Norén G Niklas, Meldau Eva-Lisa, Chandler Rebecca E
Uppsala Monitoring Centre, Uppsala, Sweden.
Uppsala Monitoring Centre, Uppsala, Sweden.
Artif Intell Med. 2021 Dec;122:102199. doi: 10.1016/j.artmed.2021.102199. Epub 2021 Oct 22.
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.
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.
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.
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.
描述和评估vigiGroup——一种共识聚类算法，该算法能够识别涉及相似疑似药物不良反应的个体病例报告组，并描述相关的不良事件概况，同时考虑共同报告的不良事件术语。
通过对一组基于混合模型的聚类分析中反复被归为同一聚类的报告对进行分组来实现共识聚类。后者使用经验贝叶斯统计收缩法以提高性能。作为基线比较，我们考虑了基于常规混合模型的聚类分析。对世界卫生组织全球个体病例安全报告数据库VigiBase中随机选择的三种药物进行了分析：舒马曲坦、氨溴索和他克莫司。使用调整后的兰德指数评估聚类稳定性，范围在-1至+1之间，并通过入侵者检测分析评估临床一致性。
对于所考虑的三种药物，vigiGroup取得了稳定且一致的结果，调整后的兰德指数在+0.80至+0.92之间，入侵者检测率在86%至94%之间。与单独基于混合模型的聚类相比，共识聚类提高了稳定性和临床一致性。与基线混合模型相比，统计收缩提高了聚类的稳定性以及交叉验证对数似然值。
所提出的算法在对个体病例报告进行聚类时能够实现足够的稳定性和临床一致性，从而在药物警戒中更好地识别病例系列和相关的不良事件概况。经验贝叶斯收缩法和共识聚类的使用均在性能上带来了有意义的改进。
