Study to Determine if Novel Wearable Monitoring System and Machine-Learning Algorithm Can Model Continuous Pulmonary Artery Pressure Recordings in Human Subjects
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 25
- 试验地点
- 1
- 主要终点
- The correlation of pulmonary artery pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •Vulnerable population
- •Unable to consent for any reason
- •Unstable patient
- •Known skin reaction to latex or adhesives
结局指标
主要结局
The correlation of pulmonary artery pressure values measured by Sawn Gan catheter and that derived by a machine learning algorithm
时间窗: 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
时间窗: 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.
次要结局
未报告次要终点