Why the study?
There are no established methods to predict quantitatively whether starting a drug with QTc-prolonging potential poses patients at risk for relevant QTc prolongation.
Does a general linear mixed model accurately predict the extent of QTc prolongation in adults newly exposed to QTc-prolonging medications?
Does a general linear mixed model accurately predict the extent of QTc prolongation in adults newly exposed to QTc-prolonging medications?
A predictive model for QTc prolongation risk showed weak but significant correlation with actual values, highlighting the potential for future tools to enhance medication safety.
Retrospective cohort data support initial QTc risk model development; leaves open prospective validation before clinical use.
There are currently no established methods to predict quantitatively whether the start of a drug with the potential to prolong the QTc interval poses patients at risk for relevant QTc prolongation. Therefore, this retrospective study aimed to pave the way for the development of models for estimating QTc prolongation in patients newly exposed to medications with QTc-prolonging potential. Data of patients with a documented QTc prolongation after initiation of a QTc-prolonging drug were extracted from hospital charts. Using a standard model-building approach, general linear mixed models were identified as the best models for predicting both the extent of QTc prolongation and its absolute value after the start of a QTc-time-prolonging drug. The cohort consisted of 107 adults with a mean age of 64.2 years. Patients were taking an average of 2.4 drugs associated with QTc prolongation, with amiodarone, propofol, pipamperone, ondansetron, and mirtazapine being the most frequently involved. There was a significant but weak correlation between measured and predicted absolute QTc values under medication (r2 = 0.262, p < 0.05), as well as for QTc prolongation (r2 = 0.238, p < 0.05). As the developed models are based on a relatively small number of subjects, further research is necessary to ensure their applicability and reliability in real-world scenarios. Overall, this research contributes to the understanding of QTc prolongation and its association with medications, providing insight into the development of predictive models. With improvements, these models could potentially aid healthcare professionals in assessing the risk of QTc prolongation before adding a new drug and in making informed decisions in clinical settings.
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Giovannoni et al. (2024) studied this question.
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