Recommendations for antibacterial therapy in adults with COVID-19-an evidence based guideline

Scope: The Dutch Working Party on Antibiotic Policy constituted a multidisciplinary expert committee to provide evidence-based recommendation for the use of antibacterial therapy in hospitalized adults with a respiratory infection and suspected or proven 2019 Coronavirus disease (COVID-19).Methods: We performed a literature search to answer four key questions. The committee graded the evidence and developed recommendations by using Grading of Recommendations Assessment, Development, and Evaluation methodology.Questions addressed by the guideline and Recommendations: We assessed evidence on the risk of bacterial infections in hospitalized COVID-19 patients, the associated bacterial pathogens, how to diagnose bacterial infections and how to treat bacterial infections. Bacterial co-infection upon admission was reported in 3.5% of COVID-19 patients, while bacterial secondary infections during hospitalization occurred up to 15%. No or very low quality evidence was found to answer the other key clinical questions. Although the evidence base on bacterial infections in COVID-19 is currently limited, available evidence supports restrictive antibiotic use from an antibiotic stewardship perspective, especially upon admission. To support restrictive antibiotic use, maximum efforts should be undertaken to obtain sputum and blood culture samples as well as pneumococcal urinary antigen testing. We suggest to stop antibiotics in patients who started antibiotic treatment upon admission when representative cultures as well as urinary antigen tests show no signs of involvement of bacterial pathogens after 48 hours. For patients with secondary bacterial respiratory infection we recommend to follow other guideline recommendations on antibacterial treatment for patients with hospital-acquired and ventilator-associated pneumonia. An antibiotic treatment duration of five days in patients with COVID-19 and suspected bacterial respiratory infection is recommended upon improvement of signs, symptoms and inflammatory markers. Larger, prospective studies about the epidemiology of bacterial infections in COVID-19 are urgently needed to confirm our conclusions and ultimately prevent unnecessary antibiotic use during the COVID-19 pandemic. (C) 2020 The Author(s). Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.Immunogenetics and cellular immunology of bacterial infectious disease

Automated detection of cerebral microbleeds via segmentation in susceptibility-weighted images of patients with traumatic brain injury

Cerebral microbleeds (CMBs) are a recognised biomarker of traumatic axonal injury (TAI). Their number and location provide valuable information in the long-term prognosis of patients who sustained a traumatic brain injury (TBI). Accurate detection of CMBs is necessary for both research and clinical applications. CMBs appear as small hypointense lesions on susceptibility-weighted magnetic resonance imaging (SWI). Their size and shape vary markedly in cases of TBI. Manual annotation of CMBs is a difficult, error-prone, and time-consuming task. Several studies addressed the detection of CMBs in other neuropathologies with convolutional neural networks (CNNs). In this study, we developed and contrasted a classification (Patch-CNN) and two segmentation (Segmentation-CNN, U-Net) approaches for the detection of CMBs in TBI cases. The models were trained using 45 datasets, and the best models were chosen according to 16 validation sets. Finally, the models were evaluated on 10 TBI and healthy control cases, respectively. Our three models outperform the current status quo in the detection of traumatic CMBs, achieving higher sensitivity at low false positive (FP) counts. Furthermore, using a segmentation approach allows for better precision. The best model, the U-Net, achieves a detection rate of 90% at FP counts of 17.1 in TBI patients and 3.4 in healthy controls