Abstract
Background/Aim: Epicardial adipose tissue (EAT) has been established as a quantitative imaging biomarker associated with the prognosis of several diseases, especially cardiovascular diseases. The cardiac injury by coronavirus disease 2019 (COVID-19) might be linked to the EAT. This study aimed to use this prognostic marker derived from computed tomography (CT) images to predict 30-day mortality in patients with COVID-19. Patients and Methods: Consecutive patients with COVID-19 were retrospectively screened between 2020 and 2022. Overall, 237 patients (78 female, 32.9%) were included in the present study. The study end-point was the 30-day mortality. EAT was measured using the diagnostic CT in a semiquantitative manner. EAT volume and density were measured for each patient. Results: Overall, 70 patients (29.5%) died within the 30-day observation period and 143 patients (60.3%) were admitted to the intensive care unit (ICU). The mean EAT volume was 140.9±89.1 cm3 in survivors and 132.9±77.7 cm3 in non-survivors, p=0.66. The mean EAT density was −71.9±8.1 Hounsfield units (HU) in survivors, and −67.3±8.4 HU in non-survivors, p=0.0001. EAT density was associated with 30-day mortality (p<0.0001) and ICU admission (p<0.0001). EAT volume was not associated with mortality and/or ICU admission. Conclusion: EAT density was associated with 30-day mortality and ICU admission in patients with COVID-19.
Epicardial adipose tissue (EAT) is a type of visceral fat located around the myocardium and pericardium. This type of fat is of endocrine importance, as EAT can produce pro- and anti-inflammatory factors including adiponectin, Interleukin-6, tumor-necrosis factor α and leptin (1-3). There is increasing scientific evidence that EAT regulates the function of the myocardium and coronary state by influencing the energy homeostasis as well as lipid metabolism.
The thickness of EAT as a diameter on one imaging slice as well as the volume of the whole EAT can be measured by cross-sectional imaging comprising cardiac magnetic resonance imaging (MRI), computed tomography (CT), and echocardiography (1-3). Notably, various analyses have shown that enlarged EAT is associated with the incidence and prognosis of coronary artery disease (3).
The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to affect the world and remains a threat to the health systems around the world. The clinical course of COVID-19 is highly variable with a mild course but also lethal cases. As such, a small group of the infected patients can suffer from a severe or critical course with admission to intensive care unit (ICU) or even with a fatal outcome (4-7). Noteworthy, the mortality rate during the first wave of the pandemic was high with over 10% of cases in most European countries (4, 5). There is no doubt that rapid and correct prediction of a fatal patient course of COVID-19 can help daily patient care (4, 5).
Early in the pandemic, clinical prognostic factors were identified, highlighting male sex and age with over 60 years with reported hazard ratios of 2.6 for age of 60 years and 1.4 for male sex, respectively (8, 9). CT plays a crucial role in diagnosis of COVID-19, especially in detecting pulmonary consolidations and the amount of involvement (10-12).
Early in the course of the pandemic, cardiovascular diseases were identified as a crucial risk factor in COVID-19 (9). Moreover, the direct cardiac injury of infected myocardial cells was mediated by the ACE2 receptors. This resulted in the release of immune-related factors, termed inflammatory storm, that could further lead to an imbalance of the oxygen supply (13). The association between EAT and the prognosis of COVID-19 might be caused by this cardiac involvement of the coronavirus. EAT assessment from CT images could provide novel biomarker. The prognostic value of EAT for COVID-19 was also evaluated in preliminary studies (14-17). Yet, due to the clinical differences of COVID-19 throughout the pandemic novel data is needed to evaluate the prognostic value of EAT.
The aim of the present analysis was to investigate, whether EAT is of prognostic relevance and shows associations with mortality and clinically relevant parameters in patients with COVID-19.
Patients and Methods
Patient acquisition. This present retrospective analysis was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. It received ethical approval from the local ethics committee following blinded review.
All consecutive patients diagnosed with COVID-19 were screened within the time period 2020 to 2022. Inclusion criteria were: CT imaging at the time point of the admission to the hospital; clinical data regarding the outcome; PCR-proven COVID-19 infection. Exclusion criteria were: severe image artifacts, which could hinder the measurement of EAT; missing clinical data/follow up.
