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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)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Covid-19 ; Clinical Parameters ; Computed Tomography ; Intensive Care Unit ; Radiological Parameters ; Severe Acute Respiratory Syndrome Coronavirus 2 (sars-cov-2)
Language
english
Publication Year
2020
HGF-reported in Year
2020
ISSN (print) / ISBN
2077-0383
e-ISSN
2077-0383
Journal
Journal of Clinical Medicine
Quellenangaben
Volume: 9,
Issue: 5,
Article Number: 1514
Publisher
MDPI
Publishing Place
Basel
Reviewing status
Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-530014-001
PubMed ID
32443442
Erfassungsdatum
2022-09-13