Purpose: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient’s symptoms and clinical history. Methods: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. Results: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23–91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55–91.65%) that was higher in the mild phenotype (94.4%; CI 90.0–97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6–6.7, p < 0.0001). Conclusion: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients’ management during a pandemic surge.

Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study

Gargani L.;Spinelli S.;Barbieri G.;Corradi F.;Vetrugno L.;Secco G.;Frumento P.;Forfori F.;Ghiadoni L.;Falcone M.;Menichetti F.;Vezzoni G.;Franchini D.
2021-01-01

Abstract

Purpose: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient’s symptoms and clinical history. Methods: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. Results: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23–91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55–91.65%) that was higher in the mild phenotype (94.4%; CI 90.0–97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6–6.7, p < 0.0001). Conclusion: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients’ management during a pandemic surge.
2021
Volpicelli, G.; Gargani, L.; Perlini, S.; Spinelli, S.; Barbieri, G.; Lanotte, A.; Casasola, G. G.; Nogue-Bou, R.; Lamorte, A.; Agricola, E.; Villen, T.; Deol, P. S.; Nazerian, P.; Corradi, F.; Stefanone, V.; Fraga, D. N.; Navalesi, P.; Ferre, R.; Boero, E.; Martinelli, G.; Cristoni, L.; Perani, C.; Vetrugno, L.; Mcdermott, C.; Miralles-Aguiar, F.; Secco, G.; Zattera, C.; Salinaro, F.; Grignaschi, A.; Boccatonda, A.; Giostra, F.; Infante, M. N.; Covella, M.; Ingallina, G.; Burkert, J.; Frumento, P.; Forfori, F.; Ghiadoni, L.; Fraccalini, T.; Vendrame, A.; Basile, V.; Cipriano, A.; Frassi, F.; Santini, M.; Falcone, M.; Menichetti, F.; Barcella, B.; Delorenzo, M.; Resta, F.; Vezzoni, G.; Bonzano, M.; Briganti, D. F.; Cappa, G.; Zunino, I.; Demitry, L.; Vignaroli, D.; Dipietro, L. S. S.; Bazzini, M.; Capozza, V.; Gonzalez, M. M.; Gibal, R. V.; Ibarz, R. P.; Alfaro, L. M.; Alfaro, C. M.; Alins, M. G.; Brown, A.; Dunlop, H.; Ralli, M. L.; Persona, P.; Russel, F. M.; Pang, P. S.; Rovida, S.; Deana, C.; Franchini, D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1101382
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