Faculty Bibliography

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.

OBJECTIVES: The aim of this study was to characterize the association between decongestion therapy and 30-day outcomes in patients hospitalized for heart failure (HF). BACKGROUND: Loop diuretic agents are commonly prescribed for the treatment of symptomatic congestion in patients hospitalized for HF, but the association between loop diuretic agent dose response and post-discharge outcomes has not been well characterized. METHODS: Cox proportional hazards models were used to estimate the association among average loop diuretic agent dose, congestion status at discharge, and 30-day post-discharge all-cause mortality and HF rehospitalization in 3,037 subjects hospitalized with worsening HF enrolled in the EVEREST (Efficacy of Vasopressin Antagonism in Heart Failure: Outcome Study With Tolvaptan) study. RESULTS: In univariate analysis, subjects exposed to high-dose diuretic agents (>/=160 mg/day) had greater risk for the combined outcome than subjects exposed to low-dose diuretic agents (18.9% vs. 10.0%; hazard ratio: 2.00; 95% confidence interval: 1.64 to 2.46; p < 0.0001). After adjustment for pre-specified covariates of disease severity, the association between diuretic agent dose and outcomes was not significant (hazard ratio: 1.11; 95% confidence interval: 0.89 to 1.38; p = 0.35). Of the 3,011 subjects with clinical assessments of volume status, 2,063 (69%) had little or no congestion at hospital discharge. Congestion status at hospital discharge did not modify the association between diuretic agent exposure and the combined endpoint (p for interaction = 0.84). CONCLUSIONS: Short-term diuretic agent exposure during hospital treatment for worsening HF was not an independent predictor of 30-day all-cause mortality and HF rehospitalization in multivariate analysis. Congestion status at discharge did not modify the association between diuretic agent dose and clinical outcomes.

STUDY OBJECTIVE: Observation unit admissions have been increasing, a trend that will likely continue because of recent changes in reimbursement policies. The purpose of this study is to determine the effect of the availability of observation units on hospitalizations and discharges to home for emergency department (ED) patients. METHODS: We studied ED visits with a final diagnosis of chest pain in the National Hospital Ambulatory Medical Care Survey from 2007 to 2010. ED visits that resulted in an observation unit admission were propensity-score matched to visits at hospitals without an observation unit. We used logistic regression to develop a prediction model for hospitalization versus discharge home for matched patients treated at nonobservation hospitals. The model was applied to matched observation unit patients to determine the likely alternative disposition had the observation unit not been available. RESULTS: There were 1,325 eligible visits that represented 5,079,154 visits in the United States. Two hundred twenty-seven visits resulted in an observation unit admission. The predictive model for hospitalization had a c statistic of 0.91; variables significantly associated with subsequent hospitalization included age, history of coronary atherosclerosis, systolic blood pressure less than 115 beats/min, and administration of antianginal medications. When the model was applied to matched observation unit patients, 49.9% of them were categorized as discharge home likely. CONCLUSION: In this study, we estimated that half of ED visits for chest pain that resulted in an observation unit admission were made by patients who may have been discharged home had the observation unit not been available. Increased availability of observation units may result in both decreased hospitalizations and decreased discharges to home.

BACKGROUND: Comorbid diabetes is common in heart failure and associated with increased hospitalization and mortality. Nonetheless, the association between glycemic control and outcomes among patients with heart failure and diabetes remains poorly characterized, particularly among low income and minority patients. METHODS: We performed a retrospective cohort study of outpatients with heart failure and diabetes in the New York City Health and Hospitals Corporation, the largest municipal health care system in the United States. Cox proportional hazard models were used to measure the association between HbA1c levels and outcomes of all-cause hospitalization, heart failure hospitalization, and mortality. RESULTS: Of 4723 patients with heart failure and diabetes, 42.6 % were black, 30.5 % were Hispanic/Latino, 31.4 % were Medicaid beneficiaries and 22.9 % were uninsured. As compared to patients with an HbA1c of 8.0-8.9 %, patients with an HbA1c of <6.5, 6.5-6.9, 7.0-7.9, and >/=9.0 % had an adjusted hazard ratio (aHR) (95 % CI) for all-cause hospitalization of 1.03 (0.90-1.17), 1.05 (0.91-1.22), 1.03 (0.90-1.17), and 1.13 (1.00-1.28), respectively. An HbA1c >/= 9.0 % was also associated with an increased risk of heart failure hospitalization (aHR 1.33; 95 % CI 1.11-1.59) and a non-significant increased risk in mortality (aHR 1.20; 95 % CI 0.99-1.45) when compared to HbA1c of 8.0-8.9 %. CONCLUSIONS: Among a cohort of primarily minority and low income patients with heart failure and diabetes, an increased risk of hospitalization was observed only for an HbA1c greater than 9 %.