COVID-19 mortality prediction model, 3C-M, built for use in resource limited settings - understanding the relevance of neutrophilic leukocytosis in predicting disease severity and mortality

  1. Niharika Agarwal
  2. Devika Dua
  3. Ritika Sud
  4. Madhur Yadav
  5. Aparna Agarwal
  6. Vijesh Vijayan

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Abstract

In this study, a combination of clinical and hematological information, collected on day of presentation to the hospital with pneumonia, was evaluated for its ability to predict severity and mortality outcomes in COVID-19. Ours is a retrospective, observational study of 203 hospitalized COVID-19 patients. All of them were confirmed RT-PCR positive cases. We used simple hematological parameters (total leukocyte count, absolute neutrophil count, absolute lymphocyte count, neutrophil to lymphocyte ration and platelet to lymphocyte ratio); and a severity classification of pneumonia (mild, moderate and severe) based on a single clinical parameter, the percentage saturation of oxygen at room air, to predict the outcome in these cases. The results show that a high absolute neutrophil count on day of onset of pneumonia symptoms correlated strongly with both severity and survival in COVID-19. In addition, it was the primary driver of an initial high neutrophil-to-lymphocyte ratio (NLR) observed in patients with severe disease. The effect of low lymphocyte count was not found to be very significant in our cohort. Multivariate logistic regression was done using Python 3.7 to assess whether these parameters can adequately predict survival. We found that clinical severity and a high neutrophil count on day of presentation of pneumonia symptoms could predict the outcome with 86% precision. This model is undergoing further evaluation at our centre for validation using data collected during the second wave of COVID-19. We present the relevance of an elevated neutrophil count in COVID-19 pneumonia and review the advances in research which focus on neutrophils as an important effector cell of COVID-19 inflammation.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.05.21261565: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was performed in line with the principles of the declaration of Helsinki after approval from the local ethics committee.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    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:
    They have also, simultaneously, highlighted the potential limitations of this score like the ‘treatment paradox’. As the disease continues to evolve and our understanding of it continues to change, the impact assessment of these models will require rigorous and continued assessment in real time in order to retain validity. Keeping these relevant critiques in mind we decided to wait to prospectively evaluate our model before recommending it for general use.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.08.05.21261565v1 on medRxiv