ADOPTS: Non-invasive Pulmonary Artery Prediction
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Catheterization Arm 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. |
Device: catheterization
Swan-Ganz catheterization (also called right heart catheterization or pulmonary artery catheterization) is the passing of a thin tube (catheter) into the right side of the heart and the arteries leading to the lungs. It is done to monitor the heart's function and blood flow and pressures in and around the heart.
|
Outcome Measures
Primary Outcome Measures
- The correlation of pulmonary artery pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm [the Swan-Ganz catheter obtains the pulmonary artery pressures for a minimum of 5 minutes.]
The primary objective of this study is to determine if a machine-learning algorithm with data from a wearable device can reproduce simultaneous pulmonary artery pressure obtained during right heart catheterization or data obtained from a Sawn Ganz catheter already in place in the setting of cardiac care unit admission.
- The correlation of pulmonary artery wedge pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm [the Swan-Ganz catheter obtains wedge pressures first for a minimum of 20 seconds (20-30 seconds).]
The second objective of this study is to determine if a machine-learning algorithm with data from a wearable device can reproduce simultaneous pulmonary artery wedge pressure obtained during right heart catheterization or data obtained from a Sawn Ganz catheter already in place in the setting of cardiac care unit admission.
Eligibility Criteria
Criteria
Inclusion Criteria:
-
Subjects age 18+ years
-
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:
-
Vulnerable population
-
Unable to consent for any reason
-
Unstable patient
-
Known skin reaction to latex or adhesives
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | PIH Good Samaritan Hospital | Los Angeles | California | United States | 90017 |
Sponsors and Collaborators
- Silverleaf Medical Sciences INC
- PIH Health Good Samaritan Hospital
Investigators
- Study Chair: Jianwei Zheng, Ph.D., Silverleaf Medical Sciences
- Principal Investigator: Ihab Alomari, Dr., PIH Good Samaritan Hospital
- Study Director: Islam Abudayyeh, Dr., Loma Linda University Health
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- ADOPTS_GOODSAM