A Network Analysis of Drug Combinations Associated with Acute Generalized Exanthematous Pustulosis (AGEP)

Abstract

Acute generalized exanthematous pustulosis (AGEP) is a rare skin adverse drug reaction. The pathophysiology and causative drugs associated with AGEP are poorly understood, with the majority of studies in AGEP focusing on a single-drug-outcome association. We therefore aimed to explore and characterize frequently reported drug combinations associated with AGEP using the WHO pharmacovigilance database VigiBase. In this explorative cross-sectional study of a pharmacovigilance database using a data-driven approach, we assessed individual case safety reports (ICSR) with two or more drugs reported to VigiBase. A total of 2649 ICSRs reported two or more drugs. Cardiovascular drugs, including antithrombotics and beta-blockers, were frequently reported in combination with other drugs, particularly antibiotics. The drug pair of amoxicillin and furosemide was reported in 57 ICSRs (2.2%), with an O/E ratio of 1.3, and the combination of bisoprolol and furosemide was recorded 44 times (1.7%), with an O/E ratio of 5.5. The network analysis identified 10 different communities of varying sizes. The largest cluster primarily consisted of cardiovascular drugs. This data-driven and exploratory study provides the largest real-world assessment of drugs associated with AGEP to date. The results identify a high frequency of cardiovascular drugs, particularly used in combination with antibiotics.

Drug Triggers and Clinic of Acute Generalized Exanthematous Pustulosis (AGEP): A Literature Case Series of 297 Patients

“…The patient demographics in this case series are generally comparable to other observational studies. Indeed, the higher frequency of women patients in our study is in line with the existing evidence [7,11,265,266], and there is agreement on the lower age observed among men in comparison to women patients [266]. However, we note that the overall mean age in our case series (48.6 years [SD 21.6]) was slightly lower than the 51.7-62 years mean age reported in other observational and multi-center studies [7,8,10,12], as well as the 57.3 years reported in a recent pharmacovigilance study including 2649 case reports from the World Health Organization pharmacovigilance database (WHO-VigiBase) [266].…”

“…Antibiotics are often the most prevalent drugs associated with AGEP onset [7][8][9][10][11][12][13][14]265,268,269], accounting for 17 out of the 26 (61.4%) AGEP cases studied by Barbaud et al [8], 26 of the 58 (44.8%) AGEP cases studied by Hotz et al [265], and 14 (66.7%) out of the 21 AGEP patients studied by Choon et al [11]. Additionally, the beta-lactam antibacterial amoxicillin was the most reported drug (21.6%) in the study of 2649 AGEP individual case safety reports (ICSRs) with at least two reported drugs in the World Health Organization (WHO) pharmacovigilance database (WHO VigiBase) [266]. In the multinational EuroSCAR case-control study, which included 97 validated AGEP cases, antibiotics, such as pristinamycin, aminopenicillins, and quinolones, were suspect drugs in 10%, 19%, and 9% of the AGEP patients, respectively.…”

“…A previous study identified the potential for drug interactions to play a role in the onset of Stevens-Johnson syndrome [272]. Similarly, it has been suggested that the high frequency of cardiovascular drugs observed in a data-driven pharmacovigilance study may suggest potential off-target drug-drug interactions [266]. Conversely, an alternative hypothesis could be the consequence of cardiovascular drugs being less commonly known as AGEP triggers, and therefore being less frequently identified as the suspect drug.…”

Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety

“…DEC in signal detection) reduces the chances of having a real connection between the investigated drug-adverse event pair. Previous publications in the ICSR network analysis field were mainly focused on descriptive analysis of spontaneous reporting databases [18,19,30], while other studies provided support for case review during signal evaluation by network visualization [23,29], or described certain adverse events [21,22]. Furthermore, two recent studies applied network metrics for signal detection purposes [24,25], but the approach was different from NEWS scores.…”

“…Studies were published in relation to the development of improved statistical signal detection methodologies that showed promising results, such as vigiRank [11], combination of supervised learning and Bradford Hill's causality considerations [12], application of machine learning [13], false discovery rate detection [14,15], competition bias removal [16], and co-prescription bias and associated unmasking [17], but currently these methods are not used widely by regulatory bodies or pharmaceutical companies. In recent years, the field of network theory and analysis was researched extensively, and various methods were applied on spontaneous reporting databases in order to describe the network characteristics of spontaneous adverse event report databases [18][19][20], certain adverse events [21,22] and support decision rules [23]. Network analysis as a part of signal detection methods has also been published recently, for co-reported community detection [24] and adverse event severity estimation and risk profiling [25].…”