Lung Involvement In COVID-19 Research Articles

Unveiling the significance of Gremlin-1 as a novel biomarker for enhanced severity prediction in COVID-19 patients.

Gremlin-1 is associated with lung disease and plays a role in the initiation and progression of pulmonary fibrosis. This suggests that Gremlin-1 may be associated with lung involvement in COVID-19 and poor clinical outcomes and warrants further investigation. This prospective, cross-sectional, single-blind study was the first to investigate Gremlin-1 levels in COVID-19 patients and whether Gremlin-1 levels could be used to predict and guide clinical follow-up in outpatients and inpatients. In this context, serum Gremlin-1 levels were measured and compared in 2 groups of patients who were diagnosed as positive for COVID-19 by RT-PCR and were followed up as outpatients (n = 28) or required hospitalization (n = 30). The median Gremlin-1 values were statistically significantly different between outpatients and intensive care unit patients (0.367 ng/mL [IQR = 0.377], 1.858 ng/mL [IQR = 2.245], respectively) (P < .0001). The area under the ROC curve value to determine the discriminative power of Gremlin-1 was found to be 0.772 (95% CI: 0.672-0.871; P < .0001), and the cutoff value of Gremlin-1 to discriminate between outpatients and hospitalized patients was found to be 1.242 ng/mL with 66.7% sensitivity and 67.2% specificity. Serum Gremlin-1 level is an important biomarker that can be used as a clinical decision-making tool for COVID-19 positive patients.

Thyroid Density in CT Imaging as a Potential Marker of Lung Involvement in COVID-19: A Retrospective Analysis.

Background The SARS-CoV-2 pandemic has underscored the multifaceted impact of the virus on human health, extending beyond the respiratory system to involve other organ systems, including the endocrine system. Emerging evidence suggests a notable interaction between COVID-19 and thyroid function, characterized by alterations in thyroid hormone levels and structural changes within the gland. This study aims to explore the association between thyroid density on CT imaging and lung involvement in patients with COVID-19, potentially offering new insights into the systemic effects of the virus. Methodology A retrospective cross-sectional analysis was conducted on 1,066 patients withCOVID-19 who underwent chest CT scans without contrast at Government Medical College, Omandurar Government Estate, Chennai, which was designated as theCOVID-19 care center from April to June 2021. Thyroid density and lung involvement were quantitatively assessed, and their correlation was analyzed using descriptive and inferential statistics, including the Kruskal-Wallis H test and Shapiro-Wilk test for normality. Results The study population predominantly exhibited normal thyroid density (749, 70.3%), followed by altered (212, 19.9%), nodular (104, 9.8%), and a single instance (0.1%) of absent thyroid density. Despite variability in lung involvement across different thyroid density categories, statistical analysis revealed no significant association between thyroid density and the extent of lung involvement in patients with COVID-19. Conclusions This study found no significant correlation between thyroid density and lung involvement in patients with COVID-19, suggesting that thyroid density on CT imaging may not serve as a reliable marker for lung involvement in this population. Further research is warranted to explore the complex interactions between COVID-19 and thyroid function, as well as the potential implications for patient management and prognosis.

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients.

During the early stages of the pandemic, computed tomography (CT) of the chest, along with serological and clinical data, was frequently utilized in diagnosing COVID-19, particularly in regions facing challenges such as shortages of PCR kits. In these circumstances, CT scans played a crucial role in diagnosing COVID-19 and guiding patient management. The COVID-19 Reporting and Data System (CO-RADS) was established as a standardized reporting system for cases of COVID-19 pneumonia. Its implementation necessitates a high level of agreement among observers to prevent any potential confusion. This study aimed to assess the inter-observer agreement between physicians from different specialties with variable levels of experience in their CO-RADS scoring of CT chests for confirmed COVID-19 patients, and to assess the feasibility of applying this reporting system to those having little experience with it. All chest CT images of patients with positive RT-PCR tests for COVID-19 were retrospectively reviewed by seven observers. The observers were divided into three groups according to their type of specialty (three radiologists, three house officers, and one pulmonologist). The observers assessed each image and categorized the patients into five CO-RADS groups. A total of 630 participants were included in this study. The inter-observer agreement was almost perfect among the radiologists, substantial among a pulmonologist and the house officers, and moderate-to-substantial among the radiologists, the pulmonologist, and the house officers. There was substantial to almost perfect inter-observer agreement when reporting using the CO-RADS among observers with different experience levels. Although the inter-observer variability among the radiologists was high, it decreased compared to the pulmonologist and house officers. Radiologists, house officers, and pulmonologists applying the CO-RADS can accurately and promptly identify typical CT imaging features of lung involvement in COVID-19.

