Pharmacoepidemiological study of drug–drug interactions in onco-hematological pediatric patients

Background Onco-hematological patients are particularly susceptible to drug–drug interactions (DDIs) because they often undergo multiple combined treatments. Some studies have analyzed the frequency of DDIs in adult patients with cancer; however, the prevalence of DDIs in children, and especially among pediatric cancer patients, remains unknown. Objective To determine the prevalence of DDIs in treatment sheets comparing two commonly used drug interaction databases, to describe the most common clinically relevant DDIs (CR-DDIs) and to investigate the risk factors associated with them. Setting An onco-hematological pediatric unit from a tertiary hospital in Spain. Method A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient’s treatment sheet was collected. Each medication list was screened through two databases: Thomson Micromedex™ and Drug Interaction Facts™. All identified DDIs were graded by their level of severity. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of CR-DDIs. Multivariate analysis was used to identify risk factors associated with CRDDIs. Main outcome measure Prevalence of CR-DDIs was measured as percentage. Results A total of 506 potential DDIs were detected in 150 treatment sheets. The prevalence of CR-DDIs by Micromedex database and Drug Interaction Facts database were 44.7 and 51.3 % respectively. Amikacin, azole antifungals, antiemetics and cyclosporine were the most frequent drugs involved in CR-DDIs. In multivariate analysis, the main risk factor associated with increased odds for CR-DDIs was a higher number of drugs. Conclusion The frequency of potential DDIs was related to a higher number of drugs, being immunosuppressant and azole antifungal agents the most commonly involved drugs. The lack of agreement between different databases enhances the complexity to detect drug interactions in clinical practice.