Clinical data. The retrieved clinical data from the patients’ records comprised: Age, sex, admission to ICU, duration of ventilation in hours, 30-day mortality.
CT imaging. All CT scans were obtained on a multidetector CT scanner (Siemens Somatom Definition AS+; Siemens Healthcare, Erlangen, Germany). During the first time of the pandemic, the scanner was used to scan every patient suspected or confirmed to be infected with COVID-19. Typical imaging parameters included a slice thickness 1 mm with 5 mm reconstructions, tube voltage 120 kV, automatic tube current modulation, pitch factor 1.2, and collimation 0.6 mm. In all cases contrast media was given.
Epicardial adipose tissue. A trained radiologist blinded to the clinical results, measured the EAT volume with the software Intellispace portal (Version 11; Philips, Amsterdam, the Netherlands). EAT volume was calculated using the density threshold between −30 and −190 Hounsfield units (HU) to semiautomatically segment fat tissue. Then, the anatomical limits were manually drawn to include the epicardial fat only. Of this segmented volume, the volume and the mean density in HU was calculated. This calculation was previously described in the literature (17). Figure 1 provides visualization of the EAT volume segmentation of a representative patient of the study cohort.
A representative case of a patient sample with COVID-19. The EAT segmentation is visualized with a green overlay. The resulting EAT volume is 40.9 cm3 and the density is −79.9 HU.
Statistical analysis. The statistical analysis was performed using SPSS (IBM SPSS Statistics for Windows. version 225.0: IBM corporation, Armonk, NY, USA). The figures were created using GraphPad Prism 5 (GraphPad Software, La Jolla, CA, USA). The retrieved data were first evaluated with descriptive statistics. Associations between EAT parameters and clinical features were assessed using Spearman’s correlation coefficient. Discrimination analysis between groups were performed using Mann-Whitney test and Fisher exact test, when suitable. Uni- and multivariable logistic regression analysis were further used to investigate the associations between EAT parameters with 30-day mortality. In all instances, p-values <0.05 were taken to indicate statistical significance.
Results
Overall, 237 patients (78 female, 32.9%) were included into the analysis. The mean age at the time of CT-acquisition was 63.4±15.3 years, median age 65 years. In total, 70 patients (29.5%) died within the 30-day study observation period and 143 patients (60.3%) needed the admission to the ICU. The mean EAT volume in survivors and non-survivors was 140.9±89.1 cm3 and 132.9±77.7 cm3, respectively (p=0.66). Mean EAT density was −71.9±8.1 HU in survivors and −67.3±8.4 HU in non-survivors, (p=0.0001, Figure 2; Table I). A moderate inverse correlation was found between EAT volume and EAT density (r=−0.38, p<0.0001, Figure 3).
Scatter plot of the EAT density in survivors and non-survivors. Mean EAT density was −71.9±8.1 HU in survivors and −67.3±8.4 HU in non-survivors, p=0.0001.
Comparison of the investigated EAT parameters between survivors and non-survivors.
Correlation analysis between EAT density and EAT volume. A moderate inverse correlation was identified (r=−0.38, p<0.0001).
In patients with ICU admission, the mean EAT volume was 138.9±82.3 cm3. It was 138.0±91.5 in patients who did not need ICU admission, (p=0.71; Table II). Furthermore, EAT density was associated with 30-day mortality according to univariable analysis (OR=1.08; 95%CI=1.04-1.1; p<0.0001) and to multivariable analysis (OR=1.07; 95%CI=1.03-1.1; p<0.0001). EAT volume showed no association with 30-day mortality (OR=1.0; 95%CI=1.0, 1.0; p=0.88) (Table III). Finally, EAT density was associated with ICU admission according to univariable analysis (OR=1.11; 95%CI=1.06-1.13; p<0.0001) and multivariable analysis (OR=1.11; 95%CI=1.07-1.2; p<0.0001) (Table IV).
Comparison of EAT volume and density between patients with and without need for ICU admission.
Uni- and multivariable regression analysis to predict 30-day mortality.
