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Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.
Caster, Ola; Dietrich, Juergen; Kürzinger, Marie-Laure; Lerch, Magnus; Maskell, Simon; Norén, G Niklas; Tcherny-Lessenot, Stéphanie; Vroman, Benoit; Wisniewski, Antoni; van Stekelenborg, John.
Afiliação
  • Caster O; Uppsala Monitoring Centre, Box 1051, Uppsala, 75140, Sweden. ola.caster@who-umc.org.
  • Dietrich J; Bayer AG, Berlin, Germany.
  • Kürzinger ML; Sanofi, Epidemiology and Benefit-Risk Evaluation, Chilly-Mazarin Cedex, France.
  • Lerch M; Lenolution GmbH, Berlin, Germany.
  • Maskell S; University of Liverpool, Liverpool, UK.
  • Norén GN; Uppsala Monitoring Centre, Box 1051, Uppsala, 75140, Sweden.
  • Tcherny-Lessenot S; Sanofi, Epidemiology and Benefit-Risk Evaluation, Chilly-Mazarin Cedex, France.
  • Vroman B; UCB Pharma, Braine-l'Alleud, Belgium.
  • Wisniewski A; AstraZeneca Global Regulatory Affairs, Cambridge, UK.
  • van Stekelenborg J; Janssen R&D, Horsham, PA, USA.
Drug Saf ; 41(12): 1355-1369, 2018 12.
Article em En | MEDLINE | ID: mdl-30043385
ABSTRACT
INTRODUCTION AND

OBJECTIVE:

Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events.

METHODS:

Performance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed.

RESULTS:

Across all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64-0.69 and 0.55-0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%).

CONCLUSIONS:

Our results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coleta de Dados / Armazenamento e Recuperação da Informação / Sistemas de Notificação de Reações Adversas a Medicamentos / Farmacovigilância / Mídias Sociais Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coleta de Dados / Armazenamento e Recuperação da Informação / Sistemas de Notificação de Reações Adversas a Medicamentos / Farmacovigilância / Mídias Sociais Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article