Serial lung ultrasound in monitoring viral pneumonia: the lesson learned from COVID-19.

Lung ultrasound (LUS) has proven to be useful in the evaluation of lung involvement in COVID-19. However, its effectiveness for predicting the risk of severe disease is still up for debate. The aim of the study was to establish the prognostic accuracy of serial LUS examinations in the prediction of clinical deterioration in hospitalised patients with COVID-19. Prospective single-centre cohort study of patients hospitalised for COVID-19. The study protocol consisted of a LUS examination within 24 h from admission and a follow-up examination on day 3 of hospitalisation. Lung involvement was evaluated by a 14-area LUS score. The primary end-point was the ability of LUS to predict clinical deterioration defined as need for intensive respiratory support with high-flow oxygen or invasive mechanical ventilation. 200 patients were included and 35 (17.5%) of them reached the primary end-point and were transferred to the intensive care unit (ICU). The LUS score at admission had been significantly higher in the ICU group than in the non-ICU group (22 (interquartile range (IQR) 20-26) versus 12 (IQR 8-15)). A LUS score at admission ≥17 was shown to be the best cut-off point to discriminate patients at risk of deterioration (area under the curve (AUC) 0.95). The absence of progression in LUS score on day 3 significantly increased the prediction accuracy by ruling out deterioration with a negative predictive value of 99.29%. Serial LUS is a reliable tool in predicting the risk of respiratory deterioration in patients hospitalised due to COVID-19 pneumonia. LUS could be further implemented in the future for risk stratification of viral pneumonia.

An evaluation of matrix metalloproteinase-9 (Mmp-9) and tissue inhibitor metalloproteinase-1 (Timp-1) Serum levels and the Mmp-9/Timp-1 Ratio in Covid-19 patients.

The progression of COVID-19 has different clinical presentations, which raises a number of immunological questions. This study aimed to investigate MMP-9 and TIMP-1 levels in patients diagnosed with COVID-19 and whether the MMP-9/TIMP-1 ratio is associated with lung involvement in COVID-19. This study was conducted with 192 patients and 45 healthy controls. ELISA was used to measure the MMP-9 and TIMP-1. The MMP-9 and TIMP-1 levels of the patients were found to be higher than those of the controls. MMP-9 and TIMP-1 were detected more in patients with lung involvement on chest CT scans than in those with no lung involvement on chest CT scans. A comparison of lung involvement levels revealed no difference was found between the groups. The MMP-9/TIMP-1 ratio was 5.8 in the group with lung involvement on chest CT scans and 6.1 in the group without lung involvement on chest CT scans. No difference was found between the two groups. A comparison with respect to lung involvement levels showed that the MMP-9/TIMP-1 ratio difference was found between the groups. Diagnostic and treatment methods targeting MMP-9 activity or neutrophil activation may be important in predicting lung involvement in COVID-19 and directing clinical outcomes.

Three Zone Scanning Protocol For Lung Ultrasound: An Anatomical Basis.

Lung ultrasound is rapidly gaining popularity based on point of care ease of use, diagnostic fidelity and lack of ionizing radiation. This was particularly notable at the beginning of the COVID-19 pandemic, where concerns of contamination of the x-ray department led to a reluctance to order frequent chest x-rays. Early COVID-19 lung involvement is of a bronchopneumonia, and patches of consolidation adjacent to the chest wall were easily detectable by ultrasound. A large number of proposed scanning protocols were advocated and are often complex and largely based on traditional stethoscope examination or access points on the chest wall rather than the underlying lung anatomy. A surgical understanding of lung anatomy and related surface anatomy has led us to develop a simplified three zone scanning protocol in 2013. The anterior zone corresponds to the upper lobe, and the posterior zone is divided between upper lobe and lower lobe. The relationship between lung lobes and the surface of the chest wall provides the anatomical basis for a simple three scanning zone lung ultrasound protocol.

