Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.
Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.
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DOI：
10.1007/s40264-014-0204-5
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发表时间：
2014-08
期刊：
影响因子：
4.2
通讯作者：
Noren, G. Niklas
中科院分区：
文献类型：
--
作者：
Caster, Ola;Juhlin, Kristina;Watson, Sarah;Noren, G. Niklas
检测上市药物的未知风险是确保个体患者获得最佳护理和减少药物不良反应的社会负担的关键。大量的个案报告仍然是主要的信息来源，需要有效的分析来指导临床评估人员了解可能的药物安全性信号。不相称性分析完全基于报告的总数，天真地忽视了报告的质量和内容。然而，这些后一特征是随后的临床评估的基础。我们的目标是开发和评估一种数据驱动的筛选算法，用于新出现的药物安全性信号，该算法考虑了报告质量和内容。vigiRank是一种新出现的安全性信号的预测模型，在这里使用收缩逻辑回归来识别预测变量并估计其各自的贡献。考虑纳入的变量涵盖了证据强度的不同方面，包括个体报告的质量和临床内容，以及时间和地理分布的趋势。使用264个阳性对照（2003年至2007年的历史安全性信号）和5，280个阴性对照（2012年该药物的产品特性概要中未列出的药物和不良事件对）的参考集进行模型拟合和评价;后者使用五重交叉验证以防止过度拟合。所有分析都是在2004年12月31日的VigiBase®重建版本上进行的，大约在那个时候，我们的参考集中出现了大多数安全性信号。选择以下方面的证据强度纳入vigiRank：信息性报告和近期报告的数量;不成比例的报告;对病例进行自由文本描述的报告数量;以及报告的地理分布。vigiRank分别基于信息成分（IC）和原始报告数量（0.775 vs. 0.736和0.707，交叉验证）提供了相对于筛选的受试者工作特征曲线下面积（AUC）的统计学显著改善。对证据强度的多个方面进行会计处理，在概念和经验上都明显优于证据性分析。vigiRank是首个在首过筛选中考虑报告质量和内容的预测模型，以更好地满足未来上市后药物安全性监测的需求。
Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment. Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content. vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase® as of 31 December 2004, at around which time most safety signals in our reference set were emerging. The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver operating characteristics curve (AUC) over screening based on the Information Component (IC) and raw numbers of reports, respectively (0.775 vs. 0.736 and 0.707, cross-validated). Accounting for multiple aspects of strength of evidence has clear conceptual and empirical advantages over disproportionality analysis. vigiRank is a first-of-its-kind predictive model to factor in report quality and content in first-pass screening to better meet tomorrow’s post-marketing drug safety surveillance needs.
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