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Completed

NCT Number: NCT05835024

Acorai Machine Learning Generalization (MLG) Study

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.

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Key information

Conditions

Heart Failure Cardiovascular Diseases Heart Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Onze-Lieve-Vrouwziekenhuis (OLV) Hospital Aalst, Aalst, Belgium

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Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Acorai Sensor Data Collection (ASDC) system 1.0 - Phase 1

Device

A 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.

Acorai Sensor Data Collection (ASDC) system 1.0 - Phase 2

Device

A 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.

Primary outcomes

  1. 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

  1. Evaluation of the ML's performance to estimate other hemodynamic parameters

    Time frame: 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.

  2. Diagnostic accuracy of ML model

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Acorai AB

Industry

Registry information

Official study title

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

Acronym: CAPTURE-HF

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Apr 28, 2023
Registry last updated
Dec 3, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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