Evidence map›Paper›PMID 39691210›Full record

ArticleClinical epidemiology2024

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

Tuomo A Nieminen, Arto A Palmu, Raija Auvinen, Sangita Kulathinal, Kari Auranen, Ritva K Syrjänen, Heta Nieminen, Tamala Mallett Moore, Stephanie Pepin, Jukka Jokinen

Abstract read
In one paragraph

Article in Clinical epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tuomo A NieminenData and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.ORCID 0000-0002-6901-2208
Arto A PalmuData and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.
Raija AuvinenInternal Medicine, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0003-1300-1048
Sangita KulathinalDepartment of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Kari AuranenMathematics and Statistics and Clinical Medicine, University of Turku, Turku, Finland.
Ritva K SyrjänenData and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.
Heta NieminenData and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.
Tamala Mallett MooreGlobal Pharmacovigilance, Sanofi, Swiftwater, PA, USA.ORCID 0000-0001-9325-4779
Stephanie PepinGlobal Clinical Development, Sanofi, Marcy-l'Étoile, France.
Jukka JokinenData and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: 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.

Indexed as

Bayesian statisticsobservational studypharmacovigilancepragmatic trialregister-based studyvaccine safety

Identifiers

PMID39691210
PMCPMC11651140

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.