Diagnosis Of COVID-19 Pneumonia Research Articles

The Comparison of Ultrasound and Tomographic Images of Lung Involvement in Critically Ill Patients With COVID-19 Pneumonia: A Prospective Observational Study.

Introduction Computed tomography (CT) has a high sensitivity for diagnosing COVID-19 pneumonia in critically ill patients, but it has significant limitations. Lung ultrasonography (LUS) is an imaging method increasingly used in intensive care units. Our primary aim is to evaluate the relationship between LUS and CT images by scoring a critically ill patient who was previously diagnosed with COVID-19 pneumonia and underwent CT, as well as to determine their relationship with the patient's oxygenation. Methods This was a single-center, prospective observational study. The study included COVID-19 patients (positive reverse transcription polymerase chain reaction, RT-PCR) who were admitted to the intensive care unit between June 2020 and December 2020, whose oxygen saturation (SpO2) was below 92%, and who underwent a chest tomography scan within the last 12 hours. CT findings were scored by the radiologist using the COVID-19 Reporting and Data System (CO-RADS). The intensivist evaluated 12 regions to determine the LUS score. The ratio of the partial pressure of oxygen in the arterial blood to the inspiratory oxygen concentration (PaO2/FiO2) was used to assess the patient's oxygenation. Results The study included 30 patients and found a weak correlation (ICC = 0.45, 95% CI = 0.25-0.65, p < 0.05) between total scores obtained from LUS and CT scans. The correlation between the total LUS score and oxygenation (r = -0.514, p = 0.004) was stronger than that between the CT score and oxygenation (r = -0.400, p = 0.028). The most common sonographic findings were abnormalities in the pleural line, white lung, and subpleural consolidation. On the other hand, the CT images revealed dense ground-glass opacities and consolidation patterns classified as CO-RADS 5. Conclusion A weak correlation was found between LUS and CT scores in critically ill COVID-19 pneumonia patients. Also, as both scores increased, oxygenation was detected to be impaired, and such a correlation is more evident with the LUS score.

Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos

IntroductionOur objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology. MethodsData was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed. ResultsThe sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70±14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%). ConclusionFor expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.

Laboratory changes and parenchymal changes in computed tomography after COVID-19 pneumonia

Aims: Novel coronavirus-2019 (2019-nCoV) has caused a global pandemic. Fort his reason, our study is to determine the variables of thorax tomography findings and laboratory data after covid 19 pneumonia in cases with severe covid 19 pneumonia and to detect the findings of possible interstitial lung diseases. Methods: In this single-center study, 61 consecutive patients were examined. These patients were admitted to the COVID-19 Pandemic Clinic of Malatya Training and Research Hospital between July 15, 2020 and August 28, 2020 and were hospitalized with a diagnosis of COVID-19 pneumonia. Patients were discharged after the illness, and after 6 months, they applied to the outpatient clinic for follow-up. In this study, we compared the changes in laboratory variables and thorax CT scans at the time of diagnosis and 6 months later. Patients were divided into groups 1 and 2. Group 1: patients who were diagnosed with COVID 19 pneumonia at initial presentation and had thorax CT and laboratory parameters at the time of diagnosis and group 2: patients who presented to the outpatient clinic for 6-month follow-up during the postcovid pneumonia period and had control thorax CT and laboratory parameters. Results: When the laboratory parameters of group 1 and group 2 patients were statistically compared. In addition to the increase in glucose, creatinine, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, lactate dehydrogenase, aptt, INR, fibrinogen, neutrophil percentage, mean erythrocyte hemoglobin, albumin, the decrease in calcium, sodium, leukocyte, platelet, hemoglobin, lymphocyte count, erythrocyte distribution range variables were found to be statistically significant (p&lt;0.05). In our study, ground glass opacity was seen most frequently in Group 1 Thorax CT and was found in 57 patients. In 10 of these 57 patients, ground-glass opacity was positive on Group 1 and Group 2 Thorax CT, while ground-glass opacity was negative on Group 2 Thorax CT in 47 patients. In 50 patients, consolidation was found on Group 1 Thorax CT and consolidation was positive on Group 1 and Group 2 Thorax CT in 5 patients. The changes in fibrosis, parenchymal band, reticular opacity, traction bronchiectasis, irregular interfaces, ground glass opacity, consolidation, and pulmonary nodule variables in Group 2 Thorax CT were statistically significant (p&lt;0.05). Conclusion: Laboratory data is very important in terms of COVID-19 infection diagnosis, prognosis and guiding treatment. It may be due to the fact that patients with residual abnormalities on control thorax CT after COVID-19 pneumonia were older, had more comorbid diseases, and had severe clinical disease at the time of hospitalization.

Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%–81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%–74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images

A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images

An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia

PurposeMedical imaging is one of the main methods of diagnosing COVID-19, along with real-time reverse trans­cription-polymerase chain reaction (RT-PCR) tests. The purpose of the study was to analyse the texture parameters of chest X-rays (CXR) of patients suspected of having COVID-19.Material and methodsTexture parameters of the CXRs of 70 patients with symptoms typical of COVID-19 infection were analysed using LIFEx software. The regions of interest (ROIs) included each lung separately, for which 57 para­meters were tested. The control group consisted of 30 healthy, age-matched patients with no pathological findings in CXRs.ResultsAccording to the ROC analysis, 13 of the tested parameters differentiate the radiological image of lungs with COVID-19 features from the image of healthy lungs: GLRLM_LRHGE (AUC 0.91); DISCRETIZED_Q3 (AUC 0.90); GLZLM_HGZE (AUC 0.90); GLRLM_HGRE (AUC 0.89); DISCRETIZED_mean (AUC 0.89); DISCRETIZED_Q2 (AUC 0.61); GLRLM_SRHGE (AUC 0.87); GLZLM_LZHGE (AUC 0.87); GLZLM_SZHGE (AUC 0.84); DISCRETIZED_Q1 (AUC 0.81); NGLDM_Coarseness (AUC 0.70); DISCRETIZED_std (AUC 0.64); CONVENTIONAL_Q2 (AUC 0.61).ConclusionsSelected texture parameters of radiological CXRs make it possible to distinguish COVID-19 features from healthy ones.

Can chest computed tomography be useful to emergency residents in the diagnosis of COVID-19 pneumonia?

&lt;b&gt;Aim:&lt;/b&gt; During the novel coronavirus disease (COVID-19) pandemic, the workload of emergency department residents (ER) has increased since emergency departments (EDs) are among the first health units to which patients apply. The aim of this study was to investigate the diagnostic value of chest computed tomography (CT) interpretations of emergency department residents in the diagnosis of COVID-19 pneumonia.&lt;br /&gt; &lt;b&gt;Material and methods: &lt;/b&gt;Patients who admitted to the tertiary ED between 30.03.2020 and 30.12.2020 with suspected COVID-19 pneumonia and underwent chest CT imaging were retrospectively analyzed. Chest CT images previously reported by the radiologist were reinterpreted by the ER and the diagnostic performance of the ER for COVID-19 pneumonia was calculated.&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;The study included 2045 patients with suspected COVID-19 pneumonia. Chest CT images of all patients were interpreted and compared by a radiologist and the ER In this study, interpretation by a radiologist was considered as the gold standard. In all patients, the diagnostic performance capacity of ER for COVID-19 pneumonia included a low sensitivity of 66%, a high specificity of 92%, a positive predictive value (PPV) of 83% and a negative predictive value (NPV) of 82%. When patients with positive COVID-19 RT-PCR test results were analyzed, the highest sensitivity, specificity, PPV and NPV of ER were 96%, 82%, 95%, 95% and 86%, respectively.&lt;br /&gt; &lt;b&gt;Conclusion: &lt;/b&gt;ER detected COVID-19 pneumonia in COVID-19 (+) patients with high diagnostic value and excluded inconsistent findings. Chest CT interpretation in the ER has a high diagnostic value. It is as important as any other radiologist in outbreak management in the ED.

The application value of lung ultrasound scoring in assessing disease severity: Evaluation of small-scale outbreaks of COVID-19.

