1. Added value of patient and drug related factors to stratify drug-drug interaction alerts for risk of QT prolongation
- Author
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Katoo M. Muylle, Sven van Laere, Luigi Pannone, Samuel Coenen, Carlo de Asmundis, Alain G. Dupont, Pieter Cornu, Clinical Pharmacology and Clinical Pharmacy, Biostatistics and medical informatics, Clinical sciences, Faculty of Medicine and Pharmacy, Heartrhythmmanagement, Pharmaceutical and Pharmacological Sciences, Artificial Intelligence supported Modelling in clinical Sciences, and UZB Other
- Subjects
Pharmacology ,drug-drug interactions ,Pharmacology, Toxicology and Pharmaceutics(all) ,drug safety ,Pharmacology. Therapy ,Public Health, Environmental and Occupational Health ,Pharmacology (medical) ,Health Informatics ,Human medicine ,Cardiology and Cardiovascular Medicine ,Clinical Decision Support ,drug-induced QT prolongation ,risk prediction model - Abstract
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%], padj = 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%], padj ≤ 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.
- Published
- 2023