Diagnosis Of COVID-19 Pneumonia Research Articles

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia.

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

Differential alterations in peripheral lymphocyte subsets in COVID-19 patients: upregulation of double-positive and double-negative T cells.

BackgroundViral infections cause alteration in the total number of lymphocytes and their subset distribution. We aimed to study peripheral blood lymphocyte subsets in COVID-19 patients and to correlate these subsets with clinical and laboratory data, which may help in clarifying the pathogenesis to develop novel diagnostic and prognostic biomarkers for COVID-19.MethodsTwenty-six reverse-transcription polymerase chain reaction (RT-PCR) confirmed COVID-19 patients were subjected to medical history-taking and a thorough clinical examination. Laboratory tests included complete blood count, D dimer, ferritin, and C-reactive protein (CRP). Chest CT was used to diagnose COVID-19 pneumonia. Lymphocyte subsets were compared with those in 20 healthy controls using flow cytometry.ResultsLeucopenia, relative neutrophilia, lymphopenia, eosinopenia together with marked elevation in neutrophil/lymphocyte ratio were observed in our COVID-19 patients. A marked reduction was observed in T cells, including both CD4 and CD8 cells, natural killer (NK), and natural killer T cells (NKT). Double-positive T (DPT) cells, double-negative T (DNT) cells, and B cells were elevated in the patients relative to the other lymphocyte subsets.ConclusionImmune-inflammatory parameters are of utmost importance in understanding the pathogenesis and in the provisional diagnosis of COVID-19. Yet, adequate care must be taken during their interpretation because of the vast discrepancies observed between studies even in the same locality. Further studies are needed to clarify the role of B cells, DPT, and DNT cells in the pathogenesis and control of COVID-19.

The administration of interferon-beta1b and favipiravir in patients with COVID-19 pneumonia

Purpose: To assess the efficacy of interferon-beta1b (INF-beta1b) in combination with favipiravir in early treatment of patients with COVD-19 pneumonia. Methods: This prospective non-controlled study was performed on admitted patients with moderate to severe COVID-19 disease to Amir-Alam hospital. The diagnosis of COVID-19 pneumonia was based on positive RT-PCR test from nasopharyngeal samples and typical spiral chest CT scan findings. At the time of this study, there was the shortage of efficient anti-COVID drugs especially “Remdesivir” due to sanction of Iran. All the patients received subcutaneous IFN-beta1b plus favipiravir. The patients with severe infection received intravenous dexamethasone. Those who underwent invasive or noninvasive mechanical ventilation, at the admission onset, were excluded from the study. Results: One hundred and eighty-two patients with moderate to severe COVID-19 pneumonia were included in this study. Among182 patients, 90 cases had moderate and 92 had severe pneumonia. Twenty-one cases with severe pneumonia and 14 cases with moderate pneumonia were transferred to ICU to receive invasive or non-invasive mechanical ventilation. The mean duration of hospital ward admission and ICU stay was 10.69±7.54 and 5.45±2.72 days, respectively. One hundred and seventy-four patients (80.7 %) recovered completely and discharged without need to ICU admission. The response rate was the same in patients with moderate and severe pneumonia. Nine cases (4.9 %) were passed away. Diabetic patients had higher mortality rates. Conclusion: This study showed the efficacy of IFN-beta1b plus favipiravir in early treatment of patients with moderate to severe COVID-19 pneumonia who did not need to mechanical ventilation.

Boosting Performance of Transfer Learning Model for Diagnosis of COVID-19 from Computer Tomography Scans

