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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Keywords Decision support systems, clinical, Drug interactions, Risk factors, ROC curve, Sensitivity and specificity
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Journal B M C Medical Informatics and Decision Making
Berger, F.A. (Florine A.), van der Sijs, I.H, Becker, M.L, van Gelder, T, & van den Bemt, P.M.L.A. (2020). Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice. B M C Medical Informatics and Decision Making, 20(1). doi:10.1186/s12911-020-01181-3