This study explored the use of transthoracic lung ultrasound for evaluating COVID-19 patients, compared it with computed tomography (CT), and examined its effectiveness using 8 and 12 lung regions. A total of 100 patients with COVID-19 and 40 healthy volunteers were assessed using 12 regions (bilateral upper/lower regions of the anterior/lateral/posterior chest) and simplified 8 zones (bilateral upper/lower regions of the anterior/lateral chest) transthoracic lung ultrasound. The relationships between ultrasound, CT, and clinical indicators were analyzed to evaluate the diagnostic value of ultrasound scores in COVID-19. Increased disease severity correlated with increased 8- and 12-zone ultrasound and CT scores (all p < 0.05). The modified 8-zone method strongly correlated with the 12-zone method (Pearson's r = 0.908, p < 0.05). The 8- and 12-zone methods correlated with CT scoring (correlation = 0.568 and 0.635, respectively; p < 0.05). The intragroup correlation coefficients of the 8-zone, 12-zone, and CT scoring methods were highly consistent (intragroup correlation coefficient = 0.718, p < 0.01). The 8-zone ultrasound score correlated negatively with oxygen saturation (rs = 0.306, p < 0.05) and Ca (rs = 0.224, p < 0.05) and positively with IL-6 (rs = 0.0.335, p < 0.05), erythrocyte sedimentation rate (rs = 0.327, p < 0.05), alanine aminotransferase (rs = 0.230, p < 0.05), and aspartate aminotransferase (rs = 0.251, p < 0.05). The 12-zone scoring method correlated negatively with oxygen saturation (rs = 0.338, p < 0.05) and Ca (rs = 0.245, p < 0.05) and positively with IL-6 (rs = 0.354, p < 0.05) and erythrocyte sedimentation rate (rs = 0.495, p < 0.05). Lung ultrasound scores represent the clinical severity and have high clinical value for diagnosing COVID-19 pneumonia. The 8-zone scoring method can improve examination efficiency and reduce secondary injuries caused by patient movement.

Retrospective review of the code status of individuals with Down syndrome during the COVID-19 era.

Code status is a label in the medical record indicating a patient's wishes for end-of-life (EOL) care in the event of a cardiopulmonary arrest. People with intellectual disabilities had a higher risk of both diagnosis and mortality from coronavirus infections (COVID-19)than the general population. Clinicians and disability advocates raised concerns that bias, diagnostic overshadowing, and ableism could impact the allocation of code status and treatment options, for patients with intellectual disabilities, including Down syndrome (DS). To study this, retrospective claims data from the Vizient® Clinical Data Base (used with permission of Vizient, all rights reserved.) of inpatient encounters with pneumonia (PNA) and/or COVID-19 at 825 hospitals from January 2019 to June 2022 were included. Claims data was analyzed for risk of mortality and risk of "Do Not Resuscitate" (DNR) status upon admission, considering patient age, admission source, Elixhauser comorbidities (excluding behavioral health), and DS. Logistic regression models with backward selection were created. In total, 1,739,549 inpatient encounters with diagnoses of COVID-19, PNA, or both were included. After controlling for other risk factors, a person with a diagnosis of DS and a diagnosis of COVID-19 PNA had 6.321 odds ratio of having a DNR status ordered at admission to the hospital compared with those with COVID-19 PNA without DS. The diagnosis of DS had the strongest association with DNR status after controlling for other risk factors. Open and honest discussions among healthcare professionals to foster equitable approaches to EOL care and code status are needed.

Systemic lymphangitic carcinomatosis from unknown primary cancer mimicking lung disease in a new coronavirus infection (COVID-19): a case report

lymphangitic carcinomatosis is characterized by the spread of cancer cells through pulmonary lymphatic vessels, which results in secondary interstitial inflammation. The diagnosis of lymphangitic carcinomatosis is challenging due to nonspecific clinical symptoms and radiological findings that similar to those of interstitial lung disease.The purpose of the study was to demonstrate difficulties in the diagnosis of lymphangitic carcinomatosis with a systemic dissemination from unknown primary site. Case presentation. We report on a case of lymphangitic carcinomatosis from cancer of unknown primary origin with systemic involvement of internal organs imitating pulmonary disease caused by coVid-19. patient K., 58 years old, was treated for 12 bed-days in therapy departments with the diagnosis of bilateral viral coVid-19 pneumonia. clinical, laboratory and instrumental data were nonspecific. despite treatment, heart failure and kidney disease progressed to terminal stages, leading to the death of the patient. Autopsy revealed systemic lymphangitic carcinomatosis from cancer of unknown primary origin with involvement of the lungs, heart, liver, pancreas, spleen, kidneys and adrenal glands. lymphangitic carcinomatosis mimicked interstitial lung disease in a novel coronavirus infection coVid-19. metastasis to the heart mimicked coronary disease, and metastases to the kidneys led to acute kidney failure, which along with acute respiratory and heart failure caused the death of the patient.Conclusion. lymphangitic carcinomatosis, which does not have specific clinical manifestations, is able to mimic interstitial lung diseases, including a new coronavirus infection. cancer of unknown primary and multiple visceral metastases in this case confirm the concept of the independence of different types of tumor progression.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists

PurposeSeveral reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia.MethodsFor this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar’s test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar’s test.ResultsA comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009).ConclusionThis multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.

