The COVID-19 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant (SOT) recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting SARS-CoV-2 vaccine responses 12 months post-vaccination in this high-risk group. Utilizing data from 303 SOT recipients from a Canadian multicenter cohort, models were developed to forecast anti-receptor binding domain (RBD) antibody levels. The study compared traditional machine learning models-logistic regression, epsilon-support vector regression, random forest regressor, and gradient boosting regressor-and deep learning architectures, including long short-term memory (LSTM), recurrent neural networks, and a novel model, routed LSTM. This new model combines capsule networks with LSTM to reduce the need for large datasets. Demographic, clinical, and transplant-specific data, along with longitudinal antibody measurements, were incorporated into the models. The routed LSTM performed best, achieving a mean square error (MSE) of 0.02±0.02 and a Pearson correlation coefficient (PCC) of 0.79±0.24, outperforming all other models. Key factors influencing vaccine response included age, immunosuppression, breakthrough infection, BMI, sex, and transplant type. These findings suggest that AI could be a valuable tool in tailoring vaccine strategies, improving health outcomes for vulnerable transplant recipients.
Keywords: COVID-19 vaccination; antibodies; machine learning; solid organ transplantation.
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.