Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort
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. W...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/9/5/1514 |
| _version_ | 1797567724581289984 |
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| author | Egon Burian Friederike Jungmann Georgios A. Kaissis Fabian K. Lohöfer Christoph D. Spinner Tobias Lahmer Matthias Treiber Michael Dommasch Gerhard Schneider Fabian Geisler Wolfgang Huber Ulrike Protzer Roland M. Schmid Markus Schwaiger Marcus R. Makowski Rickmer F. Braren |
| author_facet | Egon Burian Friederike Jungmann Georgios A. Kaissis Fabian K. Lohöfer Christoph D. Spinner Tobias Lahmer Matthias Treiber Michael Dommasch Gerhard Schneider Fabian Geisler Wolfgang Huber Ulrike Protzer Roland M. Schmid Markus Schwaiger Marcus R. Makowski Rickmer F. Braren |
| author_sort | Egon Burian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:45:38Z |
| format | Article |
| id | doaj.art-5428ce7af51040149541e95e8b809ead |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T19:45:38Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-5428ce7af51040149541e95e8b809ead2023-11-20T00:48:36ZengMDPI AGJournal of Clinical Medicine2077-03832020-05-0195151410.3390/jcm9051514Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich CohortEgon Burian0Friederike Jungmann1Georgios A. Kaissis2Fabian K. Lohöfer3Christoph D. Spinner4Tobias Lahmer5Matthias Treiber6Michael Dommasch7Gerhard Schneider8Fabian Geisler9Wolfgang Huber10Ulrike Protzer11Roland M. Schmid12Markus Schwaiger13Marcus R. Makowski14Rickmer F. Braren15Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine I, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyClinic for Anesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyInstitute of Virology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanySchool of Medicine, Dean, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, GermanyThe 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.https://www.mdpi.com/2077-0383/9/5/1514COVID-19severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)clinical parametersradiological parameterscomputed tomographyintensive care unit |
| spellingShingle | Egon Burian Friederike Jungmann Georgios A. Kaissis Fabian K. Lohöfer Christoph D. Spinner Tobias Lahmer Matthias Treiber Michael Dommasch Gerhard Schneider Fabian Geisler Wolfgang Huber Ulrike Protzer Roland M. Schmid Markus Schwaiger Marcus R. Makowski Rickmer F. Braren Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort Journal of Clinical Medicine COVID-19 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical parameters radiological parameters computed tomography intensive care unit |
| title | Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort |
| title_full | Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort |
| title_fullStr | Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort |
| title_full_unstemmed | Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort |
| title_short | Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort |
| title_sort | intensive care risk estimation in covid 19 pneumonia based on clinical and imaging parameters experiences from the munich cohort |
| topic | COVID-19 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical parameters radiological parameters computed tomography intensive care unit |
| url | https://www.mdpi.com/2077-0383/9/5/1514 |
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