Faculty Bibliography

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.

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:Sarcopenia has been associated with adverse postoperative outcomes in older age cohorts, but has not been assessed in older adults with inflammatory bowel disease (IBD). Further, current assessments of sarcopenia among all aged individuals with IBD have used various measures of muscle mass as well as cutoffs to define its presence, leading to heterogeneous findings. METHODS:In this single-institution, multihospital retrospective study, we identified all patients aged 60 years and older with IBD who underwent disease-related intestinal resection between 2012 and 2022. Skeletal Muscle Index (SMI) and Total Psoas Index (TPI) were measured at the superior L3 endplate on preoperative computed tomography scans and compared through receiver operating characteristic curve. We then performed multivariable logistic regression to assess risk factors associated with an adverse 30-day postoperative outcome. Our primary outcome included a 30-day composite of postoperative mortality and complications, including infection, bleeding, cardiac event, cerebrovascular accident, acute kidney injury, venous thromboembolism, reoperation, all-cause rehospitalization, and need for intensive care unit-level care. RESULTS:A total of 120 individuals were included. Overall, 52% were female, 40% had ulcerative colitis, 60% had Crohn's disease, and median age at time of surgery was 70 years (interquartile range: 65-75). Forty percent of older adults had an adverse 30-day postoperative outcome, including infection (23%), readmission (17%), acute kidney injury (13%), bleeding (13%), intensive care unit admission (10%), cardiac event (8%), venous thromboembolism (7%), reoperation (6%), mortality (5%), and cerebrovascular accident (2%). When evaluating the predictive performance of SMI vs TPI for an adverse 30-day postoperative event, SMI had a significantly higher area under the curve of 0.66 (95% CI, 0.56-0.76) as compared to 0.58 (95% CI, 0.48-0.69) for TPI (P = .02). On multivariable logistic regression, prior IBD-related surgery (adjusted odds ratio [adjOR] 6.46, 95% CI, 1.85-22.51) and preoperative sepsis (adjOR 5.74, 95% CI, 1.36-24.17) significantly increased the odds of adverse postoperative outcomes, whereas increasing SMI was associated with a decreased risk of an adverse postoperative outcome (adjOR 0.88, 95% CI, 0.82-0.94). CONCLUSIONS:Sarcopenia, as measured by SMI, is associated with an increased risk of postoperative complications among older adults with IBD. Measurement of SMI from preoperative imaging can help risk stratify older adults with IBD undergoing intestinal resection.

OBJECTIVE:To compare anonymized and non-anonymized approaches for imputing race and ethnicity in descriptive studies of chronic disease burden using electronic health record (EHR)-based datasets. STUDY SETTING AND DESIGN/METHODS:In this New York City-based study, we first conducted simulation analyses under different missing data mechanisms to assess the performance of Bayesian Improved Surname Geocoding (BISG), single imputation using neighborhood majority information, random forest imputation, and multiple imputation with chained equations (MICE). Imputation performance was measured using sensitivity, precision, and overall accuracy; agreement with self-reported race and ethnicity was measured with Cohen's kappa (κ). We then applied these methods to impute race and ethnicity in two EHR-based data sources and compared chronic disease burden (95% CIs) by race and ethnicity across imputation approaches. DATA SOURCES AND ANALYTIC SAMPLE/UNASSIGNED:Our data sources included EHR data from NYU Langone Health and the INSIGHT Clinical Research Network from 3/6/2016 to 3/7/2020 extracted for a parent study on older adults in NYC with multiple chronic conditions. PRINCIPAL FINDINGS/RESULTS: = 0.33). When these methods were applied to the NYU and INSIGHT cohorts, however, racial and ethnic distributions and chronic disease burden were consistent across all imputation methods. Slight improvements in the precision of estimates were observed under all imputation approaches compared to a complete case analysis. CONCLUSIONS:BISG imputation may provide a more accurate racial and ethnic classification than single or multiple imputation using anonymized covariates, particularly if the missing data mechanism is MNAR. Descriptive studies of disease burden may not be sensitive to methods for imputing missing 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.

