http://socictopen.socict.org/files/original/2b956a50358b369e15107efe27aadc62.pdf 083a0331e4b1eabffbf4620adcb2d8cd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort Creator An entity primarily responsible for making the resource Wolfgang Huber, Ulrike Protzer, Roland M Schmid, Marcus R. Makowski, Gerhard Schneider, Markus Schwaiger, Tobias Lahmer, Christoph D. Spinner, Michael Dommasch, Fabian Geisler, Egon Burian, Rickmer F Braren, Georgios A Kaissis, Fabian K Lohöfer, Friederike Jungmann, Matthias Treiber Description An account of the resource The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Computed tomography, Intensive care unit, clinical parameters, radiological parameters, COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/jcm9051514 Source A related resource from which the described resource is derived Journal of Clinical Medicine Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine