Key result
A risk prediction model incorporating patient- and drug-related factors significantly increased appropriate QT-DDI stratification from 37% to 54% compared to current practice (P=0.006).
Why the study?
Clinical decision support systems suffer from overalerting and underalerting for QT-prolonging drug-drug interactions because stratification relies only on fixed severity levels.
Does a risk prediction model including patient- and drug-related factors improve appropriate stratification of QT-DDI alerts compared to fixed severity levels?
Observational (n=493)
Does a risk prediction model including patient- and drug-related factors improve appropriate stratification of QT-DDI alerts compared to fixed severity levels?
Effect estimate: increase of 17.5% (95% CI +5.4% to +29.6%)
Absolute Event Rate: 54% vs 37%
p-value: p=0.006
A risk prediction model incorporating patient- and drug-related factors significantly improves the accuracy of QT-prolonging drug-drug interaction alerts compared to fixed severity levels, reducing both overalerting and underalerting.
May aid QT-DDI alert management; hypothesis-generating and requires prospective validation before adoption.
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.
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Muylle et al. (2022) conducted an observational in QT prolongation and torsades de pointes risk from drug-drug interactions (n=493). Risk prediction model including patient- and drug-related factors vs. Current practice (stratification based on fixed QT-DDI severity level) was evaluated on Appropriate stratification of QT-DDIs (predicted risk = true risk) (increase of 17.5%, 95% CI +5.4% to +29.6%, p=0.006). A risk prediction model incorporating patient- and drug-related factors significantly increased appropriate QT-DDI stratification from 37% to 54% compared to current practice (P=0.006).
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