Faculty Bibliography

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:Disruption of ambulatory healthcare in New York City (NYC) during the COVID-19 pandemic was common, but the impact on the cardiometabolic health of vulnerable patient groups is unknown. Therefore, we estimated the effect of total care disruption (TCD) on blood pressure (BP) control among older NYC residents with hypertension and at least one other chronic condition, and examined whether neighborhood poverty moderated this impact. METHODS:From the INSIGHT Clinical Research Network, we identified NYC residents ≥50 years of age with hypertension and at least one other chronic condition. TCD was defined as no ambulatory or telehealth visit during the pandemic. We contrasted the change in prevalence of controlled BP (BP <140/90) before and after the pandemic among those with and without TCD via an inverse probability weighted (IPW) difference-in-difference regression model. RESULTS:Among 212,673 eligible individuals, mean age was 69.5 years (SD: 10.2 years) and 15.1% experienced TCD. BP control declined from 52.4% to 45.9% among those with TCD and from 53.6% to 48.9% among those without TCD. After IPW adjustment, a larger decline in BP control was noted among those with TCD (adjusted difference-in-difference = 1.13 percentage points (95% CI 0.32-1.94, p-value=0.0058)). There was no consistent difference in the relationship between TCD and post-pandemic BP control across neighborhood poverty levels. CONCLUSION/CONCLUSIONS:COVID-19-related TCD was associated with a modest decline in BP control among older adults with hypertension in NYC; this was not moderated by neighborhood poverty level.

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.

OBJECTIVE:Individuals of racially and ethnically diverse backgrounds are underrepresented in psoriatic arthritis (PsA) research/clinical trials, despite evidence that their disease presentation, severity and course may be distinct. Here we aim to describe how race, ethnicity and other socioeconomic factors inform disease characteristics in PsA. METHODS:817 consecutive patients with PsA from a large, diverse metropolitan area, were enrolled in an observational, longitudinal registry. Demographics, medical history, medication use, and psoriatic disease phenotype and activity were all recorded and analyzed. RESULTS:The population was 77.4% non-Hispanic White, 2.2% Black, 7.1% Asian, and 9.9% identified as other races or multiracial, and 11.8% identified as Hispanic. Hispanic and non-White individuals had higher tender joint counts (p= 0.033) with similar swollen joint counts (p= 0.308) and medication use (p= 0.171). They also had high rates of radiographic axial disease. Hispanic individuals were significantly more likely to have higher tender joint counts (p= 0.029), higher RAPID3 scores (p= 0.004), and moderate-severe psoriasis (p= 0.010) compared with non-Hispanic White individuals. CONCLUSION/CONCLUSIONS:In this diverse cohort, 22.6% of patients identified as underrepresented racial and/or ethnic groups, mostly Asian or Hispanic. Despite similar swollen joint counts and medication use, non-white individuals have higher tender joint counts compared with white individuals. Phenotypically, they also were more likely to have radiographic axial involvement. These findings may reflect differences in PsA presentation, experience and outcomes in individuals of various racial and ethnic groups, which need to be taken into consideration in clinical care and research design.

