dc.contributor.author
Hosse, Clarissa
dc.contributor.author
Büttner, Laura
dc.contributor.author
Fleckenstein, Florian Nima
dc.contributor.author
Hamper, Christina Maria
dc.contributor.author
Jonczyk, Martin
dc.contributor.author
Scholz, Oriane
dc.contributor.author
Aigner, Annette
dc.contributor.author
Böning, Georg
dc.date.accessioned
2022-01-21T15:33:48Z
dc.date.available
2022-01-21T15:33:48Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/33686
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-33406
dc.description.abstract
We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
quantification
en
dc.subject
risk assessment
en
dc.subject
intensive care
en
dc.subject
resource allocation
en
dc.subject
developing countries
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients—A Simple Solution
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1616
dcterms.bibliographicCitation.doi
10.3390/diagnostics11091616
dcterms.bibliographicCitation.journaltitle
Diagnostics
dcterms.bibliographicCitation.number
9
dcterms.bibliographicCitation.originalpublishername
MDPI AG
dcterms.bibliographicCitation.volume
11
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
34573957
dcterms.isPartOf.eissn
2075-4418