Bilgin Osmanodja, Johannes Stegbauer, Marta Kantauskaite, Lars Christian Rump, Andreas Heinzel, Roman Reindl-Schwaighofer, Rainer Oberbauer, Ilies Benotmane, Sophie Caillard, Christophe Masset, Clarisse Kerleau, Gilles Blancho, Klemens Budde, Fritz Grunow, Michael Mikhailov, Eva Schrezenmeier, Simon Ronicke, Freie Universität Berlin, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], University Hospital Düsseldorf, Medizinische Universität Wien = Medical University of Vienna, CHU Strasbourg, Immuno-Rhumatologie Moléculaire, Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Team 4 : Deciphering organ immune regulation in inflammation and transplantation (DORI-t) (U1064 Inserm - CR2TI), Centre de Recherche en Transplantation et Immunologie - Center for Research in Transplantation and Translational Immunology (U1064 Inserm - CR2TI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Centre hospitalier universitaire de Nantes (CHU Nantes), Institut de transplantation urologie-néphrologie (ITUN), Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes), Team 3 : Integrative transplantation, HLA, Immunology and genomics of kidney injury (U1064 Inserm - CR2TI), Berlin Institute of Health (BIH), and KERANDEL-DION, Céline
BackgroundRepeated 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 in KTR.MethodsWe 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 KTR. Using 27 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), LASSO-regularized LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 585 vaccinations were used. External validation was performed in four independent, international validation datasets comprising 191, 184, 254, and 323 vaccinations, respectively.FindingsLASSO-regularized LR performed on the whole development dataset yielded a 23- and 11- variable model, respectively. External validation showed AUC-ROC of 0.855, 0.749, 0.828, and 0.787 for the sparser 11-variable model, yielding an overall performance 0.819.InterpretationAn 11-variable LASSO-regularized LR model predicts vaccination response in KTR with good overall accuracy. Implemented as an online tool, it can guide decisions when choosing between different immunization strategies to improve protection against COVID-19 in KTR.