Early-stage rapid and accurate diagnosis of COVID-19 pneumonia is of great importance as a measure to the fight against the pandemic. Even if real-time reverse transcription-polymerase chain reaction (RT-PCR) test seems like a gold standard for determining COVID-19, the availability and the accuracy is still a challenge. Thus, alternative diagnostic techniques are required for controlling the spreading of the disease. Amongst the radiodiagnostic methods, the computer tomography (CT) technique is one of the most powerful candidates for screening COVID-19 pneumonia accurately. In this study, it is aimed to develop a reliable transfer learning-based CNN model tailored to detect the COVID-19 from chest CT scans with high accuracy and sensitivity to help to accelerate the application of the required treatment and taking of measures. The CT scan dataset used in the training process of the CNN model was obtained from “SARS-CoV-2 CT-Scan Dataset”. This dataset contains 1252 CT scans for positive COVID-19 case and 1230 CT scans for the non-COVID-19 case, 2482 CT scans in total, all data have been collected from real patients from hospitals in Sao Paulo, Brazil. ResNet18, ResNet50, VGG16, AlexNet, and SqueezeNet1_1 architectures were re-trained to extract COVID-19 classes by transfer learning. The highest classification performance parameters were obtained for ResNet50 architecture and were calculated as 99.80% accuracy, 99.61 % precision, and 100.00% sensitivity. The activation maps were created to highlight the crucial areas of the CT images and improve causality and intelligibility. The developed transfer learning model can be utilized for reliable clinical diagnosis of COVID-19 cases from CT images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

Appropriateness of lung ultrasound for the diagnosis of COVID-19 pneumonia.

BackgroundChest radiography (CXR) and computerized tomography (CT) are the standard methods for lung imaging in diagnosing COVID‐19 pneumonia in the intensive care unit (ICU), despite their limitations. This study aimed to assess the performance of bedside lung ultrasound examination by a critical care physician for the diagnosis of COVID‐19 pneumonia during acute admission to the ICU.MethodThis was an observational, prospective, single‐center study conducted in the intensive care unit of Adan General Hospital from April 10, 2020, to May 26, 2020. The study included adults with suspicion of COVID‐19 Infection who were transferred to the ICU. Patients were admitted to the ICU directly from the ED after reverse transcriptase‐polymerase chain reaction (RT‐PCR) swabs were sent to the central virology laboratory in Kuwait, and the results were released 16 to 24 hours after the time of admission. A certified intensivist in critical care ultrasound performed the lung ultrasound within 12 hours of the patient's admission to the ICU.The treating physician confirmed the diagnosis of COVID‐19 pneumonia based on a set of clinical features, inflammatory markers, biochemical profile studies, RT‐PCR test results, and CXR.ResultsOf 77 patients with suspected COVID‐19 pneumonia, 65 (84.4%) were confirmed. The median age of the patients was 48 (31‐68) years, and 51 (71%) were men.In the group of patients with confirmed COVID‐19 pneumonia, LUS revealed four signs suggestive of COVID‐19 pneumonia in 63 patients (96.9%) (sensitivity 96.9%, CI 85%‐99.5%). Two patients presented with unilateral lobar pneumonia without other ultrasonic signs of COVID‐19 pneumonia but with positive RT‐PCR results. Among patients in the group without COVID‐19 pneumonia who had negative RT‐PCR results, 11 (91.7%) were LUS negative for COVID‐19 pneumonia (specificity 91.7%, 95% CI 58.72%‐99.77%).ConclusionsDuring the COVID‐19 outbreak, LUS allows the identification of early signs of interstitial pneumonia. LUS patterns that show a combination of the four major signs offer high sensitivity and specificity compared to nasopharyngeal RT‐PCR.

Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images

X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

Evaluation of clinical parameters of patients with Covid-19 pneumonia on admission and medications used for their comorbidities

Background: Risk factors and molecular mechanisms of severe respiratory involvement of Covid-19 are investigated more intensely and some doubts about non-steroidal antiinflammatory drugs (NSAID) and renin-angiotensin-aldosterone system (RAAS) inhibitors has also emerged.
 Aims and Objective: We aimed to detect potential associations between medications taken regularly for comorbid diseases before the diagnosis of Covid-19 pneumonia and demographic or clinical features on admission.
 Materials and Methods: Patients diagnosed with pneumonia between March 11th, 2020 and April 15th, 2020 in emergency, internal medicine and cardiology outpatient clinics were recruited and their clinical and demographic features including blood tests, oxygen saturations (SO2) along with degree of lung infiltrations in computed tomography on admission were analyzed in regard to their medications used regularly.
 Results: Angiotensin converting enzyme inhibitors (ACE inh.) or angiotensin receptor blockers (ARB) users were older than no-ACE inh./ARB group and had significantly lower SO2 on admission, whereas NSAID users with Covid-19 displayed a marked female predominance and lower hemoglobin levels than no-NSAID group. Proton pump inhibitor (PPI) users had also a significant female predominance and older age with lower hemoglobin levels. Lastly, antiaggregant users with Covid-19 had a lower SO2 on admission and overall, cases with regular drug intake were markedly associated with older age.
 Conclusion: Noteworthy associations were observed between demographic and clinical features on admission for Covid-19 pneumonia and major drug groups used regularly including RAAS inhibitors, NSAIDs and proton pump inhibitors.

