Establishing Care Post Discharge Following a Heart Failure Hospitalization in an Uninsured Heart Failure Population.

Multidisciplinary interprofessional outpatient care improves mortality for patients with heart failure (HF) but is underutilized. We sought to identify factors associated with not establishing outpatient care among uninsured individuals with HF. We included uninsured individuals referred to an interprofessional clinic after a hospitalization with HF from 2016 to 2019. The primary outcome was establishing care, defined as presenting to clinic within 7 days of discharge from the hospital. We constructed multivariable adjusted logistic regression models to identify predictors of establishing care. A total of 698 uninsured individuals were referred, of whom 583 (84%) established care. Mean age was 49.5 ± 11 years, 15% were rural-dwelling, 59% were black, and 31% were female. Black participants who were rural-dwelling (adusted odds ratio [aOR] 0.07, 95% confidence interval [CI] 0.03 to 0.17) or reported alcohol use (aOR 0.32, 95% CI 0.16 to 0.64) had lower odds of establishing care. White participants who were rural-dwelling (aOR 2.63, 95% CI 1.17 to 5.90) had higher odds of establishing care. Uninsured black individuals with HF who live in rural communities or who are active alcohol users represent a group that is at high risk of not establishing outpatient follow-up after a hospitalization with HF. Efforts to reduce this disparity are warranted to improve health outcomes in this population.

Outcomes of Individuals With and Without Heart Failure Presenting With Acute Coronary Syndrome.

Major adverse cardiac event (MACE) and bleeding risks following percutaneous coronary intervention (PCI) for acute coronary syndromes (ACS) are not well defined in individuals with heart failure (HF). We followed 1,145 individuals in the Pharmacogenomic Resource to improve Medication Effectiveness Genotype Guided Antiplatelet Therapy cohort for MACE and bleeding events following PCI for ACS. We constructed Cox proportional hazards models to compare MACE and bleeding in those with versus without HF, adjusting for sociodemographics, comorbidities, and medications. We also determined predictors of MACE and bleeding events in both groups. 370 (32%) individuals did and 775 (68%) did not have HF prior to PCI. Mean age was 61.7 ± 12.2 years, 31% were female, and 24% were African American. After a median follow-up of 0.78 years, individuals with HF had higher rates of MACE compared to those without HF (48 vs. 24 events per 100 person years) which remained significant after multivariable adjustment (hazard ratio [HR] 1.31, 95% confidence interval [CI] 1.00 to 1.72). Similarly, bleeding was higher in those with versus without HF (22 vs. 11 events per 100 person years), although this was no longer statistically significant after multivariable adjustment (HR 1.29, 95% CI 0.86 to 1.93). Diabetes and peripheral vascular disease were predictors of MACE, and end-stage renal disease was a predictor of bleeding among participants with HF. MACE risk is higher in individuals with versus without HF following PCI for ACS. However, the risk of bleeding, especially among those with end-stage renal disease , must be considered when determining post-PCI anticoagulant strategies.

Heart failure in an elderly man: where is that coronary?

Anomalous coronary arteries from the pulmonary artery are uncommon causes of heart failure in the adult population. This case demonstrates the unusual presentation in a patient with anomalous right coronary artery from the pulmonary artery and discusses the complex pathophysiology of this lesion and the role of guideline-directed medical therapy in the management of these patients.

The Use of Machine Learning for the Care of Hypertension and Heart Failure.

Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.