Dataset Information

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

ABSTRACT:

Background and objective

Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).

Methods

We prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.

Results

We collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.

Conclusions

Our proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.

SUBMITTER: Banda JM

PROVIDER: S-EPMC4712252 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Publications

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

Banda Juan M JM Callahan Alison A Winnenburg Rainer R Strasberg Howard R HR Cami Aurel A Reis Ben Y BY Vilar Santiago S Hripcsak George G Dumontier Michel M Shah Nigam Haresh NH

Drug safety 20160101 1

<h4>Background and objective</h4>Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published data ...[more]

PMID: 26446143

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Dataset Information

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

Background and objective

Methods

Results

Conclusions

Publications

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

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