Myocardial injury in COVID-19 patients is associated with the thickness of epicardial adipose tissue

Use and Prognostic Implications of Cardiac Troponin in COVID-19

Myocardial injury is common in patients with COVID-19 and is associated with an adverse prognosis. Cardiac troponin (cTn) is used to detect myocardial injury and assist with risk stratification in this population. SARS-CoV-2 infection can play a role in the pathogenesis of acute myocardial injury due to both direct and indirect damage to the cardiovascular system. Despite the initial concerns about an increased incidence of acute myocardial infarction (MI), most cTn increases are related to chronic myocardial injury due to comorbidities and/or acute nonischemic myocardial injury. This review will discuss the latest findings on this topic.

COVID-19 and Cardiovascular Diseases: A Literature Review From Pathogenesis to Diagnosis

The coronavirus disease 2019 (COVID-19) took the world by storm after the first case of COVID-19 emerged in China on December 8, 2019. The disease is generally considered as an infection of the respiratory system, but serious life-threatening myocardial injuries have been reported with this infection. Coronavirus can damage cardiac myocytes by entering the cell through angiotensin-converting enzyme 2 (ACE-2) receptor binding. Myocardial infarction, myocarditis, heart failure, cardiac arrhythmias, and Takotsubo cardiomyopathy are cardiac clinical manifestations commonly seen among patients affected by COVID-19. These cardiac pathologies are seen both during ongoing infection and post-infection. Elevated levels of myoglobin, troponin, creatine kinase-MB, plasma interleukin-6, lactate dehydrogenase (LDH), and N-terminal pro-b-type natriuretic peptide (NT-proBNP) have been found in COVID-19-associated myocardial injuries. The diagnostic modalities used in myocardial injuries due to COVID-19 include electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), endomyocardial biopsy, echocardiography (Echo), and computerized tomography (CT-Scan). This literature review will discuss, in detail, the pathogenesis, clinical manifestations, and diagnosis of myocardial injuries due to COVID-19.

Cut-off point of CT-assessed epicardial adipose tissue volume for predicting worse clinical burden of SARS-CoV-2 pneumonia

Objective

To identify a cut-off value of epicardial adipose tissue (EAT) volume quantified by CT associated with a worse clinical outcome in patients with SARS-CoV-2 pneumonia.

Materials and methods

In this retrospective study, sixty patients with a diagnosis of laboratory-confirmed COVID-19 pneumonia and a chest CT exam on admission were enrolled. Based on a total severity score (range 0–20), patients were divided into two groups: ordinary group (total severity score < 7) and severe/critical group (total severity score > 7). Clinical results and EAT volume were compared between the two groups.

Results

The severe/critical patients, compared to the ordinary ones, were older (66.83 ± 11.72 vs 58.57 ± 16.86 years; p = 0.031), had higher body mass index (27.77 ± 2.11 vs 25.07 ± 2.80 kg/m²; p < 0.001) and higher prevalence of comorbidities. EAT volume was higher in severe/critical group, compared with the ordinary group (151.40 ± 66.22 cm³ vs 92.35 ± 44.46 cm³, p < 0.001). In severe/critical group, 19 (73%) patients were admitted in intensive care unit (ICU), compared with 6 (20%) patients in the ordinary group (p < 0.001). The area under the ROC curve (AUC) is equal to 0.781 (p < 0.001) (95% CI: 0.662–0.900). The cut-off found, in correspondence with the highest value of the Youden Index, is 97 cm³: the sensitivity is equal to 83.3%, while the specificity is equal to 70% for predicting a worse outcome. The risk (odds ratio) of belonging to the severe/critical group in this population due to EAT ≥ 97 cm³ is 11.667 (95% CI: 3.384–40.220; p < 0.001).

Conclusion

An EAT volume of 97 cm³ has good sensitivity and specificity to predict a greater extent of pulmonary involvement and therefore a worse clinical outcome in patients with SARS-CoV-2 pneumonia.

Association of epicardial adipose tissue with the severity and adverse clinical outcomes of COVID-19: A meta-analysis

Objectives

Epicardial adipose tissue (EAT) has been proposed to be an independent predictor of visceral adiposity. EAT measures are associated with coronary artery disease, diabetes, and chronic obstructive pulmonary disease, which are risk factors for COVID-19 poor prognosis. Whether EAT measures are related to COVID-19 severity and prognosis is controversial.

Methods

We searched 6 databases for studies until January 7, 2022. The pooled effects are presented as the standard mean difference (SMD) or weighted mean difference with 95% confidence intervals (CIs). The primary end point was COVID-19 severity. Adverse clinical outcomes were also assessed.

Results

A total of 13 studies with 2482 patients with COVID-19 were identified. All patients had positive reverse transcriptase-polymerase chain reaction results. All quantitative EAT measures were based on computed tomography. Patients in the severe group had higher EAT measures compared with the nonsevere group (SMD = 0.74, 95% CI: 0.29–1.18, P = 0.001). Patients with hospitalization requirement, requiring invasive mechanical ventilation, admitted to intensive care unit, or with combined adverse outcomes had higher EAT measures compared to their controls (all P < 0.001).

Conclusions

EAT measures were associated with the severity and adverse clinical outcomes of COVID-19. EAT measures might help in prognostic risk stratification of patients with COVID-19.

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.