Cardiac remote monitoring devices have expanded our ability to track physiological changes used in the diagnosis and management of patients with cardiac disease. Implantable remote monitoring technologies have been shown to predict heart failure events, and guide therapy to reduce heart failure hospitalizations. The CardioMEMs System, the most studied and established remote monitoring system, relies on a pulmonary artery implant for continuous PAP measurement. However, there are no commercially available wearable systems that can reproduce continuous PAP tracings.
This study aims to determine if a machine-learning algorithm with data from a wearable cardiac remote-monitoring system incorporating EKG, heart sounds, and thoracic impedance can reproduce a continuous PAP tracing obtained during right heart catheterization.
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