COVID-19 Vaccine and Death: Causality Algorithm According to the WHO Eligibility Diagnosis

Giovanni Giammanco, Antonino Giarratano, Francesca Rappa, Claudio Tripodo, Marcello Ciaccio, Francesco Dieli, Paolo Zamboni, Pier Mannuccio Mannucci, Marcello Ciaccio, Sebastiano Fabio Garozzo, Massimiliano Esposito, Monica Salerno, Francesco Sessa, Antonino Giarratano, Pier Mannuccio Mannucci, Daniele Prati, Cristoforo Pomara

Risultato della ricerca: Articlepeer review

40 Citazioni (Scopus)


The current challenge worldwide is the administration of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. Even if rarely, severe vascular adverse reactions temporally related to vaccine administration have induced diffidence in the population at large. In particular, researchers worldwide are focusing on the so-called “thrombosis and thrombocytopenia after COVID-19 vaccination”. This study aims to establish a practical workflow to define the relationship between adverse events following immunization (AEFI) and COVID-19 vaccination, following the basic framework of the World Health Organization (WHO). Post-mortem investigation plays a pivotal role to support this causality relationship when death occurs. To demonstrate the usefulness and feasibility of the proposed workflow, we applied it to two exemplificative cases of suspected AEFI following COVID-19 vaccination. Based on the proposed model, we took into consideration any possible causality relationship between COVID-19 vaccine administration and AEFI. This led us to conclude that vaccination with ChAdOx1 nCov-19 may cause the rare development of immune thrombocytopenia mediated by platelet-activating antibodies against platelet factor 4 (PF4), which clinically mimics heparin-induced autoimmune thrombocytopenia. We suggest the adoption of the proposed methodology in order to confirm or rule out a causal relationship between vaccination and the occurrence of AEFI.
Lingua originaleEnglish
pagine (da-a)1-17
Numero di pagine17
Stato di pubblicazionePublished - 2021

All Science Journal Classification (ASJC) codes

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