Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine...

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Vydáno v:	Radiology Ročník 296; číslo 2; s. E86
Hlavní autoři:	Colombi, Davide, Bodini, Flavio C, Petrini, Marcello, Maffi, Gabriele, Morelli, Nicola, Milanese, Gianluca, Silva, Mario, Sverzellati, Nicola, Michieletti, Emanuele
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.08.2020
Témata:	Aged Betacoronavirus Coronavirus Infections - diagnostic imaging Coronavirus Infections - pathology COVID-19 Emergency Service, Hospital Female Hospitalization Humans Intensive Care Units Male Middle Aged Pandemics Patient Admission - statistics & numerical data Pneumonia, Viral - diagnostic imaging Pneumonia, Viral - pathology Predictive Value of Tests Prognosis Radiographic Image Interpretation, Computer-Assisted - methods Retrospective Studies ROC Curve SARS-CoV-2 Tomography, X-Ray Computed - methods
ISSN:	1527-1315, 1527-1315
On-line přístup:	Zjistit podrobnosti o přístupu
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Abstract	Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters ( = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020
AbstractList	Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters ( = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Online supplemental material is available for this article.Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Online supplemental material is available for this article.
Author	Sverzellati, Nicola Bodini, Flavio C Milanese, Gianluca Petrini, Marcello Michieletti, Emanuele Morelli, Nicola Silva, Mario Maffi, Gabriele Colombi, Davide
Author_xml	– sequence: 1 givenname: Davide orcidid: 0000-0002-2794-5237 surname: Colombi fullname: Colombi, Davide organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 2 givenname: Flavio C orcidid: 0000-0003-0832-2605 surname: Bodini fullname: Bodini, Flavio C organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 3 givenname: Marcello surname: Petrini fullname: Petrini, Marcello organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 4 givenname: Gabriele orcidid: 0000-0002-4126-3478 surname: Maffi fullname: Maffi, Gabriele organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 5 givenname: Nicola orcidid: 0000-0003-3787-2243 surname: Morelli fullname: Morelli, Nicola organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 6 givenname: Gianluca orcidid: 0000-0003-1974-4854 surname: Milanese fullname: Milanese, Gianluca organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 7 givenname: Mario orcidid: 0000-0002-2538-7032 surname: Silva fullname: Silva, Mario organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 8 givenname: Nicola orcidid: 0000-0002-4820-3785 surname: Sverzellati fullname: Sverzellati, Nicola organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.) – sequence: 9 givenname: Emanuele orcidid: 0000-0002-6483-4798 surname: Michieletti fullname: Michieletti, Emanuele organization: From the Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Via Taverna 49, 29121, Piacenza, Italy (D.C., F.C.B., M.P., G. Maffi, N.M., E.M.); and Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy (G. Milanese, M.S., N.S.)
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References	32553834 - Int J Infect Dis. 2020 Aug;97:233-235
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SubjectTerms	Aged Betacoronavirus Coronavirus Infections - diagnostic imaging Coronavirus Infections - pathology COVID-19 Emergency Service, Hospital Female Hospitalization Humans Intensive Care Units Male Middle Aged Pandemics Patient Admission - statistics & numerical data Pneumonia, Viral - diagnostic imaging Pneumonia, Viral - pathology Predictive Value of Tests Prognosis Radiographic Image Interpretation, Computer-Assisted - methods Retrospective Studies ROC Curve SARS-CoV-2 Tomography, X-Ray Computed - methods
Title	Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
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