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Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice

BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automat...

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Autores principales: Berger, Florine A., van der Sijs, Heleen, Becker, Matthijs L., van Gelder, Teun, van den Bemt, Patricia M. L. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376881/
https://www.ncbi.nlm.nih.gov/pubmed/32703198
http://dx.doi.org/10.1186/s12911-020-01181-3
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author Berger, Florine A.
van der Sijs, Heleen
Becker, Matthijs L.
van Gelder, Teun
van den Bemt, Patricia M. L. A.
author_facet Berger, Florine A.
van der Sijs, Heleen
Becker, Matthijs L.
van Gelder, Teun
van den Bemt, Patricia M. L. A.
author_sort Berger, Florine A.
collection PubMed
description BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden’s index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51–0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54–0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368.
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spelling pubmed-73768812020-07-23 Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice Berger, Florine A. van der Sijs, Heleen Becker, Matthijs L. van Gelder, Teun van den Bemt, Patricia M. L. A. BMC Med Inform Decis Mak Research Article BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden’s index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51–0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54–0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368. BioMed Central 2020-07-23 /pmc/articles/PMC7376881/ /pubmed/32703198 http://dx.doi.org/10.1186/s12911-020-01181-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Berger, Florine A.
van der Sijs, Heleen
Becker, Matthijs L.
van Gelder, Teun
van den Bemt, Patricia M. L. A.
Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title_full Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title_fullStr Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title_full_unstemmed Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title_short Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice
title_sort development and validation of a tool to assess the risk of qt drug-drug interactions in clinical practice
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376881/
https://www.ncbi.nlm.nih.gov/pubmed/32703198
http://dx.doi.org/10.1186/s12911-020-01181-3
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