The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

BACKGROUND AND AIM: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. METHODS: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. RESULTS: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. CONCLUSIONS: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration.

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

A Comparison of Signals of Designated Medical Events and Non-designated Medical Events: Results from a Scoping Review.

INTRODUCTION AND OBJECTIVE: The European Medicines Agency (EMA) maintains a list of designated medical events (DMEs), events that are inherently serious and are prioritized for signal detection, irrespective of statistical criteria. We have analysed the results of our previously published scoping review to determine whether DME signals differ from those of other adverse events in terms of time to communication and characteristics of supporting reports of suspected adverse drug reactions. METHODS: For all signals, we obtained the launch year of medicinal products from textbooks or regulatory agencies, extracted the year of the first report in VigiBase and calculated the interval between the first report and communication (time to communication, TTC). We further retrieved the average completeness (via vigiGrade) of the reports in each case series in the years before the communication. We categorised as DME signals those concerning an event in the EMA's list. We described the two groups of signals using medians and interquartile ranges (IQR) and compared them using the Brunner-Munzel test, calculating 95% confidence intervals (95% CI) and P values. RESULTS: Of 4520 signals, 919 concerned DMEs and 3601 concerned non-DMEs. Signals of DMEs were supported by a median of 15 reports (IQR 6-38 reports) with a completeness score of 0.52 (IQR 0.43-0.62) and signals of non-DMEs by 20 reports (IQR 6-84 reports) with a completeness score of 0.46 (IQR 0.38-0.56). The probability that a random DME signal was supported by fewer reports than non-DME signals was 0.56 (95% CI 0.54-0.58, P < 0.001) and that of one having lower average completeness was 0.39 (95% CI 0.36-0.41, P < 0.001). The median TTCs of DME and non-DME signals did not differ (10 years), but the TTC was as low as 2 years when signals (irrespective of classification) were supported by reports whose average completeness was > 0.80. CONCLUSIONS: Signals of designated medical events were supported by fewer reports and higher completeness scores than signals of other adverse events. Although statistically significant, the differences in effect sizes between the two groups were small. This suggests that listing certain adverse events as DMEs is not having the expected effect of encouraging a focus on reports of the types of suspected adverse reactions that deserve special attention. Further enhancing the completeness of the reports of suspected adverse drug reactions supporting signals of designated medical events might shorten their time to communication and reduce the number of reports required to support them.