Faculty Bibliography

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.

INTRODUCTION/BACKGROUND:The coronavirus disease 2019 (COVID-19) pandemic caused unprecedented disruptions to healthcare systems worldwide, significantly affecting patients with chronic kidney disease (CKD). In this study, we evaluated the impact of the pandemic on healthcare-seeking behavior and CKD progression among patients in New York City. METHODS:Using electronic health records from PCORnet's INSIGHT Clinical Research Network, we conducted a retrospective cohort study focused on 84,062 patients with CKD aged 50 years or older with multiple chronic conditions seen between 2017 and 2022. Patients were identified using pre-pandemic CKD diagnostic codes, and confirmed by estimated glomerular filtration rate (eGFR) measurements. Care disruption was defined as receiving fewer visits than recommended by Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We used linear mixed-effects models to estimate annual eGFR changes and analyze trends in care visits stratified by CKD stage and care disruption. RESULTS:. Care visits declined sharply in 2020 across patients at all but the end stage, with incomplete recovery by 2022. Patients with adequate pre-pandemic care maintained their visits above KDIGO levels, while those with inadequate care increased visits during the pandemic. Pronounced eGFR decline occurred in 2020 (10.6%), with slower declines observed thereafter. CONCLUSION/CONCLUSIONS:The COVID-19 pandemic disrupted CKD care, potentially leading to reduced healthcare-seeking behavior and accelerated kidney function decline in 2020. Slower decline post-2020 may reflect improved healthcare utilization, better medication adherence, and new therapies, and other factors.

OBJECTIVE:To evaluate the risks of incident dyslipidemia and abnormal body mass index (BMI) during the 28-179-day post-acute phase after documented SARS-CoV-2 infection in a large pediatric sample. STUDY DESIGN/METHODS:A retrospective cohort study using the RECOVER pediatric electronic health record (EHR) datasets from 25 US children's hospitals and health institutions, from March 2020 to September 2023. This study included 384,289 COVID-19-positive patients aged 0-21 years for dyslipidemia analyses and 285,559 aged 2-21 years for BMI analyses, each with at least 6 months of follow-up. COVID-19-negative controls included 1,080,413 and 817,315 patients, respectively. SARS-CoV-2 infection was defined by a positive polymerase-chain-reaction (PCR), antigen, or serologic test; a clinical diagnosis of COVID-19; or a documented diagnosis of post-acute sequelae of SARS-CoV-2 (PASC). Incident dyslipidemia and abnormal BMI were identified using age-specific laboratory or anthropometric thresholds. Adjusted relative risks (aRRs) were estimated using propensity-score-stratified modified Poisson regression with multiple sensitivity analyses. RESULTS:During the post-acute phase, the COVID-19-positive cohort had higher rates of new-onset composite dyslipidemia (aRR 1.24; 95% CI 1.18-1.29) and abnormal BMI (aRR 1.15; 95% CI, 1.12-1.18). Results were robust to sensitivity and stratified analyses. CONCLUSION/CONCLUSIONS:Children and adolescents with documented COVID-19 infection were associated with an increased risk of new-onset dyslipidemia and abnormal BMI during the post-acute phase, highlighting the need for metabolic monitoring after infection.

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/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.

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.