Risk Of Torsade De Pointes Research Articles

Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors

ObjectiveA prolonged QTc (LQT) is a surrogate for the risk of torsade de pointes (TdP). QTc interval duration is influenced by sex hormones: oestradiol prolongs and testosterone shortens QTc. Drugs...

Proton Pump Inhibitors and Serum Magnesium Levels in Patients With Torsades de Pointes.

Background: Torsades de pointes (TdP) is a life-threatening ventricular tachycardia occurring in long QT-syndrome patients. It usually develops when multiple QT-prolonging factors are concomitantly present, more frequently drugs and electrolyte imbalances. Since proton–pump inhibitors (PPIs)-associated hypomagnesemia is an increasingly recognized adverse event, PPIs were recently included in the list of drugs with conditional risk of TdP, despite only few cases of TdP in PPI users have been reported so far.Objectives: Aim of the present study is to evaluate whether PPI-induced hypomagnesemia actually has a significant clinical impact on the risk of TdP in the general population.Methods: Forty-eight unselected patients who experienced TdP were consecutively enrolled (2008-2017). Shortly after the first TdP episode, in those patients who did not receive magnesium sulfate and/or potassium or calcium replacement therapy, serum electrolytes were measured and their relationship with PPI usage analyzed.Results: Many patients (28/48, 58%) were under current PPI treatment when TdP occurred. Among TdP patients in whom serum electrolyte determinations were obtained before replacement therapy (27/48), those taking PPIs had significantly lower serum magnesium levels than those who did not. Hypomagnesemia occurred in ~40% of patients receiving PPIs (6/14), in all cases after an extended treatment (>2 weeks). In patients taking PPIs the mean QT-prolonging risk factor number was significantly higher than in those who did not, a difference which was mainly driven by lower magnesium levels.Conclusions: In unselected TdP patients, PPI-induced hypomagnesemia was common and significantly contributed to their cumulative arrhythmic risk. By providing clinical support to current recommendations, our data confirm that more awareness is needed when a PPI is prescribed, specifically as regards the risk of life-threatening arrhythmias.

Torsades de Pointes in Patients with Polymyalgia Rheumatica.

Polymyalgia rheumatica (PMR) represents the most common inflammatory rheumatic disease of the elderly. It is characterized by synovitis of proximal joints and extra-articular synovial structures, along with chronic high-grade systemic inflammation. PMR is closely related to giant cell arteritis (GCA), a large-vessel vasculitis that involves the major branches of the aorta, particularly the extracranial branches of carotid artery including temporal arteries. It is currently believed that PMR and GCA may represent different manifestations of the same disease process. Chronic systemic inflammation is presently recognized as one of the key pathogenic mechanisms underlying cardiovascular disease and associated complications, including cardiac arrhythmias and sudden death. In this regard, several studies demonstrated that besides promoting structural heart disease, inflammatory activation may also be per se arrhythmogenic, via cytokine-mediated effects on cardiac electrophysiology. In particular, increasing evidence points to inflammation as a novel risk factor for QTc prolongation and related life-threatening arrhythmias, specifically Torsade de Pointes (TdP). Starting from the report of two cases of TdP occurring in PMR patients with active disease and elevated circulating IL-6 levels, we here reviewed literature data regarding heart involvement and arrhythmic events in PMR/GCA, as well as TdP risk in inflammatory diseases. Potential underlying mechanisms were dissected, by focusing on the driving role of inflammatory activation.

Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin

Drug-induced cardiac arrhythmia, especially occurrence of torsade de pointes (TdP), has been a leading cause of attrition and post-approval re-labeling and withdrawal of many drugs. TdP is a multifactorial event, reflecting more than just drug-induced cardiac ion channel inhibition and QT interval prolongation. This presents a translational gap in extrapolating pre-clinical and clinical cardiac safety assessment to estimate TdP risk reliably, especially when the drug of interest is used in combination with other QT-prolonging drugs for treatment of diseases such as tuberculosis. A multi-scale mechanistic modeling framework consisting of physiologically based pharmacokinetics (PBPK) simulations of clinically relevant drug exposures combined with Quantitative Systems Toxicology (QST) models of cardiac electro-physiology could bridge this gap. We illustrate this PBPK-QST approach in cardiac risk assessment as exemplified by moxifloxacin, an anti-tuberculosis drug with abundant clinical cardiac safety data. PBPK simulations of moxifloxacin concentrations (systemic circulation and estimated in heart tissue) were linked with in vitro measurements of cardiac ion channel inhibition to predict the magnitude of QT prolongation in healthy individuals. Predictions closely reproduced the clinically observed QT interval prolongation, but no arrhythmia was observed, even at ×10 exposure. However, the same exposure levels in presence of physiological risk factors, e.g., hypokalemia and tachycardia, led to arrhythmic event in simulations, consistent with reported moxifloxacin-related TdP events. Application of a progressive PBPK-QST cardiac risk assessment paradigm starting in early development could guide drug development decisions and later define a clinical “safe space” for post-approval risk management to identify high-risk clinical scenarios.

