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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Arterial-Ventricular Coupling Impairment is Evidenced in Both Normal and Ischemic Subjects by Applying Cluster

N Aguirre, L J Cymberknop, I Farro

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed

    Insights

    Cluster Analysis (CA) effectively identifies arterial-ventricular coupling (AVC) impairments in patients using Stress Echocardiography (SE) data. This machine learning approach aids in distinguishing between normal and ischemic conditions based on cardiac performance.

    Area of Science:

    • Cardiovascular Physiology
    • Medical Imaging
    • Machine Learning

    Background:

    • Arterial-ventricular coupling (AVC) is crucial for cardiovascular performance and cardiac energetics.
    • Stress Echocardiography (SE) is a key tool for diagnosing and stratifying coronary artery disease (CAD) risk.
    • Cluster Analysis (CA) is an unsupervised machine learning technique for identifying natural groupings within data.

    Purpose of the Study:

    • To assess the capability of Cluster Analysis (CA) in identifying arterial-ventricular coupling (AVC) impairments.
    • To determine if CA can identify distinct patient groups with ischemic conditions using baseline Stress Echocardiography (SE) data.

    Main Methods:

    • Cluster Analysis (CA) was applied to SE data from baseline and peak exercise (PE) conditions.
    • Obtained clusters were analyzed for arterial-ventricular coupling (AVC) status and left ventricular (LV) wall motion abnormalities.
    • CA was validated using PE data to confirm findings from baseline analysis.

    Main Results:

    • Significant differences in AVC were observed between clusters based on baseline data and wall motion changes.
    • The CA approach successfully identified distinct groups with varying AVC conditions.
    • Findings were corroborated by CA applied to peak exercise data, confirming AVC alterations.

    Conclusions:

    • Cluster Analysis (CA) effectively identifies arterial-ventricular coupling (AVC) impairments.
    • CA can distinguish between normal and ischemic conditions based on SE data.
    • This machine learning method offers a novel approach to analyzing cardiovascular performance and identifying disease.
    Abstract

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