Uncovering Pregnancy Exposures in Pharmacovigilance Case Report Databases: A Comprehensive Evaluation of the VigiBase Pregnancy Algorithm.

Abstract

Background: Information on the safety of medicine use during pregnancy is limited at the time of marketing, making post-marketing surveillance essential. However, the lack of a specific indicator for pregnancy-related case reports within the international standard for transmission of individual case safety reports complicates the retrieval of such reports in pharmacovigilance databases. To address this, an algorithm to identify reports of exposures during pregnancy was developed in VigiBase, the World Health Organization global database of adverse event reports.

Objective: We aimed to evaluate and characterise the VigiBase pregnancy algorithm.

Methods: The rule-based algorithm uses multiple structured data elements in the International Council of Harmonisation (ICH) E2B transmission format that could potentially hold pregnancy-related information, to determine if a case report qualifies as a pregnancy case. Free text information is not considered. Three datasets were used for the evaluation. The "Full dataset" comprised deduplicated VigiBase data up to January 2023. The "Downsampled dataset" was a subsample of the Full dataset, adjusted to increase the prevalence of pregnancy reports by excluding individuals aged 45 years or older and male individuals aged 18 years or older, used to evaluate recall (i.e. sensitivity). The "Random dataset" was a straight random sample of the Full dataset, used to evaluate precision (i.e. positive predictive value). As a baseline for comparison, the Standardised Medical Dictionary for Regulatory Activities (MedDRA^®) Query (SMQ) "Pregnancy and neonatal topics (narrow)" was used. To provide a gold standard for the evaluation, case reports were manually annotated as either "pregnancy case" or "non-pregnancy case", for all reports in the Downsampled dataset, and for the reports flagged as pregnancy cases by the algorithm or the SMQ baseline in the Random dataset.

Results: In the Downsampled dataset with 7874 annotated reports, 253 reports were annotated as pregnancy cases. Of those, the algorithm recalled 75% (95% confidence interval [CI] 69-80), increasing to 91% (95% CI 86-95) when restricting the analysis to reports adhering to the ICH E2B format. Preprocessing obstacles of incomplete mapping of specific pregnancy terms to MedDRA^® led to most false negatives followed by pregnancy information confined to free text information. The SMQ baseline had a lower recall of 62% (95% CI 56-68). In the Random dataset with 30,000 reports, the algorithm flagged 344 reports, among which 316 were annotated as pregnancy cases, leading to a precision of 92% (95% CI 88-95). The main reasons for false positives were postpartum indications, non-pregnancy-specific events or information miscoded as pregnancy related. The SMQ baseline had a lower precision of 74% (95% CI 69-78).

Conclusions: The VigiBase pregnancy algorithm demonstrates robust performance, highlighting its potential to facilitate pharmacovigilance related to pregnancy. Our evaluation establishes a valuable benchmark for future research and emphasises the need for global harmonisation of standards for reporting pregnancy exposures.