Faculty Bibliography

OBJECTIVES:This study assessed the association of diabetes duration with incident heart failure (HF). BACKGROUND:Diabetes increases HF risk. However, the independent effect of diabetes duration on incident HF is unknown. METHODS: ≥7%), with tests for interaction. RESULTS:, women, and Blacks (all P interactions <0.05). CONCLUSIONS:Delaying diabetes onset may augment HF prevention efforts, and therapies to improve HF outcomes might target those with long diabetes duration.

IMPORTANCE/UNASSIGNED:The prevalence of obesity and cardiovascular-kidney-metabolic (CKM) syndrome continues to rise. Indications for novel CKM therapies, including glucagonlike peptide 1 receptor agonists (GLP-1RAs), sodium-glucose cotransporter-2 inhibitors (SGLT2is), and nonsteroidal mineralocorticoid antagonists (nsMRAs) continue to expand, yet the proportion of adults meeting expanded indications, including for multiple medications remains unclear. OBJECTIVE/UNASSIGNED:To examine proportion of adults meeting US Food and Drug Administration (FDA)-approved indications for GLP1-RAs, SGLT2is, and nsMRAs across national survey, community-based, and ambulatory health care samples. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This study used a representative cross-sectional survey of US adults (National Health and Nutrition Examination Survey [NHANES], weighted 245 million; mean [SD] age, 47 [18] years; 126.8 million [52%] female), 5 pooled community-based cohort studies (the Framingham Heart Study, the Multi-Ethnic Study of Atherosclerosis, the Prevention of Renal and Vascular Endstage Disease Study, the Atherosclerosis Risk in Communities Study, and the Cardiovascular Health Study; n = 30 929; mean [SD] age, 63 [14] years; 16 749 [54%] female), and 2 ambulatory health care samples (the Beth Israel Deaconess Medical Center cohort [BIDMC], n = 84 714; mean [SD] age, 46 [17] years; 51 113 [60%] female] and the Mass General Brigham cohort [MGB], n = 362 485; mean [SD] age, 48 [17] years; 227 206 [61%] female). Data were analyzed from November 2024 to November 2025. EXPOSURES/UNASSIGNED:FDA-approved indications for GLP-1RAs, SGLT2is, and nsMRAs. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Medication class eligibility within each study sample. RESULTS/UNASSIGNED:The proportion of individuals who met current FDA-approved indications for 1 or more CKM medication was 60% in NHANES (representing 148 million US adults), 61% in the pooled cohorts, 42% in the BIDMC ambulatory cohort, and 46% in the MGB ambulatory cohort. Eligibility for GLP-1RA therapy was most common, with 56% (representing 137.1 million US adults) in NHANES, 49% in the pooled cohorts, 41% in the BIDMC cohort, and 46% in the MGB cohort. This was followed by SGLT2i therapy (24% [57.9 million] in NHANES, 33% in the pooled cohorts, 14% for both BIDMC and MGB) and nsMRA (5% [11.7 million] in NHANES, 5% in the pooled cohorts, and 1% to 2% in ambulatory samples). Overlapping eligibility for multiple classes was common, with 12% to 17% for GLP1-RA and SGLT2i therapies and 1% to 5% for all 3 classes (an estimated 11.7 million US adults in NHANES). CONCLUSIONS AND RELEVANCE/UNASSIGNED:This study found that up to 61% of adults met FDA-approved indications for at least 1 of 3 novel CKM therapy classes. This represents an estimated 148 million US adults, including 11.7 million US adults with potential FDA indications for triple therapy, highlighting the urgent need to optimize implementation and utilization of CKM syndrome therapies.

BACKGROUND:Prior authorizations could hinder the filling of life-saving heart failure (HF) medications, such as angiotensin receptor neprilysin inhibitors (ARNIs) and sodium glucose cotransporter 2 inhibitors (SGLT2is). OBJECTIVES/OBJECTIVE:The aim of the study was to determine whether prior authorizations were associated with delayed or decreased filling for ARNI and SGLT2i. METHODS:This was a retrospective cohort study using electronic health record, pharmacy fill, and neighborhood-level data from a large, academic health system. We included patients with HF and a new prescription for ARNI or SGLT2i between April 1, 2021, and April 30, 2023, and assessed for presence of prior authorization requirement. Outcomes included days to first fill and never filling the prescription. Analyses were conducted using inverse probability weighting methods. RESULTS:Among 2,183 patients, 12.2% (152/1,243) and 14.3% (165/1,150) had a prior authorization requirement for ARNI or SGLT2i, respectively. Patients requiring prior authorization tended to be younger, identify as non-Hispanic Black or Hispanic, have non-Medicare insurance, and have fewer comorbidities. In weighted models, patients requiring prior authorization took 3.03 (95% CI: 2.16-4.25) times longer to fill ARNI, 6.75 (95% CI: 4.44-10.3) times longer to fill SGLT2i, and were 2.23 (95% CI: 1.37-3.65) times more likely to never fill SGLT2i prescriptions (all P < 0.001). CONCLUSIONS:Prior authorization requirements were more common for patients identifying as Black or Hispanic and were associated with decreased and delayed filling of ARNI and SGLT2i. Our findings highlight an important barrier to mortality-reducing, guideline-recommended medications for HF.

