DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR
Guglielmo, Marco; Penso, Marco; Carerj, Maria Ludovica; Giacari, Carlo Maria; Volpe, Alessandra; Fusini, Laura; Baggiano, Andrea; Mushtaq, Saima; Annoni, Andrea; Cannata, Francesco; Cilia, Francesco; Del Torto, Alberico; Fazzari, Fabio; Formenti, Alberto; Frappampina, Antonio; Gripari, Paola; Junod, Daniele; Mancini, Maria Elisabetta; Mantegazza, Valentina; Maragna, Riccardo; Marchetti, Francesca; Mastroiacovo, Giorgio; Pirola, Sergio; Tassetti, Luigi; Baessato, Francesca; Corino, Valentina; Guaricci, Andrea Igoren; Rabbat, Mark G.; Rossi, Alexia; Rovera, Chiara; Costantini, Pietro; van der Bilt, Ivo; van der Harst, Pim; Fontana, Marianna; Caiani, Enrico G.; Pepi, Mauro; Pontone, Gianluca
(2024) Atherosclerosis, volume 397
(Article)
Abstract
Background and aims: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. Methods: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or
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suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. Results: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903–10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765–7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822–10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045–1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336–1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. Conclusions: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.
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Keywords: Cardiac magnetic resonance, Cardiac segmentation, Coronary artery disease, Deep learning, Epicardial adipose tissue, Epicardial fat, Outcome, Cardiology and Cardiovascular Medicine, Journal Article
ISSN: 0021-9150
Publisher: Elsevier
Note: Publisher Copyright: © 2024 Elsevier B.V.
(Peer reviewed)