Background: QTc-prolongation is an independent risk factor for developing life-threatening arrhythmias. Risk management of drug-induced QTc-prolongation is complex and digital support tools could be of assistance. Bindraban et al. and Berger et al. developed two algorithms to identify patients at risk for QTc-prolongation.
Objective: The main aim of this study was to compare the performances of these algorithms for managing QTcprolonging drug-drug interactions (QT-DDIs).
Materials and Methods: A retrospective data analysis was performed. A dataset was created from QT-DDI alerts generated for inand outpatients at a general teaching hospital between November 2016 and March 2018. ECGs recorded within 7 days of the QT-DDI alert were collected. Main outcomes were the performance characteristics of both algorithms. QTc-intervals of > 500 ms on the first ECG after the alert were taken as outcome parameter, to which the performances were compared. Secondary outcome was the distribution of risk scores in the study cohort.
Results: In total, 10,870 QT-DDI alerts of 4987 patients were included. ECGs were recorded in 26.2 % of the QT-DDI alerts. Application of the algorithms resulted in area under the ROC-curves of 0.81 (95 % CI 0.79-0.84) for Bindraban et al. and 0.73 (0.70-0.75) for Berger et al. Cut-off values of >= 3 and >= 6 led to sensitivities of 85.7 % and 89.1 %, and specificities of 60.8 % and 44.3 % respectively.
Conclusions: Both algorithms showed good discriminative abilities to identify patients at risk for QTcprolongation when using >= 2 QTc-prolonging drugs. Implementation of digital algorithms in clinical decision support systems could support the risk management of QT-DDIs.