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Purpose: Recent safety issues involving medical devices have highlighted the need for better postmarket surveillance (PMS) evaluation. This article aims to describe and to assess the quality of the PMS data for a medical device and, finally, to provide recommendations to improve the data gathering process. Methods: A descriptive analysis of medical device reports (MDRs) on the use of MRA, a specific type of hip implant replacement submitted to the Food and Drug Administration Manufacturer and User Facility Device Experience database from 1 January 2008 to 31 December 2017. The number of reports was described as the number of MDRs per unique MDR number and stratified by different variables. The quality was assessed by the level of completeness of the collected PMS data. Results: The total number of reports related to MRA was 2377, and the number of MDRs per year ranged between 84 in 2009 and 452 in 2017. Most of the reports were reported by manufacturer Depuy Johnson & Johnson and were reported by a physician. In 44.9% of the reports, the device problem was reported as “Unknown.” When the device problem was known, in the majority of cases, it was related to an implant fracture. The quality of the collected data was assessed as low due to missing information. Conclusion: The underlying data should meet high quality standards to generate more evidence and to ensure a timely signal generation. This case study shows that the completeness and quality of the MDRs can be improved. The authors propose the development of tools to ensure a more dynamic complaint data collection to contribute to this enhancement.

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. The purpose of the present analysis was to evaluate its prospective performance. Methods: The evaluation of vigiRank was based on real-world signal detection in VigiBase. In May 2014, vigiRank scores were computed for pairs of new drugs and WHO Adverse Reaction Terminology critical terms with at most 30 reports from at least 2 countries. Initial manual assessments were performed in order of descending score, selecting a subset of drug-adverse drug reaction pairs for in-depth expert assessment. The primary performance metric was the proportion of initial assessments that were decided signals during in-depth assessment. As comparator, the historical performance for disproportionality-guided signal detection in VigiBase was computed from a corresponding cohort of drug-adverse drug reaction pairs assessed between 2009 and 2013. During this period, the requirement for initial manual assessment was a positive lower endpoint of the 95% credibility interval of the Information Component measure of disproportionality, observed for the first time. Results: 194 initial assessments suggested by vigiRank's ordering eventually resulted in 6 (3.1%) signals. Disproportionality analysis yielded 19 signals from 1592 initial assessments (1.2%; P <.05). Conclusions: Combining multiple strength-of-evidence aspects as in vigiRank significantly outperformed disproportionality analysis alone in real-world pharmacovigilance signal detection, for VigiBase.

Background and Objective: The availability of accurate product-specific exposure information is essential in the pharmacovigilance of biologicals, because differences in the safety profile may emerge between products containing the same active substance. In spontaneous adverse drug reaction (ADR) reports, drug exposure may, however, be misclassified, that is, attributed to the incorrect product. The aim of this study was to explore the effect of exposure misclassification on the time to detection of product-specific risks in spontaneous reporting systems. Methods: We used data simulations to explore the effect of exposure misclassification. We simulated an active substance-specific subset of a spontaneous reporting system and used the proportional reporting ratio for signal detection. The effect of exposure misclassification was evaluated in three test cases representing product-specific ADRs that may occur for biologicals and studied in relative terms by varying the model parameters (market share and relative risk). Results: We found that exposure misclassification results in the largest delay in identification of risks that have a weak association (relative risk50%) market share. The absolute public health impact of exposure misclassification, in terms of cases/time to detection, varied considerably across the test cases. Conclusion: Exposure misclassification in ADR reports may result in a delayed detection of product-specific risks, particularly in the detection of weak drug-event associations. Our findings can help inform the future implementation and refinement of product-specific and batch-specific signal detection procedures.

Background: Quantifying a medicine's risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports. Methods: We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug. Results: An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies. Conclusions: Our framework enables quantification of medicine risks in, AuthorCount:3

Objective of the research - to study safety of medicinal preparations (MP) for children by analyzing a Roszdravnadzor national database of spontaneous reports (SR). This study includes a retrospective analysis of 467 primary SR on severe adverse reactions (AR) in children recorded to database in 2015. Children of preschool age have higher risk of medical therapy complications. Most frequently, skin and whole body (38,7% and 21,1%, respectively) were involved in the pathological process. Unexpected AR were identified in children in 10,8% of cases. They were often caused by antimicrobial system action drugs (43,8%) and drugs for nervous system treatment (26,1%). SR method allows to obtain new information about MP safety in the pediatric population, and to develop measures to reduce risks of medical therapy. © 2017, Pediatria Ltd. All rights reserved.

The use of medicinal products (MP) with contraventions of officially approved instructions regulations (off-label), especially in pediatrics, is an actual problem of health care. This article studies MP off-label use structure in children and role of such drugs use in development of serious adverse reactions (AR), on the basis of Russian spontaneous reports database analysis. The results confirm that off -label MP prescription is an independent factor for AR developing risk. The study proved that spontaneous reports method is effective in studying of medicines use security problems and allows to develop recommendations that can significantly reduce the risk of pharmacotherapy complications, including in pediatric patients. © 2016, Pediatria Ltd. All rights reserved.