COVID-19 co-infection in a patient with brucellosis (preprint)

An 89-year-old male case was hospitalized in the COVID-19 department. His computerized chest tomography scan showed nodular opacities with glass halo including peripheral distribution. The patient showed active brucellosis.Finally, his respiratory symptoms and the radiologic images had got better and the second SARS-COV-2 test and the serologic tests were negative

MANAGEMENT OF SPONTANEOUS PNEUMOTHORAX IN PATIENTS WITH COVID-19 (preprint)

The coronavirus diseases 2019 (COVID-19) pneumonia may cause cystic features of lung parenchyma which can resolve or progress to larger blebs. Spontaneous pneumothorax (SP) was reported as a complication of COVID-19 with an incidence of 1% in hospitalized patients, in 3% of patients hospitalized with pneumonia, in 6% mechanically ventilated patients and in 1% of decased patients. Pneumothorax was more likely in patients with neutrophilia, severe lung injury and a prolonged clinical course. We present 11 cases of SP managed with chest tube thoracostomy (CTT) or high dose oxygen therapy. Isolated SP was detected in all cases. Eight cases were male and three cases were female. There were bilateral ground-glass opacities or pulmonary infiltrates in the parenchyma of the ten cases. We detected neutrophilia, lymphopenia and increased CRP, Ferritin, LDH, D-Dimer, IL-6 levels in almost all cases. CTT was sufficient to treat pneumothorax in our nine of case. In two cases, pneumothorax healed with high dose oxygen therapy. Favipiravir and antibiotic treatment were given to different ten patients. In our institution, all patients with COVID-19 infection were placed on prophylactic or therapeutic anticoagulation, unless contraindicated. The treatments of patients diagnosed with secondary spontaneous pneumothorax during the pandemic period and those diagnosed with secondary spontaneous pneumothorax in the previous three years were compared with the durations of tube thoracostomy performed in both groups. The increased number of cases of pneumothorax suggests that pneumothorax may be a complication of covid-19 infection. During medical treatment of covid-19, pneumothorax may be the only reason for hospitalization. Although tube thoracostomy is a sufficient treatment option in most cases, clinicians should be aware of the difficulties that may arise in diagnosis and treatment.

Assessment of Pulmonary Artery Diameters in Pediatric COVID-19 Patients (preprint)

ABSTRACT Background: This study was conducted to investigate the relationship between clinical course and pulmonary artery (PA) diameters in children diagnosed with COVID-19. Method: The study included 62 patients who presented COVID-19 symptoms between March 2020 and April 2021. Group 1 consisted of 32 pediatric patients who were COVID-19 PCR (+), while Group 2 consisted of 30 pediatric patients who were COVID-19 PCR(-). The data were collected retrospectively from medical records. Patients who developed pneumonia due to causes other than COVID-19 and those who had a history of pulmonary hypertension or pulmonary thromboembolism were excluded. The patients were examined based on their Computerized Tomographic (CT) findings, simultaneous whole blood parameters and biochemical parameters. Results: The thoracic CT findings of 18 of the patients in Group 1 were found normal. The CT images of 14 patients showed pulmonary involvement. Among the patients with pulmonary involvement, 8 had moderate pneumonia characterized by a ground-glass pattern, and 6 had severe pneumonia indicated by consolidation and linear opacities. The right pulmonary artery, left pulmonary artery and inferior vena cava (IVC) diameters of the patients in Group 1 were significantly higher than those of the patients in Group 2. Conclusion: The results of this study suggested that increased PA diameters in children diagnosed with COVID-19 may be accompanied by increased inflammation, high vascular resistance, hypoxemia and thromboembolic events. While it is thought that increased PA and IVC diameters are a factor that may indicate clinical deterioration in COVID-19 patients, more comprehensive studies are needed.

