Clinical trial NCT05622695: Non-invasive Pulmonary Artery Prediction
Official title: Study to Determine if Novel Wearable Monitoring System and Machine-Learning Algorithm Can Model Continuous Pulmonary Artery Pressure Recordings in Human Subjects
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Heart Failure
Pulmonary Arterial Hypertension
Lead sponsor: Silverleaf Medical Sciences INC
Sponsor class: INDUSTRY
Collaborators: name: PIH Health Good Samaritan Hospital
First posted: 2022-11-21
Enrollment: 25
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults with heart failure conditions.

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.


Eligibility criteria: 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

Primary outcomes:
measure: The correlation of pulmonary artery pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm
timeFrame: the Swan-Ganz catheter obtains the pulmonary artery pressures for a minimum of 5 minutes.
measure: The correlation of pulmonary artery wedge pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm
timeFrame: the Swan-Ganz catheter obtains wedge pressures first for a minimum of 20 seconds (20-30 seconds).
Secondary outcomes:
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: PIH Good Samaritan Hospital
state: California
Study officials:
affiliation: Silverleaf Medical Sciences
name: Jianwei Zheng, Ph.D.
role: STUDY_CHAIR
affiliation: PIH Good Samaritan Hospital
name: Ihab Alomari, Dr.
role: PRINCIPAL_INVESTIGATOR
affiliation: Loma Linda University Health
name: Islam Abudayyeh, Dr.
role: STUDY_DIRECTOR
References:
citation: Zheng J, Abudayyeh I, Mladenov G, Struppa D, Fu G, Chu H, Rakovski C. An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure. Front Cardiovasc Med. 2022 Aug 24;9:855356. doi: 10.3389/fcvm.2022.855356. eCollection 2022.