In the ED, LUS and CT did not differ for sensitivity or specificity for diagnosing COVID-19 pneumonia

Lieveld AW, Kok B, Schuit FH, et al. Diagnosing COVID-19 pneumonia in a pandemic setting: Lung Ultrasound versus CT (LUVCT)-a multicentre, prospective, observational study. ERJ Open Res. 2020;6:00539-2020. 33442553.

Radiographers and COVID-19 pneumonia: Diagnostic performance using CO-RADS

IntroductionA more structured role of radiographers is advisable to speed up the management of patients with suspected COVID-19. The purpose of our study was to evaluate the diagnostic performance of radiographers in the detection of COVID-19 pneumonia on chest CT using CO-RADS descriptors. MethodsCT images of patients who underwent RT-PCR and chest CT due to COVID-19 suspicion between March and July 2020 were analysed retrospectively. Six readers, including two radiologists, two highly experienced radiographers and two less experienced radiographers, independently scored each CT using the CO-RADS lexicon. ROC curves were used to investigate diagnostic accuracy, and Fleiss’κ statistics to evaluate inter-rater agreement. Results714 patients (419 men; 295 women; mean age: 64 years ±19SD) were evaluated. CO-RADS> 3 was identified as optimal diagnostic threshold. Highly experienced radiographers achieved an average sensitivity of 58.7% (95%CI: 52.5–64.7), an average specificity of 81.8% (95%CI: 77.9–85.2), and a mean AUC of 0.72 (95%CI: 0.68–0.75). Among less experienced radiographers, an average sensitivity of 56.3% (95%CI: 50.1–62.2) and an average specificity of 81.5% (95%CI: 77.6–84.9) were observed, with a mean AUC of 0.71 (95%CI: 0.68–0.74). Consultant radiologists achieved an average sensitivity of 60.0% (95%CI: 53.7–65.8), an average specificity of 81.7% (95%CI: 77.8–85.1), and a mean AUC of 0.73 (95%CI: 0.70–0.77). ConclusionRadiographers can adequately recognise the classic appearances of COVID-19 on CT, as described by the CO-RADS assessment scheme, in a way comparable to expert radiologists. Implications for practiceRadiographers, as the first healthcare professionals to evaluate CT images in patients with suspected SARS-CoV-2 infection, could diagnose COVID-19 pneumonia by means of a categorical reporting scheme at CT in a reliable way, hence playing a primary role in the early management of these patients.

Pneumocystis jirovecii Pneumonia Mimicking COVID-19 Pneumonia In A Patient With Newly Diagnosed Advanced HIV Disease

