Background
Remote hemodynamics-guided management of heart failure (HF) with implantable pulmonary artery pressure sensors has been shown to reduce HF hospitalizations. The widespread clinical adoption of this procedure is constrained by its invasive nature and high cost. We present a noninvasive technology based on a wearable sensor (CardioTag; Cardiosense) and machine learning (ML) for estimating pulmonary capillary wedge pressure (PCWP) in patients with heart failure with reduced ejection fraction (HFrEF).