DETERMINING THE ROLE OF HLA-B27 ON COVID-19

OBJECTIVE: Human leukocyte antigen-B27 (HLA-B27) binds antigenic peptides in the structure of some viruses and presents them to cytotoxic T lymphocytes and plays a role in the immune response against these viruses. It has also been found to be associated with auto-immunity as an inverse relationship and is associated with a number of auto-immune inflammatory diseases, especially Ankylosing Spondylitis (AS). Our aim in this study is to determine the role of HLA-B27 in Severe Acute Respiratory Syndrome (SARS)-Cov-2 (Coronavirus Disease-COVID-19). METHOD: 90 HLA-B27 positive and 96 HLA-B27 negative cases were included in the study. COVID-19 Polymerase Chain Reaction (PCR) results and Thorax Computed Tomography (CT) results, hospitalization and death records of the cases were retrospectively analyzed from the system records. Results were compared between HLA-B27 positive and negative groups. RESULTS: Of the HLA-B27 positive cases, 32.2% had COVID-19 positivity, 5.6% had COVID-19 lung involvement. 44.8% of HLA-B27 negative cases had COVID-19 positivity and 11.5% had lung involvement. There was no significant difference between the HLA-B27 positive and negative groups in terms of having COVID-19 infection, lung involvement, hospitalization and death rates. CONCLUSION: The results of our study show that HLA-B27 does not have a protective role in terms of having COVID-19 and lung involvement. Again, since no auto-immunity-related SARS was observed in any of the patients, it can be said that it did not increase the severity of the hyper-immunity-related disease and did not increase the risk of death in particular for COVID-19. These results need to be supported by more studies.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

BackgroundChest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and methodWe used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. ResultsSwin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. ConclusionsOur handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Dynamics of global and segmental strain as a marker of right ventricular contractility recovery in patients after COVID-19 pneumonia

Aim. To study the changes of morphological and functional right ventricular (RV) parameters depending on the severity of coronavirus infection 2019 (COVID-19) pneumonia over long-term follow-up.Material and methods. A total of 200 patients (men, 51,5%, mean age, 51,4±10,9 years) were examined at 2 control visits (3, 12 months after receiving two negative polymerase chain reaction tests). Patients were divided into following groups: group I (n=94) — lung tissue involvement ≥50% according to inhospital chest computed tomography (chest CT), group II (n=106) — lung tissue involvement˂50% according to chest CT.Results. The groups were comparable in key clinical and functional parameters 3 months after COVID-19 pneumonia. Speckle tracking echocardiography (STE) revealed a significant increase in following global longitudinal strain (LS) parameters: RV free wall endocardial LS (-22,7±3,2% and -24,3±3,8% in group I, p&lt;0,001; -23,2±3,5% and -24,5±3,4% in group II, p&gt;&lt;0,001), and RV endocardial LS (-21,0±3,1% and -22,5±3,7% in group I, p&gt;&lt;0,001, -21,5±3,2% and -22,6±3,3% in group II, p=0,001 ). Significant increase of segmental endocardial LS was revealed in group I in the basal segments of RV free wall (-26,2±5,1% and -28,1±5,1%, p=0,004) and interventricular septum (IVS) (-16,2 [13,9; 19,5]% and -17,5 [14,6; 21,4]%, p=0,024), IVS middle segment (-20,3±4,1% and -21,5±4,8%, p=0,030), as well as in group II in the apical segments of RV free wall (-21,9±6,7% and -24,4±5,2%, p=0,001) and IVS (-23,7±4,7% and -24,9±4,8%, p=0,014). Conclusion. Recovery of RV function during a 12-month follow-up period in patients with both severe and moderate/mild lung involvement in COVID-19 was detected using the STE method.&gt;˂0,001; -23,2±3,5% and -24,5±3,4% in group II, p˂0,001), and RV endocardial LS (-21,0±3,1% and -22,5±3,7% in group I, p˂0,001, -21,5±3,2% and -22,6±3,3% in group II, p=0,001 ). Significant increase of segmental endocardial LS was revealed in group I in the basal segments of RV free wall (-26,2±5,1% and -28,1±5,1%, p=0,004) and interventricular septum (IVS) (-16,2 [13,9; 19,5]% and -17,5 [14,6; 21,4]%, p=0,024), IVS middle segment (-20,3±4,1% and -21,5±4,8%, p=0,030), as well as in group II in the apical segments of RV free wall (-21,9±6,7% and -24,4±5,2%, p=0,001) and IVS (-23,7±4,7% and -24,9±4,8%, p=0,014).Conclusion. Recovery of RV function during a 12-month follow-up period in patients with both severe and moderate/mild lung involvement in COVID-19 was detected using the STE method.