A preliminary study of commercially available general-purpose chest radiography artificial intelligence-based software for detecting airspace opacity lesions in COVID-19 patients (preprint)

Purpose To validate commercially available general-purpose artificial intelligence (AI)-based software for detecting airspace opacity in chest radiographs (CXRs) of COVID-19 patients. Materials and Methods We used the ieee8023-covid-chestxray-dataset to validate commercial AI software capable of detecting "Nodule/Mass" and "Airspace opacity" as regions of interest with probability scores. From this dataset, we excluded computed tomography images and CXR images taken using an anteroposterior spine view and analyzed CXR images tagged with "Pneumonia/Viral/COVID-19" and "no findings". A radiologist then reviewed the images and rated them on a 3-point opacity score for the presence of airspace opacity. The maximum probability score of airspace opacity for each image was calculated using this software. The difference in each maximum probability for each opacity score was evaluated using Wilcoxon's rank sum test. The threshold of the probability score was determined by receiver operator characteristic curve analysis for the presence or absence of COVID-19, and the true positive rate (TPR) and false positive rate (FPR) were determined for the individual and overall opacity scores. Results Images from 342 patients with COVID-19 and 15 normal images were included. Opacity scores of 1, 2, and 3 were observed in 44, 70, and 243 images, respectively, of which 33 (75%), 66 (94.2%), and 243 (100%), respectively, were from COVID-19 patients. The overall TPR and FPR were 0.82 and 0.13, respectively, at an area under the curve of 0.88 and a threshold of 0.06, while the FPR for opacity score 1 was 0.18 and the TPR for score 3 was 0.97. Conclusion Using a public database containing CXR images of COVID-19 patients, commercial AI software was shown to be able to detect airspace opacity in severe pneumonia. Summary Commercially available AI software was capable of detecting airspace opacity in CXR images of COVID-19 patients in a public database.

Natural History of Radiological Changes on CT Chest from COVID-19 Infection Rehabilitation Patients (preprint)

Object： To investigate the chest CT manifestations of convalescent patients with COVID-19 in recovery phase. Methods: : 118 convalescent patients diagnosed as COVID-19 were followed up. According to their medical history, they were divided into 47 cases of severe disease group and 71 cases of mild disease group. Multi-slice spiral CT, high-resolution CT and pulmonary function were examined. Results: : 67 rehabilitated cases are normal on CT scan. The other CT findings were: (1) ground glass opacity lesions, including: localized patchy ground glass density lesions; Multi lobes and multi-stage ground glass opacities; Diffuse ground glass opacities lesions in both lungs. (2) The interlobular interstitium and interlobular septum were thickened. (3) Subpleural arc shadow/Subpleural lines. (4) Irregular fiber cord shadow/Irregular lines. (5) Tractive bronchiectasis. (6) Nodular consolidation of air space. (7) Cavitary lesions. No obvious mediastinal lymph node enlargement and pleural effusion were found. Pulmonary imaging and pulmonary function were improved after repeated reexamination. There was significant difference in CT findings and pulmonary function indexes between severe group and mild group (P < 0.05). Conclusion: The pulmonary manifestations of some convalescent patients with COVID-19 are basically normal. Ground glass density lesions are the main CT manifestations of convalescent patients, accompanied by varying degrees of pulmonary interstitial hyperplasia. The severe group had more severe pulmonary manifestations and poor pulmonary function than the mild group. With the extension of time, pulmonary lesions and pulmonary function gradually improved.

Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks (preprint)

With the ongoing outbreak of the COVID-19 global pandemic, the research community still struggles to develop early and reliable prediction and detection mechanisms for this infectious disease. The commonly used RT-PCR test is not readily available in areas with limited testing facilities, and it lags in performance and timeliness. This paper proposes a deep transfer learning-based approach to predict and detect COVID-19 from digital chest radiographs. In this study, three pre-trained convolutional neural network-based models (VGG16, ResNet18, and DenseNet121) have been fine tuned to detect COVID-19 infected patients from chest X-rays (CXRs). The most efficient model is further used to identify the affected regions using an unsupervised gradient-based localization technique. The proposed system uses a classification approach (normal vs. COVID-19 vs. pneumonia vs. lung opacity) using three supervised classification algorithms followed by gradient-based localization. The training, validation and testing of the system are performed using 21165 CXR images (10192 normal, 1345 pneumonia, 3616 COVID-19, and 6012 lung opacity). Simulation and evaluation results are presented using standard performance metrics, viz, accuracy, sensitivity, and specificity.