IMPORTANCE/UNASSIGNED:Among older adults with ischemic heart disease, participation in traditional ambulatory cardiac rehabilitation (CR) remains low. While mobile health CR (mHealth-CR) provides a novel opportunity to deliver care, age-specific impairments to technology use may limit uptake, and efficacy data are currently lacking. OBJECTIVE/UNASSIGNED:To test whether mHealth-CR improves functional capacity in older adults. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:The RESILIENT phase 2, multicenter, randomized clinical trial recruited patients aged 65 years or older with ischemic heart disease (defined as a hospital visit for myocardial infarction or coronary revascularization) from 5 academic hospitals in New York, Connecticut, and Massachusetts between January 9, 2020, and April 22, 2024. INTERVENTION/UNASSIGNED:Participants were randomized 3:1 to mHealth-CR or usual care. mHealth-CR consisted of commercially available software delivered on a tablet computer, coupled with remote monitoring and weekly exercise therapist telephone calls, delivered over a 3-month duration. As RESILIENT was a trial conducted in a routine care setting to inform decision-making, participants in both arms were also allowed to receive traditional CR at their cardiologist's discretion. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was change from baseline to 3 months in functional capacity, measured by 6-minute walk distance (6MWD). Secondary outcomes were health status (12-Item Short Form Health Survey [SF-12]), residual angina, and impairment in activities of daily living. RESULTS/UNASSIGNED:A total of 400 participants (median age, 71.0 years [range, 65.0-91.0 years]; 291 [72.8%] male) were randomized to mHealth-CR (n = 298) or usual care (n = 102) and included in the intention-to-treat analysis. Of those, 356 participants (89.0%) returned in person for 6MWD assessment at 3 months. For the primary outcome, there was no adjusted difference in 6MWD between participants receiving mHealth-CR vs usual care (15.6 m; 95% CI, -0.3 to 31.5 m; P = .06). Among subgroups, there was an improvement in 6MWD among women (36.6 m; 95% CI, 8.7-64.4 m). There were no differences in any secondary outcomes between groups (eg, adjusted difference in SF-12 physical component scores at 3 months: -1.9 points; 95% CI, -3.9 to 0.2 points). Based on inverse propensity score weighting, there was no effect of mHealth-CR on 6MWD among those who did not attend traditional CR (25.7 m; 95% CI, -8.7 to 60.2 m). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this randomized clinical trial of mHealth-CR vs usual care, mHealth-CR did not significantly increase 6MWD or result in improvements in secondary outcomes. The findings suggest the older adult population may require more age-tailored mHealth strategies to effectively improve outcomes. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT03978130.

PURPOSE/OBJECTIVE:Hypertension (HTN) is common and represents a major modifiable risk factor for ischemic heart disease in older adults. While home blood pressure monitoring (HBPM) is important in HTN management, patterns of HBPM engagement in older adults undergoing mobile health cardiac rehabilitation (mHealth-CR) are unknown. We aimed to identify patterns of adherence to HBPM in a cohort of older adults undergoing mHealth-CR to optimize HBPM use in the future. METHODS:We used interim data from the ongoing Rehabilitation using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting (RESILIENT) randomized trial, in which intervention arm participants (adults ≥ 65 years with ischemic heart disease) were instructed to monitor blood pressure (BP) at least weekly. Engagement groups were determined by latent class analysis and compared using ANOVA or Chi-Square tests. Longitudinal mixed effect modeling determined the associations between weekly HBPM and baseline covariates including uncontrolled HTN, obesity, diabetes, depression, alcohol, and tobacco use. RESULTS:Of the 111 participants, the mean age was 71.9 ± 5.6 years, and 83% had HTN. Over the 12-week study, mean HBPM engagement was 2.3 ± 2.3 d/wk. We observed 3 distinct patterns of engagement: high engagement (22%), gradual decline (10%), and sustained baseline engagement (68%). HBPM adherence decreased in two of the engagement groups over time. Of the covariates tested, only depression was associated with weekly HBPM after adjusting for relevant covariates (OR 9.09, P  = .03). CONCLUSIONS:In this older adult cohort undergoing mHealth-CR, we found three main engagement groups with declining engagement over time in two of the three groups. These patterns can inform future mHealth-CR interventions.