BACKGROUND/UNASSIGNED:There is no gold standard for measuring adherence to a complex medication regimen. Heart failure is a chronic disease state that requires multiple medications for optimal control, known as guideline-directed-medical therapy (GDMT), and can be used a model to explore approaches to assessing multi-medication adherence. We aimed to compare seven proportion of days covered (PDC) measures for assessing adherence to multiple GDMT medications and to evaluate their association with clinical outcomes. METHODS/UNASSIGNED:We conducted a large, single center, retrospective cohort study of 34,603 patients with heart failure who filled a GDMT prescription between April 2021 and July 2022. The primary outcomes were the seven PDC-based adherence measures derived from electronic health record and pharmacy data. PDC was defined as the proportion of days in which patients had possession of GDMT medications. Our secondary outcome was the combined clinical outcome of ED visits, hospitalizations, and death. RESULTS/UNASSIGNED:The seven measures provided a wide range of measured PDC adherence. Using the strictest measure, 'each' (>= 80% of days with each drug available) 54% of patients were considered adherent, compared with 73% measured adherence for the least restrictive measure, 'at least one'. Variability increased with increasing number of medications across all non-average based measures. For example, using the 'all' measure (a more restrictive PDC measure) adherence ranged from 0.53 to 0.41 with increasing number of GDMT prescriptions. Higher PDC for each of the seven measures was associated with increased number of ED, visits, hospitalizations, and death. There was no association with the combined outcome in patients with heart failure with reduced ejection fraction. CONCLUSIONS AND RELEVANCE/UNASSIGNED:There was a wide variability in adherence measures for assessing adherence to GDMT depending on the measure used. This variability has significant implications for the policy, clinical, and intervention context to which the measure is applied.

OBJECTIVE:While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF). MATERIALS AND METHODS/METHODS:We utilized a large electronic health record (EHR) cohort of 34,697 HF patients seen at NYU Langone Health with an active prescription for ≥1 GDMT between April 01, 2021 and October 31, 2022. The outcome was adherence to GDMT measured as proportion of days covered (PDC) at 6 months following a clinical encounter. Over 120 predictors included patient-, therapy-, healthcare-, and neighborhood-level factors guided by the World Health Organization's model of barriers to adherence. We compared performance of several ML models and their ensemble (superlearner) for predicting PDC with traditional regression model (OLS) using mean absolute error (MAE) averaged across 10-fold cross-validation, % increase in MAE relative to superlearner, and predictive-difference across deciles of predicted PDC. RESULTS:Superlearner, a flexible nonparametric prediction approach, demonstrated superior prediction performance. Superlearner and quantile random forest had the lowest MAE (mean [95% CI] = 18.9% [18.7%-19.1%] for both), followed by MAEs for quantile neural network (19.5% [19.3%-19.7%]) and kernel support vector regression (19.8% [19.6%-20.0%]). Gradient boosted trees and OLS were the 2 worst performing models with 17% and 14% higher MAEs, respectively, relative to superlearner. Superlearner demonstrated improved predictive difference. CONCLUSION/CONCLUSIONS:This development phase study suggests potential of linked EHR-pharmacy data and ML to identify HF patients who will benefit from medication adherence interventions. DISCUSSION/CONCLUSIONS:Fairness evaluation and external validation are needed prior to clinical integration.

Cardiometabolic risk factors, obesity, diabetes and hyperlipidemia contribute to cardiovascular disease (CVD). While platelets are involved in CVD pathogenesis, the relationship between risk factor burden on platelet indices and the platelet transcriptome remains uncertain. Blood was collected from CVD-free adults, measuring platelet count, mean platelet volume (MPV), immature platelet fraction (IPF), and absolute immature platelet fraction (AIPF) by hemogram. Platelets were isolated and analyzed via RNA sequencing. Participants were stratified by number of cardiometabolic risk factors (diabetes, obesity, hyperlipidemia). We calculated median (IQR) values of platelet indices and p-for-trend via linear regression across risk factor burden. To evaluate the association between risk factor burden and platelet transcripts, we performed multivariable linear regression adjusting for age, sex, and race/ethnicity. Among 141 participants, (50.5 ± 14.8 years, 42% male, 26% Black) risk factor burden was associated with increasing platelet size, IPF, and AIPF but not platelet count. Platelet RNA sequencing identified 100 differentially expressed transcripts (p < .01; 66 upregulated, 34 downregulated). Gene ontology enrichment analysis demonstrated upregulated pathways of secondary metabolic processes (NES = 1.96, p < .01), and hematopoietic stem cell proliferation (NES = 1.95, p < .01). Greater cardiometabolic risk factor burden is associated with increased platelet size and immaturity and suggesting novel platelet-mediated mechanisms linking risk factor burden with CVD.