Faculty Bibliography

OBJECTIVE:We compared health care spending and utilization associated with semaglutide relative to bariatric surgery in patients with obesity and type 2 diabetes (T2D). METHODS:Using MarketScan insurance claims of patients with BMI ≥ 35 and T2D from 2016 to 2021, we examined associations between choice of semaglutide, sleeve gastrectomy, or gastric bypass; 3-year health care spending (out-of-pocket [OOP] and total); and clinical outcomes (ED visits, hospital admissions, and major adverse cardiovascular events [MACE]). Analyses were adjusted using generalized linear models, inverse probability weighting, and instrumental variables. RESULTS:Among 6748 patients (2797 semaglutide, 2300 sleeve gastrectomy, 1651 gastric bypass), bariatric surgery patients had higher BMI and more comorbidities. In IPTW-adjusted analysis, semaglutide was associated with the highest 3-year OOP costs ($7752 vs. $5980 [sleeve gastrectomy] vs. $6591 [gastric bypass], p < 0.001), but total spending was not statistically different across the groups. Relative to semaglutide, the gastric bypass group showed higher observed ED visits (hazard ratio relative to semaglutide [95% CI]: 1.36 [1.28-1.45]) and inpatient admissions (1.25 [1.13-1.37]) and fewer MACE (0.71 [0.59-0.88]). Sleeve gastrectomy was associated with fewer long-term admissions (0.79 [0.72-0.86]) and MACE (0.79 [0.66-0.93]). CONCLUSIONS:For patients with T2D and obesity, compared with semaglutide, bariatric surgery is associated with lower OOP spending and similar total spending at 3 years, as well as lower long-term MACE rates.

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.

IMPORTANCE/OBJECTIVE:Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality. OBJECTIVE:To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated. DESIGN/METHODS:A pragmatic, randomised clinical trial. SETTING/METHODS:A large academic health system consisting of over 350 ambulatory practices. PARTICIPANTS/METHODS:Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months. EXPOSURES/METHODS:A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care. MAIN OUTCOMES/RESULTS:The primary outcome was a follow-up HbA1c test result within 90 days of randomisation. RESULTS:The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001). CONCLUSIONS:Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

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.

BACKGROUND:Patients with heart failure (HF) often have difficulty obtaining life-saving medications due to coverage barriers, such as prior authorizations and high out-of-pocket costs. To promote better coverage for high value therapies and inform policymakers about cost effectiveness, the American Heart Association/American College of Cardiology/Heart Failure Society of America added Value Statements to HF guidelines. We assessed whether these guidelines influenced Medicare drug coverage policies for 2 life-saving, costly HF medications: angiotensin receptor neprilysin inhibitors (ARNI-guideline "high value") and sodium glucose cotransporter-2 inhibitors (SGLT2i-guideline "intermediate value"). METHODS:We performed an observational study using Medicare prescription drug plan formulary files from April 2020 to April 2023 to separately assess for changes in coverage barriers to ARNI and SGLT2i after Value Statement publication (April 2022), and subsequent Medicare plan online update (October 2022). The primary outcome was the percentage of plans each month with any barrier to drug coverage (prior authorizations, tier ≥3 out-of-pocket cost-sharing, step therapy, or no coverage). Analyses used interrupted time series and difference-in-differences approaches. Difference-in-differences analyses used direct oral anticoagulants as a control due to their comparable cost and use as ARNI and SGLT2i, but without a Value Statement. RESULTS:Among 7396 Medicare drug plans, monthly rates of any coverage barrier ranged from 94.3% to 97.4% for ARNI and 93.2% to 96.6% for SGLT2i. Most barriers were due to tier ≥3 out-of-pocket cost-sharing requirements (ARNI: 94.3%-95.8%; SGLT2i: 93.2%-95.6%). Coverage barriers remained stable in April 2022 and declined slightly in October 2022. In difference-in-differences analyses, the presence of a Value Statement was associated with a ~1 percentage point decline in coverage barriers for both ARNI (difference-in-differences estimate, -1.07% [95% CI, -1.44% to -0.70%]) and SGLT2i (-1.32% [95% CI, -1.63% to -1.00%]). CONCLUSIONS:Coverage barriers to ARNI and SGLT2i were common and changed only slightly after publication of Value Statements in HF guidelines. There is a critical need for robust strategies to improve access to life-saving HF medications.

BACKGROUND:Despite the widespread use of electronic health records, a standardized approach to identify heart failure patients is lacking. We sought to create and validate definitions for identifying patients with heart failure using electronic health record data. METHODS:To define heart failure, we developed 8 distinct definitions created from combinations of heart failure diagnosis based on ICD-10 codes listed in the clinical encounter, problem list or past medical history, and/or ejection fraction ≤40%. To validate our definitions, we used stratified sampling and physician chart review guided by the Universal Definition of Heart Failure as our gold standard and compared their performance using sensitivity and positive predictive value. RESULTS:We identified 41,392 patients who met at least one of our eight definitions for heart failure, plus an additional 2,692 patients with an ICD-10 diagnosis of heart failure outside of a standard clinical setting and 696,896 patients with a cardiovascular diagnosis other than heart failure. Using these groups, we randomly sampled a total of 528 charts for physician chart review. Sensitivities of the eight definitions of heart failure ranged from 10.3% to 42.0%, and positive predictive values ranged from 80.7% to 98.6%. CONCLUSIONS:We found that patients meeting EHR-based definitions of heart failure likely represented true clinical cases of disease. Nevertheless, the definitions captured less than half of the patients with heart failure, thus severely underestimating the prevalence of disease and underlining a need for more comprehensive methods to effectively use this data to understand the epidemiological burden of heart failure.

There is a pressing need to translate evidence for heart failure (HF) therapies into contemporary practice. Medications that improve HF morbidity and mortality remain underused because of complex barriers at multiple levels across the health care ecosystem. High-quality implementation trials demonstrate that specific interventions can increase prescription, intensification, and persistence of HF medication. However, evidence-based interventions have not been widely implemented across health care organizations in the United States. This review explores 5 key strategies-patient activation, audit and feedback, rapid intensive initiation of medical therapy, virtual care teams, and clinical decision support tools-and discusses barriers to their widespread adoption. Although some barriers are specific to an intervention, others stem from systemic limitations among health care organizations and the health policy landscape. Using lessons learned from recent trials, this review also highlights future investigations needed to address these barriers, encourages uptake of successful implementation strategies, and discusses common approaches that should be abandoned.