Objectives: To describe the clinical characteristics of patients with coronavirus disease 2019 (COVID-19) treated in hospital emergency departments (EDs) in Spain, and to assess associations between characteristics and outcomes.

Material And Methods: Prospective, multicenter, nested-cohort study. Sixty-one EDs included a random sample of all patients diagnosed with COVID-19 between March 1 and April 30, 2020. Demographic and baseline health information, including concomitant conditions; clinical characteristics related to the ED visit and complementary test results; and treatments were recorded throughout the episode in the ED. We calculated crude and adjusted odds ratios for risk of in-hospital death and a composite outcome consisting of the following events: intensive care unit admission, orotracheal intubation or mechanical ventilation, or in-hospital death. The logistic regression models were constructed with 3 groups of independent variables: the demographic and baseline health characteristics, clinical characteristics and complementary test results related to the ED episode, and treatments.

Results: The mean (SD) age of patients was 62 (18) years. Most had high- or low-grade fever, dry cough, dyspnea, and diarrhea. The most common concomitant conditions were cardiovascular diseases, followed by respiratory diseases and cancer. Baseline patient characteristics that showed a direct and independent association with worse outcome (death and the composite outcome) were age and obesity. Clinical variables directly associated with worse outcomes were impaired consciousness and pulmonary crackles; headache was inversely associated with worse outcomes. Complementary test findings that were directly associated with outcomes were bilateral lung infiltrates, lymphopenia, a high platelet count, a D-dimer concentration over 500 mg/dL, and a lactate-dehydrogenase concentration over 250 IU/L in blood.

Conclusion: This profile of the clinical characteristics and comorbidity of patients with COVID-19 treated in EDs helps us predict outcomes and identify cases at risk of exacerbation. The information can facilitate preventive measures and improve outcomes.

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