Back to Search
Start Over
Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients
- Source :
- ANNALS OF HEMATOLOGY, r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA, Consejo Superior de Investigaciones Científicas (CSIC), r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe, instname, r-FISABIO. Repositorio Institucional de Producción Científica, r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau, Digital.CSIC. Repositorio Institucional del CSIC
- Publication Year :
- 2022
- Publisher :
- SPRINGER, 2022.
-
Abstract
- Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3–6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin’s lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.<br />REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research. We thank the Spanish Society of Hematology (SEHH) for its support on the study. We sincerely want to thanks the invaluable aid of microbiology services for their commitment in SARS-CoV-2-reactive IgG antibody monitoring in these highly immunosuppressed patients from all participating centers. Finally, we also want to thank the patients, nurses, and study coordinators for their foremost contributions in this study.
- Subjects :
- COVID-19 Vaccines
Antineoplastic Agents
Autologous stem cell transplantation
Respiratory virus
Antibodies, Viral
Moderna mRNA-1273
Hematological malignancies
Humans
Pfizer-BioNTech BNT162b2
BNT162 Vaccine
Early Detection of Cancer
Probabilistic graphical models
Non-Hodgkin lymphoma
SARS-CoV-2
Vaccination
Antibodies, Monoclonal
COVID-19
Hematology
General Medicine
CAR-T therapy
SARS-CoV-2 vaccines
Allogeneic stem cell transplantation
mRNA vaccine
Bayesian Networks
Immunocompromised patients
Chronic lymphocytic leukemia
Subjects
Details
- ISSN :
- 09395555
- Database :
- OpenAIRE
- Journal :
- ANNALS OF HEMATOLOGY, r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA, Consejo Superior de Investigaciones Científicas (CSIC), r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe, instname, r-FISABIO. Repositorio Institucional de Producción Científica, r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau, Digital.CSIC. Repositorio Institucional del CSIC
- Accession number :
- edsair.doi.dedup.....b3ee58c62e7825d035489b66d008a3ba