During the pandemic, diagnosis of COVID-19 infection must be excluded in patients presenting with respiratory features, especially in the area where the incidence was high. This case report highlights a man who came with respiratory infection symptoms and the chest radiograph suspicious of COVID-19 pneumonia; however, his swab test for RT-PCR was negative for COVID-19. Later he was found to have a positive anti-HIV antibody and developed advanced HIV infection by multiple opportunistic infections. A 45-year-old man presented with chronic cough and dyspnea for three months which worsened five days before the presentation. It was associated with diarrhoea, dysphagia, weight loss, left lower limb weakness, with bowel and urinary incontinence. Chest auscultation revealed generalized crepitations. The first chest radiograph showed bilateral reticular opacities extending to the lung peripheries when the suspected diagnosis of COVID-19 pneumonia was made. The nasopharynx swab for RT-PCR for COVID-19 was negative, and the sputum was positive for Pneumocystis jirovecii. The contrast-enhanced CT (CECT) brain revealed changes consistent with brain abscess. The cerebrospinal fluid (CSF) examination was consistent with cryptococcal infection. He was initially treated in the Severe Acute Respiratory Infection (SARI) ward because of the initial impression of COVID-19. He was tested positive for Human Immunodeficiency Virus (HIV) rapid test and PCR, RPR and TPHA for syphilis. He was diagnosed with Pneumocystis jirovecii pneumonia, cryptococcal meningitis, brain abscess, oral and oesophageal candidiasis and syphilis. He was treated with co-trimoxazole, Amphotericin B, flucytosine, fluconazole, penicillin G and Nystatin. He initially improved upon treatment but later succumbed to his illnesses. Given the current situation of COVID-19, any patient with acute respiratory symptoms must be tested to rule out COVID-19 pneumonia. However, in a newly diagnosed advanced HIV patient presented with respiratory infection symptoms and extrapulmonary symptoms, opportunistic infections should be excluded along with COVID-19.

Evaluation of spleen volume in thoracic computed tomography in patients with COVID-19 pneumonia

Abstract Objectives There is not enough data on the effect of COVID 19 on spleen volume in patients with COVID-19. Our aim was to compare the spleen volume of the COVID-19 patients with the control group. Methods 214 Patients (121 men, 93 women) who have a diagnosis of COVID 19 who have thorax CT were included in the study. In the patient group, there was evidence of viral pneumonia on thoracic CT or PCR was positive, all of them were patients who received treatment for COVID. The control group consisted of 185 patients (106 men, 79 women). Interobserver agreement was calculated. Analysis of the receiver operating characteristic (ROC) curve was used to identify a spleen volume cutoff value at which the accuracy for COVID 19 diagnosis was maximised. Results The mean spleen volume of the patient group was found to be 260 (range, 96.3-565, SD: 82.5) which was statistically significantly higher than the control group which was 220 (range, 125.9-331.9, SD: 34.5) (p <0.05). Excellent agreement was found between two blind observers and between intraobserver spleen volume measurements. In our study, the spleen volume for the diagnosis of COVID-19 pneumonia was 208.4 (AUC 0.639; 95% CI 0.584-0.695), sensitivity and specificity were 69.6% and 66.7%, respectively. Conclusions Quantitative evaluation of spleen volume can give an idea about the COVID diagnosis. Spleen volume is higher in COVID group than control group.

Efficacy of chest CT scan for COVID-19 diagnosis in a low prevalence and incidence region.

ObjectivesValue of chest CT was mainly studied in area of high COVID-19 incidence. The aim of this study was therefore to evaluate chest CT performances to diagnose COVID-19 pneumonia with regard to RT-PCR as reference standard in a low incidence area.MethodsA survey was sent to radiology department in 4 hospitals in an administrative French region of weak disease prevalence (3.4%). Study design was approved by the local institutional review board and recorded on the clinicaltrial.gov website (NCT04339686). Written informed consent was waived due to retrospective anonymized data collection. Patients who underwent a RT-PCR and a chest CT scan within 48 h for COVID-19 pneumonia suspicion were consecutively included. Diagnostic accuracy including the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of chest CT regarding RT-PCR as reference standard were calculated.ResultsOne hundred twenty-nine patients had abnormal chest CT findings compatible with a COVID-19 pneumonia (26%, 129/487). Among the 358 negative chest CT findings, 3% (10/358) were RT-PCR positive. Chest CT sensitivity, specificity, positive, and negative predictive value were respectively 87% (IC95: 85, 89; 69/79), 85% (IC95: 83, 87; 348/408), 53% (IC95: 50, 56; 69/129), and 97% (IC95: 95, 99; 348/358).ConclusionsIn a low prevalence area, chest CT scan is a good diagnostic tool to rule out COVID-19 infection among symptomatic suspected patients.Key Points • In a low prevalence area (3.4% in the administrative area and 5.8% at mean in the study) chest CT sensitivity and specificity for diagnosing COVID-19 pneumonia were 87% and 85% respectively. • In patients with negative chest CT for COVID-19 pneumonia, the negative predictive value of COVID-19 infection was 97% (348/358 subjects). • Performance of CT was equivalent between the 4 centers participating to this study. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-07863-4.

