The Development and Evaluation of Triage Algorithms for Early Discovery of Adverse Drug Interactions
The Development and Evaluation of Triage Algorithms for Early Discovery of Adverse Drug Interactions
复制标题
DOI：
10.1007/s40264-013-0053-7
复制
发表时间：
2013-05-01
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：
Strandell, Johanna;Caster, Ola;Noren, G. Niklas
背景 大约 20% 的药物不良反应 (ADR) 是由药物相互作用引起的。其中一些只会在上市后环境中检测到。对大量个案安全报告 (ICSR) 进行有效筛查需要自动分类来识别不良药物相互作用的信号。迄今为止的研究主要集中在统计测量上，但临床信息和药理学特征在临床评估中至关重要，并且可能对潜在不良药物相互作用信号的首过过滤具有重要价值。目的本研究的目的是开发不良药物相互作用监测的分类，并相对于临床评估进行前瞻性评估。方法考虑将一系列广泛的变量纳入分类，包括细胞色素P450（CYP）活性、报告者指出的对药物相互作用的明确怀疑、剂量和治疗重叠以及相互作用不成比例的衡量标准。通过逻辑回归确定了它们在预测不良药物相互作用信号方面的独特贡献。这是基于 WHO 全球 ICSR 数据库 VigiBase (TM) 的报告，其中包含一组已知的不良药物相互作用和相应的阴性对照。开发了三种分类，每种分类都会产生给定药物-药物-ADR 三联体构成不良药物相互作用信号的估计概率。根据专家临床评估得出的两个独立基准对分类进行评估：文献中已知的不良药物相互作用和预期不良药物相互作用信号。作为参考，使用相同的基准将分类与单独的不成比例分析进行比较。 结果 以下内容被确定为不良药物相互作用信号的有价值的预测因子：合理的 CYP 代谢；记者疑似互动的记录；以及意想不到的治疗反应、剂量信息改变的治疗效果以及仅使用两种药物时改变的治疗效果的报告。新的分类确定了与不良药物相互作用的预期信号和已经确定的信号相对应的报告模式。相对于两个基准，它们的表现优于单独的不成比例分析。 结论 已经确定了一系列不良药物相互作用信号的预测因子。与单独的不成比例分析相比，它们大大提高了信号检测能力。在首次筛选中纳入临床和药理学信息的价值是显而易见的。
Background Around 20 % of all adverse drug reactions (ADRs) are due to drug interactions. Some of these will only be detected in the postmarketing setting. Effective screening in large collections of individual case safety reports (ICSRs) requires automated triages to identify signals of adverse drug interactions. Research so far has focused on statistical measures, but clinical information and pharmacological characteristics are essential in the clinical assessment and may be of great value in first-pass filtering of potential adverse drug interaction signals.Objective The aim of this study was to develop triages for adverse drug interaction surveillance, and to evaluate these prospectively relative to clinical assessment.Methods A broad set of variables were considered for inclusion in the triages, including cytochrome P450 (CYP) activity, explicit suspicions of drug interactions as noted by the reporter, dose and treatment overlap, and a measure of interaction disproportionality. Their unique contributions in predicting signals of adverse drug interactions were determined through logistic regression. This was based on the reporting in the WHO global ICSR database, VigiBase (TM), for a set of known adverse drug interactions and corresponding negative controls. Three triages were developed, each producing an estimated probability that a given drug-drug-ADR triplet constitutes an adverse drug interaction signal. The triages were evaluated against two separate benchmarks derived from expert clinical assessment: adverse drug interactions known in the literature and prospective adverse drug interaction signals. For reference, the triages were compared with disproportionality analysis alone using the same benchmarks.Results The following were identified as valuable predictors of adverse drug interaction signals: plausible CYP metabolism; notes of suspected interaction by the reporter; and reports of unexpected therapeutic response, altered therapeutic effect with dose information and altered therapeutic effect when only two drugs had been used. The new triages identified reporting patterns corresponding to both prospective signals of adverse drug interactions and already established ones. They perform better than disproportionality analysis alone relative to both benchmarks.Conclusions A range of predictors for adverse drug interaction signals have been identified. They substantially improve signal detection capacity compared with disproportionality analysis alone. The value of incorporating clinical and pharmacological information in first-pass screening is clear.