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.

Validation and Clinical Utility of the hERG IC50:Cmax Ratio to Determine the Risk of Drug-Induced Torsades de Pointes: A Meta-Analysis.

Use of the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG) to predict torsades de pointes (TdP) risk from culprit drugs is neither sensitive nor specific. The ratio of the half-maximum inhibitory concentration of the hERG channel (hERG IC50) to the peak serum concentration of unbound drug (Cmax ) is used during drug development to screen out chemical entities likely to cause TdP. To validate the use of the hERG IC50:Cmax ratio to predict TdP risk from a culprit drug by its correlation with TdP incidence. Medline (between 1966 and March 2017) was accessed for hERG IC50 and Cmax values from the antihistamine, fluoroquinolone, and antipsychotic classes to identify cases of drug-induced TdP. Exposure to a culprit drug was estimated from annual revenues reported by the manufacturer. Inclusion criteria for TdP cases were provision of an ECG tracing that demonstrated QTc prolongation with TdP and normal serum values of potassium, calcium, and magnesium. Cases reported in patients with a prior rhythm disturbance and those involving a drug interaction were excluded. The Meta-Analysis of Observational Studies in Epidemiology checklist was used for epidemiological data extraction by two authors. Negligible risk drugs were defined by an hERG IC50:Cmax ratio that correlated with less than a 5% chance of one TdP event for every 100 million exposures (relative risk [RR] 1.0). The hERG IC50:Cmax ratio correlated with TdP risk (0.312; 95% confidence interval 0.205-0.476, p<0.0001), a ratio of 80 (RR 1.0). The RR from olanzapine is on par with loratadine; ziprasidone is comparable with ciprofloxacin. Drugs with an RR greater than 50 include astemizole, risperidone, haloperidol, and thioridazine. The hERG IC50:Cmax ratio was correlated with TdP incidence for culprit drugs. This validation provides support for the potential use of the hERG IC50:Cmax ratio for clinical decision making in instances of drug selection where TdP risk is a concern.

Prevalence of the risk factors for QT prolongation and associated drug-drug interactions in a cohort of medical inpatients.

Medical inpatients are at increased risk of QT interval prolongation due to multiple risk actors and QT prolonging drugs. This study aimed to identify the prevalence of risk factors for QT prolongation; QT prolonging medications; associated drug-drug interactions (QT-DDIs); their predictors; and TdP (torsades de pointes) risks of drugs. This cohort study was carried out in medical wards of two tertiary hospitals in Khyber Pakhtunkhwa, Pakistan. The QT-DDIs were identified using Micromedex DrugReax® and AZCERT (Arizona Center for Education and Research on Therapeutics) QT drugs lists. AZCERT QT drugs lists were used to identify TdP risks. Logistic regression analysis was performed to identify predictors of QT-DDIs. Total 400 patients were included in this study. The most frequent QT prolonging risk factors included use of ≥1 QT prolonging drugs (74.5%), female gender (55%) and diabetes mellitus (36.3%). Total 487 QT prolonging drugs were identified. According to AZCERT classification, 33.8% of the interacting drugs were included in list-1 (known risk of TdP), 0.9% in list-2 (possible risk of TdP) and 58.8% in list-3 (conditional risk of TdP). The occurrence of QT-DDIs was significantly associated with ≥10 prescribed medications (p=0.01), chronic liver disease (p=0.05), chronic obstructive pulmonary disease (p=0.03), gastroenteritis (p=0.02), antimicrobials (p<0.001), antiemetics (p<0.001) and antinausea (p<0.001). A substantial number of patients were exposed to risk factors for QT prolongation; and QT prolonging drugs such as proton pump inhibitors, antimicrobials and diuretics which may lead to serious outcomes.

