Faculty Bibliography

BACKGROUND:The COVID-19 pandemic had a significant impact on healthcare delivery. Older adults with multimorbidities were at risk of healthcare disruptions for the management of their chronic conditions. OBJECTIVE:To characterize healthcare disruptions during the COVID-19 healthcare shutdown and recovery period (March 7, 2020-October 6, 2020) and their effects on disease management among older adults with multimorbidities who were patients of NYC Health + Hospitals (H + H), the largest municipal safety-net system in the United States. DESIGN/METHODS:Observational. PATIENTS/METHODS:Patients aged 50 + with hypertension or diabetes and at least one other comorbidity, at least one H + H ambulatory visit in the six months before COVID-19 pandemic onset (March 6, 2020), and at least one visit in the post-acute shutdown period (October 7, 2020 to December 31, 2023). MAIN MEASURES/METHODS:We characterized disruption in care (defined as no ambulatory or telehealth visits during the acute shutdown) and estimated the effect of disruption on blood pressure control, hemoglobin A1c (HbA1c), and low-density lipoprotein (LDL) cholesterol using difference-in-differences models. KEY RESULTS/RESULTS:Out of 73,889 individuals in the study population, 12.5% (n = 9,202) received no ambulatory or telehealth care at H + H during the acute shutdown. Low pre-pandemic healthcare utilization, Medicaid insurance, and self-pay were independent predictors of care disruption. In adjusted analyses, the disruption group had a 3.0-percentage point (95% CI: 1.2-4.8) greater decrease in blood pressure control compared to those who received care. Disruption did not have a significant impact on mean HbA1c or LDL. CONCLUSIONS:Care disruption was associated with declines in blood pressure control, which while clinically modest, could impact risk of cardiovascular outcomes if sustained. Disruption did not affect HbA1c or LDL. Telehealth mitigated impacts of the pandemic on care disruption and subsequent disease management. Targeted outreach to those at risk of care disruption is needed during future crises.

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:Medication nonadherence is a common issue among patients with hypertension. Healthcare professionals often overlook medication nonadherence due to limited tools, time constraints, and competing demands. Integrating pharmacy medication fill data into electronic health records (EHRs) presents an opportunity to enhance medication adherence measurement and monitoring in real-time. This study identified facilitators and barriers to addressing adherence to antihypertensive medications by Medical Assistants (MAs), Registered Nurses (RNs), and Primary Care Providers (PCPs) in primary care settings. METHODS:We conducted a qualitative study with, 15 healthcare professionals (5 MAs, 5 RNs, and 5 PCPs) caring for patients with hypertension. Semi-structured interviews, guided by the Consolidated Framework for Implementation Research (CFIR), explored barriers and facilitators related to screening and addressing medication non-adherence during primary care clinical encounters. Thematic analysis and deductive coding were used to analyze the data. RESULTS:Four major themes emerged: motivation, work infrastructure, capability, and opportunity. MAs and PCPs were motivated to discuss medication adherence and build relationships. Capability varied; RNs were confident in their counseling skills based on their training and patient trust, and PCPs described adherence counseling as part of their role, particularly through motivational interviewing. Work infrastructure presented structural hurdles due to RN workflow limitations and MA role constraints. Opportunity to address non-adherence were constrained by tight schedules and competing clinical demands during brief visits. CONCLUSIONS:RNs and PCPs felt capable in their ability to address medication adherence but cited time and competing demands as significant barriers; conversely, MAs reported motivation but were limited by their role. These findings suggest opportunities for effective management of medication adherence in practice settings through a more coordinated strategy across multiple healthcare professionals. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov; NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422.

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.

IMPORTANCE/UNASSIGNED:Medication nonadherence is present in nearly half of patients with hypertension but is underrecognized in clinical care. Data linkages between electronic health records and pharmacies have created opportunities for scalable assessment of medication adherence at the point of care. OBJECTIVE/UNASSIGNED:To test the effectiveness of a multicomponent intervention that identified patients with uncontrolled hypertension and medication nonadherence using linked electronic health record-pharmacy data combined with team-based care to address adherence barriers. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:TEAMLET (Leveraging Electronic Health Record Technology and Team Care to Address Medication Adherence) was a pragmatic, 2-arm, cluster randomized clinical trial conducted between October 2022 and November 2024 in 10 primary care sites in New York. The study included adults with uncontrolled hypertension and low medication adherence, defined as proportion of days covered (PDC) less than 80%. Data analysis was performed from November 2024 to January 2025. INTERVENTION/UNASSIGNED:The intervention consisted of the following: (1) automated identification of patients with medication nonadherence at the time of the visit; (2) prompting of medical assistants to screen for barriers to adherence; (3) clinical decision support alerting the primary care physicians and nurse practitioners to barriers to adherence; and (4) adherence discussion between the primary care physician or nurse practitioner and the patient. The comparator was usual care. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was change in PDC from baseline to 12 months. RESULTS/UNASSIGNED:Among 1726 patients (mean [SD] age, 67.2 [13.9] years; 887 [51.4%] female), the mean (SD) baseline PDC was 33.2% (30.5%) overall (32.4% [30.4%] in the intervention group and 34.0% [30.6%] in the control group). The mean (SD) PDC at 12 months was 51.1% (39.5%) for the intervention group and 53.1% (39.6%) for the control group. No difference was found in the change in PDC from baseline to 12 months between the intervention and control groups (mean [SD] absolute change in PDC, 18.5 [41.1] vs 18.2 [40.9] percentage points, respectively; adjusted difference, -0.15 percentage point; 95% CI, -4.06 to 3.76 percentage points). Change in systolic blood pressure and patients who became adherent (PDC ≥80%) at 12 months were also similar between groups. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this pragmatic trial, an intervention that combined team-based primary care with automated identification of patients with antihypertensive medication nonadherence did not lead to improvements in adherence or blood pressure. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT05349422.

BACKGROUND:One hypothesis for the respiratory syncytial virus (RSV) surge in 2022 was suppression of immune responses following SARS-CoV-2 infection. Our objective was to compare the risk of subsequent RSV and other infections among children with and without SARS-CoV-2 infection. METHODS:We conducted a retrospective cohort study using electronic health record data from 27 US health systems analyzing children aged under 5 years with SARS-CoV-2 infection (test/coded) between March and July 2022. The comparison groups were children with (a) influenza infection and (b) respiratory tract infection with no evidence of SARS-CoV-2/influenza infection. The primary and secondary outcomes were RSV infection and respiratory or any infection in the subsequent 15 to 180 days, respectively. We applied inverse probability of treatment weighting and performed weighted logistic regression modeling. RESULTS:Our primary and secondary cohorts consisted of 18 767 and 114 414 children with SARS-CoV-2 infection, and 6697 and 30 424 with influenza infection, respectively, and 46 697 with another acute respiratory tract infection. The odds of subsequent RSV were lower in the SARS-CoV-2 group compared with the influenza group (adjusted odds ratio [aOR] 0.73, 95% CI 0.61-0.86) and compared with the group with any respiratory tract infection (aOR 0.78, 95% CI 0.7-0.87). The odds of a respiratory tract infection and any infection were lower in the SARS-CoV-2 group than the influenza group (aOR 0.62, 95% CI 0.59-0.64 and aOR 0.67, 95% CI 0.65-0.7, respectively). CONCLUSIONS:We did not find an increased risk of RSV or a respiratory or any type of infection within 6 months of SARS-CoV-2 infection, compared with influenza and other respiratory illnesses.

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.