Safety Profiles of mRNA COVID-19 Vaccines Using World Health Organization Global Scale Database (VigiBase): A Latent Class Analysis

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Messenger RNA (mRNA) coronavirus disease 2019 (COVID-19) vaccines have been rapidly developed and widely utilized owing to organizations and government funding and technological collaborations in response to the COVID-19 pandemic [1]. As of November 2022, approximately 12.8 billion cumulative vaccine doses have been administered globally [2]. The administration of COVID-19 vaccines might also be expanded in the future because of variants and booster doses [3].

Authorized mRNA COVID-19 vaccines have introduced a new technology platform. Although the products have undergone clinical trials, limitations remain regarding their safety information, such as restricted trial participants who meet inclusion and exclusion criteria, short study periods, and the detection of rare events, particularly serious adverse events following immunizations (AEFIs) [4]. To overcome the limited evidence regarding the safety of COVID-19 vaccines, the vaccines are being evaluated by each country's safety system such as the Vaccine Adverse Event Reporting System (VAERS) and the Vaccine Safety Datalink (VSD) in the USA. Vaccine manufacturers are required to submit safety update reports routinely to the national regulatory authorities [1]. Thus far, mild to moderate local or systemic AEFIs such as myocarditis [5], anaphylaxis [6], Bell's palsy [7], and Guillain-Barré syndrome (GBS) [8] have been reported. However, factors that are associated with unknown adverse events (AEs) following mRNA COVID-19 vaccination, such as age, gender, and existing diseases or high-risk subgroups, have not been well established [9‐11]. The identification of patterns and factors can provide insights into preventive measures and management strategies concerning unknown AEFIs [12].

We aimed to (1) identify the patterns of serious AEFI profiles after COVID-19 vaccination within the VigiBase, the World Health Organization (WHO)’s international database of suspected adverse drug reactions, and (2) describe potential factors to identify subgroups in mRNA COVID-19 vaccine reports with similar serious AEFI profiles. To achieve these aims, we applied latent class analysis (LCA), a statistical method used to identify the relationships among a set of unobserved dichotomous or polytomous variables that can be viewed as indicators [12]. This study was conducted in accordance with the Helsinki Declaration. The study protocol was approved for exemption from review by the Institutional Review Board of Chung-Ang University (IRB number: 1041078-201903-HR-071-01), because this study analyzed a secondary database. Informed consent from subjects was waived due to the database containing anonymized data that cannot identify study subjects.

A total of 312,878 spontaneous reports on AEs after mRNA COVID-19 vaccination were identified between December 28, 2020, and February 28, 2022, in VigiBase (Fig. 1). The most frequently reported AEs in terms of SOC level were nervous system disorders (37.9%) and respiratory, thoracic, and mediastinal disorders (19.1%) (Table 1).

Using the LCA, we identified five clusters of groups with similar AEFI profiles after mRNA COVID-19 administration. Although the six-cluster solution had lower AIC, BIC, and G2-statistic scores than the five-cluster solution, the difference between evaluation tool scores for the two solutions was very small. Specifically, the five-cluster solution presented lower AIC, BIC, and G2 fit statistics than the four-cluster solution. Therefore, the five-cluster solution was selected as the optimal solution in our study (Table S1).

Table 1 shows the estimated probability for each LCA indicator. Item-response probabilities were divided into the following five clusters: cluster 1: infection AEs (43,606 reports, 13.9%); cluster 2: cardiac AEs (25,301 reports, 8.1%); cluster 3: respiratory/thrombotic AEs (67,881 reports, 21.7%); cluster 4: systemic AEs (25,529 reports, 8.2%); cluster 5: nervous system AEs (150,561 reports, 48.1%). All of the clusters were reported alongside general disorders and administration site conditions (cluster 1: 66.3%, cluster 2: 29.7%, cluster 3: 44.5%, cluster 4: 81.1%, and cluster 5: 48.0%) (Table S2); the most frequently reported PTs were pyrexia (11.8%) and fatigue (10.6%) (Table 2).

Table 3 shows the characteristics of all the five clusters. All the clusters contained a significant proportion of the number of tozinameran (76.8%) and spontaneous reports (99.1%) with a completeness score > 0.8.

