Development of Real-Time Surveillance for Serious Adverse Events in a Pragmatic Clinical Trial Using National Registers in Finland

Tuomo A Nieminen,1,2 Arto A Palmu,1,3 Raija Auvinen,4 Sangita Kulathinal,2 Kari Auranen,5 Ritva K Syrjänen,1,6 Heta Nieminen,1,6 Tamala Mallett Moore,7 Stephanie Pepin,8 Jukka Jokinen1 1Data and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland; 2Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland; 3Management Team, FVR – Finnish Vaccine Research, Tampere, Finland; 4Internal Medicine, Helsinki University Hospital, Helsinki, Finland; 5Mathematics and Statistics and Clinical Medicine, University of Turku, Turku, Finland; 6RWE Unit, FVR – Finnish Vaccine Research, Tampere, Finland; 7Global Pharmacovigilance, Sanofi, Swiftwater, PA, USA; 8Global Clinical Development, Sanofi, Marcy-l’Étoile, FranceCorrespondence: Tuomo A Nieminen, Data and Analytics, Finnish Institute for Health and Welfare, Mannerheimintie 166, Helsinki, 00271, Finland, Tel +358 29 524 7534, Email [email protected]: We developed a hybrid safety surveillance approach for a large, pragmatic clinical trial of a high-dose quadrivalent influenza vaccine (QIV-HD), using both active and passive data collection methods. Here, we present the methods and results for the passive register-based surveillance of serious adverse events (SAEs), which replaced conventional SAE reporting during the trial.Patients and Methods: The trial recruited over 33,000 older adults of whom 50% received the QIV-HD while the rest received a standard-dose vaccine (QIV-SD) as a control vaccine. We collected diagnoses related to all acute hospitalizations during the six months following vaccination from national registers. During the blinded phase of the trial, we utilized a cohort study design and compared the incidences of 1811 ICD10 diagnosis groups (SAE categories) between the trial population and older adults vaccinated with the QIV-SD outside the trial, either during the study or the previous influenza season. Based on a real-time probabilistic comparison, we flagged SAE categories with higher incidence in the trial population and then evaluated possible causal associations between each flagged category and the trial intervention.Results: Our novel approach to safety surveillance provided information, which we could evaluate in real-time during the trial. The trial participants experienced 1217 hospitalizations related to any SAE categories, contributed by 941 patients. We flagged 10 SAE categories for further analysis during the study but based on further data review, none presented strong evidence of causality with vaccination.Conclusion: Safety signals can be detected and evaluated in real-time during a pragmatic vaccine trial with register-based follow-up, utilizing passive data collection and population level comparison. Compared to conventional methods of safety follow-up, this method is likely to be more comprehensive, objective and resource effective.Keywords: vaccine safety, Bayesian statistics, pharmacovigilance, observational study, register-based study, pragmatic trial