Disproportionality analysis of the safety profile of rufinamide in the real world: an evaluation of the FDA adverse event reporting system database

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Wang, Lingman;Gui, Jianxiong;Zhang, Xiaofang;Tian, Bing;Meng, Linxue;Liu, Jie;Jiang, Li

Description

The antiepileptic drug rufinamide (RUF), was recently introduced to alleviate seizures in patients with Lennox-Gastaut syndrome (LGS). However, little is known about its adverse reactions. The objective of this study is to probe, assess, and share evidence on RUF safety profiles via data from the FDA Adverse Event Reporting System (FAERS) database. Disproportionality analysis of RUF-associated adverse drug events (ADEs) was assessed through the calculation of the reporting odds (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma-Poisson shrinker (MGPS). A total of 338 ADE reports related to RUF were collected. Nervous system disorders were the most frequently reported positive signals. Notably, new unexpectedly significant ADEs were detected. Among them, atonic seizures, sudden unexplained death with epilepsy, seizure clusters, multiple-drug resistance, Stevens‒Johnson syndrome, and other possible novel signals deserve awareness. An examination of age-specific differences in the detected signals indicated that nearly half of the ADEs observed in children receiving RUF were categorized as psychological or neurological system diseases. Our disproportionality analysis of ADEs within 4 weeks of treatment revealed high RORs for electrocardiogram Qt shortened, sudden unexplained death in patients with epilepsy, and atonic seizures. Our study revealed prospective signals of new ADEs linked to RUF as well as revealed that both prescribers and patients were more conscious of the risks involved in its clinical use.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.9

FAIR Score

85%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Taylor & Francis

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Toxicology

Field

Pharmacology, Toxicology and Pharmaceutics

Domain

Life Sciences

Confidence Score

56%

Source

Scholar Data Model

Keywords

MedicineNeurosciencePharmacologyBiotechnologyCancerMental Health

Normalization Factors

FT

53.85

CTw

1.00

MTw

1.00