Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients

  1. Bilgin Osmanodja
  2. Johannes Stegbauer
  3. Marta Kantauskaite
  4. Lars Christian Rump
  5. Andreas Heinzel
  6. Roman Reindl-Schwaighofer
  7. Rainer Oberbauer
  8. Ilies Benotmane
  9. Sophie Caillard
  10. Christophe Masset
  11. Clarisse Kerleau
  12. Gilles Blancho
  13. Klemens Budde
  14. Fritz Grunow
  15. Michael Mikhailov
  16. Eva Schrezenmeier
  17. Simon Ronicke

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Abstract

Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. No decision support tool exists to predict SARS-CoV-2 vaccination response to third or fourth vaccination in KTR. We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in previously seronegative, COVID-19-naïve KTR. Using 20 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), least absolute shrinkage and selection operator (LASSO)-regularized LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 590 vaccinations were used. External validation was performed in four independent, international validation cohorts comprising 191, 184, 254, and 323 vaccinations, respectively. LASSO-regularized LR performed on the whole development dataset yielded a 20- and 10-variable model, respectively. External validation showed AUC-ROC of 0.840, 0.741, 0.816, and 0.783 for the sparser 10-variable model, yielding an overall performance 0.812. A 10-variable LASSO-regularized LR model predicts vaccination response in KTR with good overall accuracy. Implemented as an online tool, it can guide decisions whether to modulate immunosuppressive therapy before additional active vaccination, or to perform passive immunization to improve protection against COVID-19 in previously seronegative, COVID-19-naïve KTR.

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  1. Version published to 10.3389/fimmu.2022.997343
  2. ScreenIT

    SciScore for 10.1101/2022.06.02.22275894: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    For the enzyme-linked immunosorbent assays (ELISA) for the detection of IgG antibodies against the S1 domain of the SARS-CoV-2 spike (S) protein in serum (Anti-SARS-CoV-2-ELISA (IgG)
    Anti-SARS-CoV-2-ELISA (IgG
    suggested: None
    y) detecting human immunoglobulins, including IgG, IgA and IgM against the spike receptor binding (RBD) domain protein, samples with ≥ 264 U/ml were considered to be positive as recommended by Caillard et al.8,9 Any non-zero antibody level below this cutoff was considered low positive (with limit of detection being 0.4 U/mL).
    IgM against the spike receptor binding (RBD) domain protein
    suggested: None
    Outcome and Predictors: The single outcome variable was a positive serological response defined by the maximum anti-SARS-CoV-2 spike (S) IgG or antibody level after a minimum of 14 days following the date of vaccination and before any further immunization event such as SARS-CoV-2 infection, passive or active immunization.
    anti-SARS-CoV-2 spike (S) IgG
    suggested: (Leinco Technologies Cat# S540, RRID:AB_2831778)
    Generally, IgG or antibody positivity was determined based on local laboratory’s pre-defined positivity cutoff, which was mostly the one provided by the manufacturer.
    IgG
    suggested: None
    For each validation set, we excluded vaccinations with missing SARS-CoV-2 IgG data, missing information about the SARS-CoV-2 spike IgG or antibody assay used, missing medication data, or missing eGFR, lymphocyte count, or hemoglobin level.
    SARS-CoV-2 spike IgG
    suggested: None

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  3. Version published to 10.1101/2022.06.02.22275894v1 on medRxiv