Faculty Bibliography

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.

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:Nonadherence to antihypertensive medications is common. Mobile health (mHealth)-based behavioral economic interventions may improve adherence, but remain largely untested, especially in vulnerable populations. OBJECTIVE:The study sought to test whether an mHealth incentive lottery would lower systolic blood pressure (SBP) and improve adherence. METHODS:BETTER-BP (Behavioral Economics Trial To Enhance Regulation of Blood Pressure) was a randomized trial conducted in 3 safety-net clinics in New York City. Eligible participants were adults with hypertension prescribed at least 1 antihypertensive medication, with SBP >140 mm Hg, and poor self-reported adherence. In the intervention arm, an incentive lottery was administered via SMS messaging. All participants received passive adherence monitoring. The intervention lasted 6 months, with continued monitoring until 12 months. The primary clinical endpoint was change in SBP at 6 months. The primary process endpoint was adequate antihypertensive medication adherence (≥80% days adherent) from baseline to 6 months. RESULTS:Four-hundred participants (265 intervention:135 control) were enrolled with median age 57 years, 60.5% women, 61.5% Hispanic, and 20.3% non-Hispanic Black. Over 70% had Medicaid or no insurance. At 6 months, intervention arm participants were twice as likely to achieve adequate adherence (71% vs 34%; adjusted risk ratio: 2.04; 95% CI: 1.58-2.63), but there was no significant change in mean SBP (-6.7 mm Hg intervention vs -5.8 mm Hg control; P = 0.62). From 6 to 12 months, adherence was similar (31% intervention vs 26% control; adjusted risk ratio: 1.17; 95% CI: 0.83-1.65). CONCLUSIONS:In a diverse safety-net population, the BETTER-BP intervention doubled the rate of adequate antihypertensive medication adherence but did not reduce SBP at 6 months.

BACKGROUND:Wearable accelerometers, which continuously record physical activity metrics, are commonly used in mobile health-enabled cardiac rehabilitation (mHealth-CR). The association between adherence to accelerometer use during mHealth-CR and improvement in clinical outcomes, such as functional capacity, is understudied. The emergence of artificial intelligence (AI) technology provides novel opportunities to investigate accelerometry use patterns in relation to mHealth-CR outcomes. OBJECTIVE:In this study, we sought to use an AI clustering framework to identify distinct behavioral phenotypes of adherence to accelerometer use. We then aimed to quantify the association of these adherence phenotypes with functional capacity improvements in older adults undergoing mHealth-CR. METHODS:We analyzed data from the RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized clinical study to date comparing mHealth-CR versus usual care in older adults (aged ≥65 years). Intervention arm participants were instructed to wear a Fitbit accelerometer for the 3-month study duration. Adherence to accelerometer use was quantified as overall adherence (percentage of days worn) via k-means clustering AI-derived measures and compared with changes in 6-minute walk distance (6-MWD), adjusted for demographic and clinical covariates. RESULTS:Among 271 participants with a mean age of 71 years (SD 8), of whom 198 (73%) were male, accelerometers were worn for an average of 76 days (95% confidence limits 73,78) over 3 months. Adjusted analyses showed a weak association between days of wear and improvement in 6-MWD, with every 30 additional days associated with an 11-meter improvement (P=.08). Our k-means clustering framework identified adherence phenotypes at two resolutions: low resolution (k=2 clusters) and high resolution (k=8 clusters). The consistently high adherence cluster trended toward a 24.6-meter improvement in 6-MWD compared to the low and declining adherence clusters (n=39; 95% CI 0.7-49.9; P=.06). The 8-cluster phenotyping revealed a richer set of adherence patterns, with the consistently high adherence cluster in this analysis having a 38.5-meter (95% CI 2.2-74.7; P=.04) improvement in 6-MWD than the low adherence cluster, as well as greater average daily steps over the 3-month intervention (mean 7518, SD 3415 vs mean 4800, SD 2920 steps; P=.008). CONCLUSIONS:A time-series AI clustering framework identified a range of behavioral phenotypes representing different degrees of adherence to accelerometer use. Regression analysis identified a weak association between the higher adherence phenotype and functional capacity improvement in older adults undergoing mHealth-CR. Our AI-derived accelerometry adherence phenotypes may offer a new approach to tailor mHealth-CR regimens to individual patients, potentially leading to better outcomes in this high-risk population. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT03978130; https://clinicaltrials.gov/study/NCT03978130. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:RR2-10.2196/32163.

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:Data on patient-centered outcomes of mobile health cardiac rehabilitation (mHealth-CR) for older adults with ischemic heart disease are limited. The RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized study of mHealth-CR in this population, found no significant improvements in functional capacity, health status, angina, or disability compared with usual care. OBJECTIVES/OBJECTIVE:The purpose of this study was to evaluate whether mHealth-CR affects personalized goal attainment-a prespecified secondary endpoint of RESILIENT-using goal attainment scaling (GAS). METHODS:A total of 400 patients (≥65 years) with ischemic heart disease were randomized to mHealth-CR or usual care. Participants specified goals for CR at baseline using the five-category goal attainment scale: much-less-than-expected (-2), less-than-expected (-1), expected (0), better-than-expected (+1), and much-better-than-expected (+2). Goal attainment was assessed at 3 months. RESULTS:Of 400 patients (median age, 71.0 years [range 65.0-91.0]; 72.8% male; 65.2% prefrail/frail) randomized to mHealth-CR (n = 298) or usual care (n = 102), 353 (88.3%) completed GAS. Most goals addressed physical activity (54.0% mHealth-CR vs 59.0% usual care), health care behaviors (14.4% vs 11.9%), or symptom management (13.1% vs 9.0%). Rates of attaining or exceeding goals (GAS ≥0) were similar between groups (80.5% vs 77.6%; P = 0.492). However, in the intervention arm, there was a higher rate of exceeding expected level of goal attainment (GAS +1, +2) compared with usual care (52.6% vs 34.2%; P = 0.006). CONCLUSIONS:In a trial that did not demonstrate differences on traditional endpoints, those receiving mHealth-CR were more likely to exceed personalized CR goals. These findings suggest the intervention facilitated greater progress toward individualized goals and underscore the importance of patient-centered outcomes in CR.