Prevalence of Adrenal Incidentaloma by Chest Computed Tomography in Patients with a Preliminary Diagnosis of COVID-19 Pneumonia

Aim: Adrenal incidentalomas are typically discovered as an incidental finding during routine computed tomography (CT) or magnetic resonance imaging (MRI) scans conducted for unrelated purposes. Our objective was to examine the frequency of adrenal incidentaloma, in individuals who underwent thoracic CT scan as a result of Covid-19 infection.&#x0D; Material and Methods: In the retrospective descriptive study, 808 patients who applied to the emergency radiology department with the suspicion of covid 19 and underwent thoracic CT between March 2020 and July 2020 were included. The presence of incidentaloma in the CT images of the patients was evaluated.&#x0D; Results: Adrenal incidentaloma was detected in 78 (9.7%) of the 808 patients, and of those 78 adrenal incidentalomas, 70 (8.7%) were unilateral and 8 (1%) were bilateral. The mean diameter of the adrenal incidentaloma was 20 mm, and it was 18.5 mm in females and 21.5 mm in males. A total of 808 patients, 351 (43.4%) females and 457 (56.6%) males, were included in the study.&#x0D; Conclusion: Adrenal incidentaloma have become more common radiological findings with the recent COVID-19 pandemic due to the increased frequency of CT scans, and it is important to appropriately manage these patients.

A detailed statistical analysis of the performance of CO-RADS and CT-severity score in the diagnosis of COVID-19 pneumonia compared to RT-PCR test: a prospective cohort study

BackgroundReports from international studies regarding the role of CT scan and RT-PCR test in the diagnosis of coronavirus disease has been a subject of controversy. The purpose of this study was to statistically compare the performance of CT in reporting chest CT scans of coronavirus disease according to Coronavirus Disease Reporting and Data System (CO-RADS) and CT severity score (CTSS) with the performance of RT-PCR test.ResultsThe analyzed CT scans of 144 participants were consistent with CO-RADS 1 (n = 38), CO-RADS 2 (n = 11), CO-RADS 3 (n = 35), CO-RADS 4 (n = 23), and CO-RADS 5 (n = 37). CTSS in CO-RADS 1 was (0.9 ± 4), CO-RADS 2 (4 ± 2), CO-RADS 3 (10.2 ± 2), CO-RADS 4 (14 ± 6) and CO-RADS 5 (19 ± 7). There was direct correlation between CO-RADS groups and CTSS (p < 0.001). The mean total CTSS was 10 ± 9 for the whole study population. Ninety-five CT scans were compatible with CO-RADS 3, 4 or 5 and 49 CT scans were compatible with CO-RADS 1 or 2, with a positive rate of 66% (95% CI 49%, 65%), PPV (55.41%), NPV (45.18%), accuracy (86.8%) and the overall sensitivity (93.18%) and specificity (76.8%) of CT in detecting COVID-19 pneumonia when categorized and analyzed according to CO-RADS and CTSS. Sixty-four patients had positive initial RT-PCR tests and 80 patients had negative initial RT-PCR test, with a positive rate of 44.4% (95% CI 35%, 51%), PPV (41.13%), NPV (59.51%), accuracy (74.3%), sensitivity (64.2%) and specificity (93.9%). The Kappa (κ) value of average inter-reader agreement was 88% (95% CI 80%, 96%).ConclusionsRT-PCR test showed higher specificity and NPV compared to CT in detecting COVID-19 pneumonia, while CT showed higher sensitivity, PPV, accuracy and positive rate, respectively. CT was superior to RT-PCR test in detecting COVID-19 pneumonia especially at early stages of the disease.

Validation of the CALL score as a mortality prediction tool in a cohort of hospitalized COVID-19 patients in Chile.

