z9l9v0b2

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?:abstract	Abstract Background Lung ultrasound (LUS) can detect interstitial alveolar changes confined to the subpleural region, like those described in Covid-19. Objetive To evaluate how LUS findings correlate with chest computed tomography (CT) in patients admitted to the emergency department (ED) with suspicion of Covid-19. Methods Cross-sectional study of 20 patients (median age 43 years; interquartile range, 37-63 years; 50% male). All patients underwent LUS and chest CT on the day of ED admission. Each hemithorax was divided into 6 segments with similar landmarks, and equivalent scores (sc) of lesion severity were defined for both methods. The number of affected segments on LUS (LUSseg) was divided into tertiles (0-1, 2-5, and ≥6), and compared with number of affected segments on CT (CTseg), LUSsc, CTsc, and percentage of affected lung parenchyma through visual analysis (CTvis). ANOVA or Kruskal-Wallis test for continuous variables, chi-square test for categorical variables, and receiver operating characteristic (ROC) curve analysis to define optimal cutoff points were performed. P<0.05 was considered statistically significant. Results Median LUSsc, CTsc, CTseg, and CTvis were significantly different between groups. A clear separation between groups was demonstrated; patients with <2 affected segments on LUS were defined as low risk. The ROC curve showed good discriminative power to predict ≥6 affected segments on CT, with an area under the curve (AUC) of 0.97 and 0.98 for >7 LUSsc and >3 LUSseg, respectively. Conclusion LUS findings correlate with chest CT, and can help identify patients with normal lung or minor pulmonary involvement secondary to Covid-19. Int J Cardiovasc Sci. 2020; [online].ahead print, PP.0-0 Background Lung ultrasound (LUS) can detect interstitial alveolar changes confined to the subpleural region, like those described in Covid-19 Objetive To evaluate how LUS findings correlate with chest computed tomography (CT) in patients admitted to the emergency department (ED) with suspicion of Covid-19 Methods Cross-sectional study of 20 patients (median age 43 years;interquartile range, 37-63 years;50% male) All patients underwent LUS and chest CT on the day of ED admission Each hemithorax was divided into 6 segments with similar landmarks, and equivalent scores (sc) of lesion severity were defined for both methods The number of affected segments on LUS (LUSseg) was divided into tertiles (0-1, 2-5, and ≥6), and compared with number of affected segments on CT (CTseg), LUSsc, CTsc, and percentage of affected lung parenchyma through visual analysis (CTvis) ANOVA or Kruskal-Wallis test for continuous variables, chi-square test for categorical variables, and receiver operating characteristic (ROC) curve analysis to define optimal cutoff points were performed P<0 05 was considered statistically significant Results Median LUSsc, CTsc, CTseg, and CTvis were significantly different between groups A clear separation between groups was demonstrated;patients with <2 affected segments on LUS were defined as low risk The ROC curve showed good discriminative power to predict ≥6 affected segments on CT, with an area under the curve (AUC) of 0 97 and 0 98 for >7 LUSsc and >3 LUSseg, respectively Conclusion LUS findings correlate with chest CT, and can help identify patients with normal lung or minor pulmonary involvement secondary to Covid-19 Int J Cardiovasc Sci 2020;[online] ahead print, PP 0-0
is ?:annotates of	<https://research.tib.eu/covid-19/entity/z9l9v0b2_hasAnnotation_C0027627> <https://research.tib.eu/covid-19/entity/z9l9v0b2_hasAnnotation_C0036679> <https://research.tib.eu/covid-19/entity/z9l9v0b2_hasAnnotation_C1413811>
?:creator	<https://research.tib.eu/covid-19/entity/Alcantara%2C_Monica_Luiza_de%3B_Bernardo%2C_Marcos_Paulo_Lacerda%3B_Autran%2C_Tatiana_Bagrichevsky%3B_Lustosa%2C_Rodolfo_de_Paula%3B_Tayah%2C_Marcelo%3B_Chagas%2C_Lucia_Antunes%3B_Machado%2C_Dequitier_Carvalho> <https://research.tib.eu/covid-19/entity/Alcantara%2C_Monica_Luiza_de_Bernardo_Marcos_Paulo_Lacerda_Autran_Tatiana_Bagrichevsky_Lustosa_Rodolfo_de_Paula_Tayah_Marcelo_Chagas_Lucia_Antunes_Machado_Dequitier_Carvalho>
?:journal	Int._j._cardiovasc._sci._(Impr.)
?:license	unk
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:source	WHO
?:title	Lung Ultrasound as a Triage Tool in an Emergency Setting during the Covid-19 Outbreak: comparison with CT Findings
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:who_covidence_id	#742126 #895946
?:year	2020

