Search

Media reports frequently cite observational studies and meta-analyses to promote the reputed cardiovascular benefits of moderate alcohol consumption; however, it is unclear whether public opinion or drinking behavior align with these reports. We administered an anonymous, single-center, 35-question, cross-sectional survey among patients hospitalized for acute cardiac illnesses from June to September 2019, who were eligible for cardiac rehabilitation. We assessed patient opinions toward alcohol use, perceptions of alcohol's health impact, and role of media in forming these beliefs. We hypothesized that drinking habits are associated with beliefs about the health benefits of alcohol consumption. Of 300 patients approached, 290 completed the survey (97%). Most (69%) reported having heard moderate alcohol use is heart healthy from 1 or more sources including: TV (61%), family/friends (33%), newspapers (21%), and the internet (10%); although, only 19% reported believing these reports. In total, 12 patients (4%) reported intentionally increasing alcohol intake because of the reported beneficial health effects. There was a strong association between binge drinking and increasing alcohol used to improve cardiac health (odds ratio 8.8, 95% confidence interval 2.7, 29). Given the known cardiotoxic effects of alcohol, particularly in large doses, strategies aimed at population-based education regarding the unhealthy cardiovascular impact of alcohol use is needed, especially among binge drinkers.

The purpose of this study was to test the hypothesis that an individualized exercise training target heart rate (HR) based on a maximal graded exercise test (GXT) is associated with greater improvements in exercise tolerance during cardiac rehabilitation (CR) compared with no GXT. In this retrospective study, we identified patients who completed 9 to 36 visits of CR between 2001 and 2016, with a length of stay ≤18 weeks and a visit frequency of 1 to 3 days per week. Patients were grouped based on whether their exercise was guided by a target HR determined from a GXT. To assess the relation between GXT and change in exercise training metabolic equivalents of task (METs), we used generalized linear models adjusted for age, gender, race, referral reason, CR visits, CR frequency, METs at start, CR location, and year of participation. Out of 4,455 patients (37% female, 48% White, median age = 62 years), 53% were prescribed a target HR based on a GXT. Compared with no GXT, a GXT was associated with a significantly greater increase in covariate-adjusted METs during CR and percentage change from start (+0.44 METs [95% confidence interval [CI] 0.38 to 0.51] and +17% [95% CI 14% to 19%], respectively). In a sensitivity analysis limited to patients with 24 to 36 visits at ≥2 days per week (n = 1,319), a GXT was associated with a significantly greater increase in covariate-adjusted exercise training METs (+0.51 [95% CI 0.36 to 0.66]; +19% [95% CI 13% to 24%]). In conclusion, to maximize the potential increase in exercise capacity during CR, patients should undergo a GXT to determine an individualized exercise training target HR.

Background: Clinical predication models for hospitalized patients with heart failure have good performance characteristics but require medical record review, which is both costly and time-consuming. In contrast, administrative claims data are readily available for research but have limited clinical detail. Methods: We used highly detailed billing data to derive a multivariable in-hospital mortality prediction model for patients with heart failure. We validated the model in a separate multi-hospital dataset derived from electronic health record (EHR) data. For the derivation, we included patients aged ≥18 years admitted to one of 433 hospitals that participate in the Premier, Inc. Data Warehouse (PDW) between January 2009 and June 2011. Patients with a principal ICD-9-CM diagnosis of heart failure or principal diagnosis of respiratory failure with secondary diagnosis of heart failure were included. After dividing the dataset into derivation and validation sets, we used generalized estimating equations to estimate the parameters of a generalized linear model that adjusted for clustering within hospitals. The model included selected patient demographics, comorbidities, history of prior heart failure admissions, and initial medications and therapies (e.g., inotropes, mechanical ventilation) administered in the first 2 hospital days. We applied the models to the validation set. Finally, we applied the coefficients from the model to a novel multi-hospital dataset, HealthFacts (Cerner Corp), which contains information about hospitalizations and is derived from the electronic health record of 76 participating sites (years 2010-2012). Results: In PDW, we identified 200,832 patients ≥18 years old with a diagnosis of heart failure. Of these, 80% (160,839) were randomly assigned to the derivation cohort and the remaining 20% (39,993) were assigned to the validation cohort. Mortality was 4.0%. In the derivation cohort, the model showed a c-statistic of 0.79 Factors that were most strongly associated with mortality included age over 80 years, early inotropes, early vasopressors, and diagnosis of acute kidney injury that was present on admission. In the validation cohort, the c-statistic was 0.79. We then validated the model in the HealthFacts dataset, which had a higher percentage of African American patients (28% vs. 17%). In this cohort, the c-statistic was 0.82. Conclusions: A heart failure mortality model based on detailed administrative data available in the first 2 hospital days had excellent performance characteristics in a derivation and an external, EHR-based validation dataset. When clinical data are not available, this model may be useful for severity adjustment in comparative effectiveness studies of heart failure patients.

