Coronary artery calcification and epicardial adipose tissue as independent predictors of mortality in COVID-19

Phenotypic switch of vascular smooth muscle cells in COVID-19: Role of cholesterol, calcium, and phosphate

Although the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), primarily manifests as severe respiratory distress, its impact on the cardiovascular system is also notable. Studies reveal that COVID-19 patients often suffer from certain vascular diseases, partly attributed to increased proliferation or altered phenotype of vascular smooth muscle cells (VSMCs). Although the association between COVID-19 and VSMCs is recognized, the precise mechanism underlying SARS-CoV-2's influence on VSMC phenotype remains largely under-reviewed. In this context, while there is a consistent body of literature dissecting the effect of COVID-19 on the cardiovascular system, few reports delve into the potential role of VSMC switching in the pathophysiology associated with COVID-19 and the molecular mechanisms involved therein. This review dissects and critiques the link between COVID-19 and VSMCs, with particular attention to pathways involving cholesterol, calcium, and phosphate. These pathways underpin the interaction between the virus and VSMCs. Such interaction promotes VSMC proliferation, and eventually potentiates vascular calcification as well as worsens prognosis in patients with COVID-19.

Association of cardiometabolic and vascular atherosclerosis phenotypes on non-contrast chest CT with incident heart failure in patients with severe hypercholesterolemia

BACKGROUND

Coronary artery calcium (CAC), thoracic aorta calcification (TAC), non-alcoholic fatty liver disease (NAFLD), and epicardial adipose tissue (EAT) are associated with atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF).

OBJECTIVES

We aimed to determine whether these cardiometabolic and atherosclerotic risk factors identified by non-contrast chest computed tomography (CT) are associated with HF hospitalizations in patients with LDL-C≥ 190 mg/dL.

METHODS

We conducted a retrospective cohort analysis of patients with LDL-C ≥190 mg/dL, aged ≥40 years without established ASCVD or HF, who had a non-contrast chest CT within 3 years of LDL-C measurement. Ordinal CAC, ordinal TAC, EAT, and NAFLD were measured. Kaplan-Meier curves and multivariable Cox regression models were built to ascertain the association with HF hospitalization.

RESULTS

We included 762 patients with median age 60 (53-68) years, 68% (n=520) female, and median LDL-C level of 203 (194-216) mg/dL. Patients were followed for 4.7 (interquartile range 2.75-6.16) years, and 107 (14%) had a HF hospitalization. Overall, 355 (47%) patients had CAC=0, 210 (28%) had TAC=0, 116 (15%) had NAFLD, and median EAT was 79 mL (49-114). Moderate-Severe CAC (log-rank p<0.001) and TAC (log-rank p=0.006) groups were associated with increased HF hospitalizations. This association persisted when considering myocardial infarction (MI) as a competing risk. NAFLD and EAT volume were not associated with HF.

CONCLUSIONS

In patients without established ASCVD and LDL-C≥190 mg/dL, CAC was independently associated with increased HF hospitalizations while TAC, NAFLD, and EAT were not.

Epicardial adipose tissue measured from analysis of adipose tissue area using chest CT imaging is the best potential predictor of COVID-19 severity

BACKGROUND

Computed tomography (CT) imaging is widely used for diagnosing and determining the severity of coronavirus disease 2019 (COVID-19). Chest CT imaging can be used to calculate the epicardial adipose tissue (EAT) and upper abdominal visceral adipose tissue (Abd-VAT) areas. The EAT is the main source of inflammatory cytokines involved in chest inflammatory diseases; thus, the EAT area might be a more useful severity predictor than the Abd-VAT area for COVID-19. However, to the best of our knowledge, there are no large-scale reports that sufficiently consider this issue. In addition, there are no reports on the characteristics of patients with normal body mass index (BMI) and high adipose tissue.

AIM

The purpose of this study was to analyze whether the EAT area, among various adipose tissues, was the most associated factor with COVID-19 severity. Using a multicenter COVID-19 patient database, we analyzed the associations of chest subcutaneous, chest visceral, abdominal subcutaneous, and Abd-VAT areas with COVID-19 outcomes. In addition, the clinical significance of central obesity, commonly disregarded by BMI, was examined.

