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Burian, E.* ; Jungmann, F.* ; Kaissis, G.* ; Lohöfer, F.K.* ; Spinner, C.D.* ; Lahmer, T.* ; Treiber, M.* ; Dommasch, M.* ; Schneider, G.* ; Geisler, F.* ; Huber, W.* ; Protzer, U.* ; Schmid, R.M.* ; Schwaiger, M.* ; Makowski, M.R.* ; Braren, R.F.*

Intensive care risk estimation in COVID-19 pneumonia based on clinical and imaging parameters: Experiences from the Munich cohort.

J. Clin. Med. 9:1514 (2020)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Covid-19 ; Clinical Parameters ; Computed Tomography ; Intensive Care Unit ; Radiological Parameters ; Severe Acute Respiratory Syndrome Coronavirus 2 (sars-cov-2)
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2077-0383
e-ISSN 2077-0383
Zeitschrift Journal of Clinical Medicine
Quellenangaben Band: 9, Heft: 5, Seiten: , Artikelnummer: 1514 Supplement: ,
Verlag MDPI
Verlagsort Basel
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530014-001
DOI 10.3390/jcm9051514
PubMed ID 32443442
Erfassungsdatum 2022-09-13
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