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... Show moreScope: 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. Show less
Wynants, L.; Calster, B. van; Bonten, M.M.J.; Collins, G.S.; Debray, T.P.A.; Vos, M. de; ... ; Smeden, M. van 2020
OBJECTIVETo review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis... Show moreOBJECTIVETo review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia.DESIGNRapid systematic review and critical appraisal.DATA SOURCESPubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020.STUDY SELECTIONStudies that developed or validated a multivariable covid-19 related prediction model.DATA EXTRACTIONAt least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).RESULTS2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.CONCLUSIONPrediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Show less
