TY - JOUR
T1 - DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR
AU - Guglielmo, Marco
AU - Penso, Marco
AU - Carerj, Maria Ludovica
AU - Giacari, Carlo Maria
AU - Volpe, Alessandra
AU - Fusini, Laura
AU - Baggiano, Andrea
AU - Mushtaq, Saima
AU - Annoni, Andrea
AU - Cannata, Francesco
AU - Cilia, Francesco
AU - Del Torto, Alberico
AU - Fazzari, Fabio
AU - Formenti, Alberto
AU - Frappampina, Antonio
AU - Gripari, Paola
AU - Junod, Daniele
AU - Mancini, Maria Elisabetta
AU - Mantegazza, Valentina
AU - Maragna, Riccardo
AU - Marchetti, Francesca
AU - Mastroiacovo, Giorgio
AU - Pirola, Sergio
AU - Tassetti, Luigi
AU - Baessato, Francesca
AU - Corino, Valentina
AU - Guaricci, Andrea Igoren
AU - Rabbat, Mark G.
AU - Rossi, Alexia
AU - Rovera, Chiara
AU - Costantini, Pietro
AU - van der Bilt, Ivo
AU - van der Harst, Pim
AU - Fontana, Marianna
AU - Caiani, Enrico G.
AU - Pepi, Mauro
AU - Pontone, Gianluca
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Cardiac magnetic resonance
KW - Cardiac segmentation
KW - Coronary artery disease
KW - Deep learning
KW - Epicardial adipose tissue
KW - Epicardial fat
KW - Outcome
UR - http://www.scopus.com/inward/record.url?scp=85191317782&partnerID=8YFLogxK
U2 - 10.1016/j.atherosclerosis.2024.117549
DO - 10.1016/j.atherosclerosis.2024.117549
M3 - Article
C2 - 38679562
SN - 0021-9150
VL - 397
JO - Atherosclerosis
JF - Atherosclerosis
M1 - 117549
ER -