Faculty Bibliography

OBJECTIVE:To examine the associations between patient out-of-pocket (OOP) costs and nonadherence to glucagon-like peptide 1 receptor agonists (GLP-1a), and the consequent impact on adverse outcomes, including hospitalizations and emergency department (ED) visits. RESEARCH DESIGN AND METHODS/METHODS:This retrospective cohort study used MarketScan Commercial data (2016-2021). The cohort included nonpregnant adults aged 18-64 years with type 2 diabetes who initiated GLP-1a therapy. Participants were continuously enrolled in the same private insurance plan for 6 months before the prescription date and 1 year thereafter. Exposures included average first 30-day OOP costs for GLP-1a, categorized into quartiles (lowest [Q1] to highest [Q4]). Primary outcomes were the annual proportion of days covered (PDC) for GLP-1a and nonadherence, defined as PDC <0.8. Secondary outcomes included diabetes-related and all-cause hospitalizations and ED visits 1 year post-GLP-1a initiation. RESULTS:Among 61,907 adults who initiated GLP-1a, higher 30-day OOP costs were associated with decreased adherence. Patients in the highest OOP cost quartile (Q4: $80-$3,375) had significantly higher odds of nonadherence (odds ratio [OR]1.25; 95% CI 1.19-1.31) compared with those in Q1 ($0-$21). Nonadherence was linked to increased incidence rates of diabetes-related hospitalizations or ED visits (incidence rate ratio [IRR] 1.86; 95% CI 1.43-2.42), cumulative length of hospitalization (IRR 1.56; 95% CI 1.41-1.72), all-cause ED visits (IRR 1.38; 95% CI 1.32-1.45), and increased ED-related costs ($69.81, 95% CI $53.54-$86.08). CONCLUSIONS:Higher OOP costs for GLP-1a were associated with reduced adherence and increased rates of adverse outcomes among type 2 diabetes patients.

BACKGROUND:Medication non-adherence, which is common in chronic diseases such as heart failure, is often estimated using proportion of days covered (PDC). PDC is typically calculated using medication fill information from pharmacy or insurance claims data, which lack information on when medications are prescribed. Many electronic health records (EHRs) have prescription and pharmacy fill data available, enabling enhanced PDC assessment that can be utilized in routine clinical care. OBJECTIVE:To describe our approach to calculating PDC using linked EHR-pharmacy data and to compare to PDC calculated using pharmacy-only data for patients with heart failure. METHODS:We performed a retrospective cohort study of adult patients with heart failure who were prescribed guideline-directed medical therapy (GDMT) and seen in a large health system. Using linked EHR-pharmacy data, we estimated medication adherence by PDC as the percent of days in which a patient possessed GDMT based on medication pharmacy fills over the number of days the prescription order was active. We also calculated PDC using pharmacy-only data, calculated as medications possessed over days with continued medication fills. We compared these two approaches for days observed and PDC using a paired t-test. RESULTS:Among 33,212 patients with heart failure who were prescribed GDMT, 2226 (6.7%) never filled their medications, making them unavailable in the assessment of PDC using pharmacy-only data (n = 30,995). Linked EHR-pharmacy data had slightly longer days observed for PDC assessment (164.7 vs. 163.4 days; p < 0.001) and lower PDC (78.5 vs. 90.6, p < 0.001) as compared to assessment using pharmacy-only data. CONCLUSIONS:Linked EHR-pharmacy data can be used to identify patients who never fill their prescriptions. Estimating adherence using linked EHR-pharmacy data resulted in a lower mean PDC as compared to estimates using pharmacy-only data.

BACKGROUND:Cardiometabolic comorbidities such as obesity, diabetes, and hypertension are highly prevalent in heart failure (HF). We aimed to examine the association between severity of cardiometabolic comorbidities and hospitalization in patients with HF. METHODS: RESULTS: CONCLUSIONS:Greater cardiometabolic comorbidity burden was associated with increased risk of all-cause hospitalization in HF. This reinforces the role for targeting severely uncontrolled cardiometabolic comorbidities to reduce morbidity in HF.

BACKGROUND/UNASSIGNED:Despite the exponential growth in telemedicine visits in clinical practice due to the COVID-19 pandemic, it remains unknown if telemedicine visits achieved similar adherence to prescribed medications as in-person office visits for patients with heart failure. OBJECTIVE/UNASSIGNED:Our study examined the association between telemedicine visits (vs in-person visits) and medication adherence in patients with heart failure. METHODS/UNASSIGNED:This was a retrospective cross-sectional study of adult patients with a diagnosis of heart failure or an ejection fraction of ≤40% using data between April 1 and October 1, 2020. This period was used because New York University approved telemedicine visits for both established and new patients by April 1, 2020. The time zero window was between April 1 and October 1, 2020, then each identified patient was monitored for up to 180 days. Medication adherence was measured by the mean proportion of days covered (PDC) within 180 days, and categorized as adherent if the PDC was ≥0.8. Patients were included in the telemedicine exposure group or in-person group if all encounters were video visits or in-person office visits, respectively. Poisson regression and logistic regression models were used for the analyses. RESULTS/UNASSIGNED:A total of 9521 individuals were included in this analysis (telemedicine visits only: n=830 in-person office visits only: n=8691). Overall, the mean age was 76.7 (SD 12.4) years. Most of the patients were White (n=6996, 73.5%), followed by Black (n=1060, 11.1%) and Asian (n=290, 3%). Over half of the patients were male (n=5383, 56.5%) and over half were married or living with partners (n=4914, 51.6%). Most patients' health insurance was covered by Medicare (n=7163, 75.2%), followed by commercial insurance (n=1687, 17.7%) and Medicaid (n=639, 6.7%). Overall, the average PDC was 0.81 (SD 0.286) and 71.3% (6793/9521) of patients had a PDC≥0.8. There was no significant difference in mean PDC between the telemedicine and in-person office groups (mean 0.794, SD 0.294 vs mean 0.812, SD 0.285) with a rate ratio of 0.99 (95% CI 0.96-1.02; P=.09). Similarly, there was no significant difference in adherence rates between the telemedicine and in-person office groups (573/830, 69% vs 6220/8691, 71.6%), with an odds ratio of 0.94 (95% CI 0.81-1.11; P=.12). The conclusion remained the same after adjusting for covariates (eg, age, sex, race, marriage, language, and insurance). CONCLUSIONS/UNASSIGNED:We found similar rates of medication adherence among patients with heart failure who were being seen via telemedicine or in-person visits. Our findings are important for clinical practice because we provide real-world evidence that telemedicine can be an approach for outpatient visits for patients with heart failure. As telemedicine is more convenient and avoids transportation issues, it may be an alternative way to maintain the same medication adherence as in-person visits for patients with heart failure.

