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.

OBJECTIVES: To determine the relationship between caregiver status and outcomes after durable left ventricular assist device (LVAD) implantation. BACKGROUND: The absence of a caregiver is a relative contraindication to durable LVAD support. METHODS: Forty-three patients that underwent primary LVAD implantation were divided into three groups: those with caregivers that retained their roles for the duration of LVAD support (CG group), those with caregivers that resigned their roles (CG-QUIT group), and those implanted without an assigned caregiver (No-CG group). Group-specific characteristics and post-implant outcomes were compared. RESULTS: In the CG-QUIT group, caregivers did not live in the same home (0.0% vs. 80.6% of CG group) and none were spouses (0.0% vs. 64.5% for CG group). Thirty-day readmission rate was highest in the CG-QUIT group (83.3% vs. 25.8% in CG group and 16.7% in No-CG group). CONCLUSIONS: Caregiver relationships were more likely to be maintained for the duration of LVAD support when the caregiver was a spouse and resided in the same home as the patient. Carefully selected patients implanted without a designated caregiver had comparable outcomes to those who retained their original caregiver.

PURPOSE: Driveline exit site (DLES) management following left ventricular assist device implantation is important for preventing driveline infection (DLI). While chlorhexidine gluconate (CHG) is generally recommended for DLES antisepsis, CHG intolerance can develop, resulting in a need for alternative antiseptics. We reviewed our institutional experience with DLES antisepsis methods in HeartMate II patients, comparing outcomes of patients with and without CHG intolerance. METHODS: Between October 2011 and March 2016, 44 patients underwent primary HeartMate II implantation. CHG was used for DLES antisepsis and povidone-iodine (PVP-I) was used in patients with CHG intolerance. DLI was defined by Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) criteria. RESULTS: Of 44 patients, 37 (84%) received CHG and 7 (16%) received PVP-I antisepsis due to CHG intolerance. Five patients (11.4%) developed a DLI, with an event per patient-year rate of 0.07. Median length of support was 521 days (interquartile range 202-881 days). Characteristics were similar between patients with and without DLI. However, a larger proportion of patients with DLI had CHG intolerance compared to patients without DLI (60.0% vs. 10.3%, p:0.05). Causative organisms were Staphylococcus aureus in CHG-intolerant patients and Stenotrophomonas and Acinetobacter in CHG-tolerant patients. Kaplan-Meier method and log-rank test demonstrated decreased infection-free days in patients using PVP-I rather than CHG (p:0.01). CONCLUSIONS: While the etiology of DLI is multifactorial, CHG intolerance appears to be a risk factor. Our findings highlight the need for larger studies comparing the efficacy of antiseptics for DLES care, particularly for patients with CHG contraindications.

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.

Heart failure continues to be a leading cause of morbidity and mortality throughout the United States. The pathophysiology of heart failure involves activation of complex neurohormonal pathways, many of which mediate not only hypertrophy and fibrosis within ventricular myocardium and interstitium, but also activation of platelets and alteration of vascular endothelium. Platelet activation and vascular endothelial dysfunction may contribute to the observed increased risk of thromboembolic events in patients with chronic heart failure. However, current data from clinical trials do not support routine use of chronic antiplatelet or oral anticoagulation therapy for ambulatory heart failure patients without other indications (atrial fibrillation and/or coronary artery disease) as the risk of bleeding seems to outweigh the potential benefit related to reduction in thromboembolic events. In this review, we consider the potential clinical utility of targeting specific pathophysiological mechanisms of platelet and vascular endothelial activation to guide clinical decision making in heart failure patients.