Prognostic value of epicardial adipose tissue volume in combination with coronary plaque and flow assessment for the prediction of major adverse cardiac events. Issue 148 (March 2022) - Brandt, Verena; Bekeredjian, Raffi; Schoepf, U. Joseph; Varga-Szemes, Akos; Emrich, Tilman; Aquino, Gilberto J.; Decker, Josua; Bayer, Richard R.; Ellis, Lauren; Ebersberger, Ullrich; Tesche, Christian
Highlights: CT-derived EAT assessment demonstrates high discriminatory power to predict MACE. EAT shows superior diagnostic performance over Morise score, CCTA-derived plaque measures and CT-FFR. A combined model of these markers demonstrated incremental MACE prediction beyond clinical risk score. Abstract: Purpose: The purpose of this study was to determine whether EAT volume in combination with coronary CT angiography (CCTA)-derived plaque quantification and CT-derived fractional flow reserve (CT-FFR) has prognostic implication with major adverse cardiac events (MACE). Methods: Patients ( n = 117, 58 ± 10 years, 61% male) who had previously undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. Follow-up was performed to record MACE. EAT volume and plaque measures were derived from non-contrast and contrast-enhanced CT images using a semi-automatic software approach, while CT-FFR was calculated using a machine-learning algorithm. The diagnostic performance to identify MACE was evaluated using univariable and multivariable Cox proportional hazards analysis and concordance (C)-indices. Results: During a median follow-up period of 40.4 months, 19 events were registered. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in patients developing MACE (all p < 0.05). The following parameters were predictors of MACE in adjusted multivariable Cox regression analysis (hazard ratio [HR]): EAT volume (HR 2.21, p = 0.023), indexed EAT volume (HR 2.03, p = 0.035), and CCTA ≥ 50% (HR 1.05, p = 0.048). A model including Morise score, CCTA ≥ 50% stenosis, and EAT volume showed significantly improved C-index to Morise score alone (AUC 0.83 vs. 0.66, p = 0.004). Conclusions: Facing limitations in conventional cardiovascular risk scoring models, this observational study demonstrates that the prediction performance of our proposed method achieves a significant improvement in prognostic ability, especially when compared to models such as Morise score alone or its combination with CCTA and CT-FFR.