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Intravenous phenytoin: a retrospective analysis of Bayesian forecasting versus conventional dosing in patients


Background In the hospital, medication management for effective antiepileptic therapy with phenytoin (PHT) often needs rapid IV loading and subsequent dose adjustment according to therapeutic drug monitoring (TDM). Objective To investigate PHT performance in reaching therapeutic target serum concentration rapidly and sustainably, a Bayesian forecasting (BF) regimen was compared to conventional dosing (CD), according to the official summary of product characteristics. Setting A 500–600 bed acute care teaching hospital in Switzerland, serving as a referral centre for neurology and neurosurgery. Method In a retrospective, single centre, long-term analysis of hospitalized in- and out-patients, all PHT serum tests from the central hospital laboratory from 1997 to 2007 were assessed. The BF regimen consisted of a guided, body weight-adapted rapid IV PHT loading over 5 days with pre-defined TDM time points. The conventional dosage was performed without written guidance. Assuming non-normally distributed data, non-parametric statistical methods for analysis were applied. Main outcome measure The extent of target therapeutic PHT serum levels (40–80 μmol/L) was measured and compared between the two regimens. Also, the influence of gender and age was analysed. Results A total of 6,120 PHT serum levels (2,819 BF and 3,301 conventionally dosed) from 2,589 patients (869 BF and 1,720 conventionally dosed) were evaluated and compared. 63.6 % of the PHT serum levels from the BF group were within the therapeutic range, compared with only 34.0 % in the conventional group (p < 0.0001). The mean BF serum level was 52.0 ± 22.1 μmol/L (within target range) (n = 2,819), whereas the mean serum level of the CD was 39.8 ± 28.2 μmol/L (sub-target range) (n = 3,301). In the BF group, men had small but significantly lower PHT serum levels compared to women (p < 0.0001). The conventionally dosed group showed no significant gender differences (p = 0.187). A comparative sub-analysis of age-related groups (children, adolescents, adults, seniors, and elderly) showed significantly lower target levels (p < 0.0001) for each group in the conventional dosed group, compared to BF. Conclusion Comparing the two cohorts, BF with the well-defined dose regimen showed significantly better performance in reaching therapeutic PHT serum levels rapidly and for longer duration.

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The authors would like to thank Prof. H. Landolt, MD, head of Neurosurgery at the Kantonsspital Aarau, Switzerland, for his collaboration and contributions to this study and to Prof. A. Huber, MD, head of the Central Medicinal Laboratory also at the Kantonsspital Aarau, for his support. The authors wish to acknowledge the help of PD W. Berchtold, PhD, emeritus from the University of Applied Sciences, Northwestern Switzerland in statistical evaluation as well as Prof. S. Krähenbühl and Prof. HJ. Huwyler from the Pharmaceutical Department of the University of Basel for critically reviewing this manuscript.


The investigation and the PhD grant was supported by the Kantonsspital Aarau (Switzerland); (Fund for Science and Continuing Education) and by the University of Basel (third-party grant FO119900 for Clinical Pharmacy, project on CNS drug kinetics and patient monitoring).

Conflicts of interest

The submitting author is a scientific director at Vifor Pharma Ltd., Glattbrugg, Switzerland and holds an additional appointment at the Medical Faculty and the Dept. of Pharmaceutical Sciences at the University of Basel, with a professorship in pharmacology and hospital pharmacy. Vifor Pharma Ltd. is not involved in this investigation and has no pharmaceutical products, services or R&D related to the topic. Their focus is on iron deficiency treatment especially with IV iron preparations.

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Correspondence to Stefan Mühlebach.

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Tobler, A., Mühlebach, S. Intravenous phenytoin: a retrospective analysis of Bayesian forecasting versus conventional dosing in patients. Int J Clin Pharm 35, 790–797 (2013).

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  • Bayesian forecasting
  • Conventional dosing
  • Hospital population
  • Medication management
  • Phenytoin
  • Therapeutic drug monitoring