Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.

Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.

Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.

ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure.

Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary Wedge Pressure (mPCWP), which is ideally measured invasively. Accurate non-invasive estimates of the mPCWP in patients with heart failure would help identify individuals at the greatest risk of a HF exacerbation.

Incidence and implications of idiopathic thrombocytopenia following transcatheter aortic valve replacement with the Edwards Sapien(©) valves: a single center experience.

Objectives: To determine the incidence and etiology of thrombocytopenia following transcatheter Aortic valve replacement (TAVR).

Background: the use of TAVR in the United States has grown rapidly. Anecdotally, thrombocytopenia following TAVR with the Sapien valves has been observed, though little is known about this phenomenon.