BACKGROUND: Significant variability in outcomes after left ventricular assist device (LVAD) implantation emphasize the importance of accurately assessing patients' risk before surgery. This study assesses the Machine Learning Assessment of Risk and E...
BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease.
BACKGROUND: Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise.
BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: ...
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...
AIMS: To evaluate the effects of MitraClip on left ventricular (LV) and left atrial (LA) myocardial wall stress as assessed with the use of N-terminal pro-B-type natriuretic peptide (NT-proBNP) and strain imaging.
BACKGROUND: Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute de...