HRCT Chest in Patient of COVID-19 Pneumonia at a tertiary College of Medical Sciences, Bharatpur, Nepal

Background: COVID19 pneumonia is caused by novel corona virus. The HRCT chest is helpful in identification of COVID-19 Pneumonia. CT chest play a crucial role in assessing the pattern of manifestation and severity of lung involvement in COVID-19. The objective of this study is to described high resolution computed tomography (HRCT) chest findings in COVID-19 Pneumonia patients in college of medical sciences Bharatpur Nepal. Method: A prospective, HRCT Chest findings were analaysed in 375 patients. Patients with high CORADS score and suspicious for COVID-19 pneumonia were further tested positive in PCR examination. The study was done for a year period of time Result: A total 375 patients suspected for COVID-19 pneumonia in HRCT, 341 were PCR positive. The mean age affected by viral pneumonia was 51.9 (51-60 year group). The male were 240 and female were 135. Ground glass opacities is noted in 188 patients, followed by GGO and consolidation in 177, consolidations only in 7 and others findings chest findings in 3 patients. Conclusion: HRCT chest can help in early detection of COVID-pneumonia. It can also asses the percentages of lung involvement and severity of disease that can guide physician.

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Abstract Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT. Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists. Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy. Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

BackgroundThe role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.MethodsThe algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.ResultsCompared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx.ConclusionThe results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

The D allele of angiotensin-converting enzyme gene insertion/deletion polymorphism is associated with the lung involvement in COVID-19

Abstract Objectives In COVID-19, severe lung involvement develops in some patients. The reason for the predisposition to lung involvement in some patients is not yet fully understood. Genetic variabilities in angiotensin-converting enzyme (ACE) may explain why some patients are more susceptible to lung injury. Thus, the ACE gene insertion/deletion (I/D) polymorphism was investigated in COVID-19 patients with and without lung involvement. Methods The study involved 216 patients who were divided into two groups as with and without pulmonary involvement according to their thoracic computed tomography (CT) scan findings. The ACE I/D gene polymorphism was determined. Results Carriers of the DD genotype had a 4.05-fold (OR=4.05, 95% CI: 1.66–9.86, p=0.001) greater incidence of pulmonary involvement. The probability of lung involvement was 2.41-fold higher in D allele carriers (OR=2.41, 95% CI: 1.62–3.60, p=0.000). The I allele was found to be protective and diminished the occurrence of lung involvement (OR=0.41, 95% CI: 0.28–0.62, p=0.000). Conclusions In COVID-19 patients, the I allele may lower the risk of lung injury and provide a protective effect. Conversely, the D allele may raise the risk of lung injury and lead to poor outcomes.

Age-Adjusted D-Dimer Levels May Improve Diagnostic Assessment for Pulmonary Embolism in COVID-19 Patients.

