Acorai Machine Learning Generalization (MLG) Study
- Conditions
- Heart Failure
- Registration Number
- NCT05835024
- Lead Sponsor
- Acorai AB
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1602
- 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.
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Evaluation of the ML's performance to estimate pressure 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 Outcome Measures
Name Time Method Diagnostic accuracy of ML model Day 0 to Day 90 The diagnostic accuracy of the ML model trained on data collected from the ASDC System to detect clinically significant abnormal right heart catheterization measurements.
Evaluation of the ML's performance to estimate other hemodynamic parameters Day 0 to Day 90 The performance of the ML model trained on data collected from the ASDC System to estimate other hemodynamic parameters (such as right atrial pressure) compared to right heart catheterization.
Trial Locations
- Locations (18)
Conway Regional Cardiovascular Clinic
🇺🇸Conway, Arkansas, United States
Baptist Health Medical Centre
🇺🇸Little Rock, Arkansas, United States
Mayo Clinic
🇺🇸Jacksonville, Florida, United States
Cleveland Clinic Martin Health
🇺🇸Stuart, Florida, United States
Saint Luke's Hospital of Kansas City
🇺🇸Kansas City, Missouri, United States
WakeMed
🇺🇸Raleigh, North Carolina, United States
University of Pennsylvania
🇺🇸Philadelphia, Pennsylvania, United States
Medical University of South Carolina (MUSC)
🇺🇸Charleston, South Carolina, United States
Austin Heart Central and San Marcos
🇺🇸Austin, Texas, United States
Austin Heart Round Rock
🇺🇸Austin, Texas, United States
Scroll for more (8 remaining)Conway Regional Cardiovascular Clinic🇺🇸Conway, Arkansas, United States