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

Author

Listed:
  • Mátyás Pétervári

    (Semmelweis University
    Sanovigado Kft)

  • Bettina Benczik

    (Semmelweis University
    Pharmahungary Group)

  • Olivér M. Balogh

    (Semmelweis University)

  • Balázs Petrovich

    (Semmelweis University)

  • Bence Ágg

    (Semmelweis University
    Pharmahungary Group)

  • Péter Ferdinandy

    (Semmelweis University
    Pharmahungary Group)

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

Suggested Citation

  • Mátyás Pétervári & Bettina Benczik & Olivér M. Balogh & Balázs Petrovich & Bence Ágg & Péter Ferdinandy, 2022. "Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety," Drug Safety, Springer, vol. 45(11), pages 1423-1438, November.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:11:d:10.1007_s40264-022-01225-9
    DOI: 10.1007/s40264-022-01225-9
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    Cited by:

    1. Maria Antonietta Barbieri & Andrea Abate & Olivér M. Balogh & Mátyás Pétervári & Péter Ferdinandy & Bence Ágg & Vera Battini & Marianna Cocco & Andrea Rossi & Carla Carnovale & Manuela Casula & Edoard, 2025. "Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infa," Drug Safety, Springer, vol. 48(5), pages 513-526, May.

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