Predicting severity of Covid-19 using standard laboratory parameters

  1. Thimo Buchmüller
  2. Ingmar Gröning
  3. Ralf Ihl

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Abstract

Background

More than 1.6 million people have already deceased due to a COVID-19 infection making it a major public health concern. A prediction of severe courses can enhance treatment quality and thus lower fatality and morbidity rates. The use of laboratory parameters has recently been established for a prediction. However, laboratory parameters have rarely been used in combination to predict severe outcomes.

Method

We used a retrospective case-control design to analyze risk factors derived from laboratory parameters. Patients treated for COVID-19 at an hospital in Krefeld, Germany, from March to May 2020 were included (n =42). Patients were classified into two categories based on their outcome (Mild course vs. treatment in intensive care unit). Laboratory parameters were compared across severity categories using non-parametric statistic. Identified laboratory parameters were used in a logistic regression model. The model was replicated using a) clinical standardized parameters b) aggregated factors derived from a factor analysis.

Results

Patients in intensive care unit showed elevated ALT, CRP and LDH levels, a higher leukocyte and neutrophile count, a higher neutrophile ratio and a lowered lymphocyte ratio. We were able to classify 95.1% of all cases correctly (96.6% of mild and 91.7% of severe cases, p <.001).

Conclusion

A number of routinely collected laboratory parameters is associated with a severe outcome of COVID-19. The combination of these parameters provides a powerful tool in predicting severity and can enhance treatment effectiveness.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.07.21249392: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe average age was 70 years (31-94, SD: 18 yrs.), 17 patients were women (40%).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analysis were performed with SPSS version 26.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Our analysis revealed two limitations regarding the clinical utility of previous approaches. Firstly, widely used clinical thresholds are not well suited to predict the course of COVID-19. This stresses the need to adapt the thresholds to COVID-19 patients to enhance their clinical utility. A second caveat can be derived from the results of our factor analysis. Here, factor structures of the laboratory parameters differed between patients with mild as compared to those with severe infection courses (although a larger sample is needed to replicate the factor structure in severe cases). Hence, researchers developing a clinical ‘risk score’ derived from laboratory parameters and demographic variables might need to consider differential clinical profiles depending on infection severity. Given our relatively small sample size our results should be interpreted with care. Moreover, one should bear in mind that our findings were based on cross-sectional data and thus point towards laboratory abnormalities in acutely infected severe cases. Here, longitudinal studies are needed to predict future outcomes using the obtained parameters at the beginning of an infection. The differences between predictive quality of raw values as compared to binary values obtained from clinical thresholds and the difference in factor structures points towards the need of estimating a potential course of infection by assessing the combined laboratory profile rather than on defined deviations on specific par...

    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 10.1101/2021.01.07.21249392v1 on medRxiv