Publications

BACKGROUND: Risks related to long-term opioid therapy for chronic pain are high and may increase over time with aging. Deprescribing may be a beneficial intervention for older adults prescribed chronic opioids.

METHODS: Semi-structured interviews with hypothetical clinical cases of older adults prescribed opioids for chronic pain: (1) low-risk case: a patient prescribed low-dose opioids without concerns; (2) moderate-risk case: a patient with multimorbidity and concurrent benzodiazepine use prescribed moderate opioid doses; (3) high-risk case: a patient prescribed high-dose opioids with signs of an opioid use disorder (OUD). PCPs were asked, in an open-ended fashion, to discuss whether they would initiate a deprescribing conversation, how they would approach deprescribing, and how they would approach a patient who declined recommendations to deprescribe.

PARTICIPANTS AND SETTING: PCPs from a Massachusetts health system.

RESULTS: 18 PCPs participated (56% female, 78% academic). More than half of PCPs would initiate a deprescribing conversation across the three cases. PCPs' approach to deprescribing and mitigating risks differed based on clinical risk. In low and moderate-risk cases, PCPs emphasized a patient-directed taper plan and education on opioid risks. In the high-risk case, some PCPs were uncertain about initiating a deprescribing conversation due to concerns about the patient's mental health and the risk of illicit opioid use. Naloxone was infrequently recommended across the three cases, but in the high-risk case, approximately half of PCPs suggested medications for OUD.

CONCLUSIONS: PCPs reported that they would often initiate opioid deprescribing conversations with older adults, but were less confident in managing older adults with signs of OUD. PCPs require additional support to implement successful conversations on opioid deprescribing with older adults.

BACKGROUND: Healthcare utilization and cost impacts, of medication adherence above or below the 80% threshold remains unclear for cardiometabolic medications.

OBJECTIVE: To evaluate the differences in cardiovascular (CV)-related emergency department (ED) visits and total cost-of-care associated with changes in medication adherence around the 80% threshold.

METHOD: Retrospective observational analysis of claims spanning from Jan 1, 2021, to June 30, 2023. Patients aged 50-80 years with a history of cardiovascular disease (CVD) and specific cardiometabolic medications were followed for 12 months, and pre-post index-fill outcomes compared. Patients were categorized into pre- and post-index groups: 3 pre-index groups (Pre1-moderate, Pre2-high, Pre3-very-high) based on their pre-index 12-month adherence [PDC] and 4 post-index groups based on post-index PDC (Post0-low, Post1-moderate, Post2-high, Post3-very-high), for 4 medication classes (Antidiabetics, direct oral coagulants[DOACs], antiplatelets, and anti-heart-failure meds [HF]). Group definitions: Post0-low (PDC<=69%), Pre1-moderate or Post1-moderate (PDC 70 to <80%), Pre2-high or Post2-high (PDC 80 to <90%), and Pre3-very-high or Post3-very-high (PDC >=90). Outcomes included per-member-per-year [PMPY] total cost-of-care, and cardiovascular-related ED visits.

RESULTS: There were 55,934(antidiabetics), 46,290 (DOACs), 65,659 (antiplatelets), and 49, 670 (HF) patients in the final sample. Most of the patients in the HF (46-53%) and DOAC (51-57%) groups were in the 70+ age group. Among patients in the antidiabetic (45-47%) and antiplatelet (39-43%) groups, the majority were in the 60-69 age group. In general, patients who moved from a lower adherence group to a higher adherence group had lower total cost-of-care in almost all groups and medication classes. Moving from PDC >= 90 to PDC <90%, total cost-of-care was higher in all 4 medication classes.

CONCLUSION: Improving adherence to and beyond the traditional 80% target was associated with lower total cost-of-care.

BACKGROUND: Chronic benzodiazepine use remains common among older adults, despite limited evidence of benefit and substantial risks. Evidence-based approaches that are feasible in primary care settings are needed to support benzodiazepine deprescribing for older adults.

OBJECTIVES: To determine the feasibility, acceptability, and exploratory patient-centered outcomes of a team-based approach to benzodiazepine deprescribing in primary care.

DESIGN: Single-arm prospective clinical trial.

PARTICIPANTS: Adults age 65 and older prescribed long-term benzodiazepines recruited from four primary care clinics in an academic health system.

