Fung Kin Wah, Kapusnik-Uner Joan, Cunningham Jean, Higby-Baker Stefanie, Bodenreider Olivier
National Library of Medicine, Bethesda, MD, USA.
First Databank, South San Francisco, CA, USA.
J Am Med Inform Assoc. 2017 Jul 1;24(4):806-812. doi: 10.1093/jamia/ocx010.
To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.
Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support.
The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions.
The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.
比较临床实践中用于检测和避免潜在药物相互作用（DDI）的3个商业知识库（KB）。
将来自First DataBank（FDB）、Micromedex和Multum的DDI表中的药物映射到RxNorm。在临床药物、成分和DDI规则层面比较这些知识库。根据美国卫生信息技术国家协调办公室（ONC）的高度显著DDI参考列表对这些知识库进行评估。将这些知识库和ONC列表应用于一个处方数据集，以模拟它们在临床决策支持中的使用。
这些知识库分别包含160万（FDB）、450万（Micromedex）和480万（Multum）个临床药物对。总共存在860万个独特的药物对，其中79%仅在1个知识库中出现，5%在所有3个知识库中都有。然而，在严重程度排名方面，总体上一致的情况多于不一致的情况，尤其是在禁忌类别中。这些知识库涵盖了ONC列表中99.8% - 99.9%的警示信息，每1000张处方会产生25条（FDB）、145条（Micromedex）和84条（Multum）警示。
商业知识库在生成的警示信息的规模和数量上有很大差异。DDI严重程度排名的变异性比之前的研究所表明的要小。所有知识库对ONC列表的覆盖都非常好。需要开展更多工作来规范DDI纳入的编辑政策和证据，以减少知识来源之间的差异并提高相关性。在所有3个知识库中都被视为禁忌的一些DDI可能是可添加到ONC列表中的候选对象。