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?:abstract

Abstract Background Lung ultrasound (LUS) can detect interstitial alveolar changes confined to the subpleural region, like those described in Covid-19. Objetive To evaluate how LUS findings correlate with chest computed tomography (CT) in patients admitted to the emergency department (ED) with suspicion of Covid-19. Methods Cross-sectional study of 20 patients (median age 43 years; interquartile range, 37-63 years; 50% male). All patients underwent LUS and chest CT on the day of ED admission. Each hemithorax was divided into 6 segments with similar landmarks, and equivalent scores (sc) of lesion severity were defined for both methods. The number of affected segments on LUS (LUSseg) was divided into tertiles (0-1, 2-5, and ≥6), and compared with number of affected segments on CT (CTseg), LUSsc, CTsc, and percentage of affected lung parenchyma through visual analysis (CTvis). ANOVA or Kruskal-Wallis test for continuous variables, chi-square test for categorical variables, and receiver operating characteristic (ROC) curve analysis to define optimal cutoff points were performed. P<0.05 was considered statistically significant. Results Median LUSsc, CTsc, CTseg, and CTvis were significantly different between groups. A clear separation between groups was demonstrated; patients with <2 affected segments on LUS were defined as low risk. The ROC curve showed good discriminative power to predict ≥6 affected segments on CT, with an area under the curve (AUC) of 0.97 and 0.98 for >7 LUSsc and >3 LUSseg, respectively. Conclusion LUS findings correlate with chest CT, and can help identify patients with normal lung or minor pulmonary involvement secondary to Covid-19. Int J Cardiovasc Sci. 2020; [online].ahead print, PP.0-0
Background Lung ultrasound (LUS) can detect interstitial alveolar changes confined to the subpleural region, like those described in Covid-19 Objetive To evaluate how LUS findings correlate with chest computed tomography (CT) in patients admitted to the emergency department (ED) with suspicion of Covid-19 Methods Cross-sectional study of 20 patients (median age 43 years;interquartile range, 37-63 years;50% male) All patients underwent LUS and chest CT on the day of ED admission Each hemithorax was divided into 6 segments with similar landmarks, and equivalent scores (sc) of lesion severity were defined for both methods The number of affected segments on LUS (LUSseg) was divided into tertiles (0-1, 2-5, and ≥6), and compared with number of affected segments on CT (CTseg), LUSsc, CTsc, and percentage of affected lung parenchyma through visual analysis (CTvis) ANOVA or Kruskal-Wallis test for continuous variables, chi-square test for categorical variables, and receiver operating characteristic (ROC) curve analysis to define optimal cutoff points were performed P<0 05 was considered statistically significant Results Median LUSsc, CTsc, CTseg, and CTvis were significantly different between groups A clear separation between groups was demonstrated;patients with <2 affected segments on LUS were defined as low risk The ROC curve showed good discriminative power to predict ≥6 affected segments on CT, with an area under the curve (AUC) of 0 97 and 0 98 for >7 LUSsc and >3 LUSseg, respectively Conclusion LUS findings correlate with chest CT, and can help identify patients with normal lung or minor pulmonary involvement secondary to Covid-19 Int J Cardiovasc Sci 2020;[online] ahead print, PP 0-0

is ?:annotates of