Open Access Research article

Validation of a transparent decision model to rate drug interactions

Elmira Far1, Ivanka Curkovic1, Kelly Byrne1, Malgorzata Roos2, Isabelle Egloff1, Michael Dietrich3, Wilhelm Kirch4, Gerd-A Kullak-Ublick1 and Marco Egbring1*

Author Affiliations

1 Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland

2 Division of Biostatistics, ISPM, University Zurich, Hirschengraben 8, 8001, Zurich, Switzerland

3 Department of Orthopaedic, Balgrist University Hospital, Forchstrasse 340, 8008, Zurich, Switzerland

4 Institute of Clinical Pharmacology, Medical Faculty Technical University of Dresden, Fiedlerstrasse 27, D - 01307, Dresden, Germany

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BMC Pharmacology and Toxicology 2012, 13:7  doi:10.1186/2050-6511-13-7

Published: 20 August 2012



Multiple databases provide ratings of drug-drug interactions. The ratings are often based on different criteria and lack background information on the decision making process. User acceptance of rating systems could be improved by providing a transparent decision path for each category.


We rated 200 randomly selected potential drug-drug interactions by a transparent decision model developed by our team. The cases were generated from ward round observations and physicians’ queries from an outpatient setting. We compared our ratings to those assigned by a senior clinical pharmacologist and by a standard interaction database, and thus validated the model.


The decision model rated consistently with the standard database and the pharmacologist in 94 and 156 cases, respectively. In two cases the model decision required correction. Following removal of systematic model construction differences, the DM was fully consistent with other rating systems.


The decision model reproducibly rates interactions and elucidates systematic differences. We propose to supply validated decision paths alongside the interaction rating to improve comprehensibility and to enable physicians to interpret the ratings in a clinical context.

Algorithm; Severity; Validation; Drug; Interaction; Decision; Model; Mmx;