Evaluation of Potential Drug Interactions with AiDKlinik® in a Random Population Sample

Julian Schmidberger,1 Christopher Kloth,2 Martin Müller,1 Wolfgang Kratzer,1 Jochen Klaus1 1Department of Internal Medicine I, University Hospital Ulm, Ulm, Baden-Württemberg, Germany; 2Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Baden-Württemberg, GermanyCorrespondence: Wolfgang Kratzer, Department of Internal Medicine I, University Hospital Ulm, Albert-EInstein-Allee 23, Ulm, 89081, Germany, Tel +49 731 500 44730, Fax +49 731 500 44705, Email wolfgang.kratzer@uniklinik-ulm.dePurpose: Undesirable drug interactions are frequent, they endanger the success of therapy, and they lead to adverse drug reactions. The present study aimed to evaluate statistically potentially drug interactions in a locally circumscribed, random sample population.Patients and Methods: In a random sample population of 264 patients taking medications, we performed analyses with the drug information system AiDKlinik®. Statistical analysis was performed using SAS version 9.4.Results: Statistically potentially drug interactions were recorded in 82/264 (31.1%) subjects, including 39/82 (47.56%) men, and 43/82 (52.43%) women (χ2= 0.081; p = 0.776). The average number of potential possible interactions detected per person was 1.60 ± 1.21. The regression model with the variables age, body-mass-index and number of long-term-medications shows a significant association between the number of long-term medications taken and the number of moderately severe and severe reactions to drug interactions (F(3.239) = 28.67, p < 0.0001; (t(239) 8.28; p < 0.0001)). After backward elimination, the regression model showed a significant interaction with the number of long-term medications (t (240) = 8.73, p < 0.0001) and body-mass-index (t (240) = 2.02, p = 0.0442). In descriptive analysis, the highest percentages of potential drug interactions occurred in 42/82 (51.22%) subjects with body mass indices (BMIs) > 25 kg/m2 and in 28/82 (34.15%) subjects aged 61– 70 years.Conclusion: Number of long-term medications use, age, and obesity may lead to increased drug–drug interactions in a random population sample.Keywords: long-term medications, drug interactions, random sample population, prevalence, age- and gender-specific frequency