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.
Subject is at least 18 years of age at the time of screening.
Subject is willing and physically able to comply with the specified evaluations as per the protocol.
Subject is referred for invasive hemodynamic assessment with right heart catheterization.
Subject has provided written informed consent using the approved consent form.
Discretionary exclusion when the inclusion of a potential subject is not in their best interest or not in the interest of compliant performance of the study.
Patients enrolled for a right heart catheterization procedure as part of their standard of care.
Cohort
Prospective
Data collection
DeviceA supervised session that includes 10 minutes of patient information entry, 5 minutes of sensor recording time and 10 minutes of margin for setting up the system and patient, thus a total evaluation duration of approximately 25 minutes prior to the Right Heart Catheterization. No follow-up period for the subjects will be required for this clinical investigation. Patients will be followed as per standard of care, at the hospital. A single follow-up observational data collection will be conducted at 90 days, to collect the number of unplanned hospitalizations since the procedure visit. This does not involve active patient participation.
Evaluation of the ML's performance to estimate pressure
Performance of the Machine Learning model trained on data collected from the device to estimate left-sided filling pressure compared to right heart catheterization.
