Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta-analysis

AIMS

Hypoglycaemia is associated with increased cardiovascular risk among individuals with diabetes mellitus. It has been hypothesized that hypoglycaemia may trigger autonomic changes leading to increased cardiac arrhythmia risk. We conducted a systematic review and meta-analysis to explore this association.

MATERIALS AND METHODS

Ovid Medline, Embase, Scopus, Web of Science and Cochrane were searched from inception to October 10, 2017. We included studies of adults with diabetes (Type 1 or Type 2) that compared acute electrocardiogram (ECG) changes during episodes of hypoglycaemia and euglycaemia.

RESULTS

Our search resulted in 4625 citations, among which 20 studies met the predefined inclusion criteria. Finally, 12 studies were included in the descriptive analysis and 15 in the meta-analysis. Overall hypoglycaemia was associated with a reduction in heart rate variability and an increase in arrhythmia occurrence. QTc interval length was more significantly prolonged during hypoglycaemia compared to euglycaemia (pooled mean difference [95% confidence intervals] [0.64 (0.27-1.01], P = ·001). Subgroup analysis based on diabetes type showed that QTc prolongation occurred in individuals with Type 1 and Type 2 diabetes; however, the change between euglycaemia reached statistical significance only among individuals with Type 1 diabetes.

CONCLUSION

Our findings suggest that hypoglycaemia results in ECG alterations that are associated with increased risk of cardiac arrhythmia, which is associated with increased cardiovascular events and mortality. More clinical studies are needed to determine the cardiac risks of hypoglycaemia in individuals with diabetes, especially in Type 2 diabetes.

Long-QT Syndrome and Competitive Sports

Long QT syndrome (LQTS) is an inherited channelopathy which exposes athletes to a risk of sudden cardiac death. Diagnosis is more difficult in this population because: the QT interval is prolonged by training; and the extreme bradycardia frequently observed in athletes makes the QT correction formula less accurate. Based on limited clinical data which tend to demonstrate that exercise, especially swimming, is a trigger for cardiac events, participation in any competitive sports practice is not supported by 2005 European guidelines. However, based on recent retrospective studies and adopting a different medical approach, involving the patient-athlete in shared decision making, the 2015 US guidelines are less restrictive, especially in asymptomatic genotype-positive/phenotype-negative athletes. These guidelines also consider giving medical clearance to competitive sport participation in asymptomatic athletes with appropriate medical therapy.

Bayesian fusion of physiological measurements using a signal quality extension

OBJECTIVE

The fusion of multiple noisy labels for biomedical data (such as ECG annotations, which may be obtained from human experts or from automated systems) into a single robust annotation has many applications in physiologic monitoring. Directly modelling the difficulty of the task has the potential to improve the fusion of such labels. This paper proposes a means for the incorporation of task difficulty, as quantified by 'signal quality', into the fusion process.

APPROACH

We propose a Bayesian fusion model to infer a consensus through aggregating labels, where the labels are provided by multiple imperfect automated algorithms (or 'annotators'). Our model incorporates the signal quality of the underlying recording when fusing labels. We compare our proposed model with previously published approaches. Two publicly available datasets were used to demonstrate the feasibility of our proposed model: one focused on QT interval estimation in the ECG and the other focused on respiratory rate (RR) estimation from the photoplethysmogram (PPG). We inferred the hyperparameters of our model using maximum- a posteriori inference and Gibbs sampling.

MAIN RESULTS

For the QT dataset, our model significantly outperformed the previously published models (root-mean-square error of [Formula: see text] ms for our model versus [Formula: see text] ms from the best existing model) when fusing labels from only three annotators. For the RR dataset, no improvement was observed compared to the same model without signal quality modelling, where our model outperformed existing models (mean-absolute error of [Formula: see text] bpm for our model versus [Formula: see text] bpm from the best existing model). We conclude that our approach demonstrates the feasibility of using a signal quality metric as a confidence measure to improve label fusion.

SIGNIFICANCE

Our Bayesian learning model provides an extension over existing work to incorporate signal quality as a confidence measure to improve the reliability of fusing labels from biomedical datasets.

