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Frequency, characteristics and risk factors of QT interval prolonging drugs and drug-drug interactions in cancer patients: a multicenter study.

ABSTRACT: BACKGROUND:Cancer patients may receive a high number of medications with the potential to prolong QT interval and subsequent TdP (torsades de pointes). This study aimed to identify the prevalence of QT prolonging drugs, their TdP risk, QT prolonging drug-drug interactions (QT-DDIs), levels, predictors, and TdP risk of drugs involved in QT-DDIs. METHODS:This multicenter study included cancer patients from three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Micromedex DrugReax® was used for identification of QT-DDIs. TdP risks were identified by AZCERT (Arizona Center for Education and Research on Therapeutics) classification. Logistic regression analysis was performed to identify predictors of QT-DDIs. RESULTS:Of 555 patients, 51% were females. Mean age was 46.9?±?15.7 years. Total 28 distinct QT prolonging drugs were identified in 92.6% of the patients. Overall 21.8% patients were presented with QT-DDIs. Of total 288 identified QT-DDIs, all were of major-severity and fair-documentation. According to AZCERT classification, 59.9% of the interacting drugs were included in list-1 (known risk of TdP), 4.7% in list-2 (possible risk of TdP) and 6.8% in list-3 (conditional risk of TdP). Univariate logistic regression analysis showed significant results for various predictors such as, 8-9 prescribed medications (p?8 supportive care drugs (p?

SUBMITTER: Khan Q

PROVIDER: S-EPMC5710059 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Frequency, characteristics and risk factors of QT interval prolonging drugs and drug-drug interactions in cancer patients: a multicenter study.

Khan Qasim Q Ismail Mohammad M Khan Sehrash S

BMC pharmacology & toxicology 20171201 1

<h4>Background</h4>Cancer patients may receive a high number of medications with the potential to prolong QT interval and subsequent TdP (torsades de pointes). This study aimed to identify the prevalence of QT prolonging drugs, their TdP risk, QT prolonging drug-drug interactions (QT-DDIs), levels, predictors, and TdP risk of drugs involved in QT-DDIs.<h4>Methods</h4>This multicenter study included cancer patients from three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Micromed ...[more]

PMID: 29191244

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Project description:BackgroundDrug-induced QT-interval prolongation is associated with occurrence of potentially fatal Torsades de Pointes arrhythmias (TdP). So far, data regarding the overall burden of QT-interval prolonging drugs (QT-drugs) in geriatric patients are limited.ObjectiveThis study was performed to assess the individual burden of QT-interval prolonging drugs (QT-drugs) in geriatric polymedicated patients and to identify the most frequent and risky combinations of QT-drugs.MethodsIn the discharge medication of geriatric patients between July 2009 and June 2013 from the Geriatrics in Bavaria-Database (GiB-DAT) (co)-prescriptions of QT-drugs were investigated. QT-drugs were classified according to a publicly available reference site (CredibleMeds®) as ALL-QT-drugs (associated with any QT-risk) or High-risk-QT-drugs (corresponding to QT-drugs with known risk of Torsades de Pointes according to CredibleMeds®) and in addition as SmPC-high-risk-QT-drugs (according to the German prescribing information (SmPC) contraindicated co-prescription with other QT-drugs).ResultsOf a cohort of 130,434 geriatric patients (mean age 81 years, 67% women), prescribed a median of 8 drugs, 76,594 patients (58.7%) received at least one ALL-QT-drug. Co-prescriptions of two or more ALL-QT-drugs were observed in 28,768 (22.1%) patients. Particularly risky co-prescriptions of High-risk-QT-drugs or SmPC-high-risk-QT-drugs with at least on further QT-drug occurred in 55.9% (N = 12,633) and 54.2% (N = 12,429) of these patients, respectively. Consideration of SmPCs (SmPC-high-risk-QT-drugs) allowed the identification of an additional 15% (N = 3,999) patients taking a risky combination that was not covered by the commonly used CredibleMeds® classification. Only 20 drug-drug combinations accounted for more than 90% of these potentially most dangerous co-prescriptions.ConclusionIn a geriatric study population co-prescriptions of two and more QT-drugs were common. A considerable proportion of QT-drugs with higher risk only could be detected by using more than one classification-system. Local adaption of international classifications can improve identification of patients at risk.

Project description:IntroductionDrug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia known as torsades de pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications are associated with increased risk of TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and lack of direct evidence for LQTS.ObjectiveWe hypothesized that LQTS could be identified latently using an adverse event (AE) fingerprint of more commonly reported AEs. We aimed to generate an integrated data science pipeline that addresses current limitations by identifying latent signals for QT-DDIs in the US FDA's Adverse Event Reporting System (FAERS) and retrospectively validating these predictions using electrocardiogram data in electronic health records (EHRs).MethodsWe trained a model to identify an AE fingerprint for risk of TdP for single drugs and applied this model to drug pair data to predict novel DDIs. In the EHR at Columbia University Medical Center, we compared the QTc intervals of patients prescribed the flagged drug pairs with patients prescribed either drug individually.ResultsWe created an AE fingerprint consisting of 13 latently detected side effects. This model significantly outperformed a direct evidence control model in the detection of established interactions (p = 1.62E-3) and significantly enriched for validated QT-DDIs in the EHR (p = 0.01). Of 889 pairs flagged in FAERS, eight novel QT-DDIs were significantly associated with prolonged QTc intervals in the EHR and were not due to co-prescribed medications.ConclusionsLatent signal detection in FAERS validated using the EHR presents an automated and data-driven approach for systematically identifying novel QT-DDIs. The high-confidence hypotheses flagged using this method warrant further investigation.

Dataset Information

Frequency, characteristics and risk factors of QT interval prolonging drugs and drug-drug interactions in cancer patients: a multicenter study.

Publications

Frequency, characteristics and risk factors of QT interval prolonging drugs and drug-drug interactions in cancer patients: a multicenter study.

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