Making open data useful: A drug safety case study

We’re pleased to present a guest post from data scientist Juan M. Banda, the lead author of an important, newly-available resource for drug safety research. Here, Juan shares some of the context behind the data descriptor in Scientific Data and associated data package in Dryad. – EH

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As I sit in a room full of over one hundred bio-hackers at the 2016 Biohackathon in Tsuruoka, Yamagata, Japan, the need to have publicly available and accessible data for research use is acutely evident. Organized by Japan’s National Biosciences Database Center (NBDC) and Databases Center for Life Science (DBLS), this yearly hackathon gathers people from organizations and universities all over the world, including the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI), with the purpose of extending and interlinking resources like PubChem, PhenomeCentral, Bio2RDF, and PubAnnotation.

The end goal: finding better ways to access data that will allow researchers to focus on analysis of the data rather than preparation.

#biohack16 packed with expertise and lots of project proposals pic.twitter.com/euUzNke98N

— Rafael C Jimenez (@rafajido) June 13, 2016

In the same spirit, our publication “A curated and standardized adverse drug event resource to accelerate drug safety research” (doi:10.1038/sdata.2016.26; data in Dryad at http://doi.org/10.5061/dryad.8q0s4) helps researchers in the drug safety domain with the standardization and curation of the freely available data from the Federal Food and Drug Administration (FDA) adverse events reporting system (FAERS).

FAERS collects information on adverse events and medication errors reported to the FDA, and is comprised of over 10 million records collected between 1969 to the present. As one of the most important resources for drug safety efforts, the FAERS database has been used in at least 750 publications as reported by PubMed and was probably manipulated, mapped and cleaned independently by the vast majority of the authors of said publications. This cleaning and mapping process takes a considerable amount of time — hours that could have been spent analyzing the data further.

Our publication hopes to eliminate this needless work and allow researchers to focus their efforts in developing methods to analyze this information.

As part of the Observational Health Sciences Initiative (OHDSI), whose mission is to “Improve health, by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care,” we decided to tackle the task of cleaning and curating the FAERS database for our community, and the wider drug safety community. By providing a general common data model (CDM) and a general vocabulary to standardize how electronic patient data is stored, OHDSI allows its participants to join a research network with over 655 million patients.

With a significant fraction of the community’s research being focused on drug safety, it was a natural decision to standardize the FAERS database with the OMOP vocabulary, to allow all researchers on our network access to FAERS. Since the OMOP vocabulary incorporates general vocabularies such as SNOMED, MeSH, and RxNORM, among others, the usability of this resource is not limited to participants of this community.

Figure 1 from doi:10.1038/sdata.2016.26

In order to curate this dataset, we took the source FAERS data in CSV format and de-duplicated case reports. We then performed value imputation for certain fields that were missing. Drug names were standardized to RxNorm ingredients and standard clinical names (for multi-ingredient drugs). This mapping is tricky because some drug names have spelling errors, and some are non-prescription drugs, or international brand names. We achieved coverage of 93% of the drug names, which in turn cover 95% of the case reports in FARES.

For the first time, the indication and reactions have been mapped to SNOMED-CT from their original MedRA format. Coverage for indications and reactions is around 64% and 80%, respectively. The OMOP vocabulary allows RxNorm drug codes as well as SNOMED-CT codes to reside in the same unified vocabulary space, simplifying use of this resource. We also provide the complete source code we developed in order to allow researchers to refresh the dataset with the new quarterly FAERS data releases and improve the mappings if needed. We encourage users to contribute the results of their efforts back to the OHDSI community.

With a firm commitment to making open data easier to use, this resource allows researchers to utilize a professionally curated (and refreshable) version of the FAERS data, enabling them to focus on improving drug safety analyses and finding more potentially harmful drugs, as a part of OHDSI’s core mission.