Faculty Bibliography

Geriatric syndromes are common in hospitalized elders with heart failure (HF), but association with clinical outcomes is not well characterized. The purpose of this study (N = 289) was to assess presence of geriatric syndromes using Joint Commission-mandated measures, the Braden Scale (BS) and Morse Fall Scale (MFS), and to explore prognostic utility in hospitalized HF patients. Data extracted from the electronic medical record included sociodemographics, medications, clinical data, comorbid conditions, and the BS and MFS. The primary outcome of mortality was assessed using Social Security Death Master File. Statistical analysis included Cox proportional hazards models to assess association between BS and MFS scores and all-cause mortality with adjustment for known clinical prognostic factors. Higher risk BS and MFS scores were common in hospitalized HF patients, but were not independent predictors of survival. Further study of the clinical utility of these scores and other measures of geriatric syndromes in HF is warranted.

PURPOSE: The primary aim of this study was to assess the feasibility of an exercise counseling intervention for adults of diverse race/ethnicity with heart failure (HF) and to assess its potential for improving overall physical activity, functional capacity, and HF self-care. DESIGN: This study was a quasi-experimental, prospective, longitudinal cohort design. METHODS: Twenty adults were enrolled and completed the 6-minute walk and standardized instruments, followed by exercise counseling using motivational interviewing. Each received an accelerometer, hand weights, and a diary to record self-care behaviors. Participants were followed via phone for 12 weeks to collect step-counts, review symptoms, and plan the following week's step-goal. FINDINGS: Results indicate this intervention was feasible for most participants, and resulted in improvements in physical activity, functional capacity, and self-care behaviors. CONCLUSION/CLINICAL RELEVANCE: Brief exercise counseling may be an appropriate option to improve outcomes for stable patients with HF, and may be tailored to fit different settings.

Importance: Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective: To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. Design, Setting, and Participants: We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data. Main Outcomes and Measures: Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records. Results: A total of 47119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusions and Relevance: The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

OBJECTIVES: This study sought to determine whether racial and ethnic differences exist among patients with similar access to care. We examined outcomes after heart failure hospitalization within a large municipal health system. BACKGROUND: Racial and ethnic disparities in heart failure outcomes are present in administrative data, and one explanation is differential access to care. METHODS: We performed a retrospective cohort study of 8,532 hospitalizations of adults with heart failure at 11 hospitals in New York City from 2007 to 2010. Primary exposure was ethnicity and race, and outcomes were 30- and 90-day readmission and 30-day and 1-year mortality rates. Generalized estimating equations were used to test for associations between ethnicity and race and outcomes with covariate adjustment. RESULTS: Of the number of hospitalizations included, 4,305 (51%) were for blacks, 2,449 (29%) were for Hispanics, 1,494 (18%) were for whites, and 284 (3%) were for Asians. Compared to whites, blacks and Asians had lower 1-year mortality, with adjusted odds ratios (aORs) of 0.75 (95% confidence interval [CI]: 0.59 to 0.94) and 0.57 (95% CI: 0.38 to 0.85), respectively, and rates for Hispanics were not significantly different (aOR: 0.81; 95% CI: 0.64 to 1.03). Hispanics had higher odds of readmission than whites (aOR: 1.27; 95% CI: 1.03 to 1.57) at 30 (aOR: 1.40; 95% CI: 1.15 to 1.70) and 90 days. Blacks had higher odds of readmission than whites at 90 days (aOR:1.21; 95% CI: 1.01 to 1.47). CONCLUSIONS: Racial and ethnic differences in outcomes after heart failure hospitalization were present within a large municipal health system. Access to a municipal health system may not be sufficient to eliminate disparities in heart failure outcomes.