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

Mátyás Pétervári, Bettina Benczik, Olivér M. Balogh, Balázs Petrovich, Bence Ágg and Péter Ferdinandy ()
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Mátyás Pétervári: Semmelweis University
Bettina Benczik: Semmelweis University
Olivér M. Balogh: Semmelweis University
Balázs Petrovich: Semmelweis University
Bence Ágg: Semmelweis University
Péter Ferdinandy: Semmelweis University

Drug Safety, 2022, vol. 45, issue 11, No 8, 1423-1438

Abstract: Abstract Introduction Signal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union. Objectives We aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networks. Methods ICSRs of five cardiovascular adverse events were requested from EudraVigilance. We developed Vigilace™, a web-based application to build network representation of ICSRs. Three network-based signal scores, which we termed NEWS (normalized edge weight for signals) scores, were calculated by normalizing the weight of each edge in the report-based weighted network by the weight of the same edge in topological weighted networks. Depending on the third node in topological network edges, we defined full-, adverse event-, and drug-type NEWS scores. Area under the receiver operating characteristic curves (AUROC) were analyzed to compare the reporting odds ratio (ROR) and NEWS scores. Results Overall, 72,475 ICSRs were accessed from EudraVigilance. Drug-type NEWS (NEWSD) score performed better (DeLong test, p-value

Date: 2022
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DOI: 10.1007/s40264-022-01225-9

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