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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.

Keywords

Propensity Score Acute Liver Injury Relative Risk Estimate Analysis Choice True Effect Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors wish to acknowledge the Observational Medical Outcomes Partnership (OMOP) investigators for their help in running the OMOP methods in the EU-ADR environment. The study was funded by The Foundation for the National Institutes of Health, grant STUR11OMOP. Dr. Schuemie received a fellowship from the Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration, has become an employee of Janssen Research & Development since completing this research, and is an OMOP investigator. OMOP is funded by the Foundation for the National Institutes of Health through generous contributions from the following: Abbott, Amgen Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals, Inc., Biogen Idec, Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Janssen Research and Development, Lundbeck, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc, Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi-aventis, Schering-Plough Corporation, and Takeda. Mr. Straatman and Dr. Herings are employees of the PHARMO Institute for Drug Outcomes Research, which performs financially supported studies for several pharmaceutical companies. All other authors declare to have no conflicts of interests to declare. Dr. Sturkenboom is an employee of the Erasmus University Medical Center, and coordinates studies that are financially supported by several pharmaceutical companies (Novartis, Pfizer, EliLilly, Boehringer), none related to this study.

This article was published in a supplement sponsored by the Foundation for the National Institutes of Health (FNIH). The supplement was guest edited by Stephen J.W. Evans. It was peer reviewed by Olaf H. Klungel who received a small honorarium to cover out-of-pocket expenses. S.J.W.E has received travel funding from the FNIH to travel to the OMOP symposium and received a fee from FNIH for the review of a protocol for OMOP. O.H.K has received funding for the IMI-PROTECT project.from the Innovative Medicines Initiative Joint Undertaking (http://www.imi.europa.eu) under Grant Agreement no 115004, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.

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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