Introduction: Approximately half of patients hospitalized with acute heart failure (AHF) have a preserved ejection fraction (HFpEF), for which there are few known treatments that improve clinical outcomes. We sought to examine initial clinical management in a large national cohort of patients with HFpEF compared to those with heart failure reduced ejection fraction (HFrEF). Methods: We identified patients aged ≥18 years admitted with AHF between January 2009 and June 2012 to hospitals that participate in the PREMIER, Inc. data warehouse. Patients with ICD-9-CM diagnosis codes consistent with HFpEF (428.30-428.33) and HFrEF (428.20-428.23) were included. Early treatments were defined as treatments on hospital day 0, 1 or 2. Results: We identified 52,913 patients with HFpEF and 65,502 patients with HFrEF at 427 hospitals (Table 1). HFpEF patients were more likely to be older, female with greater burden of comorbid disease. Diuretic use was similar, but patients with HFpEF were less likely to receive beta-blockers (68.8% vs. 76.6%, p-value < 0.0001), ACE-inhibitors (37.6% vs. 48.0%, p-value < 0.0001), vasodilators (7.4% vs. 8.1%, p-value < 0.0001), inotropes (2.1% vs. 7.0%, p-value < 0.0001) and were more likely to receive early non-invasive positive pressure ventilation (17.5% vs. 12.3% p-value < 0.0001) and early invasive mechanical ventilation (7.6% vs. 6.5% p-value < 0.0001). Covariate adjusted mean length of stay and in-hospital mortality were higher for HFrEF patients. Conclusions: In a large national cohort of patients hospitalized with AHF, there were several differences in the demographics, early treatment strategies and outcomes for HFpEF patients compared with HFrEF patients. Further work is needed to identify variation in the use of these treatments across hospitals and association with outcomes.

Background: Mortality prediction models, developed with the goal of improving risk stratification in hospitalized heart failure (HF) patients, show good performance characteristics in the datasets in which they were developed but have not been validated in external populations. Methods: We used a novel multi-hospital dataset [HealthFacts (Cerner Corp)] derived from the electronic health record (years 2010-2012). We examined the performance of four published HF inpatient mortality prediction models developed using data from: the Acute Decompensated Heart Failure National Registry (ADHERE), the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study, and the Get With the Guidelines-Heart Failure (GWTG-HF) registry. We compared to an administrative HF mortality prediction model (Premier model) that includes selected patient demographics, comorbidities, prior heart failure admissions, and therapies administered (e.g., inotropes, mechanical ventilation) in the first 2 hospital days. We also compared to a model that uses clinical data but is not heart failure-specific: the Laboratory-Based Acute Physiology Score (LAPS2). We included patients aged ≥18 years admitted with HF to one of 62 hospitals in the database. We applied all 6 models to the data and calculated the c-statistics. Results: We identified 13,163 patients ≥18 years old with a diagnosis of heart failure. Median age was 74 years; approximately half were women; 65% of patients were white and 27% were black. In-hospital mortality was 4.3%. Bland-Altman plots revealed that, at higher predicted mortality, the Premier model outperformed the clinical models. Discrimination of the models varied: ADHERE model (0.68); EFFECT (0.70); GWTG-HF, Peterson (0.69); GWTG-HF, Eapen (0.70); LAPS2 (0.74); Premier (0.81) (Figure). Conclusions: Clinically-derived inpatient heart failure mortality models exhibited similar performance with c statistics hovering around 0.70. A generic clinical mortality prediction model (LAPS2) had slightly better performance, as did a detailed administrative model. Any of these models may be useful for severity adjustment in comparative effectiveness studies of heart failure patients. When clinical data are not available, the administrative model performs similarly to clinical models.

Background: Prior studies suggest that cardiac rehabilitation (CR) attendance improves smoking cessation rates, but it is unclear if CR services can be credited for this beneficial impact, or, rather, if CR attendance is predicted by a successful smoking cessation attempt. Which comes first?