Introduction: SARS-CoV-2 infection leads to a hypercoagulable state. The prevalence of pulmonary embolism (PE) seems to be higher in this subgroup of patients. Patients and methods: We combined data from two tertiary referral centers specialized in the management of PE. The aims of this study were as follows: (1) to evaluate the prevalence of PE among a large population of consecutive patients admitted for COVID-19 pneumonia in two centers, (2) to identify a plasma D-dimer threshold that may be useful in PE diagnostic assessment, (3) to characterize the abnormalities associated with PE and mortality in COVID-19 patients. Results: The incidence of symptomatic acute PE was 19.3%. For diagnosing PE in COVID-19 patients, based on ROC curve analysis, we identified a D-dimer concentration/patient’s age ratio of 70, which improved D-dimer diagnostic capacity for PE and led to a reclassification improvement of 14% (NRI 0.14, p = 0.03) when compared to a cut-off level of 1000 ng/mL. Especially in severe COVID-19 lung involvement, D-dimer/age ratio cut-off equal to 70 was characterized by high diagnostic feasibility (sensitivity, specificity, negative predictive value, positive predictive value of 83%, 94%, 96%, and 73%, respectively). Apart from PE status, lung involvement and troponin T concentration were also independent predictors of in-hospital mortality. In the subgroup of PE patients, mortality was comparable with non-PE patients (19/88 (21.5%) vs. 101/368 (27.4%) for non-PE, p = 0.26) and was associated with older age, higher Bova scores, and higher troponin T concentrations. Age was the sole independent predictor for mortality in this subgroup. Conclusions: PE in COVID-19 patients is common, but it may not influence mortality when managed at a specialized center. In suspected PE, age-adjusted D-dimer levels (upper limit of normal obtained from the formula patient’s age × 70) may still be a useful tool to start the diagnostic workup. In COVID-19 patients without PE, older age, more extensive parenchymal involvement, or higher D-dimer levels are factors predicting mortality.

Protocolo diagnóstico de los infiltrados pulmonares febriles durante la pandemia de la COVID-19

Chest x-ray and computed tomography (CT) scans are important pillars for the diagnosis of lung involvement in COVID-19. The radiological image is typically characterized by peripheral, bilateral ground glass opacities (GGO), mainly located in the lower lobes. The limited sensitivity and specificity of these imaging techniques and possible atypical morphological or topographical presentations make it necessary to always rule out other infectious and non-infectious diseases. Therefore, it is fundamental to consider the patient's clinical and analytical data and the epidemiological circumstances.

Making the best use of CT Quantification Scores in Management of COVID-19 Patients

Because of the primary involvement of the respiratory system, chest computed tomography (CT) is strongly recommended in suspected COVID-19 cases, for both initial evaluation and follow-up [1]. At least seven scoring systems using chest CT have been proposed to quantify lung involvement in COVID-19 which are summarized in table 1 [1-10] and we use the term CT severity score (CTSS) to refer to them with numbers 1-7 to refer to a specific scoring system.

Effectiveness of Tocilizumab in Patients with Severe or Critical Lung Involvement in COVID-19: A Retrospective Study.

Introduction: Acute lung injury is associated with dysfunctional immune response to SARS-CoV-2. This leads to CRS, which require immunomodulatory treatments aiming to limit the excessive production of cytokines. The literature so far indicates the effectiveness of tocilizumab in patients with COVID-19-associated pneumonia, but there is no clear evidence of its effectiveness in patients with at least 50% lung involvement; therefore, we aimed to bridge this gap in knowledge. Materials and methods: Longitudinal data for 4287 patients with confirmed COVID-19 infection were collected between 1st March 2020 and 16th of January 2022. In total, 182 cases with lung involvement >50% and biochemical indicators of cytokine release storm (Il-6 >100 pg/mL) were selected and analyzed using non-parametric statistics and multivariate Cox models. Results: Among the 182 included patients, 100 (55%) were treated with TCZ, while 82 (45%) did not receive TCZ. The groups were balanced regarding demographics, lung involvement and biochemical markers. Overall mortality in the group was 63.1%. Mortality in the TCZ group was 58.0% compared to 69.5% (n = 57) in the non-TCZ group (p = 0.023). In multivariate Cox proportional hazards models, intravenous administration of tocilizumab was associated with lower probability of ICU admission (HR: 0333 (CI: 0.159–0.700, p = 0.004)) and lower mortality (HR: 0.57306 (CI: 0.354–0.927, p = 0.023)). Conclusions: Tocilizumab is effective as a treatment in the most severely ill patients, in whom the level of lung involvement by the inflammatory process can exceed 50% with coexisting biochemical indices of cytokine storm (Il-6 > 100 pg/mL).

What Is COVID 19 Teaching Us about Pulmonary Ultrasound?

In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement.

