CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia (CERTAINTY)
Author
Krebs, JulianMansi, 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
Date
2021-11-22Journal
Scientific ReportsPublisher
Springer NatureType
Article
Metadata
Show full item recordAbstract
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 Studycine cardiac magnetic resonance
Arrhythmias, Cardiac
Artificial Intelligence
Risk Assessment
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http://hdl.handle.net/10713/17221ae974a485f413a2113503eed53cd6c53
10.1038/s41598-021-02111-7
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