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care

The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal sensor data. In many real-life clinical applications, no such expert labels are available, and algorithms for processing sensor data must be relied upon, without access to the "ground truth." It is therefore extremely difficult to choose which algorithms to trust or discard at any point in time, where different algorithms may be optimal for different patients, or even for different points in time for the same patient. We propose two fully Bayesian approaches for fusing labels from independent and potentially correlated annotators (i.e., algorithms or, where available, experts). These are generative models to aggregate labels (i.e., the outputs of the algorithms, such as identified ECG morphology) in an unsupervised manner, to estimate jointly the assumed bias and precision of each algorithm without access to the ground truth. The latter fused estimate may then be used to infer the underlying ground truth. For the first time in the biomedical context, we show that modeling correlations between annotators, and fusing information concerning task difficulty (such as the estimated quality of the sensor data), improve these estimates with respect to commonly employed strategies in the literature. Also, we adopt a strongly Bayesian approach to inference using Gibbs sampling to improve estimates over the existing state of the art. We present results from applying the proposed pair of models to simulated and two publicly available biomedical datasets, to demonstrate proof-of-principle. We show that our proposed models outperform all existing approaches recreated from the literature. We also show that the proposed methods are robust when dealing with missing values (as often occurs in real-life biomedical applications), and that they are suitably efficient for use in real-time applications, thereby providing the basis for the reliable use of sensors for personalizing the care of the individual.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.

Importance of QT/RR hysteresis correction in studies of drug-induced QTc interval changes

QT/RR hysteresis and QT/RR adaptation are interlinked but separate physiological processes signifying how quickly and how much QT interval changes when heart rate changes, respectively. While QT interval duration is, as a rule, corrected for heart rate in terms of the QT/RR adaptation, the correction for QT/RR hysteresis is frequently omitted in studies of drug-induced QTc changes. This study used data from previously conducted thorough QT studies to investigate the extent of QTc errors caused by omitting the correction for QT/RR hysteresis, particularly in small clinical investigations. Statistical modeling approach was used to generate 11,000 simulated samples of 10-subject studies in which mixed effect PK/PD models were used to estimate drug-induced QTc changes at mean maximum plasma concentration of investigated compounds. Calculations of QTc intervals involving and omitting QT/RR hysteresis correction were compared. These comparisons showed that ignoring QT/RR hysteresis has two undesirable effects: (A) In the design of subject-specific heart rate corrections (needed in studies of drugs that change heart rate) omission of QT/RR hysteresis may lead to signals of QTc prolongation of more than 10 ms to be missed. (B) Irrespective of whether the investigated drug changes heart rate, omission of QT/RR hysteresis causes the widths of the confidence intervals of the PK/PD predicted QTc interval changes to be increased by 20–30% on average (exceeding 50% in some cases). This may lead to a failure of excluding meaningful QTc prolongation which would be excluded if using hysteresis correction. The study concludes that correction for QT/RR hysteresis should be incorporated into future studies of drug-induced QTc changes. Subject-specific heart rate corrections that omit hysteresis correction may lead to erroneously biased conclusions. Even when using universal (e.g. Fridericia) heart rate correction, hysteresis correction decreases the confidence intervals of QTc changes and thus helps avoiding false positive outcomes.

Cardiac repolarization during fingolimod treatment in patients with relapsing-remitting multiple sclerosis

Background

Fingolimod is a sphingosine-1-phosphate receptor modulator for the treatment of relapsing-remitting multiple sclerosis (RRMS). Despite an established effect on heart rate, the effect of fingolimod on cardiac repolarization is not completely known.

Methods

Twenty-seven patients with RRMS underwent 24-hr ambulatory ECG before fingolimod (baseline), at the day of fingolimod initiation (1D) and after three-month treatment (3M). The mean values of RR-interval as well as QT-interval corrected by Bazzet's (QTcBaz) and Fridericia's (QTcFri) formula were compared between baseline, 1D, and 3M over 24-hr period as well as at daytime and nighttime.

