The relation between thrombus burden and early mortality risk in inpatients diagnosed with COVID-19-related acute pulmonary embolism: a retrospective cohort study.

BACKGROUND: COVID-19-related acute pulmonary thromboembolism (APE) is associated with poor outcomes in patients with COVID-19. There are studies investigating the association between thrombus burden and high risk of early mortality in the pre-COVID-19 period. This study aimed to evaluate the relationship between clot burden and early mortality risk in COVID-19-related APE patients. METHODS: In this single-center retrospective cohort study, the data of hospitalized adult patients followed up for COVID-19-related APE between April 1, 2020, and April 1, 2021, were electronically collected. A radiologist evaluated the computed tomography (CT) findings and calculated the Mastora scores to determine clot burden. The early mortality risk group of each patient was determined using 2019 the European Society of Cardiology guidelines. RESULTS: Of the 87 patients included in the study, 58 (66.7%) were male, and the mean age was 62.5±16.2 years. There were 53 (60.9%) patients with a low risk of mortality, 18 (20.7%) with an intermediate-low risk, and 16(18.4%) with an intermediate-high/high risk. The median total simplified Mastora scores were 11.0, 18.5, and 31.5 in the low, the intermediate-low, and the intermediate-high/high-risk groups, respectively (p = 0.002). With the 80.61% of post-hoc power of the study, intermediate-high/high early mortality risk was associated statistically significantly with the total simplified Mastora score (adj OR = 1.06, 95%CI = 1.02-1.11,p = 0.009). Total simplified Mastora score was found to predict intermediate-high/high early mortality risk with a probability of 0.740 (95% CI = 0.603-0.877): At the optimal cut-off value of 18.5, it had 75.0% sensitivity, 66.2% specificity, 33.3% positive predictive value, and 92.2% negative predictive value. CONCLUSIONS: The total simplified Mastora score was found to be positively associated with early mortality risk and could be useful as decision support for the risk assessment in hospitalized COVID-19 patients. Evaluation of thrombus burden on CT angiography performed for diagnostic purposes can accelerate the decision of close monitoring and thrombolytic treatment of patients with moderate/high risk of early mortality.

The evaluation of subcutaneous and visceral adipose tissue changes by computed tomography in coronavirus disease 2019 and comparison with quantitative analysis of lung involvement.

BACKGROUND: This study aims to reveal the relationship between lung involvement and visceral adipose tissue changes between chest-computed tomography (CT) scans taken in short intervals in COVID-19 patients. METHODS: The retrospective study included 52 patients who tested positive for SARS-CoV-2. All patients had two chest CT exams. Lung involvement measurements were calculated by using an artificial intelligence tool. Visceral and subcutaneous fat tissue was measured at the level of the first lumbar vertebra on chest CT. Additionally, demographic and laboratory data were collected. RESULTS: 52 patients were included (36.5 % female, mean age 50). Visceral fat area and visceral fat thickness changes were significantly positive predictors of total lung involvement changes (p=0.033, p=0.00024). Subcutaneous fat area and subcutaneous fat thickness changes were not associated with lung involvement change (p>0.05). CRP, IL-6, d-dimer, and ferritin levels were higher in patients who need intensive care units. CONCLUSION: Visceral adipose tissue changes may indicate that it can have a role as a reservoir of virus involvement.

Early effects of inactivated (CoronaVac) SARS-CoV-2 vaccine on retrobulbar vascular blood flow and retinal vascular density.

AIMS: We aimed to investigate the early effects of inactivated SARS-CoV-2 vaccine on retrobulbar vascular blood flow and retinal vascular density in healthy subjects. METHODS: Thirty-four eyes of 34 healthy volunteers who received the CoronaVac (Sinovac Life Sciences, China) were included in this prospective study. Resistive index (RI), pulsatility index (PI) and peak systolic velocity (PSV) of the ophthalmic artery (OA), central retinal artery (CRA), and the temporal and nasal posterior ciliary arteries (PCA) were evaluated with color Doppler ultrasonography (CDUS) before vaccination, at the 2nd and 4th weeks after vaccination. Superficial capillary plexus (SCP) and deep capillary plexus (DCP) vessel density (VD), foveal avascular zone (FAZ), and choriocapillaris blood flow (CCF) measurements were made using optical coherence tomography angiography (OCTA). RESULTS: When compared to the pre-vaccination values, there was no significant change in OA-PSV, temporal-nasal PCA-PSV, CRA-EDV, temporal-nasal PCA-EDV at 2nd and 4th weeks after vaccination. However statistically significant reductions were found in the OA-RI, OA-PI, CRA-RI, CRA-PI, temporal-nasal PCA-RI, temporal-nasal PCA-PI values, CRA-PSV at post-vaccination 2nd week (p<0.05 for all). While there was sustained reduction in OA-RI, OA-PI, CRA-PSV, and nasal PCA-RI values at 4th week after vaccination, the change in CRA-RI, CRA-PI, temporal PCA-RI, temporal-nasal PCA-PI values were not significant compared to pre-vaccination values. There was no statistically significant difference in the SCP-VD, DCP-VD, FAZ and CCF measurements. CONCLUSIONS: Our findings demonstrating that CoronaVac vaccine did not affect retinal vascular density in the early period, but it caused alterations in the retrobulbar blood flow.

Unilateral Axillary Lymphadenopathy after the Inactivated SARS-COV-2 (CoronaVac) Vaccine: Ultrasonographic Imaging

Background: Currently, unilateral clinical and subclinical axillary adenopathy cases associated with the Pfizer-BioNTech and Moderna vaccines are increasingly reported. However, only one study on axillary adenopathy due to the CoronaVac vaccine is published. Aims: To present the incidence, severity, and ultrasonographic findings of axillary adenopathy that developed in healthcare professionals in Turkey after they were vaccinated with CoronaVac against coronavirus disease-19. Study Design: A prospective study. Methods: In Turkey, the first dose of the CoronaVac vaccine for coronavirus disease-19 was administered to healthcare professionals on January 14, 2021, and the second dose on February 11, 2021. This study covered the period from January 21, 2021 (1 week after the first dose), and April 15, 2021 (9 weeks after the second dose). Individuals who had a history of COVID-19 more than 3 weeks after vaccine doses, systemic disease, and diagnosis and treatment history of breast cancer were excluded. The axillary lymph nodes of the vaccinated and contralateral arms were evaluated in 101 volunteer healthcare professionals using axillary ultrasonography. Results: A significant difference was found in the cortical thicknesses of the lymph nodes between the vaccinated and contralateral axilla after both the first (*p < 0.01) and second (*p < 0.01) doses. Accordingly, the rates of subclinical lymphatic hyperplasia on the vaccinated side were 25.7% (n = 26/101) after the first and 31.1% (n = 28/90) after the second dose. Lymph nodes with pathological appearance based on a reduced echogenic hilum with marked cortical thickening were found only in 2.2%. Among the 39 cases in which antibodies (immunoglobulin G and immunoglobulin M) were measured, the antibody level was classified as <10 and ≥10. No statistically significant difference was found in the cortical thickness of the axillary lymph nodes between patients with high antibody levels (≥10) and those with low antibody levels (<10) (p > 0.05). Conclusion: In this study, clinical signs of axillary lymph node hyperplasia were not detected after vaccination with CoronaVac. Mild and diffuse thickening of the CoronaVac vaccine-induced lymph nodes was more common than pathological and palpable lymph nodes.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.