Faculty Bibliography

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: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.

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.

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.

OBJECTIVE:The objective of this study is to examine real-world dose titration patterns of semaglutide for weight management (Wegovy, Novo Nordisk A/S) in US adults and identify characteristics associated with early discontinuation. METHODS:We identified 15,811 commercially insured adults who started semaglutide for weight management (administrated through single-dose prefilled pens) between June 2021 and December 2023. We depicted dose-titration patterns over 5 months and identified factors associated with discontinuation using multivariable Cox regression. Sensitivity analyses examined patterns after supply shortage resolution (after October 2023). RESULTS:Most semaglutide users deviated from the recommended monthly dose-escalation schedule within the first 5 months. By the fifth month, nearly one-half (46%) had discontinued the treatment, with similar rates (48%) among those initiating after supply stabilization. Discontinuation was strongly associated with copayment amount, with rates increased from 41% in the lowest quintile ($1-$54 per month) to 51% in the highest quintile ($161-$1460 per month). Higher discontinuation rates were also associated with lower household income and education level. CONCLUSIONS:The deviations from the recommended dose-escalation schedule and high discontinuation rate among real-world semaglutide users indicate important challenges in the delivery of evidence-based care. Policy interventions that reduce financial barriers to the persistence of semaglutide are needed.

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.

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.

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.