Electronic health record (EHR)-embedded tools are known to improve prescribing of guideline-directed medical therapy (GDMT) for patients with heart failure. However, physicians may perceive EHR tools to be unhelpful, and may be therefore hesitant to implement these in their practice. We surveyed cardiologists about two effective EHR-tools to improve heart failure care, and they perceived the EHR tools to be easy to use, helpful, and improve the overall management of their patients with heart failure.

BACKGROUND:Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce heart failure (HF) hospitalizations, recurrent cardiovascular events, and chronic kidney disease (CKD) progression, and thus constitute a Class 1a recommendation in people with diabetes and atherosclerotic cardiovascular disease, HF, or CKD and in people with severe albuminuria or HF, regardless of diabetes status. OBJECTIVES/OBJECTIVE:The purpose of this study was to comprehensibly characterize the rate of SGLT2 inhibitor prescriptions among people with a Class 1a recommendation for SGLT2 inhibitor use. METHODS:Among 3,189,827 adults from 28 U.S. health systems within Optum Labs Data Warehouse between April 1, 2022, and March 31, 2023, we assessed SGLT2 inhibitor prescription rates, stratified by presence of diabetes and Class 1a recommendation. RESULTS:Among 716,387 adults with diabetes, 63.4% had a Class 1a recommendation for SGLT2 inhibitor therapy. There was little difference by Class 1a recommendation status (present: 11.9%; 95% CI: 11.9%-12.0% vs absent: 11.4%; 95% CI: 11.3%-11.6%; standardized mean difference: 1.3%). Among 2,473,440 adults without diabetes, 6.2% had a Class 1a recommendation for SGLT2 inhibitor therapy, and 3.1% (3.0%-3.2%) of those received a prescription. Internists/family practitioners initiated SGLT2 inhibitor prescriptions most commonly among people with diabetes, whereas specialists initiated SGLT2 inhibitor prescriptions most commonly among people without diabetes. No health system had >25% SGLT2 inhibitor prescription rate among people with a Class 1a recommendation. Health systems with higher proportions of patients with commercial insurance and lower proportions with Medicare had higher SGLT2 inhibitor prescription rates. CONCLUSIONS:In this analysis of U.S. data from 2022 to 2023, SGLT2 inhibitor prescription among people with a Class 1a recommendation is low. Interventions are needed to increase uptake of guideline-recommended SGLT2 inhibitor use.

BACKGROUND:Despite advances in medical therapy for heart failure with reduced ejection fraction (HFrEF), major gaps in medication adherence to guideline-directed medical therapies (GDMT) remain. Greater continuity of care may impact medication adherence and reduced hospitalizations. METHODS:We conducted a cross-sectional study of adults with a diagnosis of HF and EF ≤40% with ≥2 outpatient encounters between January 1, 2017 and January 10, 2021, prescribed ≥1 of the following GDMT: 1) Beta Blocker, 2) Angiotensin Converting Enzyme Inhibitor/Angiotensin Receptor Blocker/Angiotensin Receptor Neprilysin Inhibitor, 3) Mineralocorticoid Receptor Antagonist, 4) Sodium Glucose Cotransporter-2 Inhibitor. Continuity of care was calculated using the Bice-Boxerman Continuity of Care Index (COC) and the Usual Provider of Care (UPC) index, categorized by quantile. The primary outcome was adherence to GDMT, defined as average proportion of days covered ≥80% over 1 year. Secondary outcomes included all-cause and HF hospitalization at 1-year. We performed multivariable logistic regression analyses adjusted for demographics, insurance status, comorbidity index, number of visits and neighborhood SES index. RESULTS:Overall, 3,971 individuals were included (mean age 72 years (SD 14), 71% male, 66% White race). In adjusted analyses, compared to individuals in the highest COC quartile, individuals in the third COC quartile had higher odds of GDMT adherence (OR 1.26, 95% CI 1.03-1.53, P = .024). UPC tertile was not associated with adherence (all P > .05). Compared to the highest quantiles, the lowest UPC and COC quantiles had higher odds of all-cause (UPC: OR 1.53, 95%CI 1.23-1.91; COC: OR 2.54, 95%CI 1.94-3.34) and HF (UPC: OR 1.81, 95%CI 1.23-2.67; COC: OR 1.77, 95%CI 1.09-2.95) hospitalizations. CONCLUSIONS:Continuity of care was not associated with GDMT adherence among patients with HFrEF but lower continuity of care was associated with increased all-cause and HF-hospitalizations.

BACKGROUND:Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload. OBJECTIVES/OBJECTIVE:This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF. METHODS:This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models. RESULTS:Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90). CONCLUSIONS:An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920).