METHODS

This retrospective cohort study evaluated patients with COVID-19 aged ≥18 years In Japan. Data including from chest CT images collected between February 2020 and October 2022 in four hospitals of the Japan COVID-19 Task Force were analyzed. Patient characteristics and COVID-19 severity were compared according to the adipose tissue areas (chest and abdominal subcutaneous adipose tissue [Chest-SAT and Abd-SAT], EAT, and Abd-VAT) calculated from chest CT images.

RESULTS

We included 1077 patients in the analysis. Patients with risk factors of severe COVID-19 such as old age, male sex, and comorbidities had significantly higher areas of EAT and Abd-VAT. High EAT area but not high Abd-VAT area was significantly associated with COVID-19 severity (adjusted odds ratio (aOR): 2.66, 95 % confidence interval [CI]: 1.19-5.93). There was no strong correlation between BMI and VAT. Patients with high VAT area accounted for 40.7 % of the non-obesity population (BMI < 25 kg/m2). High EAT area was also significantly associated with COVID-19 severity in the non-obesity population (aOR: 2.50, 95 % CI: 1.17-5.34).

CONCLUSIONS

Our study indicated that VAT is significantly associated with COVID-19 severity and that EAT is the best potential predictor for risk stratification in COVID-19 among adipose tissue areas. Body composition assessment using EAT is an appropriate marker for identifying obesity patients overlooked by BMI. Considering the next pandemic of the global health crisis, our findings open new avenues for implementing appropriate body composition assessments based on CT imaging.

Correlation between coronary artery calcification and COVID-19

Background

Coronary heart disease (CHD) is an underlying cardiac condition contributing to increased COVID-19 mortality and morbidity which can be assessed by several diagnosis methods including coronary artery calcification (CAC). The goal of this study was to find out if there were potential links between CAC, clinical findings, severity of COVID-19, and in-hospital outcomes.

Methods

This retrospective study evaluated 551 suspected patients admitted to teaching hospitals of the Babol University of Medical Sciences, Babol, Iran, from March to October 2021. Data included previous diseases, comorbidities, clinical examinations, routine laboratory tests, demographic characteristics, duration of hospitalization, and number of days under ventilation were recorded in a checklist.

Results

Findings of current study provide evidence of a significant relationship between coronary artery calcification (CAC) and in-hospital mortality. Additionally, we observed significant correlations between CAC and several clinical parameters including age, duration of hospitalization, pulse rate, maximum blood pressure, erythrocyte sedimentation rate (ESR), blood urea nitrogen (BUN), neutrophil count, white blood cell (WBC) count, and oxygen saturation. However, we did not observe a significant association between CAC and the severity index of COVID-19. In addition, logistic regression tests did not find a significant value of CAC to predict in-hospital mortality.

Conclusion

Our findings showed a significant relationship between CAC and in-hospital mortality.

Assessing cardiovascular risk with mammography and non-contrast chest CT: A review of the literature and clinical implications

Coronary artery disease (CAD) is the leading cause of mortality and disability globally. In the United States, about 7.2% of adults aged 20 and older are affected by CAD. However, due to its progression over decades, CAD is often undetected and unnoticed until plaque ruptures. This leads to partial or complete artery blockage, resulting in myocardial infarction. Thus, new screening methods for early detection of CAD are needed to prevent and minimize the morbidity and mortality from CAD. Vascular calcifications seen on mammography and non-contrast chest CT (NCCT) can be used for the early detection of CAD and are an accurate predictor of cardiovascular risk. This paper aims to review the basic epidemiology, pathophysiology, imaging findings, and correlation of long-term cardiovascular outcomes with vascular calcifications on mammography and NCCT.

Coronary Calcium Predicts All-Cause Mortality in Suspected Acute Aortic Syndrome

PURPOSE

To determine long-term clinical outcomes in patients with suspected acute aortic syndrome (AAS) and evaluate the prognostic value of coronary calcium burden as assessed with CT aortography in this symptomatic population.

