Publication: The lung ultrasound "Rule of 7" in the prognosis of COVID-19 patients: Results from a prospective multicentric study.
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Date
2021-09-04
Authors
Tung-Chen, Yale
Gil-Rodrigo, Adriana
Algora-Martín, Ana
Llamas-Fuentes, Rafael
Rodríguez-Fuertes, Pablo
Marín-Baselga, Raquel
Alonso-Martínez, Blanca
Sanz Rodríguez, Elena
Llorens Soriano, Pere
Ramos-Rincón, José-Manuel
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Abstract
There is growing evidence regarding the imaging findings of coronavirus disease 2019 (COVID-19) in lung ultrasound (LUS), however the use of a combined prognostic and triage tool has yet to be explored. To determine the impact of the LUS in the prediction of the mortality of patients with highly suspected or confirmed COVID-19.The secondary outcome was to calculate a score with LUS findings with other variables to predict hospital admission and emergency department (ED) discharge. Prospective study performed in the ED of three academic hospitals. Patients with highly suspected or confirmed COVID-19 underwent a LUS examination and laboratory tests. A total of 228 patients were enrolled between March and September 2020. The mean age was 61.9 years (Standard Deviation - SD 21.1). The most common findings in LUS was a right posteroinferior isolated irregular pleural line (53.9%, 123 patients). A logistic regression model was calculated, including age over 70 years, C-reactive protein (CRP) over 70mg/L and a lung score over 7 to predict mortality, hospital admission and discharge from the ED. We obtained a predictive model with a sensitivity of 56.8% and a specificity of 87.6%, with an AUC of 0.813 [p The combination of LUS, clinical and laboratory findings in this easy to apply "rule of 7" showed excellent performance to predict hospital admission and mortality.
Description
MeSH Terms
Aged
COVID-19
Humans
Lung
Middle Aged
Prognosis
Prospective Studies
SARS-CoV-2
Ultrasonography
COVID-19
Humans
Lung
Middle Aged
Prognosis
Prospective Studies
SARS-CoV-2
Ultrasonography
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CIE Terms
Keywords
C-reactive protein, COVID-19, Chest, Emergency departments, Logistic regression, Medicina de urgencias, Point-of-care, Proteína C reactiva, Regresión logística, Tórax