Nieminen Tuomo A, Palmu Arto A, Auvinen Raija, Kulathinal Sangita, Auranen Kari, Syrjänen Ritva K, Nieminen Heta, Mallett Moore Tamala, Pepin Stephanie, Jokinen Jukka
Data and Analytics, Finnish Institute for Health and Welfare (THL), Helsinki, Finland.
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Clin Epidemiol. 2024 Dec 13;16:901-915. doi: 10.2147/CLEP.S483034. eCollection 2024.
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.
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.
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.
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.
我们针对一项大型实用的高剂量四价流感疫苗（QIV-HD）临床试验，开发了一种混合安全监测方法，采用主动和被动数据收集方法。在此，我们介绍基于被动登记的严重不良事件（SAE）监测的方法和结果，该监测在试验期间取代了传统的SAE报告。
该试验招募了超过33000名老年人，其中50%接受QIV-HD，其余接受标准剂量疫苗（QIV-SD）作为对照疫苗。我们从国家登记处收集了接种疫苗后六个月内所有急性住院相关的诊断信息。在试验的盲法阶段，我们采用队列研究设计，比较了试验人群与在试验期间或上一个流感季节接种QIV-SD的试验外老年人中1811个国际疾病分类第10版诊断组（SAE类别）的发病率。基于实时概率比较，我们标记了试验人群中发病率较高的SAE类别，然后评估每个标记类别与试验干预之间可能的因果关联。
我们新颖的安全监测方法提供了信息，我们可以在试验期间进行实时评估。试验参与者经历了1217次与任何SAE类别相关的住院治疗，由941名患者导致。我们在研究期间标记了10个SAE类别进行进一步分析，但基于进一步的数据审查，没有一个类别显示出与疫苗接种有因果关系的有力证据。
在基于登记随访的实用疫苗试验中，利用被动数据收集和人群水平比较，可以实时检测和评估安全信号。与传统的安全随访方法相比，这种方法可能更全面、客观且资源有效。
