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Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
Open accessAbstract
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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“The area under the receiver operating characteristic curve (AUC) was then computed to determine the adverse effects which support the clinical decision making for appropriate patient care.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]
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“More specifically, logistic regression models were used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit admission or death versus no ICU admission or death).”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]
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“For example, ML-based regression models have been used in [22] to estimate adverse outcome in COVID-19 pneumonia, by using chest CT scans with reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]
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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
- Published
- 2020-08
- Volume
- 296
- Issue
- 2
- Pages
- E86-E96
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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