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Ciprofloxacin-induced theophylline toxicity: a population-based study

  • Pharmacoepidemiology and Prescription
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Abstract

Purpose

Ciprofloxacin can inhibit the cytochrome P450-mediated metabolism of theophylline, but the clinical relevance of this drug interaction is uncertain. We studied the risk of theophylline toxicity associated with the co-prescription of ciprofloxacin and theophylline.

Methods

This was a population-based, nested case–control study of a cohort of Ontario residents aged 66 years of age or older treated with theophylline between April 1, 1992 and March 31, 2009. Within this group, case patients were those hospitalized with theophylline toxicity. For each case, 50 age- and sex-matched control patients were identified from the same cohort. The odds ratio (OR) for the association between hospitalization for theophylline toxicity and receipt of ciprofloxacin in the 14 days preceding hospitalization was determined.

Results

Among the 77,251 elderly patients receiving therapy with theophylline, 180 eligible case patients hospitalized for theophylline toxicity and 9000 matched controls were identified. Following multivariable adjustment, a nearly twofold increase in the risk of theophylline toxicity following the receipt of ciprofloxacin was observed [adjusted OR 1.86, 95% confidence interval (CI) 1.18–2.93]. In contrast, there was no increased risk of theophylline toxicity within a group of patients receiving neutral comparator antibiotics (levofloxacin, trimethoprim-sulfamethoxazole or cefuroxime) (adjusted OR 0.78; 95% CI 0.38–1.62).

Conclusion

Treatment with ciprofloxacin is associated with a significant increase in the risk of theophylline toxicity. When clinically appropriate, alternate antibiotics should be considered for elderly patients receiving theophylline.

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Acknowledgments

We thank Brogan Inc., Ottawa for use of their Drug Product and Therapeutic Class Database.

Conflicts of interest and financial disclosure

Tony Antoniou has received an unrestricted research grant from Glaxo-Smith-Kline Inc, Merck and Pfizer for a number of different studies. Ms. Gomes has no conflicts of interest to disclose. Dr. Mamdani has no conflicts of interest to disclose. Dr. Juurlink has no conflicts of interest to disclose.

Funding/support

Tony Antoniou is supported by a scholarship from the Canadian Observational Cohort (CANOC) Collaboration. This project was supported by research funds from the Ontario Drug Policy Research Network, the Canadian Institutes of Health Research and the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The sponsors had no role in the design and conduct of the study, in the collection, analysis and interpretation of the data or in the preparation, review or approval of the manuscript. The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.

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Authors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Tony Antoniou, Muhammad M. Mamdani & David N. Juurlink

  2. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada

    Tara Gomes

  3. St. Michael’s Hospital, Toronto, ON, Canada

    Tony Antoniou

  4. Applied Health Research Centre, St. Michael’s Hospital, Toronto, ON, Canada

    Muhammad M. Mamdani

  5. Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

    Tara Gomes, Muhammad M. Mamdani & David N. Juurlink

  6. King Saud University, Riyadh, Saudi Arabia

    Muhammad M. Mamdani

  7. Sunnybrook Research Institute, Toronto, ON, Canada

    David N. Juurlink

  8. 410 Sherbourne Street, 4th Floor, Toronto, ON, M4X 1K2, Canada

    Tony Antoniou

Authors
  1. Tony Antoniou
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  2. Tara Gomes
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  3. Muhammad M. Mamdani
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  4. David N. Juurlink
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Corresponding author

Correspondence to Tony Antoniou.

Appendix 1: Medications included in multivariable model

Appendix 1: Medications included in multivariable model

Medication use in 90 days preceding index date:

  • CYP1A2 inhibitors: Allopurinol, Cimetidine, Disulfiram, Duloxetine, Ethinyl estradiol, Fluvoxamine, Isoniazid, Mefenamic acid, Mexiletine, Norfloxacin, Propafenone, Propranolol, Propylthiouracil, Riluzole, Rofecoxib, Ticlopidine

  • CYP3A4 inhibitors: Amiodarone, Amprenavir, Aprepitant, Atazanavir, Clarithromycin, Darunavir, Delavirdine, Diltiazem, Erythromycin, Fluconazole, Fosamprenavir, Imatinib, Indinavir, Itraconazole, Lopinavir/ritonavir, Nefazodone, Nelfinavir, Ritonavir, Saquinavir, Telithromycin, Verapamil, Voriconazole

  • CYP1A2/3A4 inducers: Amobarbital, Carbamazepine, Dexamethasone, Efavirenz, Etravirine, Nevirapine, Phenobarbital, Phenytoin, Pioglitazone, Primidone, Rifampin, Secobarbital

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Antoniou, T., Gomes, T., Mamdani, M.M. et al. Ciprofloxacin-induced theophylline toxicity: a population-based study. Eur J Clin Pharmacol 67, 521–526 (2011). https://doi.org/10.1007/s00228-010-0985-0

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  • DOI: https://doi.org/10.1007/s00228-010-0985-0

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