BACKGROUND/UNASSIGNED:Despite the exponential growth in telemedicine visits in clinical practice due to the COVID-19 pandemic, it remains unknown if telemedicine visits achieved similar adherence to prescribed medications as in-person office visits for patients with heart failure. OBJECTIVE/UNASSIGNED:Our study examined the association between telemedicine visits (vs in-person visits) and medication adherence in patients with heart failure. METHODS/UNASSIGNED:This was a retrospective cross-sectional study of adult patients with a diagnosis of heart failure or an ejection fraction of ≤40% using data between April 1 and October 1, 2020. This period was used because New York University approved telemedicine visits for both established and new patients by April 1, 2020. The time zero window was between April 1 and October 1, 2020, then each identified patient was monitored for up to 180 days. Medication adherence was measured by the mean proportion of days covered (PDC) within 180 days, and categorized as adherent if the PDC was ≥0.8. Patients were included in the telemedicine exposure group or in-person group if all encounters were video visits or in-person office visits, respectively. Poisson regression and logistic regression models were used for the analyses. RESULTS/UNASSIGNED:A total of 9521 individuals were included in this analysis (telemedicine visits only: n=830 in-person office visits only: n=8691). Overall, the mean age was 76.7 (SD 12.4) years. Most of the patients were White (n=6996, 73.5%), followed by Black (n=1060, 11.1%) and Asian (n=290, 3%). Over half of the patients were male (n=5383, 56.5%) and over half were married or living with partners (n=4914, 51.6%). Most patients' health insurance was covered by Medicare (n=7163, 75.2%), followed by commercial insurance (n=1687, 17.7%) and Medicaid (n=639, 6.7%). Overall, the average PDC was 0.81 (SD 0.286) and 71.3% (6793/9521) of patients had a PDC≥0.8. There was no significant difference in mean PDC between the telemedicine and in-person office groups (mean 0.794, SD 0.294 vs mean 0.812, SD 0.285) with a rate ratio of 0.99 (95% CI 0.96-1.02; P=.09). Similarly, there was no significant difference in adherence rates between the telemedicine and in-person office groups (573/830, 69% vs 6220/8691, 71.6%), with an odds ratio of 0.94 (95% CI 0.81-1.11; P=.12). The conclusion remained the same after adjusting for covariates (eg, age, sex, race, marriage, language, and insurance). CONCLUSIONS/UNASSIGNED:We found similar rates of medication adherence among patients with heart failure who were being seen via telemedicine or in-person visits. Our findings are important for clinical practice because we provide real-world evidence that telemedicine can be an approach for outpatient visits for patients with heart failure. As telemedicine is more convenient and avoids transportation issues, it may be an alternative way to maintain the same medication adherence as in-person visits for patients with heart failure.

Anxiety disorders are highly comorbid with sleep disturbance and have also been associated with deficits in emotion regulation, the ability to control and express emotions. However, the extent to which specific dimensions of sleep disturbance and emotion regulation are associated with anxiety diagnosis is not well-explored. This study examined dimensions of emotion regulation and sleep disturbance that may predict greater likelihood of anxiety diagnosis using novel machine learning techniques. Participants (Mean(SD) age= 28.6(11.3) years, 62.7% female) with primary anxiety disorders (n = 257), including generalized anxiety disorder (n = 122) and social anxiety disorder (n = 135), and healthy controls (n = 89) completed the Difficulties in Emotion Regulation Scale and Pittsburgh Sleep Quality Index. A conditional inference tree was fit to classify likelihood of current anxiety diagnosis based on predictors. The best model fit included 4 split nodes and 5 terminal nodes. Worse scores on two emotion regulation subscales, strategies directed to manage negative emotions and nonacceptance of negative emotions, were the best predictors of current anxiety diagnosis (99.3% probability of diagnosis). For those with better emotion regulation, poor sleep quality and worse daytime functioning due to sleep were important predictors of anxiety diagnosis. Good emotion regulation and non-disturbed sleep predicted high likelihood of being a non-psychiatric control (88.2%). Limitations include cross-sectional design precluding designating directionality of effects of sleep and emotion regulation on anxiety onset; limited sample size; and self-reported sleep. Facets of emotion regulation and sleep disturbance may be important early targets for brief intervention for anxiety disorders.

The COVID-19 pandemic was an unparalleled stressor that enhanced isolation. Loneliness has been identified as an epidemic by the US Surgeon General. This study aimed to: (1) characterize longitudinal trajectories of loneliness during the acute phase of the COVID-19 pandemic; (2) identify longitudinal mediators of the relationship of loneliness with anxiety and depression; and (3) examine how loneliness naturally clusters and identify factors associated with high loneliness. Two hundred and twenty-nine adults (78% female; mean age = 39.5 ± 13.8) completed an abbreviated version of the UCLA Loneliness Scale, Perceived Stress Scale, Emotion Regulation Questionnaire, State Anxiety Inventory, and Patient Health Questionnaire-8 longitudinally between April 2020 and 2021. Trajectory analyses demonstrated relatively stable loneliness over time, while anxiety and depression symptoms declined. Longitudinal analyses indicated that loneliness effects on anxiety and depression were both partially mediated by perceived stress, while emotion regulation capacity only mediated effects on anxiety. Three stable clusters of loneliness trajectories emerged (high, moderate, and low). The odds of moderate or high loneliness cluster membership were positively associated with higher perceived stress and negatively associated with greater cognitive reappraisal use. Our results demonstrate the important interconnections between loneliness and facets of mental health throughout the early phases of the pandemic and may inform targeted future interventions for loneliness work.

BACKGROUND:This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS:We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS:The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS:We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.