MATERIALS AND METHODS

A retrospective cohort of all patients who underwent emergency CT aortography from January 2007 through January 2012 for suspected AAS was assembled. A medical record survey tool was used to evaluate subsequent clinical events over 10 years of follow-up. Events included death, aortic dissection, myocardial infarction, cerebrovascular accident, and pulmonary embolism. Coronary calcium scores were computed from original images using a validated simple 12-point ordinal method and categorized into none, low (1-3), moderate (4-6), or high (7-12) groupings. Survival analysis with Kaplan-Meier curves and Cox proportional hazard modeling was performed.

RESULTS

The study cohort comprised 1658 patients (mean age, 60 years ± 16 [SD]; 944 women), with 595 (35.9%) developing a clinical event over a median follow-up of 6.9 years. Patients with high coronary calcium demonstrated the highest mortality rate (adjusted hazard ratio = 2.36; 95% CI: 1.65, 3.37). Patients with low coronary calcium demonstrated lower mortality, but rates were still almost twice as high compared with patients with no detectable calcium (adjusted hazard ratio = 1.89; 95% CI: 1.41, 2.53). Coronary calcium was a strong predictor of major adverse cardiovascular events (P < .001), which persisted after adjustment for common significant comorbidities.

CONCLUSION

Patients with suspected AAS had a high rate of subsequent clinical events, including death. CT aortography-based coronary calcium scores strongly and independently predicted all-cause mortality.Keywords: Acute Aortic Syndrome, Coronary Artery Calcium, CT Aortography, Major Adverse Cardiovascular Events, Mortality Supplemental material is available for this article. © RSNA, 2023See also commentary by Weir-McCall and Shambrook in this issue.

The role of epicardial adipose tissue dysfunction in cardiovascular diseases: an overview of pathophysiology, evaluation, and management

In recent decades, the epicardial adipose tissue (EAT) has been at the forefront of scientific research because of its diverse role in the pathogenesis of cardiovascular diseases (CVDs). EAT lies between the myocardium and the visceral pericardium. The same microcirculation exists both in the epicardial fat and the myocardium. Under physiological circumstances, EAT serves as cushion and protects coronary arteries and myocardium from violent distortion and impact. In addition, EAT acts as an energy lipid source, thermoregulator, and endocrine organ. Under pathological conditions, EAT dysfunction promotes various CVDs progression in several ways. It seems that various secretions of the epicardial fat are responsible for myocardial metabolic disturbances and, finally, leads to CVDs. Therefore, EAT might be an early predictor of CVDs. Furthermore, different non-invasive imaging techniques have been proposed to identify and assess EAT as an important parameter to stratify the CVD risk. We also present the potential therapeutic possibilities aiming at modifying the function of EAT. This paper aims to provide overview of the potential role of EAT in CVDs, discuss different imaging techniques to assess EAT, and provide potential therapeutic options for EAT. Hence, EAT may represent as a potential predictor and a novel therapeutic target for management of CVDs in the future.

Segmentation and volume quantification of epicardial adipose tissue in computed tomography images

BACKGROUND

Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task.

PURPOSE

The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false positive segmentation.

METHODS

Firstly, the obtained Computed Tomography (CT) was preprocessed. That is, the threshold range of EAT was determined to be (-190, -30) HU according to prior knowledge, and the non-adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Secondly, the image obtained after thresholding was input into the lightweight RDU-Net network to perform the training, validating, and testing process. RDU-Net uses a residual multi-scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem and appropriately highlights the status of positive pixels.

RESULTS

In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity (JS), Accuracy (ACC), Specificity (SP), Precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland-Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices.

CONCLUSIONS

A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time-consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT_Seg/tree/main/EAT_seg. This article is protected by copyright. All rights reserved.

Role of the coronary and non-coronary cardiovascular findings on non-cardiac gated thoracic CT in predicting mortality in SARS-CoV-2 infection

Background

The potential effects of cardiovascular comorbidities on the clinical outcomes in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remain unclear. Identification of the coronary and non-coronary cardiovascular findings may help to stratify the patients' prognosis. Therefore, we aimed to evaluate the prognostic impact of the coronary and the non-coronary cardiovascular findings in SARS-CoV-2 patients.

