Probabilistic record matching, as implemented in vigiMatch, achieved good predictive value for confirmed or likely duplicates in each data source. Most of the false positives corresponded to otherwise related reports; less than 10 % were altogether unrelated. A substantial proportion of the correctly identified duplicates had not previously been detected by national centre activity. On one hand, vigiMatch highlighted duplicates that had been missed by rule-based methods, and on the other hand its lower total number of suspected duplicates to review improved the accuracy of manual review.

Subgroup analyses perform better than stratified analyses and should be considered over the latter in routine first-pass signal detection. Subgroup analyses are also clearly beneficial over crude analyses for larger databases, but further validation is required for smaller databases.

IntroductionSpontaneous reporting of suspected adverse drug reactions is key for efficient post-marketing safety surveillance. To increase usability and accessibility of reporting tools, the Web-Recognising Adverse Drug Reactions (WEB-RADR) consortium developed a smartphone application (app) based on a simplified reporting form.ObjectiveThe objective of this study was to evaluate the characteristics, quality and contribution to signals of reports submitted via the WEB-RADR app.MethodsThe app was launched in the UK, the Netherlands and Croatia between July 2015 and May 2016. Spontaneous reports submitted until September 2016 with a single reporter were included. For each country, app reports and reports received through conventional means in the same time period were compared to identify characteristic features. A random subset of reports was assessed for clinical quality and completeness. The contribution to signal detection was assessed by a descriptive analysis.ResultsHigher proportions of app reports were submitted by patients in the UK (28 vs. 18%) and Croatia (32 vs. 7%); both p < 0.01. In the Netherlands, the difference was small (60 vs. 57%; p = 0.5). The proportion of female patients and the median patient ages in app reports submitted by patients were similar to the reference. The proportion of reports of at least moderate quality was high in both samples (app: 78–85%, reference: 78–98%), for all countries. App reports contributed to detecting eight potential safety signals at the national level, four of which were eventually signalled.ConclusionThe WEB-RADR app offers a new route of spontaneous reporting that shows promise in attracting reports from patients and that could become an important tool in the future. Patient demographics are similar to conventional routes, report quality is sufficient despite a simplified reporting form, and app reports show potential in contributing to signal detection.Electronic supplementary materialThe online version of this article (10.1007/s40264-018-0679-6) contains supplementary material, which is available to authorized users.