Results

QTcBaz over 24-hr was shorter at 1D (414 ± 20 ms, p < .001) and at 3M (414 ± 20 ms, p < .001) than at baseline (418 ± 20 ms). In contrast, QTcFri over 24-hr was longer at 1D (410 ± 19 ms, p < .001) but similar at 3M (406 ± 19 ms, p = .355) compared to baseline (407 ± 19 ms). Daytime QTcBaz was shorter at 1D (p < .001) and at 3M (p = .007), whereas daytime QTcFri was longer at 1D (p < .05) but similar at 3M (p = ns) compared to baseline. During the night, changes were observed neither in QTcBaz nor in QTcFri between baseline, 1D, and 3M.

Conclusions

Changes in cardiac repolarization after fingolimod initiation were mild and occurred at daytime. Ambiguously, QTcBaz demonstrated shortening, whereas QTcFri showed prolongation in cardiac repolarization after fingolimod initiation. The formula applied for QT-interval correction needs to be taken carefully into account as evaluating pharmacovigilance issues related to fingolimod.

Individualized corrected QT interval is superior to QT interval corrected using the Bazett formula in predicting mutation carriage in families with long QT syndrome

BACKGROUND

Long QT syndrome (LQTS) is characterized by reduced penetrance and variable QT prolongation over time, resulting in an estimate of 25% carriers of a pathogenic mutation with a normal corrected QT (QTc) interval on the resting electrocardiogram (ECG).

OBJECTIVE

The purpose of this study was to test the hypothesis that an individualized corrected QT interval derived from 24-hour Holter data more accurately predicts carriage of a pathogenic LQTS mutation than did QT derived from a standard 12-lead ECG and corrected using the Bazett formula (QTc interval).

METHODS

Carriers of a pathogenic LQTS mutation and their genotype-negative family members who had both resting ECG and Holter recordings available were included. Automated and manual measurements of QTc were performed. QTi was derived from 24-hour Holter recordings and defined as the QT value at the intersection of an RR interval of 1000 ms, with the linear regression line fitted through QT-RR data points of each individual patient.

RESULTS

In total, 69 patients with LQTS (23 long QT type 1, 39 long QT type 2, and 7 long QT type 3) and 55 controls were selected. Demographic characteristics were comparable. A comparison of the receiver operating characteristic curves indicates that the test added diagnostic value compared to manual measurement (P = .02) or automated measurement (P = .005). The diagnostic accuracy of manually measured QTc using conventional cutoff criteria was 72%, while it was 92% using a sex-independent QTi cutoff of 445 ms. This was caused by a 39% increase in sensitivity without compromising the specificity.

CONCLUSION

QTi derived from Holter recordings is superior to conventional QTc measured from a standard 12-lead ECG in predicting the mutation carrier state in families with LQTS.

Universal Correction for QT/RR Hysteresis

INTRODUCTION

Clinical pharmacology QT/QTc studies can be smaller if they more efficiently use the data generated.

OBJECTIVE

The aim was to use large sets of electrocardiograms (ECGs) deposited at the US Food and Drug Administration to investigate the implications of heart rate measurement on the accuracy of QTc data.

METHODS

Using the data of 80 thorough QT studies, we investigated whether placing study subjects in supine positions during short-term time points stabilizes heart rate (part I, based on 73 studies with 747,912 measured ECGs in 6786 healthy subjects) and whether heart rate measurements different from RR intervals captured simultaneously with QT intervals decrease QTc variability (part II, based on seven studies with 897,570 ECG measurements in 751 healthy subjects).

RESULTS

In the part I data, when subjects were placed in supine undisturbed positions, heart rate instability (max-min of repeatedly measured heart rates within the same study time point) exceeding 5 beats per minute (bpm) was observed 40 % of the time and exceeded 10 bpm 10 % of the time. In the part II data, even when including QT measurements preceded by variable heart rates, correction of QT durations for RR interval values derived through a simple QT/RR hysteresis model with 95 % adaptation in 120 s led to mean intra-subject standard deviation of QTc (Fridericia formula) of only 7.14 ± 1.98 and 6.38 ± 1.50 ms in women and men, respectively.

