Development and Validation of Multivariable Prediction Models of Serological Response to SARS-CoV-2 Vaccination in Kidney Transplant Recipients

Abstract

Background 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 in KTR. Methods 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 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. Findings LASSO-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. Interpretation An 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.