Concordance and predictive value of two adverse drug event data sets.
Concordance and predictive value of two adverse drug event data sets.
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DOI：
10.1186/1472-6947-14-74
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发表时间：
2014-08-22
影响因子：
3.5
通讯作者：
Reis BY
中科院分区：
文献类型：
--
作者：
Cami A;Reis BY
准确预测药物不良事件（ADEs）是控制和降低药物相关发病率和死亡率的重要手段。由于不存在单一的“金标准”ADE数据集，因此目前用于开发ADE预测模型的是一系列不同的药物安全性数据集。目前迫切需要评估这些不同ADE数据集之间的一致性程度，并根据多个参考标准验证ADE预测模型。我们系统地评估了两个广泛使用的ADE数据集- 2010年的Lexi-comp和2012年的SIDER的一致性。Lexi-comp中ADE（药物）计数与SIDER之间的关联强度采用Spearman秩相关法进行评估，两组数据集之间的差异以药物类别、ADE类别和ADE频次为特征。我们还使用两个ADE数据集对预测性药物安全网络（PPN）模型进行了比较验证。使用两种验证集中的每一种来评估PPN的预测能力，使用受试者工作特征曲线（AUROC）下的面积。两组数据中ade数与药物的相关系数分别为0.84 （95% CI: 0.82 ~ 0.86）和0.92 （95% CI: 0.91 ~ 0.93）。相对于2005年的Lexi-comp早期快照，Lexi-comp 2010和SIDER 2012平均每年分别引入1973和4810个新的药物ade关联。这两个数据集之间的差异在神经系统和抗感染药物、胃肠道和神经系统不良反应以及上市后不良反应方面最为明显。当使用SIDER 2012代替lex -comp 2010进行验证时，发现PPN的AUROC有1.1%的微小差异。综上所述，Lexi-comp和SIDER数据集的ADE与药物计数高度相关，验证集的选择对PPN的整体预测性能影响不大。我们的研究结果还表明，重要的是要意识到ADE数据集之间存在的差异，特别是在专注于特定药物和ADE类别的建模应用中。
Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single “gold standard” ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets – Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the differences that exist among ADE data sets, especially in modeling applications focused on specific drug and ADE categories.
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影响因子：
17.1
作者：
Tatonetti NP;Ye PP;Daneshjou R;Altman RB
通讯作者：
Altman RB
影响因子：
9.9
作者：
Kuhn M;Campillos M;Letunic I;Jensen LJ;Bork P
通讯作者：
Bork P
使用药物的化学，生物学和表型特性对不良药物反应进行大规模预测。
DOI：
10.1136/amiajnl-2011-000699
发表时间：
2012-06
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Liu M;Wu Y;Chen Y;Sun J;Zhao Z;Chen XW;Matheny ME;Xu H
通讯作者：
Xu H
基于网络的外部链接预测方法对药品不良反应的预测
影响因子：
3.1
作者：
Lin, Jiao;Kuang, Qifan;Li, Menglong
通讯作者：
Li, Menglong
影响因子：
2.6
作者：
Duke, Jon;Friedlin, Jeff;Li, Xiaochun
通讯作者：
Li, Xiaochun