COVID-19 Management and Outcome of 76 Pediatric Cancer Patients, a Single Centre Experience from Developing Country (preprint)

Background: Coronavirus disease 2019 (COVID-19) is a global pandemic with no enough data regarding its impact on pediatric cancer patients. Our main aim is to describe the clinical management and outcome of COVID-19 in this vulnerable group. Methods: This prospective study included 76 pediatric oncology patients with confirmed infection in the period between 1st of May and end of November, 2020 with at least two-month follow-up from recruitment. Remdesivir (RDV) was the antiviral therapy used. Findings: The median age of patients was 9 years. Sixty patients were on first line treatment. Haematological malignancies constituted 86.8% of patients. Severe to critical form of infection represent 35.4% of cases. The commonest presentation was fever (93.4%). Chemotherapy was delayed in 59.2% of cases and doses were modified in 30.2%. Most acute lymphoblastic leukemia/lymphoblastic lymphoma (88%) were in maintenance treatment phase while 55% of acute myeloid leukemia were in induction phase. Sixty days overall survival (OS) was 86.6% and mortalities occurred only in critically ill cases. Of the sixteen acute leukemia who were in first induction phase, 13 survived and 10 achieved induction remission. The commonest CT chest finding was ground glass opacities (74.2%). A negative PCR within 2 weeks and improvement of radiological findings were statistically related to disease severity (p=0.008and, 0.002 respectively).Better OS was associated with regression of radiological finding after 30 days from infection (p=0.002). Of the forty-five cases who received RDV, 70% were severe to critically ill cases, yet morality was comparable to NoRDV with no serious adverse events observed. Interpretation: COVID-19 in pediatric cancer patients has good clinical outcome except for critical form of infection at presentation. New oncologic cases tolerate induction therapy with good disease outcome. RDS was well tolerated with no OS difference compared to patients who did not receive the drug.Funding: The present work was funded by the Children’s Cancer Hospital Foundation and Association of Friends of the National Cancer-free Initiative (AFNCI)Declaration of Interest: All authors declare no competing financial interests.Ethical Approval: The approval of hospital ethical committee and family consents were obtained.

Increased angiotensin-converting enzyme 2, sRAGE and immune activation, but lowered calcium and magnesium in COVID-19: association with chest CT abnormalities and lowered peripheral oxygen saturation (preprint)

Background The characterization of new biomarkers of COVID-19 is extremely important. Few studies measured the soluble receptor for advanced glycation end product (sRAGE), angiotensin-converting enzyme 2 (ACE2), calcium and magnesium in COVID-19. Aims To measure sRAGE, ACE2, interleukin (IL) -6, IL-10, CRP, calcium, magnesium, and albumin in COVID-19 patients in association with peripheral oxygen saturation (SpO2) and chest CT scan abnormalities (CCTA) including ground glass opacities. Methods This study measured sRAGE, ACE2, IL-6, IL-10, CRP using ELISA techniques, and calcium, magnesium, and albumin using a spectrophotometric method in 60 COVID-19 patients and 30 healthy controls. Results COVID-19 is characterized by significantly increased IL-6, CRP, IL-10, sRAGE, ACE2, and lowered levels of SpO2, albumin, magnesium and calcium. Neural networks showed that a combination of calcium, IL-6, CRP, and sRAGE yielded an accuracy of 100% in detecting COVID-19 patients with calcium being the most important predictor followed by IL-6, and CRP. COVID-19 patients with CCTAs showed lower SpO2 and albumin levels than those without CCTAs. SpO2 was significantly and inversely correlated with IL-6, IL-10, CRP, sRAGE, and ACE2, and positively with albumin, magnesium and calcium. Patients with positive IgG results showed a significant elevation in the serum level of IL-6, sRAGE, and ACE2 compared to the negatively IgG patient subgroup. Conclusion The results show that immune-inflammatory and RAGE pathway biomarkers may be used as external validating criterion for the diagnosis COVID-19. Those pathways coupled with lowered SpO2, calcium and magnesium are drug targets that may help to reduce the consequences of COVID-19.

