A comparison of four drug–drug interaction databases for patients undergoing haematopoietic stem cell transplantation


Journal of Clinical Pharmacy and Therapeutics, vol.47, no.10, pp.1711-1719, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 47 Issue: 10
  • Publication Date: 2022
  • Doi Number: 10.1111/jcpt.13728
  • Journal Name: Journal of Clinical Pharmacy and Therapeutics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, BIOSIS, CAB Abstracts, CINAHL, EMBASE, International Pharmaceutical Abstracts, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.1711-1719
  • Keywords: DDIs, drug-drug interaction databases, drug-drug interactions, haematopoietic stem cell transplantation, CLINICAL DECISION-SUPPORT, INTERACTION SOFTWARE, INFECTIOUS COMPLICATIONS, PREVALENCE, RECOMMENDATIONS
  • Kayseri University Affiliated: No


© 2022 John Wiley & Sons Ltd.What is known and objective: Patients who have undergone haematopoietic stem cell transplantation are prone to drug–drug interactions due to polypharmacy. Drug–drug interaction databases are essential tools for identifying interactions in this patient group. However, drug–drug interaction checkers, which help manage interactions, may have disagreements about assessing the existence or severance of the interactions. The study aimed to determine differences among popular drug–drug interaction databases from several angles for patients who underwent haematopoietic stem cell transplantation. Methods: The 21-day treatment sheets of one hundred patients who underwent haematopoietic stem cell transplantation were examined in two subscription-based (Uptodate and Micromedex) and two open-access databases (Drugs.com and Epocrates) in terms of several categories two years in a row. Statistical analysis was utilized to understand the compatibility of databases in terms of severity scores, evidence levels, given references, and word counts in interaction reports. Fleiss' and Cohen's kappa statistics were used to analyse the databases' agreement levels. Results and discussion: A total of 1393 and 1382 different drug–drug interactions were detected in subsequent versions of the databases, namely the 2021 and 2022 versions. The Fleiss kappa overall agreement among databases was slight. Uptodate and Micromedex showed fair agreement, and other database pairs showed slight agreement in severity ratings. Conclusion: There was a poor agreement among databases for interactions seen in bone marrow transplantation patients. Therefore, it would be safer to use more than one database in daily practice. Further work needs to be done to understand the agreement level of databases for different types of interactions.