In cluster 1 (infection AEs), COVID-19 infection (67.4%) and vaccination failure (46.6%) were more likely to be included (Table 2). This cluster had the highest proportion of reports received by physicians (45.9%) and from Europe (62.1%). It had a median TTO of 85.5 (interquartile range 16–149.5) days for each report. Cluster 2 (cardiac AEs) included myocarditis (25.0%) and pericarditis (14.7%) (Table 2). Most of the reports received included males (59.7%), an age of < 18 years (5.4%), and death (11.3%). Cluster 3 (respiratory/thrombotic AEs) was distinguished from other classes in that it was composed of AEFIs of both dyspnea (27.1%) and pulmonary embolism (14.8%). It included death (9.1%), life-threatening AEs (16.4%), and AEs that caused/prolonged hospitalization (45.7%). Patients in 17.3% of AE cases in cluster 3 were ≥ 75 years old. Cluster 4 (systemic AEs) included headache (43.8%) and nausea (31.5%). Most reports included females (69.5%), and the patients were often from the Americas (62.0%). The median TTO was 1 (interquartile range 0–9) day for each report. Cluster 5 (nervous system AEs) was the largest of the five clusters (48.1%). It differed from the other clusters in that it included a high proportion of facial paralysis (3301 reports, 85.7%) and Guillain-Barré syndrome (1494 reports, 82.6%; Table S3).

Table 4 shows the results of multinomial logistic regression. We selected cluster 4 (systemic AEs) as our reference group because the AE pattern of cluster 4 was consistent with AEs from a prior study [6]. The odds ratio (OR) (95% confidence interval [CI]) of males in cluster 2 (cardiac AEs) was 2.98 (95% CI 2.87–3.09), which was the highest estimate among clusters. A significant association was observed between cluster 5 (nervous system AEs) and congenital anomaly/birth defects (OR: 4.24; 95% CI 1.56–11.5). The ORs (95% CI) of notifier type reported by physicians were as follows: cluster 1: 6.67 (95% CI 6.27–7.09), cluster 2: 2.27 (95% CI 2.13–2.42), cluster 3: 2.46 (95% CI 2.36–2.60), and cluster 5: 1.60 (95% CI 1.51–1.68). Cluster 1 showed a strong association with TTO ≥ 14 days (OR: 16.2; 95% CI 15.5–16.9).

Our study aimed to identify the patterns of serious AEFI profiles after COVID-19 vaccination with the help of VigiBase. We identified five distinguished safety profiles using LCA and described the potential factors of serious AEFI profiles, which included age, gender, region, and TTO.

We suggest that cluster 1 (infection AEs) might be related to the ineffectiveness of mRNA vaccines. The most common PTs of cluster 1 were COVID-19 infection and vaccination failure. Furthermore, cluster 1 had a median TTO of 85.5 (interquartile range: 16.0–149.5) days, and a longer TTO of AEs (≥ 14 days) (OR 16.2; 95% CI 15.5–16.9) corresponds to the results of a randomized controlled trial [5]. The efficacy of the tozinameran vaccine decreased to 90% at 2–4 months after vaccination [5]. Moreover, as the number of reports in cluster 1 has increased steadily since December 2020, a long-term study of mRNA vaccine ineffectiveness is required [22].

We identified myocarditis and pericarditis following mRNA COVID-19 vaccination in cluster 2 (cardiac AEs). This association was not reported in the trial, and several countries are required to provide continued critical data for vaccine safety monitoring [23‐26]. Cluster 2 was mainly characterized by young males and is correlated with the results of previous studies [23, 27]. Given the AE cluster patterns, it is important to note that the majority of reports of death were found in young males.