The CALL score is a predictive tool for respiratory failure progression in COVID-19. Whether the CALL score is useful to predict short- and medium-term mortality in an unvaccinated population is unknown. This is a prospective cohort study in unvaccinated inpatients with a COVID-19 pneumonia diagnosis upon hospital admission. Patients were followed up for mortality at 28 days, 3, 6, and 12 months. Associations between CALL score and mortality were analyzed using logistic regression. The prediction performance was evaluated using the area under a receiver operating characteristic curve (AUROC). A total of 592 patients were included. On average, the CALL score was 9.25 (±2). Higher CALL scores were associated with increased mortality at 28 days [univariate: odds ratio (OR) 1.58 (95% CI, 1.34-1.88), p < 0.001; multivariate: OR 1.54 (95% CI, 1.26-1.87), p < 0.001] and 12 months [univariate OR 1.63 (95% CI, 1.38-1.93), p < 0.001; multivariate OR 1.63 (95% CI, 1.35-1.97), p < 0.001]. The prediction performance was good for both univariate [AUROC 0.739 (0.687-0.791) at 28 days and 0.869 (0.828-0.91) at 12 months] and multivariate models [AUROC 0.752 (0.704-0.8) at 28 days and 0.862 (0.82-0.905) at 12 months]. The CALL score exhibits a good predictive capacity for short- and medium-term mortality in an unvaccinated population.

Accuracy of Initial Chest Computed Tomography Scan for Diagnosis of COVID-19: A Systematic Review

Abstract Background Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus (SARS- COVID-19) with recent outbreak occurred on 11 March 2020. A computerized tomography (CT) scan for chest was proved to be a helpful tool for diagnosis of COVID-19 pneumonia. But little is known about its diagnostic accuracy. Aim of the Work This systematic review aims at determining current evidence regarding accuracy of initial chest CT scan for detection of COVID-19 infection. Patients and Methods We conducted an electronic search through different databases; PubMed, SCOPUS, Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL). We included clinical trials whether randomized or nonrandomized prospective or retrospective cohorts, case control, cross sectional studies and case series larger than 10 cases). All studies that reported data about the diagnostic accuracy of initial chest CT for diagnosis of COVID-19 infection were enrolled. We evaluated the methodological quality for included studies using QUADAS-2 Risk of Bias tool. Results From a total 4617 screened citations, 40 studies met our inclusion criteria. We divided the identified studies into two groups: studies that reported both sensitivity and specificity (6 studies, n = 1431) and studies that included only positive COVID-19 cases from which we could estimate only sensitivity (34 studies, n = 3515). Among six studies that evaluated accuracy of CT chest, there was high sensitivity ranged from to 93% to 100% but CT chest specificity was low ranged from to 10% to 56%. Rest of 34 studies had a wide range of sensitivity between 61% and 100% with few studies reported sensitivity below 80%. Conclusion Our results suggest that CT chest is a great screening tool for COVID-19 with high sensitivity but it had low specificity. However, interpretation of CT chest along with other laboratory could enhance its diagnostic accuracy.

Caracterización metabólica de los cambios pulmonares estructurales en pacientes con hallazgos incidentales sugestivos de neumonía por COVID-19 en estudios 18F-FDG PET/CT. Perspectivas fisiopatológicas de imágenes multimodales obtenidas durante la pandemia

PurposeTo evaluate the metabolic uptake of different tomographic signs observed in patients with incidental structural findings suggestive of COVID-19 pneumonia through 18F-FDG PET/CT. Material and methodsWe retrospectively analyzed 596 PET/CT studies performed from February 21, 2020 to April 17, 2020. After excluding 37 scans (non-18F-FDG PET tracers and brain studies), we analyzed the metabolic activity of several structural changes integrated in the CO-RADS score using the SUVmax of multimodal studies with 18F-FDG. ResultsForty-three patients with 18F-FDG PET/CT findings suggestive of COVID-19 pneumonia were included (mean age: 68±12.3 years, 22 male). SUVmax values were higher in patients with CO-RADS categories 5–6 than in those with lower CO-RADS categories (6.1±3.0 vs. 3.6±2.1, p=0.004). In patients with CO-RADS 5–6, ground-glass opacities, bilaterality and consolidations exhibited higher SUVmax values (p-values of 0.01, 0.02 and 0.01, respectively). Patchy distribution and crazy paving pattern were also associated with higher SUVmax (p-values of 0.002 and 0.01). After multivariate analysis, SUVmax was significantly associated with a positive structural diagnosis of COVID-19 pneumonia (odds ratio=0.63, 95% confidence interval=0.41–0.90; p=0.02). The ROC curve of the regression model intended to confirm or rule out the structural diagnosis of COVID-19 pneumonia showed an AUC of 0.77 (standard error=0.072, p=0.003). ConclusionsIn those patients referred for standard oncologic and non-oncologic indications (43/559; 7.7%) during pandemic, imaging with 18F-FDG PET/CT is a useful tool during incidental detection of COVID-19 pneumonia. Several CT findings characteristic of COVID-19 pneumonia, specifically those included in diagnostic CO-RADS scores (5–6), were associated with higher SUVmax values.

