Signal Detection in EUROmediCAT: Identification and Evaluation of Medication-Congenital Anomaly Associations and Use of VigiBase as a Complementary Source of Reference.

Introduction: Knowledge on the safety of medication use during pregnancy is often sparse. Pregnant women are generally excluded from clinical trials, and there is a dependence on post-marketing surveillance to identify teratogenic medications.

Aims: This study aimed to identify signals of potentially teratogenic medications using EUROmediCAT registry data on medication exposure in pregnancies with a congenital anomaly, and to investigate the use of VigiBase reports of adverse events of medications in the evaluation of these signals.

A Feasibility Study of Drug-Drug Interaction Signal Detection in Regular Pharmacovigilance.

Introduction: Adverse drug reactions related to drug-drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance.

Objective: The aim of this study was to evaluate the feasibility of signal detection of drug-drug interactions in collections of individual case reports of suspected adverse drug reactions.

Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project.

Introduction: A large number of studies on systems to detect and sometimes normalize adverse events (AEs) in social media have been published, but evidence of their practical utility is scarce. This raises the question of the transferability of such systems to new settings.

Objectives: The aims of this study were to develop an AE recognition system, prospectively evaluate its performance on an external benchmark dataset and identify potential factors influencing the transferability of AE recognition systems.

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection.

Correction to: vigiGrade: A Tool to Identify Well-Documented Individual Case Reports and Highlight Systematic Data Quality Issues.

The article vigiGrade: A Tool to Identify Well-Documented Individual Case Reports and Highlight Systematic Data Quality Issues, written by Tomas Bergvall. G. Niklas Norén.

Paediatric safety signals identified in VigiBase: Methods and results from Uppsala Monitoring Centre.

Purpose: The purpose of this study is to uncover previously unrecognised risks of medicines in paediatric pharmacovigilance reports and thereby advance a safer use of medicines in paediatrics.

Methods: Individual case safety reports (ICSRs) with ages less than 18 years were retrieved from VigiBase, the World Health Organization (WHO) global database of ICSRs, in September 2014. The reports were grouped according to the following age spans: 0 to 27 days; 28 days to 23 months; 2 to 11 years; and 12 to 17 years.

vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.

Purpose: vigiRank is a data-driven predictive model for emerging safety signals. In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone.

An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies.

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

Objective: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.

Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.

Background: Pharmacovigilance signal detection largely relies on individual case reports, but longitudinal health data are being explored as complementary information sources. Research to date has focused on the ability of epidemiological methods to distinguish established adverse drug reactions (ADRs) from unrelated adverse events.

Objective: The aim of this study was to evaluate a process for structured clinical and epidemiological assessment of temporally associated drugs and medical events in electronic medical records.

vigiGrade: a tool to identify well-documented individual case reports and highlight systematic data quality issues.

Background: Individual case safety reports of suspected harm from medicines are fundamental to post-marketing surveillance. Their value is directly proportional to the amount of clinically relevant information they include. To improve the quality of the data, communication between stakeholders is essential and can be facilitated by a simple score and visualisation of the results.

Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system.

Background: Observational healthcare data offer the potential to identify adverse drug reactions that may be missed by spontaneous reporting. The self-controlled cohort analysis within the Temporal Pattern Discovery framework compares the observed-to-expected ratio of medical outcomes during post-exposure surveillance periods with those during a set of distinct pre-exposure control periods in the same patients. It utilizes an external control group to account for systematic differences between the different time periods, thus combining within- and between-patient confounder adjustment in a single measure.