The Role of Chest CT in Deciphering Interstitial Lung Involvement: Systemic Sclerosis Versus COVID-19 (preprint)

Background: In clinical practice, the striking similarities observed at computed tomography (CT) between the diseases make it difficult to distinguish a COVID-19 pneumonia from a progression of interstitial lung disease (ILD) secondary to Systemic sclerosis (SSc). The aim of the present study was to identify the main CT features that may help distinguishing SSc-ILD from COVID-19 pneumonia. Methods: This multicentric study included 22 international readers divided in the radiologist group (RAD) and non-radiologist group (nRAD). A total of 99 patients, 52 with COVID-19 and 47 with SSc-ILD, were included in the study.Findings: Fibrosis inside focal ground glass opacities (GGO) in the upper lobes; fibrosis in the lower lobe GGO; reticulations in lower lobes (especially if bilateral and symmetrical or associated with signs of fibrosis) were the CT features most frequently associated with SSc-ILD. The CT features most frequently associated with COVID- 19 pneumonia were: consolidation (CONS) in the lower lobes, CONS with peripheral (both central/peripheral or patchy distributions), anterior and posterior CONS and rounded-shaped GGOs in the lower lobes. After multivariate analysis, the presence of CONS in the lower lobes (p <0.0001) and signs of fibrosis in GGO in the lower lobes (p <0.0001) remained independently associated with COVID-19 pneumonia or SSc-ILD, respectively. A predictive score weas created which resulted positively associated with the COVID-19 diagnosis (96.1% sensitivity and 83.3% specificity).Interpretation: The CT differential diagnosis between COVID-19 pneumonia and SSc-ILD is possible through the combination our score and the radiologic expertise. If an overlap of both diseases is suspected, the presence of consolidation in the lower lobes may suggest a COVID-19 pneumonia while the presence of fibrosis inside GGO may indicate a SSc-ILD.Funding: No Funding were received for this study.Declaration of Interests: SC reports personal fees from NOVARTIS-SANOFI-LILLY-CELTHER-PFIZER-JANSSEN; MK reports grants and personal fees from Boehringer-Ingelheim, personal fees from Corbus, grants and personal fees from Chugai, grants and personal fees from Ono Pharmeceuticals, personal fees from Tanabe-Mitsubishi, personal fees from Astellas, personal fees from Gilead, personal fees from Mochida; ST reports personal fees from Boehringer Ingelheim, personal fees from Roche, outside the submitted work; GS reports personal fees from Boehringer Ingelheim; CB reports personal fees from Actelion, personal fees from Eli Lilly, grants from European Scleroderma Trial and Research (EUSTAR) group, grants from New Horizon Fellowship, grants from Foundation for Research in Rheumatology (FOREUM), grants from Fondazione Italiana per la Ricerca sull'Artrite (FIRA); CV reports grants and personal fees from Boehringer Ingelheim, grants and personal fees from F. Hoffmann-La Roche Ltd.; FL reports lectures fee from Roche and from Boehringer- Ingelheim; CPD reports grants and personal fees from GSK, personal fees from Boerhinger Ingelheim, grants from Servier, grants and personal fees from Inventiva, grants and personal fees from Arxx Therapeutics, personal fees from Corbus, personal fees from Sanofi, personal fees from Roche; FL reports grants and personal fees from GSK, personal fees from Boehringer Ingelheim, personal fees from Orion Pharma, personal fees from AstraZeneca, grants from MSD, personal fees from HIKMA, personal fees from Trudell International, grants and personal fees from Chiesi Farmaceutici, personal fees from Novartis Pharma; MH reports personal fees from Speaking fees from Actelion, Eli lilly and Pfizer; D K reports personal fees from Actelion, grants and personal fees from Bayer, grants and personal fees from Boehringer Ingelhem, personal fees from CSL Behring, grants and personal fees from Horizon, grants from Pfizer, personal fees from Corbus, grants and personal fees from BMS, outside the submitted work; and Dr Khanna is the Chief Medical officer of Eicos Sciences Inc and has stock options. All the mentioned authors declared previous feed outside the submitted work. All other authors declare no competing interests.Ethics Approval Statement: This retrospective, observational, multicentric, international study was approved by the Institutional Ethics Committee of Florence Careggi hospital (protocol number 17104_oss).

Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays (preprint)

In this paper, our focus is on constructing models to assist a clinician in the diagnosis of COVID-19 patients in situations where it is easier and cheaper to obtain X-ray data than to obtain high-quality images like those from CT scans. Deep neural networks have repeatedly been shown to be capable of constructing highly predictive models for disease detection directly from image data. However, their use in assisting clinicians has repeatedly hit a stumbling block due to their black-box nature. Some of this difficulty can be alleviated if predictions were accompanied by explanations expressed in clinically relevant terms. In this paper, deep neural networks are used to extract domain-specific features(morphological features like ground-glass opacity and disease indications like pneumonia) directly from the image data. Predictions about these features are then used to construct a symbolic model (a decision tree) for the diagnosis of COVID-19 from chest X-rays, accompanied with two kinds of explanations: visual (saliency maps, derived from the neural stage), and textual (logical descriptions, derived from the symbolic stage). A radiologist rates the usefulness of the visual and textual explanations. Our results demonstrate that neural models can be employed usefully in identifying domain-specific features from low-level image data; that textual explanations in terms of clinically relevant features may be useful; and that visual explanations will need to be clinically meaningful to be useful.

Characterization of Confirmed and Suspected COVID-19 Pneumonia Patients in a Retrospective Cohort Study in Wuhan (preprint)

Background: A methodical comparison of confirmed and suspected COVID-19 patients has not been previously reported. Therefore, we thoroughly analyzed the demographic and clinical characteristics between these groups to identify mortality risk factors.Methods: A retrospective cohort of 1,276 hospitalized COVID-19 pneumonia patients at Tongren Hospital (Wuhan, China; January 27 to March 3, 2020) was studied. Cox regression analyses were performed to evaluate multiple mortality risk factors. Results: Both cohorts of confirmed (n=797) and suspected (n=479) patients exhibited typical demographic, clinical, and radiological characteristics. Treatment methods were consistent and both groups shared similarities in many demographic and clinical characteristics: age (≥65, 45.9% vs 41.8%, P=0.378) and lung disease (12.5% vs 14.6%, P=0.293). However, confirmed patients exhibited more severe disease manifestations than those in suspected patients: a higher incidence of fever (65.4% vs 58.0%, P<0.01), lower lymphocyte count (1.12×109/L vs 1.22×109/L, P=0.022), higher C-reactive protein (CRP) (11.60 mg/L vs 7.61mg/L, P=0.021), and more severe radiographic manifestations (lung infection incidence, 3.8% vs 3.0%, P=0.014; ground-glass opacity lesion incidence, 2.3% vs 2.0%, P=0.033). The dynamic profiles of lymphocytes, monocytes, D-dimer, and CRP, clearly delineated confirmed patients from suspected patients exhibiting critical illness. Cox regression analysis demonstrated that lung disease (adjusted hazard ratio 8.972, 95% CI: 3.782-21.283), cardiovascular disease (3.083, 1.347-7.059), neutrophil count (1.189, 1.081-1.307), age (1.068, 1.027-1.110), and ground-glass opacity lesions (1.039, 95% 1.013-1.065), were the main risk factors for mortality in confirmed patients; lung disease (14.725, 2.187-99.147), age (1.076, 1.004-1.153), and CRP level (1.012, 95% CI 1.004-1.020) were the primary factors in suspected patients.Conclusions: Suspected patients with serious illness should seek medical attention to reduce mortality. Multiple factors must be assessed to determine the mortality risk and the appropriate treatment. 

Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis (preprint)

Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantitative description of radiographic opacities. Most current AI assisted CXR image analysis framework do not quantify for regional variations of disease. To address these, we proposed a four region lung segmentation method to assist accurate quantification of COVID 19 pneumonia. Methods. A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs. To improve the segmentation performance of COVID 19 images, ensemble strategy incorporating five models is exploited. Using each region, we evaluated the clinical relevance of the proposed method with the Radiographic Assessment of the Quality of Lung Edema (RALE). Results. The proposed ensemble strategy showed dice score of 0.900, which is significantly higher than conventional methods (0.854 0.889). Mean intensities of segmented four regions indicate positive correlation to the extent and density scores of pulmonary opacities under the RALE framework. Conclusion. A deep learning based model in CXR can accurately segment and quantify regional distribution of pulmonary opacities in patients with COVID 19 pneumonia.

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning (preprint)

Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: : An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results: and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays (preprint)

In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detection of few pathologies, we use a hierarchical taxonomy mapped to the Unified Medical Language System (UMLS) terminology to identify 189 radiological findings, 22 differential diagnosis and 122 anatomic locations, including ground glass opacities, infiltrates, consolidations and other radiological findings compatible with COVID-19. We train the system on one large database of 92,594 frontal chest x-rays (AP or PA, standing, supine or decubitus) and a second database of 2,065 frontal images of COVID-19 patients identified by at least one positive Polymerase Chain Reaction (PCR) test. The reference labels are obtained through natural language processing of the radiological reports. On 23,159 test images, the proposed neural network obtains an AUC of 0.94 for the diagnosis of COVID-19. To our knowledge, this work uses the largest chest x-ray dataset of COVID-19 positive cases to date and is the first one to use a hierarchical labeling schema and to provide interpretability of the results, not only by using network attention methods, but also by indicating the radiological findings that have led to the diagnosis.

Clinical analysis of 207 patients with coronavirus disease 2019 (COVID-19) in Shanghai, China (preprint)

Objectives: To investigate clinical features and the chest computed tomography (CT) findings in patients with confirmed cases of coronavirus disease 2019 (COVID-19) in Shanghai. Materials and Methods: Two hundred seven patients (102 men and 105 women, 15-84 years old) with COVID-19 from 23 January 2020 to 8 February 2020 were retrospectively reviewed. The imaging findings, clinical and laboratory data of the patients were evaluated and analyzed. The CT score was determined by totaling the lobes of lungs affected ranging from 0-25. Results: The median time from onset of symptoms to first hospital admission was 5.3±3.9 days.After being tested positive, the hospital stay of patients with onset of symptoms within one week is longer than that of patients with onset of symptoms over one week (15.7 vs. 11.5 respectively, p<0.01). The initial lung findings of patients with COVID-19 on chest CT were small subpleural ground glass opacities (GGO) that grew larger with crazy-paving pattern and consolidation with or without interstitial opacity. The mean CT scores peaked at 8-10 days of illness, with a slow decline thereafter and substantial scores after the 10 days. Both age and CD4+ cell counts had a remarkable prognostic effect on imaging outcomes (p<0.05). Conclusion: For patients in mild-to-moderatecondition, the disease began to improve after 10 days from the initiation of the symptoms. Both age and baseline CD4+ cell count were pivotal predictor of the outcome of imaging of the patients with COVID-19.