Uni- and multivariable regression analysis to predict ICU admission.
Discussion
The present analysis provides the prognostic relevance of EAT density and volume quantified from CT images in patients with COVID-19. As presented, there was an association between EAT density with 30-day mortality, whereas not with EAT volume. EAT density could serve as a novel useful biomarker quantified from CT images.
Correct and rapid risk-stratification can be crucial for patients with COVID-19 due to the different clinical courses (7, 8).
Very early during the pandemic, it was shown that cardiovascular disorders, especially coronary heart disease as co-morbidities are a risk factor for a severe COVID-19 course (18). The present analysis examined whether the known important factor of EAT as an important prognostic parameter in cardiovascular disease with the clinical outcome of COVID-19, which is known to cause direct cardiac injury including myocarditis.
An already established prognostic factor provided by CT imaging is the extension of pulmonary involvement of the consolidations. In a recent meta-analysis, there were promising results regarding the prognostic relevance of CT findings regarding coronary artery calcifications, mediastinal lymph adenopathy and pleural effusion in patients with COVID-19 (19). Noteworthy, the included patients were of the first wave of the pandemic with a different severity course and outcomes compared to recent days.
EAT was established as an important prognostic and predictive parameter in cardiovascular diseases, especially in coronary heart disease (1-3). The role of EAT comprises the cardiac metabolism with vasogenic effect on coronaries, innervation, and potentially the cryoprotection. However, recent data has revealed that EAT plays additional roles in cardiac biology, myocardial redox state and intracellular calcium cycling (1-3).
In a recent study on diabetes patients, EAT volume was positively associated with age, BMI, pack-year history of smoking, and triglyceridemia but negatively correlated with HDL cholesterol level (20). Marcucci et al. showed that the threshold value of 97 cm3 had good diagnostic accuracy to predict a greater pulmonary manifestation course of COVID-19 (16). Contrary to these, in the present study the mean volume was higher than that reported by Marcucci et al., indicating a different study population (16). The proposed threshold cannot be translated to the investigated patient sample in the present analysis. Similarly, Slipczuk et al. proposed a median value of 98 ml as the cut-off with prognostic relevance for mortality (21). However, in the study by Bihan et al., the mean EAT volume was within the same scope as that in the present study (15). The importance of regional differences, co-morbidities, and overall body composition could account for these severe differences regarding EAT volume. In a recent study by Duyuler et al., EAT thickness measured on an axial CT slice was an independent predictor for ICU admission (14). Noteworthy, EAT density was not as commonly investigated in the literature compared to the volume (17, 22). In the study by Eslami et al. EAT density did not show an association with mortality. In the other study, patients with the lower third of the EAT density had a 3.6-fold increased risk for the occurrence of pulmonary embolism (22).
The present study adds to the literature that the HU values of EAT seem to be better than the sole volume of EAT. Similar findings were reported for visceral fat areas where HU quantification seems to be more predictive than the area itself. However, there is definite need to harmonize the partially inflicting results regarding EAT volume and to adjust for time of the pandemic and other important co-factors.
The present study is not free of limitations. First, the present analysis is limited to a retrospective design with possible known inherent bias. However, the EAT quantification was performed blinded to the clinical results to reduce possible bias. Second, patients from different waves of the pandemic were pooled together in the present analysis. However, due to the small sample size there could be no further subanalyses to adjust for this fact.
In conclusion, EAT density is associated with 30-day mortality and need for ICU admission in patients with COVID-19.
Acknowledgements
This research was supported by the German Federal Ministry of Education and Research (BMBF) as part of the University Medicine Network (Project RACOON, 01KX2021).
Footnotes
Authors’ Contributions
H.J.M. and A.A.: wrote the main manuscript text. H.J.M.: performed the statistical analysis. A.S. and J.B.: Study design and Supervision. M.H. and A.A.: Data extraction and analysis. All Authors reviewed and approved the final manuscript.
Conflicts of Interest
The Authors have no conflicts of interest to declare in relation to this study.
- Received July 20, 2023.
- Revision received October 9, 2023.
- Accepted October 10, 2023.
- Copyright © 2024 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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