INTERVENTIONS: Ten-week virtual primary care embedded program consisting of pharmacist-guided tapering and three psychologist-led mindfulness-informed cognitive behavioral therapy (CBT) sessions.

MAIN MEASURES: Feasibility outcomes included enrollment, retention, and intervention adherence. Acceptability outcomes were collected through qualitative interviews. Exploratory efficacy outcomes included change in mean daily benzodiazepine dose, change in PROMIS anxiety score, and change in PROMIS sleep disturbance score.

KEY RESULTS: Seventeen participants (mean age 72, 29% female, mean 17 years of benzodiazepine use) enrolled and completed all six study visits (100% fidelity). Participants' mean (SD) baseline daily benzodiazepine dose was 9.0 (8.6) diazepam milligram equivalents. At completion, participants had a mean 60.5 percentage point reduction in benzodiazepine use (95% CI -69.9% to -51.3%; p < .001), with all participants reducing their dose and 3 stopping completely. Mean PROMIS anxiety scores decreased from 55.2 to 51.8 (-3.5 point change, 95% CI -6.5 to -0.7; p = 0.02) and mean PROMIS sleep disturbance scores were unchanged at week 10 (-1.7 point change, 95% CI -4.9 to 1.5; p = 0.30). Qualitative interviews indicated the program may target increased self-efficacy to reduce benzodiazepines and endorsed utility from both pharmacist- and psychologist-led components.

CONCLUSIONS: A primary care embedded virtual intervention involving pharmacist-led tapering and mindfulness-informed CBT sessions to support benzodiazepine deprescribing is feasible, acceptable, may reduce older adults' benzodiazepine use, and warrants multi-site testing.

GOV TRIAL NUMBER: NCT06119308.

GOV REGISTRATION DATE: 10/27/2023.

BACKGROUND: In older adults with osteoarthritis (OA) and hypertension (HTN), analgesic use may elevate blood pressure and cardiovascular risk. Whether comorbid HTN influences initial analgesic choice remains unclear; we examined initial analgesic use in Medicare beneficiaries with incident OA, comparing those with and without HTN.

METHODS: We conducted a retrospective cohort study using 2011-2022 nationally representative Medicare beneficiaries (≥ 65 years) with incident OA who initiated an analgesic within 30 days of diagnosis and had continuous enrollment for ≥ 365 days prior through ≥ 30 days post-index. Patients with baseline HTN were classified as OA + HTN; others as OA-only. We assessed overall analgesic trends using the Cochran-Armitage test and evaluated differences by HTN status using logistic regression with year as an interaction term. For stratified analyses by joint type, we applied weighted logistic regression.

RESULTS: Among 179,033 beneficiaries (mean age 75 ± 7.3 years; 62.7% women; 80.7% White), 57.1% had baseline HTN. Overall, the most commonly initiated analgesic classes were intra-articular injections (30.3%), and oral NSAIDs only (28.2%). Notable changes from 2012 to 2022 were increase in topical NSAIDs use (3.1%-5.7%) and decrease in opioid combination use (25.4%-13.9%), with no significant trend differences by HTN status. In joint-specific analyses, OA + HTN versus OA-only showed no differences in odds of initiating oral opioids (OR: 0.97, 95% CI: 0.92-1.03), intra-articular injections (OR: 1.01, 95% CI: 0.96-1.07) or topical NSAIDs (OR: 0.88, 95% CI: 0.78-1.01) versus oral NSAIDs.

CONCLUSION: Baseline HTN did not influence the choice of initial analgesic in incident OA patients. Safer, evidence-based alternatives are needed for older adults with comorbid HTN.

AIM: The "2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia" retires and replaces the "2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol."

METHODS: A comprehensive literature search was conducted from October 2024 to December 2024 to identify clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE: The focus of this clinical practice guideline is to address the evaluation, management, and monitoring of individuals with dyslipidemias, including high blood cholesterol, hypertriglyceridemia, and elevated lipoprotein(a).

AIM: The "2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia" retires and replaces the "2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol."

METHODS: A comprehensive literature search was conducted from October 2024 to December 2024 to identify clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE: The focus of this clinical practice guideline is to address the evaluation, management, and monitoring of individuals with dyslipidemias, including high blood cholesterol, hypertriglyceridemia, and elevated lipoprotein(a).

