Non-invasive Pulmonary Artery Prediction (ADOPTS)

Silverleaf Medical Sciences

Status: Enrolling

Conditions: Heart Failure; Pulmonary Arterial Hypertension

Treatments: Device: catheterization

Study type: Observational

Funder types: Industry; Other

Identifiers

NCT05622695

ADOPTS_GOODSAM

Details and patient eligibility

About

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.

Enrollment: 25 estimated patients

Sex: All

Ages: 20+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Subjects age 18+ years
2. Undergoing a right heart cardiac catheterization or in the cardiac care unit with active monitoring using an arterial line or Swan-Ganz catheter.

Exclusion criteria

1. Vulnerable population
2. Unable to consent for any reason
3. Unstable patient
4. Known skin reaction to latex or adhesives

Trial design

25 participants in 1 patient group

Catheterization Arm

Description:

Participants will be limited to adults older than 18 years of age, able to consent, planned for the cardiac catheterization lab for a right heart catheterization or in the cardiac care unit with an existing arterial line or Swan-Ganz catheter actively measuring the pulmonary artery pressure on a continuous basis.

Treatment:

Device: catheterization