Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Review
Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Systematic Review.
Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. ⋯ Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Review
Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Systematic Review.
Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. ⋯ Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Review Meta Analysis
Acceptability and uptake of HIV self-testing in emergency care settings: A systematic review and meta-analysis.
Emergency departments (ED) interface with large numbers of patients that are often missed by conventional HIV testing approaches. ED-based HIV self-testing (HIVST) is an innovative engagement approach which has potential for testing gains among populations that have failed to be reached. This systematic review and meta-analysis evaluated acceptability and uptake of HIVST, as compared to standard provider-delivered testing approaches, among patients seeking care in ED settings. ⋯ Available data indicate that HIVST may be acceptable and may increase testing among patients seeking emergency care, suggesting that expanding ED-based HIVST programs could enhance HIV diagnosis. However, given the limitations of the reports, additional research is needed to better inform the evidence base.