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Cited by 457COVID-19 Clinical Research StudiesCOVID-19 diagnosis using AILong-Term Effects of COVID-19

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

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

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Colombi, Davide, Bodini, Flavio C., Petrini, Marcello, Maffi, Gabriele, Morelli, Nicola, Milanese, Gianluca, Silva, Mario, Sverzellati, Nicola, Michieletti, Emanuele·2020·Radiology
DOI 10.1148/radiol.2020201433PMC PMC7233411
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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; 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.

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Research works printed but not linked: 2

  1. American College of Radiology (ACR), ACR Recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 Infection
  2. Yoon RG, 2013, Eur Radiol, 23, 692

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Metadata

Authors
Colombi, D., Bodini, F. C., Petrini, M., Maffi, G., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E.
Affiliations
University of Parma (IT)
Corresponding author
Colombi, Davide
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2020-08
Volume
296
Issue
2
Pages
E86-E96
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.2020201433
PMC
PMC7233411
OpenAlex
W3017116151
Cited by
456
References
33
Citations by year
2026: 3 · 2025: 15 · 2024: 22 · 2023: 38 · 2022: 80 · 2021: 163 · 2020: 135
FWCI
13.66
Type
journal article
Language
en
Open access
Locations
  • Radiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
Keywords
chest CT, prognosis, intensive care unit admission, mortality, CT volumetry, prognostic factors, SARS-CoV-2, emergency department admission, risk stratification, SARS-CoV-2 serology
MeSH terms
Betacoronavirus, COVID-19, SARS-CoV-2, Aged, Emergency Service, Hospital, Female, Hospitalization, Humans, Intensive Care Units, Male, Middle Aged, Patient Admission, Pneumonia, Viral, Predictive Value of Tests, Prognosis, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, ROC Curve, Tomography, X-Ray Computed, Coronavirus Infections, Pandemics

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