Methods

We studied a total of 594 SARS-CoV-2 patients who were hospitalized and performed a non-cardiac gated thoracic computed tomography. Two blinded radiologists assessed the coronary artery calcification segment involvement score (CACSIS) and non-coronary atherosclerosis cardiovascular findings (NCACVF). The baseline characteristics of the patients and CT findings were evaluated according to survival status. Logistic regression analyses were performed to identify the independent predictors of mortality.

Results

At a mean follow-up of 8 (4–12.5) days, 44 deaths occurred (7.4%). Compared to survivors, non-survivors had increased CACSIS [27.3% (CACSIS = 0) vs 25% (CACSIS 1–5) vs 47.7% (CACSIS >5), p < 0.001]. Similarly, on NCACVF, non-survivors had much more major findings compared to survivors (29.5% vs. 2.7%, respectively, p < 0.001). At multivariable analysis, age (p = 0.009), creatinine (p < 0.001), hs-cTnI (p = 0.004) and NCACVF (HR 1.789; 95% CI 1.053–3.037; p = 0.031) maintained a significant independent association with in-hospital mortality.

Conclusion

Our study shows that coronary and non-coronary cardiovascular findings on non-cardiac gated thoracic CT may help to predict mortality in patients with SARS-CoV-2 infection.

Visual Coronary and Aortic Calcium Scoring on Chest Computed Tomography Predict Mortality in Patients With Low-Density Lipoprotein-Cholesterol ≥190 mg/dL

BACKGROUND

Current guidelines recommend coronary artery calcium (CAC) scoring for stratification of atherosclerotic cardiovascular disease risk only in patients with borderline to intermediate risk score by the pooled cohort equation with low-density lipoprotein-cholesterol (LDL-C) of 70 to 190 mg/dL. It remains unknown if CAC or thoracic aorta calcification (TAC), detected on routine chest computed tomography, can provide further risk stratification in patients with LDL-C≥190 mg/dL.

METHODS

From a multisite medical center, we retrospectively identified all patients from March 2005 to June 2021 age ≥40 years, without established atherosclerotic cardiovascular disease and LDL-C≥190 mg/dL who had non-gated non-contrast chest computed tomography within 3 years of LDL-C measurement. Ordinal CAC and TAC scores were measured by visual inspection. Kaplan-Meier curves and multivariable Cox-regression models were built to ascertain the association of CAC and TAC scores with all-cause mortality.

RESULTS

We included 811 patients with median age 59 (53-68) years, 262 (32.3%) were male, and LDL-C median level was 203 (194-217) mg/dL. Patients were followed for 6.2 (3.29-9.81) years, and 109 (13.4%) died. Overall, 376 (46.4%) of patients had CAC=0 and 226 (27.9%) had TAC=0. All-cause mortality increased with any CAC and moderate to severe TAC. In a multivariate model, patients with CAC had a significantly higher mortality compared with those without CAC: mild hazard ratio (HR), 1.71 (1.03-2.83), moderate HR, 2.12 (1.14-3.94), and severe HR, 3.49 (1.94-6.27). Patients with moderate TAC (HR, 2.34 [1.19-4.59]) and those with severe TAC (HR, 3.02 [1.36-6.74]) had higher mortality than those without TAC.

CONCLUSIONS

In patients without history of atherosclerotic cardiovascular disease and LDL-C≥190 mg/dL, the presence and severity of CAC and TAC are independently associated with all-cause mortality.

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.

Coronary artery calcium score as a prognostic factor of adverse outcomes in patients with COVID-19: a comprehensive review

BACKGROUND AND AIM

The association of known cardiovascular risk factors and poor prognosis of coronavirus disease 2019 (COVID-19) has been recently emphasized. Coronary artery calcium (CAC) score is considered to be a risk predictor of cardiovascular events. Therefore, we have conducted a review of literature on the predictive value of CAC score predictive value in COVID-19 outcome.

METHOD

A search of literature was conducted, aiming for articles published until December 2021 on PubMed and Scopus to identify potentially eligible studies.