Clusters 3 and 4 presented similar proportions of respiratory, thoracic, and mediastinal disorders in terms of SOCs (Table 1). However, these two clusters showed differences in the PTs of the most commonly reported type of AEs (Table 2). Cluster 3 showed higher diversity in reported respiratory symptoms compared to cluster 4. In cluster 3, the most common AEs were dyspnea (27.1%), pulmonary embolism (14.8%), and cough (7.4%) in respiratory, thoracic, and mediastinal disorders, while cluster 4 mostly included dyspnea (30.0%). Therefore, we labeled cluster 3 as “respiratory/thrombotic AEs.” Moreover, cluster 3 showed a high proportion of vascular disorders, including pulmonary embolism, deep vein thrombosis, and thrombosis, along with respiratory disorders. Previous case reports have reported thrombotic death immediately after the onset of symptoms of dyspnea [28] and deep vein thrombosis [29] following administration of mRNA COVID-19 vaccines. As a possible mechanism for thromboembolic events, massive platelet activation through platelet factor 4 has been proposed [30]. Prior studies have also reported thrombotic events after the onset of respiratory symptoms, which is consistent with cluster 3, which consisted primarily of patients ≥ 65 years (29.6% of cluster 3) [28, 29]. Thus, physicians need to monitor patients who complain of respiratory symptoms for the possibility of further thrombotic events. Cluster 4 demonstrated a shorter TTO and a higher number of AEs per report than other clusters. This could be due to the most frequent AEs in cluster 4 being headache and nausea, which are well-known acute systemic AEFIs [6]. Therefore, we named cluster 4 as “systemic AEs.” In cluster 5, reports of headache and dizziness without cardiac and respiratory disorders made up a high proportion. We identified 3301 reports of facial paralysis (85.7% of total reports of facial paralysis) and 1494 reports of Guillain-Barré syndrome (82.6% of total reports of facial paralysis), which are rare but serious events necessitating monitoring by regulatory agencies [31‐33]. Therefore, we labeled cluster 5 as “nervous system disorder AEs.”

This study is meaningful because we have identified potential factors determining the patterns of serious AEFI profiles after the administration of mRNA COVID-19 vaccines on a global scale using a database containing over 30 million reports. Although LCA has been applied in many domains, only a few studies have been conducted on vaccine safety [21, 34]. In particular, this is the first study to apply LCA to identify the patterns of serious AEs after the administration of mRNA COVID-19 vaccines. Using LCA, clusters of AEFIs could be identified based on underlying patterns that cannot be directly observed. Thus, the obtained evidence on the distinct safety profiles and associated factors using a spontaneous AE database will be helpful for improving the pharmacovigilance practice of safety concerns regarding mRNA COVID-19 vaccines.

This study has several limitations. First, because we used a spontaneous reporting database, the reports are prone to a notoriety bias. The number of spontaneous reports of AEs following COVID-19 vaccination increased rapidly because of either mass media intervention or scientific communications. Considering the differential reporting rate, comparison between COVID-19 vaccines and other vaccines could result in spurious perception and unbalance in disproportionality analysis [35, 36]. Thus, clustering, without other vaccines as the denominator, could be an appropriate method of analysis of COVID-19 vaccination during the COVID-19 pandemic. Second, VigiBase is designed to identify early signs of previously unknown AEs as rapidly as possible using spontaneous reports, so some of its reports are not well documented. To overcome this limitation, we applied a completeness score to identify high spontaneous report quality. The completeness score could contribute to an important causality assessment or the detection of safety signals in accuracy. Our study utilized well-documented reports with > 0.8 completeness, as suggested by WHO [16]. Third, the present results should be interpreted with caution because there are no data on pregnancy, patient comorbidities, and the dose of the COVID-19 vaccine. Fourth, we performed all of the reports without causality assessment. In contrast, some previous studies were conducted with causality assessments for possible or greater AEs. However, VigiBase contains the essential information required for causality assessment as a variable to estimate.

We found five distinguished clusters of serious AEs following mRNA COVID-19 vaccination. ICSRs in each cluster shared similar AE profiles and were distinguished by potential factors such as age, gender, and TTO. Mainly, the number of COVID-19 vaccines administrated due to the pandemic resulted in a massive number of AEs following COVID-19 vaccination, which could bias safety signals. However, using LCA, it will be possible to more efficiently detect the characteristics of reported AEs by period or unexpected AEs. Therefore, our findings are important for enhancing the understanding of the benefits and risks associated with the use of vaccines and could contribute to establishing management strategies for serious AEs of special interest following mRNA COVID-19 vaccination.