Consensus clustering for case series identification and adverse event profiles in pharmacovigilance

Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved..

OBJECTIVE: To describe and evaluate vigiGroup - a consensus clustering algorithm which can identify groups of individual case reports referring to similar suspected adverse drug reactions and describe associated adverse event profiles, accounting for co-reported adverse event terms.

MATERIALS AND METHODS: Consensus clustering is achieved by grouping pairs of reports that are repeatedly placed together in the same clusters across a set of mixture model-based cluster analyses. The latter use empirical Bayes statistical shrinkage for improved performance. As baseline comparison, we considered a regular mixture model-based cluster analysis. Three randomly selected drugs in VigiBase, the World Health Organization's global database of Individual Case Safety Reports were analyzed: sumatriptan, ambroxol and tacrolimus. Clustering stability was assessed using the adjusted Rand index, ranging between -1 and +1, and clinical coherence was assessed through an intruder detection analysis.

RESULTS: For the three drugs considered, vigiGroup achieved stable and coherent results with adjusted Rand indices between +0.80 and +0.92, and intruder detection rates between 86% and 94%. Consensus clustering improved both stability and clinical coherence compared to mixture model-based clustering alone. Statistical shrinkage improved the stability of clusters compared to the baseline mixture model, as well as the cross-validated log-likelihood.

CONCLUSIONS: The proposed algorithm can achieve adequate stability and clinical coherence in clustering individual case reports, thereby enabling better identification of case series and associated adverse event profiles in pharmacovigilance. The use of empirical Bayes shrinkage and consensus clustering each led to meaningful improvements in performance.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:122

Enthalten in:

Artificial intelligence in medicine - 122(2021) vom: 15. Dez., Seite 102199

Sprache:

Englisch

Beteiligte Personen:

Norén, G Niklas [VerfasserIn]
Meldau, Eva-Lisa [VerfasserIn]
Chandler, Rebecca E [VerfasserIn]

Links:

Volltext

Themen:

Adverse drug reaction reporting systems
Cluster analysis
Journal Article
Methods
Pharmacovigilance

Anmerkungen:

Date Completed 31.03.2022

Date Revised 01.04.2022

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.artmed.2021.102199

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM333609867