Computed tomography characterization and outcome evaluation of COVID-19 pneumonia complicated by venous thromboembolism.

BACKGROUND: COVID-19 is frequently complicated by venous thromboembolism (VTE). Computed tomography (CT) of the chest-primarily usually conducted as low-dose, non-contrast enhanced CT-plays an important role in the diagnosis and follow-up of COVID-19 pneumonia. Performed as contrast-enhanced CT pulmonary angiography, it can reliably detect or rule-out pulmonary embolism (PE). Several imaging characteristics of COVID-19 pneumonia have been described for chest CT, but no study evaluated CT findings in the context of VTE/PE. PURPOSE: In our retrospective study, we analyzed clinical, laboratory and CT imaging characteristics of 50 consecutive patients with RT-PCR proven COVID-19 pneumonia who underwent contrast-enhanced chest CT at two tertiary care medical centers. MATERIAL AND METHODS: All patients with RT-PCR proven COVID-19 pneumonia and contrast-enhanced chest CT performed at two tertiary care hospitals between March 1st and April 20th 2020 were retrospectively identified. Patient characteristics (age, gender, comorbidities), symptoms, date of symptom onset, RT-PCR results, imaging results of CT and leg ultrasound, laboratory findings (C-reactive protein, differential blood count, troponine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), fibrinogen, interleukin-6, D-dimer, lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase muscle-brain (CKmb) and lactate,) and patient outcome (positive: discharge or treatment on normal ward; negative: treatment on intensive care unit (ICU), need for mechanical ventilation, extracorporeal membrane oxygenation (ECMO), or death) were analyzed. Follow-up was performed until May 10th. Patients were assigned to two groups according to two endpoints: venous thromboembolism (VTE) or no VTE. For statistical analysis, univariate logistic regression models were calculated. RESULTS: This study includes 50 patients. In 14 out of 50 patients (28%), pulmonary embolism was detected at contrast-enhanced chest CT. The majority of PE was detected on CTs performed on day 11-20 after symptom onset. Two patients (14%) with PE simultaneously had evidence of deep vein thrombosis. 15 patients (30%) had a negative outcome (need for intensive care, mechanical ventilation, extracorporeal membrane oxygenation, or death), and 35 patients (70%) had a positive outcome (transfer to regular ward, or discharge). Patients suffering VTE had a statistically significant higher risk of an unfavorable outcome (p = 0.028). In univariate analysis, two imaging characteristics on chest CT were associated with VTE: crazy paving pattern (p = 0.024) and air bronchogram (n = 0.021). Also, elevated levels of NT-pro BNP (p = 0.043), CK (p = 0.023) and D-dimers (p = 0.035) were significantly correlated with VTE. CONCLUSION: COVID-19 pneumonia is frequently complicated by pulmonary embolism (incidence of 28% in our cohort), remarkably with lacking evidence of deep vein thrombosis in nearly all thus affected patients of our cohort. As patients suffering VTE had an adverse outcome, we call for a high level of alertness for PE and advocate a lower threshold for contrast-enhanced CT in COVID-19 pneumonia. According to our observations, this might be particularly justified in the second week of disease and if a crazy paving pattern and / or air bronchogram is present on previous non-enhanced CT.

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.