ABSTRACT
Background:Bevacizumab, an antiangiogenic drug, is being evaluated for the management of novel coronavirus disease (COVID-19) pneumonia among critically ill patients. The objective of this study was to assess the effectiveness of bevacizumab in severe COVID-19 pneumonia. Methods:This was a retrospective, observational study performed in 111 patients diagnosed with COVID-19 pneumonia. Bevacizumab was administered intravenously at 7.5 mg/kg along with standard care in a non-randomly selected subset of patients (n = 29) with evidence of acute respiratory distress syndrome (ARDS) within 72 hours of worsening of oxygenation. The primary outcome measure was intensive care unit (ICU)-related mortality. Results:Bevacizumab was administered for a median of 9.4 (4-24) days from the onset of symptoms and 2.2 (1-3) days from the day of ICU admission. Bevacizumab-treated patients showed a statistically significant improvement in PF ratio and reduction in radiological severity score. In the bevacizumab group, 13 (44.8%) of 29 patients died in ICU, and in the standard-of-care group, 37 (45.1%) of 82 patients died. The difference in clinical status assessed using the World Health Organization 7-category Ordinary Scale at 28 days between the bevacizumab group and the standard-of-care group was not statistically significant (odds ratio 1.02, 95% confidence interval 0.44-2.4, P = .94). Conclusion:Bevacizumab plus standard care was not superior to standard care alone in reducing mortality and improving clinical outcomes at day 28.
ABSTRACT
Introduction: Understanding the virus transmission patterns and routes of transmission among Healthcare Workers (HCWs) is limiting the amplification events in health care facilities. Aim: To estimate the secondary infection rate and to describe the clinical presentation of infection and the risk factors for infection among healthcare worker contacts of Coronavirus Disease-2019 (COVID-19) cases. Materials and Methods: A descriptive cross-sectional study was conducted from June 2020 to July 2021, at a tertiary care centre, in central Kerala, India, among all the healthcare workers with exposure to a COVID-19 confirmed cases within the institution, between 15 July 2020 to 15 August 2020. Data including demographic details, information on contact and possible exposure with the COVID-19 infected patient was obtained using a questionnaire adapted from the World Health Organisation (WHO) questionnaire. Data was entered into Microsoft Excel and analysed using International Business Machines (IBM) Statistical Package for Social Sciences (SPSS) version 22.0. Results: A total of 433 healthcare workers (382 females and 51 males, mean age: 34.33±10.79 years) were found to be exposed to COVID-19 confirmed cases in the institution. The 21.1% of the healthcare worker contacts were exposed while working in non-COVID Intensive Care Unit (ICU) setting. Out of the 433 HCWs who were exposed to COVID-19 patients, 9 tested positive for COVID-19 (secondary infection rate was 2.07% with a Confidence Interval (CI) of 0.7-3.4%). All 9 of the positive HCWs were females, of which 88.89% were symptomatic. Conclusion: Healthcare workers are at risk of transmission of COVID-19 while providing care, hence further explorative studies, including serologic studies are recommended to further understand the epidemiology.
ABSTRACT
The spread of Covid-19 virus around the world has taken many lives, quarantined people and shattered many industries. Due to high transmissibility of the virus and its silent incubation period in human beings, detection of the virus plays an important role to control its spread and to plan diagnostic and preventive measures. Laboratory tests such as Polymerase Chain Reaction (PCR) take more time and hence there is a need for rapid and accurate diagnostic methods to detect the virus to prevent its spread and combat it. Today PCR tests were used for diagnosing purposes and the chest x-ray was only used as the follow up of patients, hence these studies on the chest x-rays of patients with Covid-19 pneumonia or any other disease are still limited to the literature and must be improved in the future. In this project, the goal is to build an application for healthcare workers to monitor the health of lungs using the chest x-ray images of patients. The algorithm must be very accurate because it deals with the lives of people. Here we used computer vision and deep learning techniques in this project. The focus is to classify chest x-ray images and segment the abnormal region and to get more insights on the images from the available datasets. The diagnostic accuracy is the challenging part and to increase the detection efficiency due to the limited open-source data available. The data was collected from the internet. On classification, the trained model was able to achieve 93.10% accuracy and F1 Score of 0.93 after using transfer learning technique with pneumonia images. On segmentation, the Intersection Over Union value was found to be 0.91 on the validation data. © 2021 ACM.