Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Case Reports
A case-control analysis of stroke in Covid-19 patients: Results of Unusual Manifestations of Covid-19-Study 11.
We investigated the incidence, predictor variables, clinical characteristics, and stroke outcomes in patients with COVID-19 seen in emergency departments (EDs) before hospitalization. ⋯ The incidence of stroke in COVID-19 patients presenting to EDs was lower than that in the non-COVID-19 reference sample. COVID-19 patients with stroke had greater need for hospitalization and ICU admission than those without stroke and longer hospitalization and greater in-hospital mortality than non-COVID-19 patients with stroke.
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The objective was to determine the accuracy of a new, rapid blood test combining measurements of both glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1) for predicting acute traumatic intracranial injury (TII) on head CT scan after mild traumatic brain injury (mTBI). ⋯ A rapid i-STAT-based test had high sensitivity for prediction of acute TII, comparable to lab-based platforms. The speed, portability, and high accuracy of this test may facilitate clinical adoption of brain biomarker testing as an aid to head CT decision making in EDs.
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Musculoskeletal pain is a common emergency department (ED) presentation, and patient-centered care may improve quality of life, treatment satisfaction, and outcomes. Our objective was to investigate the expectations, definitions of success, and priorities of ED patients with musculoskeletal pain. ⋯ Our findings indicate that: (1) patient subgroups by outcome priorities may exist that could inform multimodal, personalized approaches from the ED and (2) patients are flexible in which treatments they are willing to try to meet their individual goals.
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Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneumonia; however, this issue remains unclear. Therefore, we conducted this study to clarify it. ⋯ A real-time interactive AIoT-based model might be a better tool for predicting adverse outcomes in ED patients with pneumonia. Further validation in other populations is warranted.
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This study aimed to (1) examine the proportion of patients presenting to an emergency department (ED) for acute cardiac symptoms with comorbid mental health conditions (MHCs) comprising current depression, generalized anxiety disorder, and panic disorder; (2) compare cardiac patients with and without MHCs regarding sociodemographic, medical, and psychological characteristics; and (3) examine recognition and treatment rates of MHCs. ⋯ MHCs are prevalent in nearly one-third of patients presenting with cardinal cardiac symptoms. Thus, the ED visit offers an opportunity to identify and refer patients with MHCs to appropriate and timely care after exclusion of life-threatening conditions.