Evolution of lung function and chest CT 6 months after COVID-19 pneumonia: Real-life data from a Belgian University Hospital

IntroductionMost post COVID-19 follow-up studies are limited to a follow-up of 3 months. Whether a favorable evolution in lung function and/or radiological abnormalities is to be expected beyond 3 months is uncertain. Materials and methodsWe conducted a real-life follow-up study assessing the evolution in lung function, chest CT and ventilation distribution between 10 weeks and 6 months after diagnosis of COVID-19 pneumonia. ResultsSeventy-nine patients were assessed at 6 months of whom 63 had chest CT at both follow-up visits and 46 had multiple breath washout testing to obtain lung clearance index (LCI). The study group was divided into a restrictive (n = 39) and a non-restrictive subgroup (n = 40) based on TLC z-score. Restriction was associated with a history of intubation, neuromuscular blockade use and critical illness polyneuropathy. Restriction significantly improved over time, but was not resolved by 6 months (median TLC z-score of −2.2 [IQR: −2.7; −1.5] at 6 months versus −2.7 [IQR: −3.1; −2.1] at 10 weeks). LCI did not evolve between both follow-up visits. Symptoms and chest CT score improved irrespective of restriction. ConclusionWe observed a disconnect between the improvement of COVID-19 related symptoms, chest CT lesions, and corresponding lung function. While CT imaging is almost normalized at 6 months, a further reduction of pulmonary restriction may be hoped for beyond 6 months in those patients showing restriction at their first follow-up visit.

Life-threatening DRESS syndrome with kidney damage following severe COVID-19 in a patient with Down syndrome

A 49-year-old man with Down syndrome and obesity was admitted to our institution with a diagnosis of COVID-19 pneumonia, confirmed by tomography and real-time PCR test for SARS-CoV-2. In less...

Clinical Features of the 60 Years and Older Patients Infected with 2019 Novel Coronavirus: Can We Predict Mortality Earlier?

Introduction: The novel coronavirus (COVID-19), which has affected over 100 countries in a short while, progresses more mortally in elderly patients with comorbidities. In this study, we examined the epidemiological, clinical, and laboratory characteristics of the patients aged 60 and over who had been infected with COVID-19. Methods: The data of the patients admitted to the hospital within 1 month from May 8, 2020 onwards and hospitalized for COVID-19 pneumonia were obtained from the hospital medical records, and the epidemiological, clinical, and laboratory parameters of the patients during the admission to the emergency department were examined. Patients were divided into 2 groups regarding the criteria of having in-hospital mortality (mortality group) and being discharged with full recovery (survivor group). The factors, which could have an impact on the mortality, were investigated using a univariate and multivariate logistic regression analysis. Results: This retrospective study included 113 patients aged 60 years and older, with a confirmed diagnosis of COVID-19 pneumonia. The mean age of the patients was 70.7 ± 7.9, and 64.6% (n = 73) of them were male. The mortality rate was 19.4% (n = 22). Among the comorbid illnesses, only renal failure was significant in the mortality group (p = 0.04). A CURB-65score ≥3 or pneumonia severity index (PSI) class ≥4 manifested a remarkable discrimination ability to predict 30-day mortality (p < 0.001). When the laboratory parameters were considered, the value of neutrophil to lymphocyte ratio (NLR) was significant in predicting mortality in univariate and multivariate analysis (odds ratio [OR] = 1.11; 95% confidence interval [95% CI], 1.03–1.21; p = 0.006, and OR = 1.51; 95% CI, 1.11–2.39; p = 0.044, respectively). Conclusion: In our study, NLR was determined to be an independent marker to predict in-hospital mortality among patients with COVID-19. PSI and CURB-65 revealed a considerably precise prognostic accuracy for the patients with COVID-19 in our study as well. Moreover, thanks to that NLR results in a very short time, it can enable the clinician to predict mortality before the scoring systems are calculated and hasten the management of the patients in the chaotic environment of the emergency room.

Higher Accuracy of Lung Ultrasound over Chest X-ray for Early Diagnosis of COVID-19 Pneumonia.

