Faculty Bibliography

BACKGROUND/UNASSIGNED:Skilled nursing facilities (SNFs) play a critical role in postacute recovery for older adults with heart failure (HF), yet the transition from SNF to home remains a vulnerable and understudied phase of care. Although discharge guidelines emphasize clear communication, HF-specific self-care education, medication management, and follow-up coordination, little is known about how these practices are implemented during SNF-to-home transitions. METHODS/UNASSIGNED:We conducted a convergent mixed-methods study across 4 nonprofit SNFs, integrating data from postdischarge patient and caregiver surveys, structured medical record abstraction of discharge instructions, and semi-structured staff interviews. Eligible patients were Medicare beneficiaries aged ≥65 years discharged from SNF to home following HF hospitalization, with SNF stays ≤60 days. Quantitative data were analyzed using descriptive statistics, while qualitative data underwent thematic and directed content analysis. Findings were triangulated across data sources to identify key challenges and actionable strategies. RESULTS/UNASSIGNED:Among 150 respondents, 59% reported receiving written discharge instructions; however, HF-specific self-care elements (eg, daily weight monitoring, low salt diet) were documented in only 15% to 41% of instructions. Although 87% reported receiving a medication list, only 53% had it reflected in the discharge instructions, and adherence support was infrequently addressed (24%). Follow-up coordination was similarly discordant: 37% of respondents reported a scheduled primary care appointment, compared with 13% documented in discharge instructions. Staff interviews revealed nonstandardized discharge workflows, workforce constraints, and reliance on verbal education, contributing to variability in patient preparation and communication across care settings. CONCLUSIONS/UNASSIGNED:SNF-to-home transitions after HF hospitalization are marked by discordance between patient-reported education and written documentation, as well as inconsistent medication and follow-up coordination. These gaps represent modifiable vulnerabilities during a high-risk recovery period. Standardized HF-focused discharge workflows and strengthened cross-setting communication may improve transitional care, while long-term solutions must address structural and workforce constraints.

PURPOSE/OBJECTIVE:Mobile health cardiac rehabilitation may improve access to care among older adults with ischemic heart disease, but engagement remains poorly understood. We analyzed weekly engagement data from the RESILIENT (Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting) trial, a large, randomized trial of mobile health cardiac rehabilitation in older adults conducted in the United States. METHODS:Data from 298 intervention participants were analyzed. Weekly engagement was scored from 0 to 11 based on exercise entry (7 points), communication with exercise therapist (2 points), video viewing (1 point), and blood pressure measurement (1 point). Latent class analysis identified digital engagement phenotypes. Participant characteristics were compared, and multivariable logistic regression identified factors associated with phenotype membership. RESULTS:Median age was 71.0 years, 28% were women, 23% were non-White, and 62% were enrolled after elective percutaneous coronary intervention. Latent class analysis identified 3 phenotypes: persistently low (n = 81), intermediate declining (n = 93), and persistently high (n = 124). Participants with persistently low engagement were more likely to be non-White (48% vs 12% vs 15%, P < .001), Medicaid enrolled (22% vs 8% vs 7%, P = .001), have less than high school education (16% vs 4% vs 3%, P < .001), have frailty phenotype (28% vs 10% vs 7%, P < .001), and have a greater mean number of comorbidities (3.1 vs 3.0 vs 2.6; P = .012). After adjustment, non-White race and frailty remained independently associated with low engagement. Improvement in 6-minute walk test distance varied: 20.8 m (low), 29.7 m (intermediate), and 54.5 m (high) (P = .003). CONCLUSIONS:Three distinct digital engagement phenotypes emerged. Persistently low engagement was more common among non-White and frail participants, underscoring ongoing disparities despite efforts to overcome the digital divide.

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.