CLINICAL AND PATHOANATOMICAL PARALLELS OF THE HEART DAMAGE DUE TO COVID-19

The aim – to investigate the clinical and pathomorphological features of the heart damagein patients who died due to COVID-19 pneumonia.Materials and methods. A retrospective analysis of the medical records of 556 patients,hospitalized to Lviv Clinical Emergency Care Hospital in January- February 2021, witha diagnosis of COVID-19 pneumonia and 90 reports of pathological examination ofthe causes of death of patients with COVID-19 pneumonia was carried out. Clinicalparameters (demographic data, comorbidities, presence of COVID-19 pneumoniacomplications, complaints, medical history, results of physical examination andadditional laboratory and instrumental investigations, performed during hospitalization,including levels of NT-proBNP and troponin, determined by immunofluorescence method)and results of pathological examination were evaluated.The results. During life, most patients who died due to COVID-19 pneumonia werediagnosed with acute respiratory distress syndrome (ARDS), the development of multipleorgan failure, rarely PE. They showed significantly lower capillary blood oxygensaturation indicators compared to similar indicators of recovered patients, as well asan increase in markers of systemic inflammation (level of leukocytes and interleukin-6),fibrin degradation products (D-dimers) and markers of multiorgan failure (creatinine,NT-proBNP, troponin I). The most frequent pathomorphological signs of heartdamage in patients with COVID-19 were interstitial edema with hyperemia (63.5 %) and fragmentation of cardiomyocytes (34.1 %). Every fourth patient had mononuclear cell infiltration, which was associated with increased level of NTproBNP, oxygen saturationdecrease in capillary blood, and increase in aspartate aminotransferase. Myocardialinfarction, pericarditis, and myocarditis occurred more rarely in patients with COVID-19.Conclusions. The variety of clinical scenarios of heart damage requires a high level ofsuspicion in the early stages of the disease and the use of appropriate investigations toidentify its cause.

Comparison of ultrasound signs, computed tomography data and morphological examination of the lungs in patients with coronavirus infection: post hoc analysis

INTRODUCTION: Ultrasound examination (US) of the lungs has shown high efficiency in the diagnosis of COVID-19 pneumonia. The aim of the research was studying the correspondence of computed tomography (CT) US signs of the lungs and morphological data in patients with COVID-19 pneumonia. MATERIALS AND METHODS: The post hoc analysis included 388 patients who simultaneously underwent ultrasound and CT of the lungs. Lung ultrasound was performed according to the 16-zone “Russian Protocol”. Morphological data were obtained from the results of pathoanatomic examination of deceased patients. RESULTS: The comparison of signs detected by CT and ultrasound of the lungs was performed during a multidimensional correspondence analysis. The analysis was carried out using a three-dimensional solution that explained 64.9 % of inertia (p &lt; 0.001). CT signs of “ground glass opacity” (100 %) corresponded to the B-line at ultrasound (100 %), CT of the consolidation sign (44.8 %) — ultrasound signs of consolidation (46.9 %), aerobronchogram of CT (34 %) — aerobronchogram of ultrasound (36.9 %), free liquid CT (11.1 %) — free liquid Ultrasound (13.9 %). CT signs of reticular changes (29.6 %) and “cobblestone pavement” (12.4 %) corresponded to various combinations of ultrasound signs of subpleural consolidation and B-lines. The B-lines were caused by the exudation of fluid and protein molecules into the intraalveolar space against the background of massive death of alveolocytes and formed by the development of intraalveolar edema and the formation of hyaline membranes. The ultrasonic sign of consolidation appeared in the airless zone of the lungs. Subpleural consolidation are caused by thickening and inflammatory infiltration of the pleura, diffuse alveolar damage, with intraalveolar edema, death and decay of alveolocytes, perivascular inflammatory cell reaction/ During treatment for more than 7 days, consolidation in the lung tissue developed due to the disorganization of the organ structure due to the progression of fibrosis. CONCLUSION: Multivariate correspondence analysis showed correlation of CT signs and US signs of the lungs. Morphological analysis showed polymorphism of histological data that caused the formation of ultrasound signs.