Drug Safety

, Volume 36, Supplement 1, pp 159–169 | Cite as

Replication of the OMOP Experiment in Europe: Evaluating Methods for Risk Identification in Electronic Health Record Databases

  • Martijn J. Schuemie
  • Rosa Gini
  • Preciosa M. Coloma
  • Huub Straatman
  • Ron M. C. Herings
  • Lars Pedersen
  • Francesco Innocenti
  • Giampiero Mazzaglia
  • Gino Picelli
  • Johan van der Lei
  • Miriam C. J. M. Sturkenboom
Original Research Article

Abstract

Background

The Observational Medical Outcomes Partnership (OMOP) has just completed a large scale empirical evaluation of statistical methods and analysis choices for risks identification in longitudinal observational healthcare data. This experiment drew data from four large US health insurance claims databases and one US electronic health record (EHR) database, but it is unclear to what extend the findings of this study apply to other data sources.

Objective

To replicate the OMOP experiment in six European EHR databases.

Research Design

Six databases of the EU-ADR (Exploring and Understanding Adverse Drug Reactions) database network participated in this study: Aarhus (Denmark), ARS (Italy), HealthSearch (Italy), IPCI (the Netherlands), Pedianet (Italy), and Pharmo (the Netherlands). All methods in the OMOP experiment were applied to a collection of 165 positive and 234 negative control drug–outcome pairs across four outcomes: acute liver injury, acute myocardial infarction, acute kidney injury, and upper gastrointestinal bleeding. Area under the receiver operator characteristics curve (AUC) was computed per database and for a combination of all six databases using meta-analysis for random effects. We provide expected values of estimation error as well, based on negative controls.

Results

Similarly to the US experiment, high predictive accuracy was found (AUC >0.8) for some analyses. Self-controlled designs, such as self-controlled case series, IC temporal pattern discovery and self-controlled cohort achieved higher performance than other methods, both in terms of predictive accuracy and observed bias.

Conclusions

The major findings of the recent OMOP experiment were also observed in the European databases.

Supplementary material

40264_2013_109_MOESM1_ESM.xlsx (178 kb)
Mapping from ATC to RxNorm identifiers (xlsx 178 kb)
40264_2013_109_MOESM2_ESM.xlsx (12 kb)
Age-stratified analysis of incidence across OMOP and EU-ADR databases (xlsx 13 kb)

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Martijn J. Schuemie
    • 1
    • 7
  • Rosa Gini
    • 1
    • 2
  • Preciosa M. Coloma
    • 1
  • Huub Straatman
    • 3
  • Ron M. C. Herings
    • 1
    • 3
  • Lars Pedersen
    • 4
  • Francesco Innocenti
    • 2
    • 5
  • Giampiero Mazzaglia
    • 5
  • Gino Picelli
    • 6
  • Johan van der Lei
    • 1
  • Miriam C. J. M. Sturkenboom
    • 1
  1. 1.Department of Medical InformaticsErasmus University Medical CenterRotterdamThe Netherlands
  2. 2.Agenzia Regionale di Sanità della ToscanaFlorenceItaly
  3. 3.PHARMO InstituteUtrechtThe Netherlands
  4. 4.Department of Clinical EpidemiologyAarhus University HospitalÅrhusDenmark
  5. 5.Health Search, Italian College of General PractitionersFlorenceItaly
  6. 6.Pedianet, Società Servizi Telematici SRLPadovaItaly
  7. 7.Observational Medical Outcomes Partnership, Foundation for the National Institutes of HealthBethesdaUSA

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