DISCUSSION

A total of 18 articles were reviewed for association between higher CAC score and adverse outcomes in COVID-19.

CONCLUSION

The coronary calcium score could be considered as a new radiological marker for risk assessment in COVID-19 patients and providing additional information in fields of prognosis and possible cardiovascular complications. High CAC score is associated with higher in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC scores.

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.

A Meta-Analysis: Coronary Artery Calcium Score and COVID-19 Prognosis

Background: Multiple studies have investigated the correlations of mortality, mechanical ventilation, and intensive care unit (ICU) admissions with CAC scores. This analysis overviews the prognostic capability of CAC scoring in mortality, mechanical ventilation, and ICU admission for hospitalized COVID-19 patients. Methods: Online search was conducted on PubMed, Cochrane Library, and Scopus from inception to 22 November 2021 to identify studies involving CAC scores in relation to ICU admission, mechanical ventilation, and death rates. Results: A total of eight studies were analyzed. In the absence of CAC group compared with the presence of CAC score, there was an increase in mortality in the presence of CAC (RR 2.24, 95% CI, 1.41−3.56; p < 0.001). In the low CAC group and high CAC group, high CAC group had increase in mortality (RR 2.74; 95% CI, 1.94−3.86; p < 0.00001). There was no statistical difference in outcomes of mechanical ventilation and ICU admission between any of the groups. Conclusion: This meta-analysis strictly examined the outcomes of interest in death, mechanical ventilation, and ICU admission while comparing the CAC scores in patients with COVID-19. Given these findings, CAC scoring can aid in stratifying patients, thus allowing earlier interventions in rapidly developing illnesses.

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

PURPOSE OF THE REVIEW

We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging.

RECENT FINDINGS

CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards. The segmentation of skeletal muscle tissue and VAT and SAT compartments is most often performed at the level of the 3rd lumbar vertebra. A decreased amount of CT-determined skeletal muscle mass is a marker of impaired survival in many patient populations, including patients with most types of cancer, some surgical patients, and those admitted to the intensive care unit (ICU). Patients with increased VAT are more susceptible to impaired survival / worse outcomes; however, those patients who are critically ill or admitted to the ICU or who will undergo surgery appear to be exceptions. The independent significance of SAT is less well established. Recently, the roles of the CT-determined decrease of muscle mass and increased VAT area and epicardial adipose tissue (EAT) volume have been shown to predict a more debilitating course of illness in patients suffering from severe acute respiratory syndrome coronavirus 2 (COVID-19) infection.

SUMMARY

The field of CT-based body composition analysis is rapidly evolving and shows great potential for clinical implementation.

Coronary calcium score as a predictor of outcomes in the hypertensive Covid-19 population: results from the Italian (S) Core-Covid-19 Registry

Hypertension is associated with more severe disease and adverse outcomes in COVID-19 patients. Recent investigations have indicated that hypertension might be an independent predictor of outcomes in COVID-19 patients regardless of other cardiovascular and noncardiovascular comorbidities. We explored the significance of coronary calcifications in 694 hypertensive patients in the Score-COVID registry, an Italian multicenter study conducted during the first pandemic wave in the Western world (March-April 2020). A total of 1565 patients admitted with RNA-PCR-positive nasopharyngeal swabs and chest computed tomography (CT) at hospital admission were included in the study. Clinical outcomes and cardiovascular calcifications were analyzed independently by a research core lab. Hypertensive patients had a different risk profile than nonhypertensive patients, with more cardiovascular comorbidities. The deceased hypertensive patients had a greater coronary calcification burden at the level of the anterior descending coronary artery. Hypertension status and the severity cutoffs of coronary calcifications were used to stratify the clinical outcomes. For every 100-mm³ increase in coronary calcium volume, hospital mortality in hypertensive patients increased by 8%, regardless of sex, age, diabetes, creatinine, and lung interstitial involvement. The coronary calcium score contributes to stratifying the risk of complications in COVID-19 patients. Cardiovascular calcifications appear to be a promising imaging marker for providing pathophysiological insight into cardiovascular risk factors and COVID-19 outcomes.