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Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare t...

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Autores principales: Moreau, Fanny, Simon, Nicolas, Walther, Julia, Dambrine, Mathilde, Kosmalski, Gaetan, Genay, Stéphanie, Perez, Maxime, Lecoutre, Dominique, Belaiche, Stéphanie, Rousselière, Chloé, Tod, Michel, Décaudin, Bertrand, Odou, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002594/
https://www.ncbi.nlm.nih.gov/pubmed/33802983
http://dx.doi.org/10.3390/metabo11030173
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author Moreau, Fanny
Simon, Nicolas
Walther, Julia
Dambrine, Mathilde
Kosmalski, Gaetan
Genay, Stéphanie
Perez, Maxime
Lecoutre, Dominique
Belaiche, Stéphanie
Rousselière, Chloé
Tod, Michel
Décaudin, Bertrand
Odou, Pascal
author_facet Moreau, Fanny
Simon, Nicolas
Walther, Julia
Dambrine, Mathilde
Kosmalski, Gaetan
Genay, Stéphanie
Perez, Maxime
Lecoutre, Dominique
Belaiche, Stéphanie
Rousselière, Chloé
Tod, Michel
Décaudin, Bertrand
Odou, Pascal
author_sort Moreau, Fanny
collection PubMed
description The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R(AUC)) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R(AUC): 90.0%, 44.2% and 75.0% for R(AUC) ≤ 0.5; R(AUC) 0.5–2 and R(AUC) > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R(AUC) category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.
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spelling pubmed-80025942021-03-28 Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively? Moreau, Fanny Simon, Nicolas Walther, Julia Dambrine, Mathilde Kosmalski, Gaetan Genay, Stéphanie Perez, Maxime Lecoutre, Dominique Belaiche, Stéphanie Rousselière, Chloé Tod, Michel Décaudin, Bertrand Odou, Pascal Metabolites Article The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R(AUC)) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R(AUC): 90.0%, 44.2% and 75.0% for R(AUC) ≤ 0.5; R(AUC) 0.5–2 and R(AUC) > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R(AUC) category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments. MDPI 2021-03-17 /pmc/articles/PMC8002594/ /pubmed/33802983 http://dx.doi.org/10.3390/metabo11030173 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Moreau, Fanny
Simon, Nicolas
Walther, Julia
Dambrine, Mathilde
Kosmalski, Gaetan
Genay, Stéphanie
Perez, Maxime
Lecoutre, Dominique
Belaiche, Stéphanie
Rousselière, Chloé
Tod, Michel
Décaudin, Bertrand
Odou, Pascal
Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title_full Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title_fullStr Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title_full_unstemmed Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title_short Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?
title_sort does ddi-predictor help pharmacists to detect drug-drug interactions and resolve medication issues more effectively?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002594/
https://www.ncbi.nlm.nih.gov/pubmed/33802983
http://dx.doi.org/10.3390/metabo11030173
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