CONCLUSION

The QT/RR hysteresis model with 95 % adaptation in 120 s is universally applicable to healthy subjects, providing small QTc variability. Supine positions do not generally stabilize heart rates in healthy subjects. Universally applicable QT/RR hysteresis correction allows clinical QT/QTc studies to include variable heart rate episodes in the time points.

Modeling the effects of amiodarone on short QT syndrome variant 2 in the human ventricles

AIMS

The short QT syndrome (SQTS) is a new genetic disorder associated with atrial and ventricular arrhythmias and sudden death. The SQT2, SQTS variant, results from a gain-of-function mutation (V307L) in the KCNQ1-encoded potassium channel. Although pro-arrhythmogenic effects of SQTS have been characterized, less is known about the pharmacology of SQTS. Therefore, this study aims to assess the effects of amiodarone on SQT2.

METHODS AND RESULTS

The ten Tusscher et al. model of the human ventricular action potential (AP) was modified to incorporate changes to I_Ks based on experimental data. Cell models were incorporated into heterogeneous one-dimensional (1D) tissue to compute the pseudo-ECG and the corresponding QT interval. The blocking effects of amiodarone on I_Ks, I_Na, I_NaK, I_CaL, I_NaCa, and I_Kr were modeled using n_H (Hill coefficient) and IC₅₀ values from the literature. At the cellular level, amiodarone both at low and high doses prolonged the SQT2 AP duration (APD); at the tissue level, amiodarone at a high dose caused QT prolongation to the physiological range, but failed at a low dose.

CONCLUSIONS

Amiodarone at a high dose produced better therapeutic effects on SQT2 than at a low dose. This study provides new evidence that amiodarone at a high dose may be a potential pharmacological treatment for SQT2.

Impact of the Norepinephrine Prodrug Droxidopa on the QTc Interval in Healthy Individuals

A double‐blind, 4‐period crossover study (NCT01327066) was conducted to assess the effect of the novel norepinephrine prodrug droxidopa on the QT interval in in healthy subjects. Subjects were randomized to receive a single dose of droxidopa 600 mg (maximal dose) and 2000 mg (supratherapeutic dose) compared with the positive control, moxifloxacin 400 mg, and placebo, each separated by a 3‐day washout period. Patients were monitored by continuous Holter monitoring, and electrocardiograms (ECGs) were extracted 0.5–23 hours after dosing. Blood samples for pharmacokinetic analysis were collected before dosing and after ECG data collection. The primary end point was the time‐matched placebo‐adjusted change from baseline in the individually corrected QT (QTcI). The time‐averaged QTcI mean placebo‐corrected changes from baseline for droxidopa 600 and 2000 mg were 0.1 milliseconds (90%CI, ‐0.9 to 1.0 milliseconds) and 0.3 milliseconds (90%CI, ‐0.6 to 1.3 milliseconds), respectively, and 9 milliseconds (90%CI, 8.4–10.3 milliseconds) for moxifloxacin. This study found no effect of either dose of droxidopa on cardiac repolarization using QTcI. Analysis of the pharmacokinetic/pharmacodynamic relationship and cardiac repolarization showed no association with droxidopa exposure. There were no clinically relevant effects of droxidopa on heart rate, atrioventricular conduction, or cardiac depolarization identified. No morphologic ECG changes were observed.

Optimizing QT Interval Measurement for the Preparticipation Screening of Young Athletes

PURPOSE

Sudden cardiac death is the leading cause of death in athletes. Long QT syndrome (LQTS) is one of the most common cardiogenetic diseases that can lead to sudden cardiac death and is identified by QT interval prolongation on an ECG. Recommendations for QT monitoring in athletes are adopted from nonathlete populations. To improve screening, ECG data of athletes are assessed to determine a more appropriate method for QT interval estimation.

METHODS

ECG (CardeaScreen) data were collected from June 2010 to March 2015. ECG data with HR greater than 100 bpm were excluded. Fiducial points of outliers were manually corrected if the QRS onset or the T wave offset was misidentified. A model of best fit was determined and compared across four QT correction factors. Classification analysis was used to compare the Bazett's corrected QT interval to the 99th percentile of uncorrected QT interval.