Background: The COVID-19 pandemic rapidly strained healthcare systems worldwide. The reference standard for diagnosis is a positive reverse transcription polymerase chain reaction (RT-PCR) test, but results are not immediate and sensibility is variable. Aim: To evaluate the diagnostic accuracy of lung ultrasound compared to chest X-ray for COVID-19 pneumonia. Design and Setting: A retrospective analysis of symptomatic patients admitted into one primary care centre in Spain between March and September 2020. Method: Patients’ chest X-rays and lung ultrasounds were categorized as normal or pathologic. RT-PCR confirmed COVID-19 infection. Pathologic lung ultrasound images were further categorized as showing either local or diffuse interstitial disease. McNemar and Fisher tests were used to compare diagnostic accuracy. Results: Most of the 212 patients presented fever at admission, either as a standalone symptom (37.74% of patients) or together with others (72.17% of patients). The positive predictive value of the lung ultrasound was 90% for the diffuse interstitial pattern and 46.92% for local pattern. The lung ultrasound had a significantly higher sensitivity (82.75%) (p < 0.001), but lower specificity (71%) than the chest X-ray (54.02% and 86%, respectively) (p = 0.008) for identifying interstitial lung disease. Moreover, sensitivity of the lung ultrasound for severe interstitial disease was 100%, and was significantly higher than the chest X-ray (58.33%) (p = 0.002). Conclusion: The lung ultrasound is more accurate than the chest X-ray for identifying patients with COVID-19 pneumonia and it is especially useful for those presenting diffuse interstitial disease.

Correlation of autopsy pathological findings and imaging features from 9 fatal cases of COVID-19 pneumonia.

We aimed to investigate the relationship of radiological features and the corresponding pulmonary pathology of patients with Coronavirus Disease (COVID-19) pneumonia.In this multicenter study, serial chest CT and radiographic images from 9 patients (51–85 years old, 56% male) were reviewed and analyzed. Postmortem lungs were sampled and studied from these autopsies, with a special focus on several corresponding sites based on imaging features.The predominant pattern of pulmonary injury in these 9 cases was diffuse alveolar damage (DAD) and interstitial inflammation. Moreover, acute fibrinous exudates, organization, inflammatory cell infiltration, hyaline membranes, pulmonary edema, pneumocyte hyperplasia, and fibrosis were all observed. The histopathology features varied according to the site and severity of each lesion. In most of the 9 cases, opacities started from a subpleural area and peripheral structures were more severely damaged based on gross views and pathological examinations. Fibrosis could occur in early stages of infection and this was supported by radiological and pathological findings. The radiological features of COVID-19 pneumonia, at the critically ill stage, were diffuse ground-glass opacities with consolidation, interstitial thickening, and fibrous stripes, which was based in the fibrous tissue proliferation in the alveolar and interlobular septa, and filled alveoli with organizing exudation. Fungal and bacterial co-infections were also observed in 6 cases.Typical imaging features can be correlated with underlying pathological findings. Combining assessments of imaging features with pathological findings therefore can enhance our understanding of the histopathological mechanism of COVID-19 pneumonia, and facilitate early radiological diagnosis and prognosis estimation of COVID-19 pneumonia, which has important implications for the development of clinical targeted treatments and research related to COVID-19 pneumonia.

High-Resolution CT Chest Findings in Suspected COVID-19 Pneumonia Patients With Negative Real-Time Polymerase Chain Reaction Assay.

ObjectiveThe study was conducted with the objective of describing High-resolution computed tomography (HRCT) chest findings of clinically suspected COVID-19 patients having a negative real-time polymerase chain reaction (RT-PCR) assay as well as prevalence and distribution of the HRCT chest manifestations consistent with the diagnosis of COVID-19 pneumonia.MethodsThis descriptive cross-sectional study was conducted prospectively on a total of 48 patients with high clinical suspicion for COVID-19 and a negative RT-PCR assay that was presented to the Diagnostic Radiology Department of Capital Hospital, Islamabad from July 2020 to December 2020. These patients were included via non-probability consecutive sampling, had a positive history of contact with a known COVID-19 patient and/or any two of the following signs and symptoms; fever, cough, malaise, body aches, arthralgia, new-onset loss of taste and smell, and dyspnea or oxygen saturation less than 85%. A detailed history was sought after informed consent and all these patients underwent non-contrast HRCT chest scans that were reported by an experienced consultant radiologist. The scans showing positive features for COVID-19 pneumonia were assessed for the nature and distribution of the disease.ResultsAmongst 48 suspects with negative RT-PCR assay, 38 (79.2%) showed ground-glass opacities, a hallmark feature of COVID-19 pneumonia. A total of 22 (57.89%) of these 38 patients had ground-glass opacities with a crazy-paving pattern, nine (23.68%) mixed ground-glass opacities with consolidation, and seven (18.42%) had pure ground-glass opacities. Among these 79.2% suspects, ground-glass opacities were multifocal in 37 (97.37%), bilateral in 35 (92.10%), peripheral in 36 (94.74%), and dorsally located in 32 (81.6%) cases. Subpleural atelectatic bands were seen in 18 (47.36%) of these, bronchovascular markings were prominent in 15 (39.47%), and reverse halo sign was positive in nine (23.68%) cases. Out of the rest of the cases, three were diagnosed as interstitial lung disease, two as chronic lung disease, and one as active pulmonary tuberculosis.ConclusionThe majority of clinically suspected cases for COVID-19 showed hallmark findings on non-contrast HRCT chest scans in keeping with coronavirus disease regardless of a negative RT-PCR assay.

Bilateral Lung Transplantation for End-Stage Respiratory Failure from COVID-19 Pneumonia

IntroductionThe SARS-CoV-2 virus is causing severe end-stage fibrosis and respiratory failure in otherwise healthy individuals. Lung transplant (LTX) has been performed internationally in select patients for this indication, but there is limited evidence on its role in COVID-19. We describe a patient who received a bilateral LTX 12 weeks after an initial diagnosis of COVID-19 pneumonia.Case ReportA 51-year-old male with hypertension and presented to an outlying hospital with dyspnea, fever and exposure to SARS-CoV-2. He was hypoxic and a diagnosis of COVID-19 pneumonia was made by nasopharyngeal swab. He was treated with dexamethasone, remdesivir, and convalescent plasma, mechanical ventilation and eventually femoral VV-ECMO cannulation to maintain oxygenation. He was extubated and was transitioned to a left subclavian dual-limb 30 Fr VV-ECMO cannula for improved rehabilitation. He was then transferred to our center for LTX consideration given refractory ARDS. Evaluation for LTX revealed pulmonary hypertension, negative SARS-CoV-2 PCR and deconditioning but no absolute contraindications. He participated in intensive rehabilitation and progressed to assisted steps despite severe deconditioning and hypoxia. He was listed for a bilateral lung transplant with a lung allocation score of 90 and received a donor offer 7 days after listing and after 82 days on ECMO. He underwent bilateral LTX via clamshell exposure with central VA ECMO support. Intraoperatively, the lungs were densely consolidated with severe hilar adenopathy without peripheral adhesions. Post-operatively, he was transitioned back to his original VV ECMO circuit and then decannulated on post-op day 3. Standard induction with basiliximab and immunosuppression with IV methylprednisolone, mycophenolate and tacrolimus was administered. He had a transient elevation of liver enzymes on post-operative day 1 and an early planned tracheostomy was performed due to deconditioning. He has since, been progressing well on oxygen via tracheostomy collar and is able to speak with a one-way valve and participate in rehabilitation.SummaryFor patients with irreversible end-stage lung disease after COVID-19 pneumonia, LTX is a viable option. Timely transfer to a lung transplant center and intensive rehabilitation are essential. Standard established immunosuppression and post-transplant protocols should be followed.

Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study.

PurposeTo analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient’s symptoms and clinical history.MethodsThis is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed.ResultsWe studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23–91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55–91.65%) that was higher in the mild phenotype (94.4%; CI 90.0–97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6–6.7, p < 0.0001).ConclusionCombining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients’ management during a pandemic surge.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00134-021-06373-7.