Can Nebulised Heparin Reduce Time to Extubation in SARS CoV 2 The CHARTER Study Protocol (preprint)

Introduction: COVID 19 is associated with the development of ARDS displaying the typical features of diffuse alveolar damage with extensive pulmonary coagulation activation resulting in fibrin deposition in the microvasculature and formation of hyaline membranes in the air sacs. The anticoagulant actions of nebulised heparin limit fibrin deposition and progression of lung injury. Serendipitously, unfractionated heparin also inactivates the SARS CoV 2 virus and prevents its entry into mammalian cells. Nebulisation of heparin may therefore limit both fibrin mediated lung injury and inhibit pulmonary infection by SARS CoV 2. For these reasons we have initiated a multicentre international trial of nebulised heparin in patients with COVID 19. Methods and intervention: Mechanically ventilated patients with confirmed or strongly suspected SARS CoV 2 infection, hypoxaemia and an acute pulmonary opacity in at least one lung quadrant on chest Xray, will be randomised to nebulised heparin 25,000 Units every 6 hours or standard care for up to 10 days while mechanically ventilated. The primary outcome is the time to separation from invasive ventilation to day 28, where non survivors to day 28 are treated as though not separated from invasive ventilation. Ethics and dissemination: The study protocol has been submitted to the human research and ethics committee of St Vincents Hospital, Melbourne, Australia. Submission is pending in other jurisdictions. Results of this study will be published in scientific journals and presented at scientific meetings.

Temporal radiographic changes in COVID-19 patients: relationship to disease severity and viral clearance (preprint)

Background: COVID-19 is “public enemy number one” and has placed an enormous burden on health authorities across the world. Given the wide clinical spectrum of COVID-19, understanding the factors that can predict disease severity will be essential since this will help frontline clinical staff to stratify patients with increased confidence. Purpose: To investigate the diagnostic value of the temporal radiographic changes, and the relationship to disease severity and viral clearance in COVID-19 patients. Methods: : In this retrospective cohort study, we included 99 patients admitted to the Renmin Hospital of Wuhan University, with laboratory confirmed moderate or severe COVID-19. Temporal radiographic changes and viral clearance were explored using appropriate statistical methods. Results: : Radiographic features from HRCT scans included ground-glass opacity, consolidation, air bronchogram, nodular opacities and pleural effusion. The HRCT scores (peak) during disease course in COVID-19 patients with severe pneumonia (median: 24.5) were higher compared to those with pneumonia (median: 10) (p=3.56×10 -12 ), with more frequency of consolidation (p=0.025) and air bronchogram (p=7.50×10 -6 ). The median values of days when the peak HRCT scores were reached in pneumonia or severe pneumonia patients were 12 vs . 14, respectively (p=0.048). Log-rank test and Spearman's Rank-Order correlation suggested temporal radiographic changes as a valuable predictor for viral clearance. In addition, follow up CT scans from 11 pneumonia patients showed full recovery. Conclusion: Given the values of HRCT scores for both disease severity and viral clearance, a standardised HRCT score system for COVID-19 is highly demanded.

Correlation between CT findings and outcomes in 46 Patients with Coronavirus Disease-19 (preprint)

Purpose: The aim of this study was to retrospectively analyze chest Computed Tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Methods: This retrospective review included 46 patients with RT-PCR confirmed COVID-19 infection. Basic clinical characteristics and detailed CT features were evaluated and compared between patients who recovered (n = 40) from coronavirus and those who expired (n = 6). Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1-5 for each lobe, range; 0, none; 25, maximum) was determined. Results: We analyzed clinical data from 46 patients (26 males and 20 females; age 9-82 years) with confirmed COVID-19 pneumonia were evaluated. The chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who expired had fibrosis, in contrast to six (15%) patients who recovered from coronavirus. Most patients had subpleural lesions (89.0%), bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus, but only one (2.5%) patient who recovered (p = 0.000). CT identified a greater area of lung lobe involvement in patients who died (p = 0.000). In the group of patients who expired, the total CT score was higher than that of the recovery group (17.2 ± 7.8 vs. 7.1 ± 4.3, p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). Conclusion: The CT of patients with COVID-19 mainly showed ground-glass opacity and ground-glass opacity plus consolidation, with a peripheral lower lobe preference. Early fibrosis may correlate with well prognosis. Lymphopenia, elevated C-reactive protein, and high CT score in conjunction with diffuse distribution of lung lesions are indicative of disease severity and short- term mortality.

