Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor?

Authors

  • Gianluca Trifirò MD, MSc,

    Corresponding author
    1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
    2. IRCCS Centro Neurolesi ‘Bonino-Pulejo’, Messina, Italy
    3. Department of Clinical and Experimental Medicine and Pharmacology, Pharmacology Unit, University of Messina, Messina, Italy
    • Departments of Medical Informatics, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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  • Antoine Pariente MD, PhD,

    1. Inserm U 657, Pharmacology Department, Bordeaux, France
    2. CHU Bordeaux, France
    3. Department of Pharmacology, University of Bordeaux, France
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  • Preciosa M. Coloma MD,

    1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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  • Jan A. Kors PhD,

    1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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  • Giovanni Polimeni PharmD, PhD,

    1. Department of Clinical and Experimental Medicine and Pharmacology, Pharmacology Unit, University of Messina, Messina, Italy
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  • Ghada Miremont-Salamé MD,

    1. Inserm U 657, Pharmacology Department, Bordeaux, France
    2. CHU Bordeaux, France
    3. Department of Pharmacology, University of Bordeaux, France
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  • Maria Antonietta Catania MD,

    1. IRCCS Centro Neurolesi ‘Bonino-Pulejo’, Messina, Italy
    2. Department of Clinical and Experimental Medicine and Pharmacology, Pharmacology Unit, University of Messina, Messina, Italy
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  • Francesco Salvo MD,

    1. Department of Clinical and Experimental Medicine and Pharmacology, Pharmacology Unit, University of Messina, Messina, Italy
    2. Inserm U 657, Pharmacology Department, Bordeaux, France
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  • Anaelle David MD,

    1. Inserm U 657, Pharmacology Department, Bordeaux, France
    2. CHU Bordeaux, France
    3. Department of Pharmacology, University of Bordeaux, France
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  • Nicholas Moore MD, PhD,

    1. Inserm U 657, Pharmacology Department, Bordeaux, France
    2. CHU Bordeaux, France
    3. Department of Pharmacology, University of Bordeaux, France
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  • Achille Patrizio Caputi MD,

    1. IRCCS Centro Neurolesi ‘Bonino-Pulejo’, Messina, Italy
    2. Department of Clinical and Experimental Medicine and Pharmacology, Pharmacology Unit, University of Messina, Messina, Italy
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  • Miriam Sturkenboom PharmD, PhD,

    1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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  • Mariam Molokhia PhD,

    1. Department of Epidemiology and Population Heath, London School of Hygiene & Tropical Medicine, London, UK
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  • Julia Hippisley-Cox MD,

    1. Division of Primary care, School of Community Health Sciences, University of Nottingham, UK
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  • Carlos Diaz Acedo,

    1. Fundació IMIM - European Projects Management Office, Barcelona, Spain
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  • Johan van der Lei MD, PhD,

    1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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  • Annie Fourrier-Reglat PharmD, PhD

    1. Inserm U 657, Pharmacology Department, Bordeaux, France
    2. CHU Bordeaux, France
    3. Department of Pharmacology, University of Bordeaux, France
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Abstract

Purpose

Data mining on electronic health records (EHRs) has emerged as a promising complementary method for post-marketing drug safety surveillance. The EU-ADR project, funded by the European Commission, is developing techniques that allow mining of EHRs for adverse drug events across different countries in Europe. Since mining on all possible events was considered to unduly increase the number of spurious signals, we wanted to create a ranked list of high-priority events.

Methods

Scientific literature, medical textbooks, and websites of regulatory agencies were reviewed to create a preliminary list of events that are deemed important in pharmacovigilance. Two teams of pharmacovigilance experts independently rated each event on five criteria: ‘trigger for drug withdrawal’, ‘trigger for black box warning’, ‘leading to emergency department visit or hospital admission’, ‘probability of event to be drug-related’, and ‘likelihood of death’. In case of disagreement, a consensus score was obtained. Ordinal scales between 0 and 3 were used for rating the criteria, and an overall score was computed to rank the events.

Results

An initial list comprising 23 adverse events was identified. After rating all the events and calculation of overall scores, a ranked list was established. The top-ranking events were: cutaneous bullous eruptions, acute renal failure, anaphylactic shock, acute myocardial infarction, and rhabdomyolysis.

Conclusions

A ranked list of 23 adverse drug events judged as important in pharmacovigilance was created to permit focused data mining. The list will need to be updated periodically as knowledge on drug safety evolves and new issues in drug safety arise. Copyright © 2009 John Wiley & Sons, Ltd.

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