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. We aimed to evaluate and characterise the VigiBase pregnancy algorithm. 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. 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). 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.

Vascular graft maturation is associated with blood flow characteristics, such as velocity, pressure, vorticity, and wall shear stress (WSS). Many studies examined these factors separately. We aimed to examine the remodeling of arterio-venous fistulas (AVFs) and loop-shaped venous interposition grafts, together with 3D flow simulation. Thirty male Wistar rats were randomly and equally divided into sham-operated, AVF, and loop-shaped venous graft (Loop) groups, using the femoral and superficial inferior epigastric vessels for anastomoses. Five weeks after surgery, the vessels were removed for histological evaluation, or plastic castings were made and scanned for 3D flow simulation. Remodeling of AVF and looped grafts was complete in 5 weeks. Histology showed heterogeneous morphology depending on the distribution of intraluminal pressure and WSS. In the Loop group, an asymmetrical WSS distribution coincided with the intima hyperplasia spots. The tunica media was enlarged only when both pressure and WSS were high. The 3D flow simulation correlated with the histological findings, identifying “hotspots” for intimal hyperplasia formation, suggesting a predictive value. These observations can be useful for microvascular research and for quality control in microsurgical training.

Background: Objective performance assessment is essential in surgical skill training, yet current methods are labor-intensive and focus on observing the trainee rather than the end-product of the procedure. Machine learning (ML) methods offer reproducible feedback but have mainly relied on kinematic or video data, often reducing assessment to binary or ternary classification. Our objective was to compare ML regression models predicting expert-assigned scores of vascular anastomoses from computational fluid dynamics (CFD) features of the final product. Additionally, we aimed to assess biomechanical plausibility of predictions. Methods: A total of 146 participants performed 419 end-to-side anastomoses on case-specific three-dimensional (3D) printed simulators. Anastomoses were digitized via 3D scanning, ranked by experts, and characterized using CFD-derived hemodynamic features. These served as input for linear models (Ridge, Partial Least Squares), support vector machines, and tree-based ensembles (Random Forest, Extremely Randomized Trees, and Extreme Gradient Boosting [XGBoost]), evaluated using 10-fold nested cross-validation with genetic hyperparameter optimization. Results: Inter-rater reliability of expert indicated strong agreement (intraclass correlation coefficient ICC3k = 0.846). XGBoost achieved the lowest mean root mean squared error of 0.758 (95% bootstrap CI: 0.722–0.799) and a coefficient of determination (R²) of 0.673 (0.617–0.725), with the most stable performance across folds. Shapley additive explanations (SHAP) identified the wall shear stress gradient, transverse wall shear stress, and maximum pressure as the most influential features—variables associated with intimal hyperplasia and atherosclerotic remodeling. Conclusions: Tree-based ensemble methods, particularly XGBoost, effectively modeled biomechanical properties against expert scores. Combining CFD and ML can provide reproducible, mechanistically relevant feedback in vascular surgical skill training.

Background: Post-marketing surveillance is essential for complementing the safety profiles of medicinal products, especially for populations generally excluded from clinical trials such as pregnant individuals. However, the absence of a standardised pregnancy indicator in the electronic transmissions of adverse event reports hampers their correct identification in pharmacovigilance databases and complicates the study of safety concerns related to pregnancy exposures. Three recently developed rule-based algorithms with the common aim to systematically retrieve pregnancy-related reports differ in scope and are tailored to different databases (A. FAERS, B. EudraVigilance, C. VigiBase). Aim: To compare the design and outputs of the three pregnancy algorithms. Methods: This study was a collaboration among the authors of the three pregnancy algorithms. We harmonised their rules, implemented them in an R package to enable execution in both VigiBase and FAERS, and analysed key characteristics of reports flagged by each algorithm. Results: The pregnancy algorithms A, B, and C flagged 235653, 279515, and 446957 reports respectively in VigiBase, and 265015, 260734, 350479 in FAERS. Reports exclusively retrieved by each algorithm (994, 3248, and 142324 in VigiBase, and 1528, 1100, and 59643 in FAERS) were mostly explained by Algorithm A having no age restriction, Algorithm B excluding normal pregnancy and ineffective contraception, and Algorithm C excluding paternal exposure. Conclusions: Differences in flagging were largely related to varying scopes. Understanding commonalities and differences is crucial for empowering professionals working with pregnancy-related pharmacovigilance to select and use the most appropriate algorithm for their specific needs. Key points: Three independently developed algorithms were designed to retrieve pregnancy-related adverse event reports and support research into pregnancy-specific safety concerns. By applying these algorithms to VigiBase and FAERS, we highlighted overlaps and differences in the reports they flag, reflecting heterogeneous scope and implementation. Awareness of these distinctions is essential to select and apply the most suitable algorithm for their specific needs.