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Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.
Radiology. 2020 08; 296(2):E86-E96.R

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; 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.

Authors+Show Affiliations

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.).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.).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.).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.).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.).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.).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.).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.).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.).

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32301647

Citation

Colombi, Davide, et al. "Well-aerated Lung On Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia." Radiology, vol. 296, no. 2, 2020, pp. E86-E96.
Colombi D, Bodini FC, Petrini M, et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology. 2020;296(2):E86-E96.
Colombi, D., Bodini, F. C., Petrini, M., Maffi, G., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E. (2020). Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology, 296(2), E86-E96. https://doi.org/10.1148/radiol.2020201433
Colombi D, et al. Well-aerated Lung On Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology. 2020;296(2):E86-E96. PubMed PMID: 32301647.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. AU - Colombi,Davide, AU - Bodini,Flavio C, AU - Petrini,Marcello, AU - Maffi,Gabriele, AU - Morelli,Nicola, AU - Milanese,Gianluca, AU - Silva,Mario, AU - Sverzellati,Nicola, AU - Michieletti,Emanuele, Y1 - 2020/04/17/ PY - 2020/4/18/pubmed PY - 2020/8/1/medline PY - 2020/4/18/entrez SP - E86 EP - E96 JF - Radiology JO - Radiology VL - 296 IS - 2 N2 - 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. SN - 1527-1315 UR - https://www.unboundmedicine.com/medline/citation/32301647/Well_aerated_Lung_on_Admitting_Chest_CT_to_Predict_Adverse_Outcome_in_COVID_19_Pneumonia_ L2 - https://pubs.rsna.org/doi/10.1148/radiol.2020201433?url_ver=Z39.88-2003&amp;rfr_id=ori:rid:crossref.org&amp;rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -