Artificial intelligence machine learning based evaluation of elevated left ventricular end-diastolic pressure: a Cleveland Clinic cohort study.

Background: Left ventricular end-diastolic pressure (LVEDP) is a key indicator of cardiac health. The gold-standard method of measuring LVEDP is invasive intra-cardiac catheterization. Echocardiography is used for non-invasive estimation of left ventricular (LV) filling pressures; however, correlation with invasive LVEDP is variable.

Contemporary Outcomes and Predictors of Inpatient Mortality for Infective Endocarditis Occurring in Renal Transplant Recipients in the United States.

Renal transplant (RT) recipients are susceptible to infections because of immunosuppression. The literature regarding the epidemiology and outcomes of infective endocarditis (IE) in RT recipients is limited. We analyzed the National Inpatient Sample in the United States to study IE in RT and identify risk factors for inpatient mortality and IE development in RT patients.

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.

Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease.