Effect of Convalescent Plasma Therapy on Clinical Improvement of COVID-19 Patients: A Randomized Clinical Trial.

Background: Due to the critical condition of COVID-19, it is necessary to evaluate the efficacy of administrating convalescent plasma to COVID-19 patients. Therefore, we decided to design a clinical trial to investigate the effect of convalescent plasma of patients recovered from COVID-19 on the treatment outcome of COVID-19-infected patients. Materials and Methods: In this parallel randomized controlled clinical trial, patients in the intervention group received standard treatment plus convalescent plasma of patients recovered from COVID-19. We allocated 60 patients to each treatment group through balanced block randomization. Then, COVID-19 outcomes, vital signs, and biochemical parameters were compared between the two treatment groups by the independent t test and ANCOVA. Results: The mean age (SD) of the patients in the intervention and standard treatment groups was 52.84 (15.77) and 55.15 (14.34) years, respectively. Although patients in the intervention group reported more hospitalization days (11.45±5.86 vs. 10.42±6.79), death rates (26.67% vs. 18.13%), ICU admission (45 vs. 41.67%), and ARDS (11.67% vs. 3.33%), these differences were not statistically significant (P>0.05). Moreover, the two groups were homogenous in vital signs and biochemical parameters before and after treatment (P>0.05). Conclusion: The present study indicated that convalescent plasma therapy has no significant effect on the survival, hospitalization, and ICU admission of COVID-19 patients.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Expanding spontaneous pneumothorax in COVID-19 pneumonia: Case report and review of literature.

Coronavirus disease 2019 has presented itself with a variety of clinical signs and symptoms. One of these has been the accordance of spontaneous pneumothorax which in instances has caused rapid deterioration of patients. Furthermore pneumothorax may happen secondary to intubation and the resulting complications. Not enough is discussed regarding cases with COVID-19 related pneumothorax and proper management of these patients. The present article reports an elderly patient with spontaneous pneumothorax secondary to COVID-19 and reviews the existing literature.

Alveolar hemorrhage in the setting of COVID-19: Report of a successful vascular intervention and embolization.

The coronavirus disease 2019 (COVID-19) is characterized by viral pneumonia with mild to moderate symptoms. Emerging studies suggest that some patients may experience uncommon complications, such as thrombotic or hemorrhagic episodes. Here we present a 59-year-old male patient who had a hemorrhage episode from a branch of the pulmonary arteries and was treated by interventional embolization. Our case report demonstrates the importance of early diagnosis of hemorrhagic complications of COVID-19 and the possible benefits of early vascular intervention.

Successful vascular interventional management of superior mesenteric vein thrombosis in a patient with COVID-19: A case report and review of literature.

Coronavirus disease (COVID-19) is associated with thrombosis formation in various vessels, including those in the abdomen. In this case report, we present a COVID-19 infected patient who had developed abdominal discomfort. The patient underwent magnetic resonance imaging, which showed signs of thrombosis formation in the superior mesenteric vein (SMV). After conservative treatment failed, the patient was considered for vascular intervention. The SMV clot underwent thrombolysis via the infusion of reteplase (dose 6 mg stat, followed by 1 mg every hour) through a 5F perfusion Cather (Cragg-McNamara, 20 cm). Control venography showed near-complete recanalization. The patient was discharged with oral anticoagulants. Our case report is one of the first incidents of successful vascular intervention in SMV thrombosis in the setting of COVID-19.

Predictors of coronavirus disease 19 (COVID-19) pneumonitis outcome based on computed tomography (CT) imaging obtained prior to hospitalization: a retrospective study.

PURPOSE: Computed tomography (CT) has been utilized as a diagnostic modality in the coronavirus disease 19 (COVID-19), while some studies have also suggested a prognostic role for it. This study aimed to assess the diagnostic and prognostic value of computed tomography (CT) imaging in COVID-19 patients. METHODS: This was a retrospective study of fifty patients with COVID-19 pneumonia. Twenty-seven patients survived, while 23 passed away. CT imaging was performed in all of the patients on the day of admission. Imaging findings were interpreted based on current guidelines by two expert radiologists. Imaging findings were compared between surviving and deceased patients. Lung scores were assigned to patients based on CT chest findings. Then, the receiver operating characteristic curve was used to determine cutoff values for lung scores. RESULTS: The common radiologic findings were ground-glass opacities (82%) and airspace consolidation (42%), respectively. Air bronchogram was more commonly seen in deceased patients (p = 0.04). Bilateral and multilobar involvement was more frequently found in deceased patients (p = 0.049 and 0.014, respectively). The mean number of involved lobes was 3.46 ± 1.80 lobes in surviving patients and 4.57 ± 0.60 lobes in the deceased patients (p = 0.009). The difference was statistically significant. The area under the curve for a lung score cutoff of 12 was 0.790. CONCLUSION: Air bronchogram and bilateral and multilobar involvement were more frequently seen in deceased patients and may suggest a poor outcome for COVID-19 pneumonia.

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

Clinical and radiological characteristics of pediatric patients with COVID-19: focus on imaging findings.

PURPOSE: CT imaging has been a detrimental tool in the diagnosis of COVID-19, but it has not been studied thoroughly in pediatric patients and its role in diagnosing COVID-19. METHODS: 27 pediatric patients with COVID-19 pneumonia were included. CT examination and molecular assay tests were performed from all participants. A standard checklist was utilized to extract information, and two radiologists separately reviewed the CT images. RESULTS: The mean age of patients was 4.7 ± 4.16 (mean ± SD) years. Seventeen patients were female, and ten were male. The most common imaging finding was ground-glass opacities followed by consolidations. Seven patients had a single area of involvement, five patients had multiple areas of involvement, and four patients had diffuse involvement. The sensitivity of CT imaging in diagnosing infections was 66.67%. Also, some uncommon imaging findings were seen, such as a tree-in-bud and lung collapse. CONCLUSION: CT imaging shows less involvement in pediatric compared to adult patients, due to pediatric patients having a milder form of the disease. CT imaging also has a lower sensitivity in detecting abnormal lungs compared to adult patients. The most common imaging findings are ground-glass opacities and consolidations, but other non-common imaging findings also exist.

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.