Comparison of two algorithms to support medication surveillance for drug-drug interactions between QTc-prolonging drugs

Berger, F.; Sijs, Heleen van der; Gelder, Teun van; Kuijper, Aaf F.M.; Bemt, Patricia M. L. A. van den; Becker, Matthijs L.

doi:10.1016/j.ijmedinf.2020.104329

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…Nevertheless, the exact risk for developing QT prolongation when using a specific combination of drugs is unknown. There have been some attempts to develop a clinical tool to determine the risk of QT prolongation when using two or more QTc-prolonging drugs with a known risk of TdP [ 90 ], but not without the need for further testing.…”

Section: Acquired Long Qt Syndromementioning

confidence: 99%

“…Although, as seen previously, there are drugs known to prolong the QT interval, the use of these drugs in the absence of other risk factors rarely causes a prolonged QT interval. In patients with no risk factors, the use of a QT-prolonging drug or a combination of these is acceptable in clinical practice [ 90 ]. Therefore, there is a need for a stratification scale to classify patients depending on their risk factors to develop diLQTS.…”

Section: Acquired Long Qt Syndromementioning

confidence: 99%

“…Nonetheless, some algorithms have been developed. A study published in 2021 evaluating the performance of two previously developed algorithms in a substantial dataset found AUROC of 0.81 and 0.73 for the Bindraban and Berger algorithms respectively [ 90 ]. This study considered risk factors such as age, the use of loop diuretics, glomerular filtration rate, serum potassium, serum calcium, female gender, cardiac comorbidities, hypertension, diabetes mellitus and the use of QT-prolonging drugs.…”

Section: Acquired Long Qt Syndromementioning

confidence: 99%

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Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Baracaldo-Santamaría

Llinás-Caballero

Corso-Ramírez

et al. 2021

IJMS

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Long QT syndromes can be either acquired or congenital. Drugs are one of the many etiologies that may induce acquired long QT syndrome. In fact, many drugs frequently used in the clinical setting are a known risk factor for a prolonged QT interval, thus increasing the chances of developing torsade de pointes. The molecular mechanisms involved in the prolongation of the QT interval are common to most medications. However, there is considerable inter-individual variability in drug response, thus making the application of personalized medicine a relevant aspect in long QT syndrome, in order to evaluate the risk of every individual from a pharmacogenetic standpoint.

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Section: Acquired Long Qt Syndromementioning

confidence: 99%

Section: Acquired Long Qt Syndromementioning

confidence: 99%

Section: Acquired Long Qt Syndromementioning

confidence: 99%

See 1 more Smart Citation

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Baracaldo-Santamaría

Llinás-Caballero

Corso-Ramírez

et al. 2021

IJMS

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“…The discriminative ability of the tool is not perfect, so optimization of the tool is required. 22 On the other hand, there is currently a complete lack of discrimination when handling these QT-DDIs. There should be a balance between the number of alerts generated by the CDS systems and its effect on patient care.…”

Section: Development Of a Prediction Modelmentioning

confidence: 99%

“…8 Therefore, a CDS prediction tool was developed to assess the risk of QT-DDIs for developing QTc-prolongation (Figure 1). 21,22 The main aim of this study was to determine the effect of such a CDS tool on the interventions made by pharmacists in primary care. We also explored the usability of the CDS tool in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

Berger¹,

Sijs²,

Gelder

et al. 2021

Therapeutic Advances in Drug Safety

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Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

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Determining sensitivity and specificity of risk scores for QTc interval prolongation in hemato-oncology patients prescribed systemic antifungal therapy: a retrospective cross-sectional study

Steinbrech,

Klein,

Kirschke

et al. 2024

Int J Clin Pharm

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Background QTc interval prolongation can result in potentially lethal arrhythmias. One risk factor is QTc-prolonging drugs, including some antifungals often used in hemato-oncology patients. Screening tools for patients at risk have not yet been investigated in this patient population. Aim Our aim was to evaluate the sensitivity and specificity of five QTc risk scores in hemato-oncology patients receiving systemic antifungal therapy. Method Data were retrieved from an internal study database including adult hemato-oncology patients prescribed systemic antifungal therapy. Data on QTc-prolonging medication, risk factors for QTc prolongation, and electrocardiograms (ECG) were collected retrospectively for a period of 12 months. The QTc risk scores according to Tisdale, Vandael, Berger, Bindraban, and Aboujaoude as well as their sensitivity and specificity were calculated. Results During the evaluated period, 77 patients were prescribed systemic antifungals resulting in 187 therapy episodes. Regarding therapy episodes, median age was 56 years (IQR 44–68), 41% (77) were female, and a median of 3 QTc-prolonging drugs were prescribed (range 0–6). ECGs were available for 45 (24%) of the therapy episodes 3–11 days after initiation of the antifungal therapy, 22 of which showed QTc prolongation. Regarding these 45 therapy episodes, sensitivity and specificity of the risk scores were calculated as follows: Tisdale 86%/22%, Vandael 91%/35%, Berger 32%/83%, Bindraban 50%/78%, Aboujaoude 14%/87%. Conclusion The QTc risk scores according to Tisdale and Vandael showed sufficient sensitivity for risk stratification in the studied patient population. In contrast, risk scores according to Berger, Bindraban, and Aboujaoude cannot be considered suitable due to poor sensitivity.

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Comparison of two algorithms to support medication surveillance for drug-drug interactions between QTc-prolonging drugs

Cited by 5 publications

References 21 publications

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

Determining sensitivity and specificity of risk scores for QTc interval prolongation in hemato-oncology patients prescribed systemic antifungal therapy: a retrospective cross-sectional study

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