Euthyroid Sick Syndrome as a Prognostic Indicator of COVID-19 Pulmonary Involvement, Associated With Poorer Disease Prognosis and Increased Mortality

ObjectiveThe prevalence of euthyroid sick syndrome (ESS) and its association with the prognosis of COVID-19 and mortality in patients with lung involvement in COVID-19 have not yet been elucidated. MethodsClinical and laboratory data of patients with COVID-19 with or without ESS were collected retrospectively and analyzed on admission. All subjects were admitted to the Department of Internal Diseases and Clinical Pharmacology at Bieganski Hospital between December 2020 and April 2021. ResultsIn total, 310 medical records of patients with COVID-19 were analyzed retrospectively. Among 215 enrolled patients, 82 cases of ESS were diagnosed. The patients with ESS had higher pro-inflammatory factor levels, longer hospitalizations, and a higher risk of requiring high-flow nasal oxygen therapy or intubation than the patients without ESS. The Kaplan-Meier curve indicated that the patients with ESS had a lower probability of survival when computed tomography showed ≤50% parenchymal involvement compared with that in patients without ESS. However, no differences in mortality were noted in those with more than 50% parenchymal involvement. The survival curve showed that ESS was associated with a higher risk of mortality during hospitalization. ConclusionESS is closely associated with a poor prognosis, including longer hospitalizations, more frequent intubation, transfer to the intensive care unit, and a higher mortality rate in patients with COVID-19. ESS is a potential prognostic predictor of survival, regardless of lung involvement in COVID-19.

Hidden systolic dysfunction of the right ventricle in patients with increased pulmonary vascular resistance 3 months after COVID-19 pneumonia

Funding AcknowledgementsType of funding sources: None.BackgroundCoronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2. Pulmonary vascular resistance (PVR) is an important hemodynamic variable used in the management of patients with cardiovascular and pulmonary pathology. Doppler echocardiography has significantly impacted clinical medicine by its ability to assess intracardiac hemodynamics noninvasively. It seems actual to reveal the relationship between right heart function and lungs involvement in COVID-19.PurposeTo assess the relationship right ventricular (RV) echocardiographic structural and functional parameters with indicators of increased pulmonary vascular resistance (PVR) in patients 3 months after COVID-19 pneumonia.Methods96 patients (mean age 47.6 ± 15.2 years) were examined during hospitalization and at control visit (3 months follow-up). The equation tricuspid regurgitation velocity / time-velocity integral of the RV outflow tract X 10 + 0.16 has been used to determine PVR. Patients were divided into 2 groups: I group (n = 31) – patients with increase of PVR ≥1.5 Wood unit (WU) and II group (n = 65) – with decrease of PVR <1.5 WU.ResultsAt baseline groups did not differ in main clinical characteristics, including severity of lung involvement in COVID-19 by computed tomography (32.7 ± 22.1% vs 36.5 ± 20.4%, р=0.418). Linear, planimetric and volumetric parameters did not differ significantly between groups. At the control visit RV free wall (FW) global endocardial longitudinal strain (LS) (-19.3 [-17.9; -25.8] % in group I vs -23.4 [-19.8; -27.8] % in group II, p = 0.048), tricuspid annular plane systolic excursion (21.7 ± 3.8 mm vs 23.3 ± 3.4 mm, p = 0.040) and RV maximum systolic velocity assessed by tissue Doppler imaging (12.3 ± 2.4 cm/s vs 13.4 ± 2.0 cm/s, p = 0.024) were significantly reduced in group I, systolic pulmonary artery pressure (sPAP) according to Otto C. (32.0 [26,0; 35.0] mm Hg vs 23.0 [20.0; 28.0] mm Hg, p <0.001) was significantly increased in group I. According to logistic regression only endocardial RV FW LS (OR 0.859; 95% CI 0.746 - 0.989; p = 0.034) and sPAP (OR 1.248; 95% CI 1.108 - 1405; p <0.001) had an independent relationship with increase of PVR. According to Spearman correlation analysis the moderate relationship was found between PVR and mean pulmonary artery pressure according to Mahan G. (r = 0.516, p = 0.003), PVR and RV–pulmonary artery coupling (r=-0.509, p = 0.007) in group I at the control visit.ConclusionHidden RV systolic dysfunction defined as decreased endocardial RV FW LS to -19.3% is associated with increased PVR ≥1.5 WU in patients 3 months after COVID-19 pneumonia. Figure. RV FW LS by STE