DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR

Marco Guglielmo, Marco Penso, Maria Ludovica Carerj, Carlo Maria Giacari, Alessandra Volpe, Laura Fusini, Andrea Baggiano, Saima Mushtaq, Andrea Annoni, Francesco Cannata, Francesco Cilia, Alberico Del Torto, Fabio Fazzari, Alberto Formenti, Antonio Frappampina, Paola Gripari, Daniele Junod, Maria Elisabetta Mancini, Valentina Mantegazza, Riccardo MaragnaFrancesca Marchetti, Giorgio Mastroiacovo, Sergio Pirola, Luigi Tassetti, Francesca Baessato, Valentina Corino, Andrea Igoren Guaricci, Mark G. Rabbat, Alexia Rossi, Chiara Rovera, Pietro Costantini, Ivo van der Bilt, Pim van der Harst, Marianna Fontana, Enrico G. Caiani, Mauro Pepi, Gianluca Pontone*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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 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.

Original languageEnglish
Article number117549
JournalAtherosclerosis
Volume397
Early online date18 Apr 2024
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Cardiac magnetic resonance
  • Cardiac segmentation
  • Coronary artery disease
  • Deep learning
  • Epicardial adipose tissue
  • Epicardial fat
  • Outcome

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