GSK, 980 Great West Road, London, TW8 9GS, UK.
GSK, Research Triangle, NC, USA.
Drug Saf. 2023 Sep;46(9):847-855. doi: 10.1007/s40264-023-01327-y. Epub 2023 Aug 3.
Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review.
Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large.
The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK's products.
The mean CUSP score for 'key cases' and 'cases of low utility' was 19.7 (median: 21; range: 7-27) and 17.3 (median: 19; range: 4-27), respectively. CUSP distribution for 'key cases' were skewed toward the higher range of scores compared with 'all cases'. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for 'key cases' for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5-23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5-20.7).
Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review.
过去几十年来，不良事件的自发报告稳步增加，尽管这种趋势有助于改善上市后监测药物警戒活动，但评估时手动审查由此产生的大量数据具有挑战性，每个单独的报告都需要药物警戒专家进行审查。这突出表明需要替代或补充方法来帮助确定优先次序。
本研究旨在开发和测试一种自动化方法，即临床效用评分优先排序法（CUSP），以帮助药物警戒专家优先考虑安全性数据的临床评估，从而在病例量较大时提高病例系列审查的速度。
在与导致标签变更的五个药物-事件对相关的个体病例安全性报告（ICSR）参考数据集上测试了 CUSP 方法。选择的药物-事件对在 GSK 产品组合中的各种特征。
“关键病例”和“低效用病例”的平均 CUSP 评分为 19.7（中位数：21；范围：7-27）和 17.3（中位数：19；范围：4-27）。与“所有病例”相比，“关键病例”的 CUSP 分布偏向于高分范围。每个药物-事件对的整体表现差异很大，其中三个药物-事件对的“关键病例”具有更高的预测能力（这三个药物-事件对的平均 CUSP 为 22.8；范围：22.5-23.0），而另外两个药物-事件对的预测能力较低（这两个药物-事件对的平均 CUSP 为 17.6；范围：14.5-20.7）。
尽管已经开发了几种工具来评估 ICSR 的完整性和监管效用，但这是首次尝试成功开发一种自动化临床效用评分系统，该系统可支持对 ICSR 进行临床审查的优先级排序。