OBJECTIVES: In 2022, the FDA issued a drug safety communication based on case studies that transmucosal buprenorphine, a medication for opioid use disorder (MOUD), may contribute to dental disease. We sought to assess longitudinal dental care utilization patterns among patients with opioid use disorder (OUD) in the 18 months before and 36 months after MOUD initiation.

METHODS: Using data from the Veterans Affairs Corporate Data Warehouse (2003-2020), we created a cohort of patients coded for OUD and prescribed MOUD. Outcomes included preventive and therapeutic dental visits and oral infections within 18 months before and up to 36 months after index MOUD. We used unadjusted Poisson models to estimate incidence per 1000 patients by MOUD. We performed analyses with and without interval censoring for edentulism, death, or a 60-day gap in MOUD.

RESULTS: Among 49,675 eligible patients, 21,551 received methadone, 17,759 transmucosal buprenorphine, 8993 oral naltrexone, and 1372 injectable naltrexone. Median (IQR) days on treatment varied by drug: methadone 95 (52, 257), buprenorphine 309 (102, 968), oral naltrexone 49 (30, 101), injectable naltrexone 28 (28, 85). We observed an immediate increase in dental visits from a baseline range of 135-144 visits/1000/6-month period to 223-686 visits/1000/6-month period after initiating any MOUD. Patterns were similar by MOUD agent and formulation. Results were similar in analyses with and without interval censoring.

CONCLUSIONS: Both preventive and therapeutic dental utilization increased immediately following initiation with MOUD. Future observational studies of the effects of MOUD on adverse dental outcomes should account for confounding due to health-seeking behavior.

BACKGROUND: Opioid use disorder (OUD) remains a critical public health crisis in the United States. Despite widespread policy and clinical interventions, early identification of individuals at risk for developing OUD remains challenging due to limitations in traditional screening approaches and a lack of individualized risk stratification methods. Machine learning (ML) methods offer an opportunity to develop timely, high-performing, and explainable predictive models that can enhance OUD prevention strategies in clinical settings.

OBJECTIVE: This study aims to develop and validate an ML model using electronic health record (EHR) data to predict the 3-month risk of incident OUD among adults initiating opioid therapy and to stratify patients into clinically actionable risk groups.

METHODS: This prognostic modeling study used 2017-2022 OneFlorida+ EHR data to develop and validate ML algorithms predicting 3-month incident OUD risk. We included 182,083 adults (≥18 y) without cancer, overdose, or OUD or hospice history who received ≥1 outpatient, noninjectable opioid prescription. Using 183 predictors measured in sequential 3-month intervals, we developed an elastic net, least absolute shrinkage and selection operator, gradient boosting machine (GBM), and random forest models on randomly split training, testing, and validation sets. Model performance was assessed using C-statistics, predictive values, and number needed to evaluate, with patients stratified into risk deciles for clinical applicability. Model explainability was assessed using Shapley additive explanations, and fairness was evaluated using standard metrics. We externally validated the best-performing model using an independent cohort from the 2018-2020 UPMC (formerly University of Pittsburgh Medical Center) health system.

RESULTS: In the validation sample (n=60,694), GBM (C-statistics=0.879, 95% CI 0.874-0.884) and elastic net (C-statistics=0.872, 95% CI 0.867-0.877) outperformed least absolute shrinkage and selection operator (C-statistics=0.846, 95% CI 0.840-0.851) and random forest (C-statistics=0.798, 95% CI 0.792-0.804), with GBM model requiring the fewest predictors (n=75) for predicting 3-month incident OUD. Using the GBM algorithm to predict the subsequent 3-month OUD risk, the top decile subgroup had a positive predictive value of 3.26%, a negative predictive value of 99.8%, and a number needed to evaluate of 31. The top decile (n=6696) captured  68% of patients with OUD. Shapley additive explanations analysis identified age, number of outpatient visits, history of back and other pain conditions, comorbidity burden, and opioid prescribing patterns as the strongest predictors of incident OUD. Fairness assessment showed an acceptable false negative rate parity across race, age, and sex. In external validation on the UPMC cohort, the GBM model maintained good discrimination (C-statistics=0.756, 95% CI 0.750-0.762) and effective risk stratification.

CONCLUSIONS: An ML algorithm predicting incident OUD derived from OneFlorida+ EHR data performed well in external validation with data using UPMC. The algorithm might be valuable for incident OUD risk prediction and stratification across health systems, with potential to inform early intervention.