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Predicting a Positive Antibody Response After 2 SARS-CoV-2 mRNA Vaccines in Transplant Recipients: A Machine Learning Approach With External Validation.
Alejo, Jennifer L; Mitchell, Jonathan; Chiang, Teresa P-Y; Chang, Amy; Abedon, Aura T; Werbel, William A; Boyarsky, Brian J; Zeiser, Laura B; Avery, Robin K; Tobian, Aaron A R; Levan, Macey L; Warren, Daniel S; Massie, Allan B; Moore, Linda W; Guha, Ashrith; Huang, Howard J; Knight, Richard J; Gaber, Ahmed Osama; Ghobrial, Rafik Mark; Garonzik-Wang, Jacqueline M; Segev, Dorry L; Bae, Sunjae.
Afiliación
  • Alejo JL; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Mitchell J; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Chiang TP; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Chang A; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Abedon AT; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Werbel WA; Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Boyarsky BJ; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Zeiser LB; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Avery RK; Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Tobian AAR; Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Levan ML; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Warren DS; Department of Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, NY.
  • Massie AB; Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD.
  • Moore LW; Department of Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, NY.
  • Guha A; Department of Surgery, Houston Methodist Hospital, Houston, TX.
  • Huang HJ; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX.
  • Knight RJ; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX.
  • Gaber AO; Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX.
  • Ghobrial RM; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX.
  • Garonzik-Wang JM; Department of Medicine, Houston Methodist Hospital, Houston, TX.
  • Segev DL; Department of Surgery, Houston Methodist Hospital, Houston, TX.
  • Bae S; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX.
Transplantation ; 106(10): e452-e460, 2022 10 01.
Article en En | MEDLINE | ID: mdl-35859275
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 http://transplantmodels.com/covidvaccine/ . 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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Receptores de Trasplantes / Vacunas contra la COVID-19 / COVID-19 Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Transplantation Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Receptores de Trasplantes / Vacunas contra la COVID-19 / COVID-19 Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Transplantation Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos