Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort

  1. Egon Burian
  2. Friederike Jungmann
  3. Georgios A. Kaissis
  4. Fabian K. Lohöfer
  5. Christoph D. Spinner
  6. Tobias Lahmer
  7. Matthias Treiber
  8. Michael Dommasch
  9. Gerhard Schneider
  10. Fabian Geisler
  11. Wolfgang Huber
  12. Ulrike Protzer
  13. Roland M. Schmid
  14. Markus Schwaiger
  15. Marcus R. Makowski
  16. Rickmer F. Braren

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Abstract

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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    SciScore for 10.1101/2020.05.04.20076349: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: All patients gave consent for scientific evaluation of clinical and imaging data at the time of admission.
    IRB: The local institutional review board of the Technical University of Munich has approved this study (protocol numbers: 245/19 S-SR and 111/20 S).
    Randomizationnot detected.
    BlindingSeverity score in CT: Two radiologists with 8 (F.L.) and 3 years of experience (E.B.) performed qualitative image assessment blinded to the clinical data.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: Statistical analyses were performed in SPSS (version 26; SPSS Inc., Chicago, IL, USA) and Python 3.7.6.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Although the presented data suggests the possibility of an estimation of disease severity in COVID-19 pneumonia using broadly available and simply assessed parameters, there are some limitations to our findings. The cohort size is small and therefore the importance of comorbidities is likely underrepresented in our analysis. Despite this, none of the ICU admitted patients seemed to suffer a clinical course defined by their comorbidity profile. Furthermore, in our current analysis we did not distinguish between different types of opacification (i.e. ground glass versus consolidated volumes) nor between clinically defined early and late stage manifestations of COVID-19 pneumonia. These may hold differential sensitivity for predicting the clinical course of disease and would first require validation in longitudinal imaging studies. Clearly, at this point we can only speculate on the clinical value of CT imaging for the prediction of individual viral load as other factors (e.g. host genetics and immune system interplay) may play in important role.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.3390/jcm9051514
  3. Version published to 10.1101/2020.05.04.20076349 on medRxiv