Aims: Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors. Methods: We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference). Results: The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], p adj = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI −17.7% to −4.7%], p adj ≤ 0.001). Conclusion: The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.

Originele taal-2English
Pagina's (van-tot)1374-1385
Aantal pagina's12
TijdschriftBritish Journal of Clinical Pharmacology
Nummer van het tijdschrift4
Vroegere onlinedatum22 nov 2022
StatusPublished - apr 2023

Bibliografische nota

Funding Information:
Katoo M. Muylle, received a research grant from Research Foundation Flanders (FWO) under the grant number 1S39820N. The funding body had no role in the study design, data collection, analysis or interpretation of the data nor in the writing of the manuscript or the decision to submit the article for publication.

Publisher Copyright:
© 2022 British Pharmacological Society.

Copyright 2023 Elsevier B.V., All rights reserved.


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