Significance of T-wave inversion triggered by spontaneous atrial premature beats in patients with long QT syndrome

In patients with the long QT syndrome (LQTS), a sudden increase in heart rate can cause T-wave alternans (TWA) with beat-to-beat alternating polarity of T wave. We hypothesized that LQTS patients at high risk for torsades de pointes (TdP) may exhibit momentary atrial or sinoatrial premature beat-induced T-wave inversion (APB-TWI). The purpose of this study was to assess the association of APB-TWI with TdP history and with microvolt TWA. Twenty-four-hour continuous 12-lead electrocardiograms (ECGs) were recorded in 18 healthy subjects and 39 consecutive patients with LQTS types 1 (n = 21), 2 (n = 4), 3 (n = 4), and unidentified (n = 10). Peak TWA was determined by the modified moving average method. The 39 LQTS patients were divided into 2 groups: 10 LQTS patients with TdP history (TdP group) and 29 without (non-TdP group). None of the healthy subjects showed APB-TWI, whereas 38.5% of the LQTS patients (15/39) exhibited APB-TWI. The incidences of APB-TWI and TWA ≥42 μV were significantly higher in the TdP group than in the non-TdP group (APB-TWI: 80% vs 24.1%, P = .006; TWA ≥42 μV: 100% vs 65.5%, P = .04). APB-TWI was inferior in sensitivity for an association with TdP history to TWA ≥42 μV (80% vs 100%) but superior in specificity (75.9% vs 51.7%). Patients with APB-TWI exhibited significantly higher TWA values than those without [median (interquartile range) 73 (55-106.5) vs 48 (37.5-71.8) μV, P = .02]. APB-TWI is an easily measurable ECG pattern and is strongly associated with TdP history as well as TWA ≥42 μV in LQTS patients. APB-TWI and TWA may share pathophysiological mechanisms.

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

BackgroundBlockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model.ResultsAn artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs.ConclusionA 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

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

BackgroundCancer 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.MethodsThis 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.ResultsOf 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 < 0.001) and ≥10 medications (p < 0.001), 2 QT drugs (p < 0.001) and ≥3 QT drugs (p < 0.001), breast cancer (p = 0.03), gastrointestinal cancer (p = 0.03), 4–5 supportive care drugs (p < 0.001), 6–8 supportive care drugs (p < 0.001) and >8 supportive care drugs (p < 0.001).ConclusionsA high prevalence of QT prolonging drugs and QT-DDIs was reported in oncology. Appropriate precautions are needed to prevent harmful consequences of these interactions.

Uncertainty Quantification Reveals the Importance of Data Variability and Experimental Design Considerations for in Silico Proarrhythmia Risk Assessment.

The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was <60%, preventing reliable estimation of IC50-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental design considerations that preclude an accurate determination of drug IC50-values in vitro. Thus, we demonstrate that UQ provides valuable information about in silico modeling predictions that can inform future proarrhythmic risk evaluation of drugs under the CiPA paradigm.

Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features.

While pre-clinical Torsades de Pointes (TdP) risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features). Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the in-silico models (derived features). The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD). In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.

Risk of Cardiac Events Associated With Antidepressant Therapy in Patients With Long QT Syndrome

Patients with long QT syndrome (LQTS) are at a high risk of cardiac events. Many patients with LQTS are treated with antidepressant drugs (ADs). We investigated the LQTS genotype-specific risk of recurrent cardiac arrhythmic events (CAEs) associated with AD therapy. The study included 59 LQT1 and 72 LQT2 patients from the Rochester-based LQTS Registry with corrected QT (QTc) prolongation and a history of AD therapy. Using multivariate Anderson-Gill models, we estimated the LQTS genotype-specific risk of recurrent CAEs (ventricular tachyarrhythmias, aborted cardiac arrest, or sudden cardiac death) associated with time-dependent ADs. Specifically, we examined the risk associated with all ADs, selective serotonin reuptake inhibitor (SSRI), and ADs classified on the CredibleMeds list (www.CredibleMeds.org) as "Conditional" or "Known risk of Torsades de pointes (TdP)." After adjusting for baseline QTc duration, sex, and time-dependent beta-blocker usage, there was an increased risk of recurrent CAEs associated with ADs in LQT1 patients (hazard ratio = 3.67, 95% confidence interval 1.98-6.82, p < 0.001) but not in LQT2 patients (hazard ratio = 0.89, 95% confidence interval 0.49-1.64, p = 0.716; LQT1 vs LQT2 interaction, p < 0.001). Similarly, LQT1 patients who were on SSRIs or ADs with "Known risk of TdP" had a higher risk of recurrent CAEs than those patients off all ADs, whereas there was no association in LQT2 patients. ADs with "Conditional risk of TdP" were not associated with the risk of recurrent CAEs in any of the groups. In conclusion, the risk of recurrent CAEs associated with time-dependent ADs is higher in LQT1 patients but not in LQT2 patients. Results suggest a LQTS genotype-specific effect of ADs on the risk of arrhythmic events.

