Categorical Analysis of Database Consistency in Reporting Drug–Drug Interactions for Cardiovascular Diseases

Author:

Suciu Liana12ORCID,Ardelean Sebastian Mihai3ORCID,Udrescu Mihai3ORCID,Goldiş Florina-Diana4ORCID,Hânda Daiana4ORCID,Tuică Maria-Medana4ORCID,Vasii Sabina-Oana4ORCID,Udrescu Lucreţia4ORCID

Affiliation:

1. Department II—Pharmacology, Pharmacotherapy, “Victor Babeş” University of Medicine and Pharmacy Timişoara, 300041 Timişoara, Romania

2. Research Center for Pharmaco-Toxicological Evaluations, “Victor Babeş” University of Medicine and Pharmacy Timişoara, 300041 Timişoara, Romania

3. Department of Computer and Information Technology, University Politehnica of Timişoara, 300223 Timişoara, Romania

4. Department I—Drug Analysis, “Victor Babeş” University of Medicine and Pharmacy Timişoara, 300041 Timişoara, Romania

Abstract

Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss’ kappa coefficients of κ=0.3363 and κ=0.3572, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss’ kappa coefficients that indicate levels of agreement varying from poor agreement (κ<0) to perfect agreement (κ=1). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset.

Funder

Romanian Ministry of Education and Research

Publisher

MDPI AG

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