A 6-mRNA host response whole-blood classifier trained on pre-pandemic data accurately predicts severity in COVID-19 and other acute viral infections

  1. Ljubomir Buturovic
  2. Hong Zheng
  3. Benjamin Tang
  4. Kevin Lai
  5. Win Sen Kuan
  6. Mark Gillett
  7. Rahul Santram
  8. Maryam Shojaei
  9. Raquel Almansa
  10. Jose Ángel Nieto
  11. Sonsoles Muñoz
  12. Carmen Herrero
  13. Nikolaos Antonakos
  14. Panayiotis Koufargyris
  15. Marina Kontogiorgi
  16. Georgia Damoraki
  17. Oliver Liesenfeld
  18. James Wacker
  19. Uros Midic
  20. Roland Luethy
  21. David Rawling
  22. Melissa Remmel
  23. Sabrina Coyle
  24. Yiran E. Liu
  25. Aditya M Rao
  26. Denis Dermadi
  27. Jiaying Toh
  28. Lara Murphy Jones
  29. Michele Donato
  30. Purvesh Khatri
  31. Evangelos J. Giamarellos-Bourboulis
  32. Timothy E Sweeney

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Abstract

Background

Determining the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19.

Methods

We developed the classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs.

Results

We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N=97) and retrospectively (N=100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay.

Conclusions

With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

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  1. Version published to 10.1101/2020.12.07.20230235v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.12.07.20230235: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The studies were conducted following approvals for the collection of biomaterial for transcriptomic analysis for patients with lower respiratory tract infections provided by the Ethics Committees of the participating hospitals.
    Consent: Participants were adults with written informed consent provided by themselves or by first-degree relatives in the case of patients unable to consent, with molecular detection of SARS-CoV-2 in respiratory secretions and radiological evidence of lower respiratory tract involvement.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Further, all samples were shown to be negative for HIV, West Nile, Hepatitis B, and Hepatitis C by molecular or antibody-based testing.
    antibody-based testing.
    suggested: None
    Software and Algorithms
    SentencesResources
    We mapped microarray probes in each dataset to Entrez Gene identifiers (IDs) to facilitate integrated analysis.
    Entrez Gene
    suggested: (Entrez Gene, RRID:SCR_002473)
    Thus, LAMP assays were designed to span exon junctions, and at least three core (FIP/BIP/F3/B3) solutions meeting these design criteria were identified for each marker and evaluated for successful amplification of cDNA and exclusion of gDNA.
    LAMP
    suggested: (LAMP, RRID:SCR_001740)
    Statistical Analyses: Analyses were performed in R version 3 and Python version 3.6.
    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:
    Our study has several limitations. First, our study uses retrospective data with large amount of heterogeneity for discovery of the 6-mRNA signature; such heterogeneity could hide unknown confounders in classifier development. However, our successful representation of biological, clinical, and technical heterogeneity also increased the a priori odds of identifying a parsimonious set of generalizable prognostic biomarkers suitable for clinical translation as a point-of-care. Second, owing to practical considerations for urgent need, we focused on a preselected panel of mRNAs. It is possible that similar analysis using the whole transcriptome data would find additional signatures, though with less clinical data. Third, we only considered linear models. It is possible that more complex models that account for non-linear relationships may be more accurate, but also may be overfit. Fourth, a common limitation in all these types of pandemic observational studies is a lack of understanding of the effect of time from symptoms onset. Finally, additional larger prospective cohorts are needed to further confirm the accuracy of the 6-mRNA model in distinguishing patients at high risk of progressing to severe outcomes from those who do not. Overall, our results show that once translated into a rapid assay and validated in larger prospective cohorts, this 6-mRNA prognostic score could be used as a clinical tool to help triage patients after diagnosis with SARS-CoV-2 or other viral infectio...

    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.

  3. Version published to 10.1101/2020.12.07.20230235v1 on medRxiv