A Comparison of Existing and New Methods for Torsade de Pointes Risk Classification Based on Multi-ion Channel Screening

A Comparison of Existing and New Methods for Torsade de Pointes Risk Classification Based on Multi-ion Channel Screening

Ciprofloxacin does not Prolong the QTc Interval: A Clinical Study in ICU Patients and Review of the Literature.

Ciprofloxacin may prolong the QT interval and increase the risk of Torsade de Pointes (TdP). Intravenous administration of ciprofloxacin in patients with additional risks may elevate the risk of QTc interval prolongation. We prospectively assessed whether intravenous ciprofloxacin prolongs the QT interval in patients with additional co-morbidities and risk factors. We also reviewed the literature on the QT prolonging effect or TdP inducing effect of ciprofloxacin. ICU Patients who were treated with intravenous ciprofloxacin as part of their therapy were recruited. ECG was recorded within 60 min before start and in the last 30 min of 1 h infusion, or within 30 min after the end of ciprofloxacin infusion. QT interval was corrected for the heart rate using both Bazett's and Fridericia's formula. The changes were analyzed using the paired Student's t-test. Ten patients were included in the study (average age 74-y, 6 males). The average baseline QTc interval corrected with Bazett's formula was 448 ms that was shortened during or after ciprofloxacin infusion by 3 ms and 2 ms based on Bazett's (p=0.67) and Fridericia's (p=0.68) formula, respectively. No observational study or cohort study thus far has shown that ciprofloxacin has a QT prolonging effect or increases the risk of TdP or (cardiovascular) mortality. Conclusion. Based on our results and the results of previous studies, it is unlikely that ciprofloxacin has a clinically relevant QT prolonging effect or an increased risk of TdP.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Is prolongation of corrected QT interval associated with seizures induced by electroconvulsive therapy reduced by atropine sulfate?

Electrocardiogram abnormalities have been reported during electroconvulsive therapy (ECT). A corrected QT interval (QTc) prolongation indicates delayed ventricular repolarization, which can trigger ventricular arrhythmias such as torsade de pointes (TdP). We examined the QTc changes during generalized tonic-clonic seizures induced by ECT, and the effects of atropine sulfate on these QTc changes. We analyzed heart rate, QT interval, and QTc in 32 patients with depression who underwent ECT (25 women, 67.4 ± 8.7 years of age). The QTc from -30 to 0 seconds prestimulation was used as baseline, which was compared with QTc at 20-30 seconds and 140-150 seconds poststimulus onset. QTc was significantly prolonged at 20-30 seconds poststimulus, then significantly decreased at 140-150 seconds poststimulus, compared with baseline. QTc prolongation induced by ECT was significantly decreased by atropine sulfate. These data suggest that the risk of TdP may be enhanced by ECT. Further, the risk of cardiac ventricular arrhythmias, including TdP, may be reduced by administration of atropine sulfate.

Safety of Oral Dofetilide Reloading for Treatment of Atrial Arrhythmias.

