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Detection of Drug–Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining

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Abstract

Background and Objective

While risk of acute kidney injury (AKI) is a well documented adverse effect of some drugs, few studies have assessed the relationship between drug–drug interactions (DDIs) and AKI. Our objective was to develop an algorithm capable of detecting potential signals on this relationship by retrospectively mining data from electronic health records.

Material and methods

Data were extracted from the clinical data warehouse (CDW) of the Hôpital Européen Georges Pompidou (HEGP). AKI was defined as the first level of the RIFLE criteria, that is, an increase ≥50 % of creatinine basis. Algorithm accuracy was tested on 20 single drugs, 10 nephrotoxic and 10 non-nephrotoxic. We then tested 45 pairs of non-nephrotoxic drugs, among the most prescribed at our hospital and representing distinct pharmacological classes for DDIs.

Results

Sensitivity and specificity were 50 % [95 % confidence interval (CI) 23.66–76.34] and 90 % (95 % CI 59.58–98.21), respectively, for single drugs. Our algorithm confirmed a previously identified signal concerning clarithromycin and calcium-channel blockers (unadjusted odds ratio (ORu) 2.92; 95 % CI 1.11–7.69, p = 0.04). Among the 45 drug pairs investigated, we identified a signal concerning 55 patients in association with bromazepam and hydroxyzine (ORu 1.66; 95 % CI 1.23–2.23). This signal was not confirmed after a chart review. Even so, AKI and co-prescription were confirmed for 96 % (95 % CI 88–99) and 88 % (95 % CI 76–94) of these patients, respectively.

Conclusion

Data mining techniques on CDW can foster the detection of adverse drug reactions when drugs are used alone or in combination.

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Acknowledgments

Cendrine Baudoin, Eric Zapletal, Antoine Rachas, Abdel-Ali Boussadi, Bastien Rance, Anne-Sophie Jannot, Jean Bouyer, Gilles Chatellier.

Author information

Authors and Affiliations

  1. Biomedical Informatics and Public Health Department, HEGP, AP-HP, Paris, France

    Yannick Girardeau, Pierre Durieux, Patrice Degoulet & Paul Avillach

  2. Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1138, Centre de Recherche des Cordeliers, 75006, Paris, France

    Yannick Girardeau, Pierre Durieux, Antoine Neuraz, Patrice Degoulet & Paul Avillach

  3. Department of Nephrology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France

    Claire Trivin

  4. Department of Pharmacovigilance, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France

    Christine Le Beller & Lillo-Le Louet Agnes

  5. Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA

    Paul Avillach

  6. Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, MA, USA

    Paul Avillach

Authors
  1. Yannick Girardeau
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  2. Claire Trivin
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  3. Pierre Durieux
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  4. Christine Le Beller
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  5. Lillo-Le Louet Agnes
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  6. Antoine Neuraz
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  7. Patrice Degoulet
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  8. Paul Avillach
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Corresponding author

Correspondence to Paul Avillach.

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Funding

No sources of funding were used to assist in the preparation of this study.

Conflicts of interest

Yannick Girardeau, Claire Trivin, Pierre Durieux, Christine Le Beller, Lillo-Le Louet Agnes, Antoine Neuraz, Patrice Degoulet and Paul Avillach have no conflicts of interest that are directly relevant to the contents of this study.

Ethical standards

All persons gave their informed consent prior to their inclusion in the study. We obtained an approval from the institutional review board of our hospital (IRB#00001072 Study #CDW_2013_0004).

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Girardeau, Y., Trivin, C., Durieux, P. et al. Detection of Drug–Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining. Drug Saf 38, 799–809 (2015). https://doi.org/10.1007/s40264-015-0311-y

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  • DOI: https://doi.org/10.1007/s40264-015-0311-y

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