Lindquist M, Ståhl M, Bate A, Edwards I R, Meyboom R H
The Uppsala Monitoring Centre, Sweden.
Drug Saf. 2000 Dec;23(6):533-42. doi: 10.2165/00002018-200023060-00004.
The detection of new drug safety signals is of growing importance with ever more new drugs becoming available and exposure to medicines increasing. The task of evaluating information relating to safety lies with national agencies and, for international data, with the World Health Organization Programme for International Drug Monitoring.
An established approach for identifying new drug safety signals from the international database of more than 2 million case reports depends upon clinical experts from around the world. With a very large amount of information to evaluate, such an approach is open to human error. To aid the clinical review, we have developed a new signalling process using Bayesian logic, applied to data mining, within a confidence propagation neural network (Bayesian Confidence Propagation Neural Network; BCPNN). Ultimately, this will also allow the evaluation of complex variables.
The first part of this study tested the predictive value of the BCPNN in new signal detection as compared with reference literature sources (Martindale's Extra Pharmacopoeia in 1993 and July 2000, and the Physicians Desk Reference in July 2000). In the second part of the study, results with the BCPNN method were compared with those of the former signalling procedure.
In the study period (the first quarter of 1993) 107 drug-adverse reaction combinations were highlighted as new positive associations by the BCPNN, and referred to new drugs. 15 drug-adverse reaction combinations on new drugs became negative BCPNN associations in the study period. The BCPNN method detected signals with a positive predictive value of 44% and the negative predictive value was 85%. 17 as yet unconfirmed positive associations could not be dismissed with certainty as false positive signals. Of the 10 drug-adverse reaction signals produced by the former signal detection system from data sent out for review during the study period, 6 were also identified by the BCPNN. These 6 associations have all had a more than 10-fold increase of reports and 4 of them have been included in the reference sources. The remaining 4 signals that were not identified by the BCPNN had a small, or no, increase in the number of reports, and are not listed in the reference sources.
Our evaluation showed that the BCPNN approach had a high and promising predictive value in identifying early signals of new adverse drug reactions.
随着越来越多的新药上市以及药物暴露量的增加，新药安全信号的检测变得愈发重要。评估安全相关信息的任务由各国机构承担，对于国际数据，则由世界卫生组织国际药物监测规划负责。
一种从超过200万份病例报告的国际数据库中识别新药安全信号的既定方法依赖于来自世界各地的临床专家。面对大量需要评估的信息，这种方法容易出现人为错误。为辅助临床审查，我们开发了一种新的信号检测流程，该流程在置信传播神经网络（贝叶斯置信传播神经网络；BCPNN）内运用贝叶斯逻辑进行数据挖掘。最终，这也将允许对复杂变量进行评估。
本研究的第一部分测试了BCPNN在新信号检测中的预测价值，并与参考文献来源（1993年和2000年7月的《马丁代尔药物大典》以及2000年7月的《医师案头参考》）进行比较。在研究的第二部分，将BCPNN方法的结果与之前的信号检测程序的结果进行比较。
在研究期间（1993年第一季度），BCPNN突出显示了107种药物不良反应组合为新的阳性关联，并涉及新药。在研究期间，15种新药的药物不良反应组合成为BCPNN的阴性关联。BCPNN方法检测到的信号阳性预测值为44%，阴性预测值为85%。17种尚未得到证实的阳性关联不能确定地排除为假阳性信号。在研究期间，前信号检测系统从发送进行审查的数据中产生的10种药物不良反应信号中，BCPNN也识别出了6种。这6种关联的报告数量均增加了10倍以上，其中4种已被纳入参考文献来源。其余4种未被BCPNN识别的信号报告数量增加很少或没有增加，且未在参考文献来源中列出。
我们的评估表明，BCPNN方法在识别新的药物不良反应早期信号方面具有很高且有前景的预测价值。
