Safety of RSV Vaccine among Pregnant Individuals: A Real-World Pharmacovigilance Study Using Vaccine Adverse Event Reporting System
Bibliographic record
Abstract
ABSTRACT Objectives To describe the post-marketing safety of RSVPreF among pregnant individuals. Design This case series study analyzed adverse event (AE) reports submitted to the U.S. Food and Drug Administration’s Vaccine Adverse Event Reporting System (VAERS) database following RSVPreF immunization from September 1, 2023, to February 23, 2024. Setting VAERS, as a national spontaneous vaccine safety surveillance system, provides insights into the safety profile of the RSVPreF vaccine in a real-world setting. Participants Surveillance data included all AE reports submitted to VAERS for pregnant individuals following vaccination. Exposure Receipt of RSVPreF vaccine among pregnant individuals in the U.S. Primary and secondary outcome measures Descriptive statistics assessed all AE reports with RSVPreF, including frequency, gestational age at vaccination, time to AE onset, and serious report proportions. The Bayesian Confidence Propagation Neural Network (BCPNN) was utilized, estimating the information component (IC) to identify disproportionate reporting of RSVPreF–event pairs. Results VAERS received 77 reports pertained to RSVPreF vaccination in pregnant individuals, with 42 (54.55%) classified as serious. The most reported non-pregnancy-specific AEs were headache, injection site erythema, and injection site pain. Preterm birth was the most frequently reported pregnancy-specific AE, followed by preterm premature rupture of membranes, cesarean section, cervical dilatation, and hemorrhage during pregnancy. The median time from immunization to reported preterm birth was 3 days, with two-thirds of cases within a week. Disproportionality analysis indicated a significant signal for various AEs, particularly highlighting preterm birth with an IC of 2.18 (95%CI, 1.54-2.63), suggesting that reports of preterm birth associated with RSVPreF vaccination occurred more frequently than statistically expected. Conclusions While reported AEs were generally consistent with the safety profile observed in prelicensure studies, this study highlights ongoing concern about preterm birth among pregnant individuals following RSVPreF vaccination. Comprehensive longitudinal follow-up, including prospective pregnancy registries and infant follow-up studies is urgently required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".