Although dofetilide labeling states that the drug must be initiated or reinitiated with continuous electrocardiographic monitoring and in the presence of trained personnel, the risks of dofetilide reloading justifying repeat hospitalization have not been investigated. Patients admitted for dofetilide reloading for atrial arrhythmias were retrospectively reviewed. The need for dose adjustment and the incidence of torsades de pointes (TdP) were identified. The incidence of TdP in dofetilide reloading was compared with patients admitted for dofetilide initial loading. Of 138 patients admitted for dofetilide reloading for atrial arrhythmias, 102 were reloaded at a previously tolerated dose, 30 with a higher dose from a previously tolerated dose and 2 at a lower dose; prior dosage was unknown in 4 patients. Dose adjustment or discontinuation was required in 44 patients (31.9%). No TdP occurred in the same dose reloading group, but TdP occurred in 2 patients admitted to increase dofetilide dosage (0% versus 6.7%; P=0.050). Dofetilide dose adjustment or discontinuation was required in 30 of 102 patients (29.4%) reloaded at a previously tolerated dose and in 11 of 30 patients (36.7%) admitted for an increase in dose. Although no TdP occurred in patients admitted to reload dofetilide at the same dose as previously tolerated, dosage adjustments or discontinuation was frequent and support the need for hospitalization for dofetilide reloading. Patients admitted for reloading with a higher dose tended to be at higher risk for TdP than patients reloaded at a prior tolerated dose.

Prevalence of QT interval prolonging drug-drug interactions (QT-DDIs) in psychiatry wards of tertiary care hospitals in Pakistan: a multicenter cross-sectional study.

Background QT prolongation and associated arrhythmias, torsades de pointes (TdP), are considerable negative outcomes of many antipsychotic and antidepressant agents frequently used by psychiatric patients. Objective To identify the prevalence, levels, and predictors of QT prolonging drug-drug interactions (QT-DDIs), and AZCERT (Arizona Center for Education and Research on Therapeutics) classification of drugs involved in QT-DDIs. Setting Psychiatry wards of three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Method This was a multicenter cross-sectional study. Micromedex DrugReax was used for identification of QT-DDIs. TdP risks were identified by the AZCERT classification. Multivariate logistic regression analysis was performed to identify predictors of QT-DDIs. Main outcome measure Prevalence of QT-DDIs (overall, age-wise and gender-wise) and their levels of severity and documentation; AZCERT classes of drugs involved in QT-DDIs; and odds ratios for predictors of QT-DDIs. Results Of 600 patients, 58.5% were female. Median age was 25years (IQR=20-35). Overall 51.7% patients had QT-DDIs. Of total 698 identified QT-DDIs, most were of major-severity (98.4%) and fair-documentation (93.7%). According to the AZCERT classification, 36.4% of the interacting drugs were included in list-1 (known risk of TdP), 26.9% in list-2 (possible risk of TdP) and 27.5% in list-3 (conditional risk of TdP). Drugs commonly involved in QT-DDI were olanzapine (n=146), haloperidol (138), escitalopram (122), risperidone (91), zuclopenthixol (87), quetiapine (n80) and fluoxetine (74). In multivariate logistic regression analysis, QT-DDIs were significantly associated with 6-7 prescribed medications (p=0.04) and >7 medications (p=0.03). Similarly, there was significant association of occurrence of QT-DDIs with 2-3 QT drugs (p<0.001) and >3 QT drugs (p<0.001). Conclusion A considerable number of patients are exposed to QT-DDIs in psychiatry.There is a need to implement protocol for monitoring the outcomes of QT-DDIs.

A review of ECG and QT interval measurement use in a public psychiatric inpatient setting.

There is an increased rate of sudden cardiac death (SCD) in mental health patients. Some antipsychotic medications are known to prolong the QT interval, thus increasing a patient's risk of SCD via the arrhythmia, torsades de pointes (TdP). Our aim was to evaluate assessment for QT prolongation within a public inpatient mental health facility by auditing electrocardiograph (ECG) use. We reviewed records of all mental health inpatient admissions to a public emergency mental health inpatient unit between 1 January 2016 and 11 February 2016. ECG availability was noted and QT interval was manually measured and assessed for risk of TdP using the QT nomogram when present. Demographic information and medication use was collected. Of 263 mental health inpatient admissions, 50 (19%) presentations had an ECG. A total of four (8%) had a prolonged QT interval. Of the 50 patients with an ECG, 12 (24%) were taking medication known to prolong the QT interval. There was very limited risk assessment for QT prolongation in a public hospital psychiatric inpatient unit, with less than 20% of patients having an ECG performed. Our study supports an association between QT-prolonging drugs and a clinically significant prolonged QT interval; however, a larger study with routine ECG screening is required.

Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment.

Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm.