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The efficacy of BNT162b2 (Pfizer-BioNTech) and CoronaVac vaccines in patients with cancer.

Melih SimsekAyse Irem YasinMehmet BesirogluAtakan TopcuZehra Sucuoglu IsleyenMesut SekerHaci Mehmet Turk
Published in: Journal of medical virology (2022)
Although vaccination is efficacious and prevents infection in the general population, there is limited data about Coronavirus disease-19 (Covid-19) occurrence after vaccination in cancer patients. It was aimed to evaluate the efficacy of BNT162b2 (Pfizer-BioNTech) and CoronaVac vaccines against Covid-19 in patients with cancer. In this single-center, retrospective, cross-sectional, and descriptive study, the data of cancer patients referred to the medical oncology clinic of a university hospital were analyzed. The sample of the study consisted of cancer patients who had Covid-19 or were vaccinated against Covid-19. A total number of 2578 patients were included in the study. Of the patients, 2000 have never been infected with severe acute respiratory syndrome coronavirus and 578 patients have had a positive reverse-transcription polymerase chain reaction (RT-PCR) for Covid-19. It was found that 2094 patients (81.2%) were fully vaccinated, and 484 patients (18.8%) did not receive full-dose vaccination. A statistically significant difference in Covid-19 occurrence was found between the patients who had full-dose vaccination or not (p = 0.000). In in-group comparisons of full-dose vaccinated patients, while no difference was observed between two doses of BNT162b2 (Pfizer-BioNTech) and three doses of CoronaVac (p = 0.432), a statistically significant difference was observed between all other groups (p < 0.005). When the data of 578 patients who experienced Covid-19 was analyzed, a statistically significant difference was observed between the groups who were full-dose vaccinated and those who were not (p = 0.000). It is recommended that this vulnerable patient group should be prioritized in vaccination programs, and full-dose vaccination (at least two doses of vaccines) should be completed as soon as possible.
Keyphrases
  • coronavirus disease
  • sars cov
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • prognostic factors
  • machine learning
  • patient reported outcomes
  • deep learning
  • transcription factor
  • big data