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Signal Detection of Potentially Drug-Induced Acute Liver Injury in Children Using a Multi-Country Healthcare Database Network

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Abstract

Background

Data mining in spontaneous reporting databases has shown that drug-induced liver injury is infrequently reported in children.

Objectives

Our objectives were to (i) identify drugs potentially associated with acute liver injury (ALI) in children and adolescents using electronic healthcare record (EHR) data; and (ii) to evaluate the significance and novelty of these associations.

Methods

We identified potential cases of ALI during exposure to any prescribed/dispensed drug for individuals <18 years old from the EU-ADR network, which includes seven databases from three countries, covering the years 1996–2010. Several new methods for signal detection were applied to identify all statistically significant associations between drugs and ALI. A drug was considered statistically significantly associated with ALI, using all other time as a reference category, if the 95 % CI lower band of the relative risk was >1 and in the presence of at least three exposed cases of ALI. Potentially new signals were distinguished from already known associations concerning ALI (whether in adults and/or in the paediatric population) through manual review of published literature and drug product labels.

Results

The study population comprised 4,838,146 individuals aged <18 years, who contributed an overall 25,575,132 person-years of follow-up. Within this population, we identified 1,015 potential cases of ALI. Overall, 20 positive drug–ALI associations were detected. The associations between ALI and domperidone, flunisolide and human insulin were considered as potentially new signals. Citalopram and cetirizine have been previously described as hepatotoxic in adults but not in children, while all remaining associations were already known in both adults and children.

Conclusions

Data mining of multiple EHR databases for signal detection confirmed known associations between ALI and several drugs, and identified some potentially new signals in children that require further investigation through formal epidemiologic studies. This study shows that EHRs may complement traditional spontaneous reporting systems for signal detection and strengthening.

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Acknowledgments

This research has been funded by the European Commission Seventh Framework Programme (FP7/2007-2013) under grant no. 215847—the EU-ADR Project. The funding agency had no role in the design and conduct of the study, the collection and management of data, the analysis or interpretation of the data or preparation, review, or approval of the manuscript.

Conflicts of interest

Martijn J. Schuemie has become an employee of Janssen R&D since completing this research and has received a grant from the Foundation for the National Institutes of Health (FNIH). Katia M.C. Verhamme received unconditional research grants from Pfizer, Boehringer-Ingelheim, Novartis and Yamanouchi. None of these are related to the content of this research. Carmen Ferrajolo, Preciosa M. Coloma, Sandra de Bie, Rosa Gini, Giampiero Mazzaglia, Gino Picelli, Carlo Giaquinto, Lorenza Scotti, Paul Avillach, Lars Pedersen, Francesco Rossi, Annalisa Capuano, Johan van der Lei, Ron Herings, Miriam C.J.M. Sturkenboom and Gianluca Trifiró declare that they have no conflicts of interest directly relevant to the content of this study.

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Authors and Affiliations

  1. Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, 3015 GE, Rotterdam, The Netherlands

    Carmen Ferrajolo, Preciosa M. Coloma, Katia M. C. Verhamme, Martijn J. Schuemie, Sandra de Bie, Johan van der Lei, Gianluca Trifiró & Miriam C. J. M. Sturkenboom

  2. Pharmacology Section, Department of Experimental Medicine, Campania Regional Center of Pharmacovigilance and Pharmacoepidemiology, Second University of Naples, Via L. De Crecchio, 7, 80138, Naples, Italy

    Carmen Ferrajolo, Francesco Rossi & Annalisa Capuano

  3. Regional Health Agency of Tuscany, Florence, Italy

    Rosa Gini

  4. PHARMO Institute, Utrecht, The Netherlands

    Ron Herings

  5. Italian College of General Practitioners, Florence, Italy

    Giampiero Mazzaglia

  6. Pedianet-Società Servizi Telematici SRL, Padua, Italy

    Gino Picelli

  7. Department of Paediatrics, University Hospital of Padua, Padua, Italy

    Carlo Giaquinto

  8. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy

    Lorenza Scotti

  9. LESIM, ISPED, University of Bordeaux 2, Bordeaux, France

    Paul Avillach

  10. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark

    Lars Pedersen

  11. Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy

    Gianluca Trifiró

  12. Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Miriam C. J. M. Sturkenboom

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  1. Carmen Ferrajolo
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  18. Miriam C. J. M. Sturkenboom
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EU-ADR consortium

Corresponding author

Correspondence to Carmen Ferrajolo.

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Ferrajolo, C., Coloma, P.M., Verhamme, K.M.C. et al. Signal Detection of Potentially Drug-Induced Acute Liver Injury in Children Using a Multi-Country Healthcare Database Network. Drug Saf 37, 99–108 (2014). https://doi.org/10.1007/s40264-013-0132-9

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