Predicting a Positive Antibody Response after 2 SARS-CoV-2 mRNA Vaccines in Transplant Recipients: A Machine Learning Approach with External Validation

Jennifer L. Alejo, Jonathan Mitchell, Teresa P.Y. Chiang, Amy Chang, Aura T. Abedon, William A. Werbel, Brian J. Boyarsky, Laura B. Zeiser, Robin K. Avery, Aaron A.R. Tobian, Macey L. Levan, Daniel S. Warren, Allan B. Massie, Linda W. Moore, Ashrith Guha, Howard J. Huang, Richard J. Knight, Ahmed Osama Gaber, Rafik Mark Ghobrial, Jacqueline M. Garonzik-WangDorry L. Segev, Sunjae Bae

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Background. Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations. Methods. Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital. Results. Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at Conclusions. Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs.

Original languageEnglish (US)
Pages (from-to)E452-E460
Issue number10
StatePublished - Oct 1 2022


  • Antibodies, Viral
  • Antibody Formation
  • BNT162 Vaccine
  • COVID-19/prevention & control
  • COVID-19 Vaccines/adverse effects
  • Humans
  • Immunosuppressive Agents/adverse effects
  • Machine Learning
  • Mycophenolic Acid
  • SARS-CoV-2
  • Transplant Recipients
  • Vaccines
  • Vaccines, Synthetic
  • mRNA Vaccines

ASJC Scopus subject areas

  • Transplantation


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