Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study

  1. Adrián Sánchez-Montalvá
  2. Daniel Álvarez-Sierra
  3. Mónica Martínez-Gallo
  4. Janire Perurena-Prieto
  5. Iria Arrese-Muñoz
  6. Juan Carlos Ruiz-Rodríguez
  7. Juan Espinosa-Pereiro
  8. Pau Bosch-Nicolau
  9. Xavier Martínez-Gómez
  10. Andrés Antón
  11. Ferran Martínez-Valle
  12. Mar Riveiro-Barciela
  13. Albert Blanco-Grau
  14. Francisco Rodríguez-Frias
  15. Pol Castellano-Escuder
  16. Elisabet Poyatos-Canton
  17. Jordi Bas-Minguet
  18. Eva Martínez-Cáceres
  19. Alex Sánchez-Pla
  20. Coral Zurera-Egea
  21. Aina Teniente-Serra
  22. Manuel Hernández-González
  23. Ricardo Pujol-Borrell
  24. the “Hospital Vall d’Hebron Group for the study of COVID-19 immune profile”

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Abstract

Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited.

Objectives

To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively.

Findings

1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests.

Conclusions

Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

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  1. Version published to 10.3389/fimmu.2022.902837
  2. ScreenIT

    SciScore for 10.1101/2022.01.29.22270016: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical review: This project was approved by the institutional ethics board of the three institutions (HUVH, HUGTP, and HUB) which waived the requirement for individual informed consent (protocol R(AG)242/2020).
    Consent: Ethical review: This project was approved by the institutional ethics board of the three institutions (HUVH, HUGTP, and HUB) which waived the requirement for individual informed consent (protocol R(AG)242/2020).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisMultivariable logistic regression was used to calculate the predictive power of different combinations of variables.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    macrophage colony-stimulating factor (GM-CSF), interferon (IFN)-alpha, IFN-gamma, interleukin (IL)-10, IL-12 p70, IL-13, IL-15, IL-17A, IL-1RA, IL-2, IL-4, IL-6, IL-7, and tumour-necrosis factor (TNF), and granzyme B were measured in sera using the ELLA microfluidic platform (Biotechne®, Minneapolis, MN, USA); for sCD163 levels an enzyme-linked immunosorbent assay was used (CD163 human kit, Thermo Fisher Societies, Waltham, MA, USA).
    Biotechne®
    suggested: None
    Thermo Fisher Societies
    suggested: None
    Data were analysed with Kaluza Beckman Software v.2.1.
    Kaluza
    suggested: (Kaluza, RRID:SCR_016182)
    R, version 4.1.0 (The R Foundation for Statistical Computing, Vienna, Austria) and Prism 9® (GraphPad, San Diego, CA, USA) packages were used for all analyses.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    The analyses revealed the limitations of currently used clinical laboratory tests used to assess the prognosis of patients with COVID-19 and tried to improve their interpretation by grouping them into categories that reflect the two main biological processes that are measured, i.e., inflammation and organ damage. As their limitations are due to redundancy, clinical management protocols could be simplified, but additional biomarkers with independent predictive power are urgently needed. This study highlights the lack of tests, for early prediction of the specific immune response to SARS-CoV-2. Such tests could provide critical non-redundant information required for prediction and clinical management. The results of the pilot study using a selection of robust immunological techniques derived from other areas of clinical immunology, suggest that better tests can be identified through systematic investigation. As well as this central message, other notable findings are: 1) The three cohorts confirmed the strong association of: SpO2/FiO2, neutrophilia, lymphopenia, APRs, coagulation factors, kidney function and the AST/ALT ratio with survival and predicting disease severity. 2) There was a high level of collinearity (redundancy) among the different variables, which explains the limited predictive ability of current tests. 3) After reducing redundancy, the best combination of variables was age, comorbidity index, SpO2/FiO2, NLR, CRP, AST/ALT ratio, fibrinogen, and GFR. 4) The class...

    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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  3. Version published to 10.1101/2022.01.29.22270016 on medRxiv