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Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients
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 externall...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576943/ https://www.ncbi.nlm.nih.gov/pubmed/36268021 http://dx.doi.org/10.3389/fimmu.2022.997343 |
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author | Osmanodja, Bilgin Stegbauer, Johannes Kantauskaite, Marta Rump, Lars Christian Heinzel, Andreas Reindl-Schwaighofer, Roman Oberbauer, Rainer Benotmane, Ilies Caillard, Sophie Masset, Christophe Kerleau, Clarisse Blancho, Gilles Budde, Klemens Grunow, Fritz Mikhailov, Michael Schrezenmeier, Eva Ronicke, Simon |
author_facet | Osmanodja, Bilgin Stegbauer, Johannes Kantauskaite, Marta Rump, Lars Christian Heinzel, Andreas Reindl-Schwaighofer, Roman Oberbauer, Rainer Benotmane, Ilies Caillard, Sophie Masset, Christophe Kerleau, Clarisse Blancho, Gilles Budde, Klemens Grunow, Fritz Mikhailov, Michael Schrezenmeier, Eva Ronicke, Simon |
author_sort | Osmanodja, Bilgin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9576943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95769432022-10-19 Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients Osmanodja, Bilgin Stegbauer, Johannes Kantauskaite, Marta Rump, Lars Christian Heinzel, Andreas Reindl-Schwaighofer, Roman Oberbauer, Rainer Benotmane, Ilies Caillard, Sophie Masset, Christophe Kerleau, Clarisse Blancho, Gilles Budde, Klemens Grunow, Fritz Mikhailov, Michael Schrezenmeier, Eva Ronicke, Simon Front Immunol Immunology 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. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9576943/ /pubmed/36268021 http://dx.doi.org/10.3389/fimmu.2022.997343 Text en Copyright © 2022 Osmanodja, Stegbauer, Kantauskaite, Rump, Heinzel, Reindl-Schwaighofer, Oberbauer, Benotmane, Caillard, Masset, Kerleau, Blancho, Budde, Grunow, Mikhailov, Schrezenmeier and Ronicke https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Osmanodja, Bilgin Stegbauer, Johannes Kantauskaite, Marta Rump, Lars Christian Heinzel, Andreas Reindl-Schwaighofer, Roman Oberbauer, Rainer Benotmane, Ilies Caillard, Sophie Masset, Christophe Kerleau, Clarisse Blancho, Gilles Budde, Klemens Grunow, Fritz Mikhailov, Michael Schrezenmeier, Eva Ronicke, Simon Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title | Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title_full | Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title_fullStr | Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title_full_unstemmed | Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title_short | Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients |
title_sort | development and validation of multivariable prediction models of serological response to sars-cov-2 vaccination in kidney transplant recipients |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576943/ https://www.ncbi.nlm.nih.gov/pubmed/36268021 http://dx.doi.org/10.3389/fimmu.2022.997343 |
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