DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.
In: Atherosclerosis, 2024-04-18, S. 117549
academicJournal
Zugriff:
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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gianluca Pontone reports a relationship with G.E. Healthcare, Bracco, Heartflow, Boheringher that includes: funding grants and speaking and lecture fees. The other authors have nothing to disclose.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Titel: |
DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.
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Autor/in / Beteiligte Person: | Guglielmo, M ; Penso, M ; Carerj, ML ; Giacari, CM ; Volpe, A ; Fusini, L ; Baggiano, A ; Mushtaq, S ; Annoni, A ; Cannata, F ; Cilia, F ; Del Torto, A ; Fazzari, F ; Formenti, A ; Frappampina, A ; Gripari, P ; Junod, D ; Mancini, ME ; Mantegazza, V ; Maragna, R ; Marchetti, F ; Mastroiacovo, G ; Pirola, S ; Tassetti, L ; Baessato, F ; Corino, V ; Guaricci, AI ; Rabbat, MG ; Rossi, A ; Rovera, C ; Costantini, P ; van der Bilt I ; van der Harst P ; Fontana, M ; Caiani, EG ; Pepi, M ; Pontone, G |
Zeitschrift: | Atherosclerosis, 2024-04-18, S. 117549 |
Veröffentlichung: | Ahead of Print, 2024 |
Medientyp: | academicJournal |
ISSN: | 1879-1484 (electronic) |
DOI: | 10.1016/j.atherosclerosis.2024.117549 |
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