convenient purification and eventual biotinylation/multimerization. Additionally, MHC class I components are covalently linked, providing the opportunity to produce a diverse set of CD8+ T cell-specific reagents bearing peptides with various affinities to MHC class I.

Deprescribing, an integral component of a continuum of good prescribing practices, is the process of medication withdrawal or dose reduction to correct or prevent medication-related complications, improve outcomes, and reduce costs. Deprescribing is particularly applicable to the commonly encountered multimorbid older adult with cardiovascular disease and concomitant geriatric conditions such as polypharmacy, frailty, and cognitive dysfunction-a combination rarely addressed in current clinical practice guidelines. Triggers to deprescribe include present or expected adverse drug reactions, unnecessary polypharmacy, and the need to align medications with goals of care when life expectancy is reduced. Using a framework to deprescribe, this review addresses the rationale, evidence, and strategies for deprescribing cardiovascular and some noncardiovascular medications.