OBJECTIVE:This study examined associations between access to leisure-time physical activity (LTPA) facilities and parks and repeated measures of glycated hemoglobin (A1C) over time, using follow-up tests among United States Veterans with newly diagnosed type-2 diabetes (T2D). METHODS:Data were analyzed from 274,463 patients in the Veterans Administration Diabetes Risk cohort who were newly diagnosed with T2D between 2008 and 2018 and followed through 2023. Generalized estimating equations with a logit link function and binomial logistic regression were used to examine associations. RESULTS:Patients were on average 60.5 years of age, predominantly male (95.0 %) and white (66.9 %), and had an average of 11.7 A1C tests during the study follow-up period. In high- and low-density urban communities, a one-unit higher LTPA facility density score was associated with 1 % and 3 % greater likelihood of in-range A1C tests during follow-up, respectively, but no association was observed among patients living in suburban/small town and rural communities. Across community types, closer park distance was not associated with subsequent greater odds of in-range A1C tests. Unexpectedly, in low-density urban areas, the likelihood of in-range A1C tests was 1 % lower at farther park distances. CONCLUSIONS:These results suggest that broader access to LTPA facilities, but not park proximity, may contribute in small ways to maintaining glycemic control after T2D diagnosis in urban communities. Tailored interventions may be needed to promote patients' effective use of LTPA facilities and parks.

BACKGROUND:Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7-October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City. METHODS:This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network (n=276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits. RESULTS:Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients (P < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age. CONCLUSIONS:This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.

OBJECTIVES/OBJECTIVE:Despite significant therapeutic advances in psoriatic arthritis (PsA), many patients do not achieve remission and cycle through multiple biologic (b)- or targeted synthetic (ts)- DMARDs. Identifying the underlying reasons for repetitive therapeutic failure remains a knowledge gap. Here we describe prescribing patterns and characteristics of PsA patients with multi-b/tsDMARD failure at the NYU Psoriatic Arthritis Center. METHODS:Nine hundred sixty PsA patients were enrolled in an observational, longitudinal registry. Demographics, medical history, medication use, and psoriatic disease phenotype were collected. Multi-b/tsDMARD failure was defined as requiring ≥ 4 b/tsDMARDs. RESULTS:Seven hundred twenty-five patients (75%) used ≥ 1 b/tsDMARD during their disease course. The initial b/tsDMARDs prescribed were predominately anti-TNF agents. 166 (17%) patients had multi-b/tsDMARD failure. Compared to those requiring 1 b/tsDMARD, female sex (OR 2.3; 95%CI 1.4-3.8), axial disease (OR 2.1; 95% CI 1.2-3.6), depression (OR 2.0; 95%CI 1.1-3.7), and obesity (OR 1.7; 95%CI 1.0-2.8) were risk factors for multi-b/tsDMARD failure disease after adjustment for age, disease duration, sex, depression, smoking, obesity, and skin severity. Patients with multi-b/tsDMARD failure PsA also had increased disease activity at their clinical visit (i.e., swollen joint count, p = 0.005). CONCLUSION/CONCLUSIONS:In this cohort, 17% patients with PsA experienced multi-b/tsDMARD failure. These patients were more likely to be female, obese, and have higher rates of axial involvement and depression, along with higher active disease activity. This highlights the inflammatory and non-inflammatory drivers of multiple therapeutic failures, underscoring the need for precision medicine strategies and potential non-pharmacologic adjuvant therapies for patients with PsA to improve outcomes and quality of life.

Mandatory prescription drug monitoring programs and cannabis legalization have been hypothesized to reduce overdose deaths. We examined associations between prescription monitoring programs with access mandates ("must-query PDMPs"), legalization of medical and recreational cannabis supply, and opioid overdose deaths in United States counties in 2013-2020. Using data on overdose deaths from the National Vital Statistics System, we fit Bayesian spatiotemporal models to estimate risk differences and 95% credible intervals (CrI) in county-level opioid overdose deaths associated with enactment of these state policies. Must-query PDMPs were independently associated with on average 0.8 (95% CrI: 0.5, 1.0) additional opioid-involved overdose deaths per 100,000 person-years. Legal cannabis supply was not independently associated with opioid overdose deaths in this time period. Must-query PDMPs enacted in the presence of legal (medical or recreational) cannabis supply were associated with 0.7 (95% CrI: 0.4, 0.9) more opioid-involved deaths, relative to must-query PDMPs without any legal cannabis supply. In a time when overdoses are driven mostly by non-prescribed opioids, stricter opioid prescribing policies and more expansive cannabis legalization were not associated with reduced overdose death rates.

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.