A Multi-Site Observational Clinical Investigation to Collect Non-invasive Sensor Data During a Right Heart Catheterization and Train Machine Learning Models to Estimate Intracardiac Hemodynamic Parameters Evaluation of a Novel maChine leArning Model's Performance for Non-invasive inTracardiac pressURE Monitoring in Heart Failure - The CAPTURE-HF Trial
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Heart Failure
- Sponsor
- Acorai AB
- Enrollment
- 1602
- Locations
- 18
- Primary Endpoint
- Evaluation of the ML's performance to estimate pressure
- Status
- Completed
- Last Updated
- last year
Overview
Brief Summary
Acorai is developing a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters in patients with suspected or confirmed heart failure, and/or pulmonary hypertension, who require hemodynamic assessment. The device will be intended as a companion test or clinical decision support tool to be used and interpreted by qualified healthcare professionals to aid standard-of-care clinical assessment in identifying hemodynamic congestion and supporting personalized treatment of heart failure and pulmonary congestion.
This study is part of the development of a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters. It will be conducted to collect the data needed to train the machine learning models retrospectively.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Subject is, at least, 18 years of age at the time of screening visit.
- •Subject is willing and physically able to comply with the specified evaluations as per the clinical investigation plan, as assessed by the investigator.
- •Subject is referred for invasive hemodynamic assessment with right heart cardiac catheterization.
- •Patient has provided written informed consent using the Ethics Committee/ Institutional Review Board approved consent form.
Exclusion Criteria
- •Discretionary exclusion when, in the opinion of the investigator, the inclusion of a potential subject is not in their best interest or not in the interest of compliant performance of the clinical investigation.
- •Subjects who are pregnant are excluded in the US
Outcomes
Primary Outcomes
Evaluation of the ML's performance to estimate pressure
Time Frame: Day 0 to Day 90
Performance of the ML model trained on data collected from the ASDC System to estimate left-sided filling pressure compared to right heart catheterization.
Secondary Outcomes
- Diagnostic accuracy of ML model(Day 0 to Day 90)
- Evaluation of the ML's performance to estimate other hemodynamic parameters(Day 0 to Day 90)