A COVID-19 patient with multiple negative results for PCR assays outside Wuhan, China: a case report (preprint)

Background: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency of major international concern. Real-time RT-PCR assays are recommended for diagnosis of COVID-19. Here we report a rare case of COVID-19 with multiple negative results for PCR assays outside Wuhan, China. Case presentation: A 32-year old male was admitted to our hospital because of 6 days of unexplained fever on January 29, 2020. He had come from Wuhan city 10 days before admission. 5 days before admission, no abnormality was noted in laboratory test, chest radiography, and nasopharyngeal swab test for the SARS-CoV-2 nucleic acid. The patient was treated with ibuprofen for alleviating fever. On admission, chest computed tomography showed multiple ground-glass opacities in right lower lung field. COVID-19 was suspected. 3 times of nasopharyngeal swab specimens were collected after admission. However, none of the specimens were positive. The patient was confirmed with COVID-19 after fifth SARS-CoV-2 nucleic acid test. He was treated with lopinavir/ritonavir, recombinant human interferon alfa-2b inhalation, methylprednisolone. After 18 days of treatment, he was discharged with improved symptoms, lung lesions and negative results of nasopharyngeal swab. Conclusion: This case reminds clinician that a patient with high clinical suspicion of COVID-19 but multiple negative RT-PCR result should not be taken out of isolation. A combination of patient’s exposure history, clinical manifestations, laboratory tests, and typical imaging findings plays a vital role in making preliminary diagnosis and guide early isolation and treatment. Repeat swab tests are helpful in diagnosis for this kind of patients. 

The impact of COVID-19 on ischemic stroke: A case report (preprint)

BackgroundThe outbreak of a novel coronavirus since December 2019 in Wuhan, became an emergency of major international concern. As of March 5, 2020, the SARS-CoV-2 epidemic has caused 80,565 confirmed infections with 3,015 fatal cases in China. The SARS-CoV-2 outbreak is a major challenge for clinicians. In our clinic, we found a rare case that a COVID-19 patient combined with ischemic stroke.Case PresentationA 79-year-old man was admitted to the Hubei Provincial Hospital of Chinese Traditional Medicine due to right limb weakness for 1 day and slight cough for 1 week. At presentation, his body temperature was 37.3°C (99.0°F) with some moist rales. Neurological examination showed right limb weakness, and the limb muscle strength was grade 4. The left leg and arms were unaffected. In addition, runs of speech were not fluent enough with tongue deviation. Laboratory studies showed lymphopenia and eosinophilic granulocytopenia. Chest CT revealed bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, with a peripheral lung distribution. Real-time polymerase chain reaction (RT-PCR) from throat swab sample was positive for SARS-CoV-2 nucleic acid. This patient was treated with antiviral drugs and anti-inflammatory drugs with supportive care until his discharge. Clopidogrel (75 mg) and atorvastatin (20 mg) were administered orally to treat acute ischemic stroke. After twelve days of treatment, he can walk normally and communicate with near fluent language.ConclusionWe report an even more unusual case, a patient who was hospitalized for right limb weakness and was later diagnosed with COVID-19. Here, SARS-CoV-2 infection caused hypoxemia and excessive secretion of inflammatory cytokines, which contribute to the occurrence and development of ischemic stroke. Once COVID-19 patients show acute ischemic stroke, neurologists should cooperate with infectious disease doctors to help patients.