Faculty Bibliography

OBJECTIVES/OBJECTIVE:The goal of this study was to evaluate the prevalence, characteristics, and outcomes of patients with advanced heart failure (HF) in a geographically defined population. BACKGROUND:Some patients with HF progress to advanced HF, characterized by debilitating HF symptoms refractory to therapy. Limited data are available on the epidemiology and outcomes of patients with advanced HF. METHODS:This was a population-based cohort study of all Olmsted County, Minnesota, adults with and without HF from 2007 to 2017. The 2018 European Society of Cardiology advanced HF diagnostic criteria were operationalized and applied to all patients with HF. Hospitalization and mortality in advanced HF, overall and according to ejection fraction (EF) type (reduced EF <40% [HFrEF], mid-range EF 40%-49% [HFmrEF], and preserved EF ≥50% [HFpEF]) were examined using Andersen-Gill and Cox models. RESULTS:Of 6,836 adults with HF, 936 (13.7%) met criteria for advanced HF. The prevalence of advanced HF increased with age and was higher in men. At advanced HF diagnosis, 396 (42.3%) patients had HFrEF, 134 (14.3%) had HFmrEF, and 406 (43.4%) had HFpEF. The median (interquartile range) time from advanced HF diagnosis to death was 12.2 months (3.7-29.9 months). The mean rate of hospitalization was 2.91 (95% CI: 2.78-3.06) per person-year in the first year after advanced HF diagnosis. There were no differences in risks of all-cause mortality or hospitalization by EF. Patients with advanced HFpEF were at lower risk for cardiovascular mortality compared with advanced HFrEF (HR: 0.79; 95% CI: 0.65-0.97). CONCLUSIONS:In this population-based study, more than one-half of patients with advanced HF had mid-range or preserved EF, and survival was poor regardless of EF.

Objectives/UNASSIGNED:To identify ICD-10-CM diagnostic codes associated with the social determinants of health (SDOH), determine frequency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code. Design/UNASSIGNED:Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indicator for homelessness. Main Outcome Measures/UNASSIGNED:Any interruption in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identification numbers (CIN) for continuity. Methods/UNASSIGNED:Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017. Results/UNASSIGNED:ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrated linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016-2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experienced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn. Conclusions/UNASSIGNED:Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness.

BACKGROUND:Intensive glycemic control is of unclear benefit and carries increased risk for older adults with diabetes. The American Geriatrics Society's (AGS) Choosing Wisely (CW) guideline promotes less aggressive glycemic targets and reduction in pharmacologic therapy for older adults with type II diabetes. Meanwhile, behavioral economic (BE) approaches offer promise in influencing hard-to-change behavior, and previous studies have shown the benefits of using electronic health record (EHR) technology to encourage guideline adherence. OBJECTIVE:This study aimed to develop and pilot test an intervention that leverages BE with EHR technology to promote appropriate diabetes management in older adults. DESIGN/METHODS:A pilot study within the New York University Langone Health (NYULH) EHR and Epic system to deliver BE-inspired nudges at five NYULH clinics at varying time points from July 12, 2018, through October 31, 2019. PARTICIPANTS/METHODS:Clinicians across five practices in the NYULH system whose patients were older adults (age 76 and older) with type II diabetes. INTERVENTIONS/METHODS:A BE-EHR module comprising six nudges was developed through a series of design workshops, interviews, user-testing sessions, and clinic visits. BE principles utilized in the nudges include framing, social norming, accountable justification, defaults, affirmation, and gamification. MAIN MEASURES/METHODS:Patient-level CW compliance. KEY RESULTS/RESULTS:CW compliance increased 5.1% from a 16-week interval at baseline to a 16-week interval post intervention. From February 14 to June 5, 2018 (prior to the first nudge launch in Vanguard clinics), CW compliance for 1278 patients was mean (95% CI)-16.1% (14.1%, 18.1%). From July 3 to October 22, 2019 (after BE-EHR module launch at all five clinics), CW compliance for 680 patients was 21.2% (18.1%, 24.3%). CONCLUSIONS:The BE-EHR module shows promise for promoting the AGS CW guideline and improving diabetes management in older adults. A randomized controlled trial will commence to test the effectiveness of the intervention across 66 NYULH clinics. NIH TRIAL REGISTRY NUMBER/UNASSIGNED:NCT03409523.

OBJECTIVE:The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico. METHODS:Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients. RESULTS:Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio, 1.57; 95% confidence interval &lsqb;CI], 1.43 to 1.72). The intervention was associated with an increase in emergency department (ED) visits (rate ratio, 1.30; 95% CI, 1.04 to 1.63) but no change in hospitalizations (rate ratio, 1.47; 95% CI, 0.95 to 2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI, 4.0% to 13.6%). We found similar increases in use of statins (DID, 8.5%; 95% CI, 3.2% to 13.8%), angiotensin-converting enzyme inhibitors (DID, 9.5%; 95% CI, 3.5% to 15.4%), or antidepressant therapies (DID, 9.4%; 95% CI, 1.1% to 18.1%). CONCLUSION/CONCLUSIONS:Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization.

Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of cases is challenging because of the heterogeneous nature of the disease, which encompasses various phenotypes that may respond differently to treatment. The increasing availability of both structured and unstructured data in the EHR has expanded opportunities for cohort construction. This article reviews the current literature on approaches to identification of heart failure, and looks toward the future of machine learning, big data, and phenomapping.