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    CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia (CERTAINTY)

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    Author
    Krebs, Julian
    Mansi, Tommaso
    Delingette, Hervé
    Lou, Bin
    Lima, Joao A C
    Tao, Susumu
    Ciuffo, Luisa A
    Norgard, Sanaz
    Butcher, Barbara
    Lee, Wei H
    Chamera, Ela
    Dickfeld, Timm-Michael
    Stillabower, Michael
    Marine, Joseph E
    Weiss, Robert G
    Tomaselli, Gordon F
    Halperin, Henry
    Wu, Katherine C
    Ashikaga, Hiroshi
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    Date
    2021-11-22
    Journal
    Scientific Reports
    Publisher
    Springer Nature
    Type
    Article
    
    Metadata
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    See at
    https://doi.org/10.1038/s41598-021-02111-7
    Abstract
    Better models to identify individuals at low risk of ventricular arrhythmia (VA) are needed for implantable cardioverter-defibrillator (ICD) candidates to mitigate the risk of ICD-related complications. We designed the CERTAINTY study (CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia) with deep learning for VA risk prediction from cine cardiac magnetic resonance (CMR). Using a training cohort of primary prevention ICD recipients (n = 350, 97 women, median age 59 years, 178 ischemic cardiomyopathy) who underwent CMR immediately prior to ICD implantation, we developed two neural networks: Cine Fingerprint Extractor and Risk Predictor. The former extracts cardiac structure and function features from cine CMR in a form of cine fingerprint in a fully unsupervised fashion, and the latter takes in the cine fingerprint and outputs disease outcomes as a cine risk score. Patients with VA (n = 96) had a significantly higher cine risk score than those without VA. Multivariate analysis showed that the cine risk score was significantly associated with VA after adjusting for clinical characteristics, cardiac structure and function including CMR-derived scar extent. These findings indicate that non-contrast, cine CMR inherently contains features to improve VA risk prediction in primary prevention ICD candidates. We solicit participation from multiple centers for external validation.
    Rights/Terms
    © 2021. The Author(s).
    Keyword
    CERTAINTY Study
    cine cardiac magnetic resonance
    Arrhythmias, Cardiac
    Artificial Intelligence
    Risk Assessment
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/17221
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-021-02111-7
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