RESULTS

High school (n = 597), college (n = 1207), and professional athletes (n = 273) (N = 2077) were analyzed. Mean age was 19 ± 3.5 yr. QT interval varied by cohort (HS = 388 ± 30, Col = 410 ± 33, Pro = 407 ± 27, p < 0.0001). A nonlinear power function with a cubic exponent of -0.349 fit the data the best (R = 0.64). Of the four common correction factors, Fridericia had the lowest residual dependence to HR (m = -0.10). With standard screening, 75% of athletes within the top 1% for QT interval were not identified for further investigation for LQTS.

CONCLUSION

Up to 75% of athletes possessing an uncorrected QT interval greater than 99% of the population are not identified for investigation for LQTS using the recommended criteria. We propose a new method of risk stratification that replaces QT interval correction. Further study is needed to establish QT interval distributions and risk thresholds in athletes.

Prevalence and Risk Factors of Drug-Associated Corrected QT Prolongation in Elderly Hospitalized Patients: Results of a Retrospective Analysis of Data Obtained Over 6 Months

OBJECTIVES

Little information exists on the frequency and determinants of drug-associated long QT syndrome in older adults. The objectives of this study were to assess the prevalence and identify risk factors of drug-associated long QT syndrome in a population of elderly hospitalized patients.

METHODS

This was a retrospective study performed over 6 months in hospital geriatric medicine. Various QT-correction equations were fitted to the individual QT-RR data to evaluate the most appropriate equation. Long QT syndrome was defined as corrected QT ≥450 ms. Available data were compared in patients with and without long QT syndrome. Logistic regression and classification and regression tree analysis were performed to identify determinants of long QT syndrome.

RESULTS

Thirty-three of 152 patients (22%) exhibited corrected QT ≥450 ms. The different QT correction equations provided similar results, except the Bazett equation. In patients with long QT syndrome, there was a higher proportion of male subjects (58 vs. 33%, p = 0.009) and a higher number of QT-prolonging drugs than in patients without long QT syndrome. Male sex (odds ratio, 3.25) and the number of prescribed QT-prolonging agents (odds ratio, 1.77) were significantly associated with the probability of long QT syndrome. The number of QT-prolonging drugs had a stronger influence on the risk of long QT syndrome in men than in women.

CONCLUSION

Male sex was found to be a significant risk factor of corrected QT prolongation in elderly hospitalized patients. The risk also increased with the number of QT-prolonging agents, especially in men. Those findings may help to mitigate the risk of long QT syndrome in elderly patients in clinical practice.

Categorization and theoretical comparison of quantitative methods for assessing QT/RR hysteresis

BACKGROUND

In the human electrocardiogram, there is a lag of adaptation of the QT interval to heart rate changes, usually termed QT/RR hysteresis (QT-hys). Subject-specific quantifiers of QT-hys have been proposed as potential biomarkers, but there is no consensus on the choice of the quantifier.

METHODS

A comprehensive literature search was conducted to identify original articles reporting quantifiers of repolarization hysteresis from the surface ECG in humans.

RESULTS

Sixty articles fulfilled our inclusion criteria. Reported biomarkers were grouped under four categories. A simple mathematical model of QT/RR loop was used to illustrate differences between the methods. Category I quantifiers use direct measurement of QT time course of adaptation. They are limited to conditions where RR intervals are under strict control. Category IIa and IIb quantifiers compare QT responses during consecutive heart rate acceleration and deceleration. They are relevant when a QT/RR loop is observed, typically during exercise and recovery, but are not robust to protocol variations. Category III quantifiers evaluate the optimum RR memory in dynamic QT/RR relationship modeling. They estimate an intrinsic memory parameter independent from the nature of RR changes, but their reliability remains to be confirmed when multiple memory parameters are estimated. Promising approaches include the differentiation of short-term and long-term memory and adaptive estimation of memory parameters.

CONCLUSION

Model-based approaches to QT-hys assessment appear to be the most versatile, as they allow separate quantification of QT/RR dependency and QT-hys, and can be applied to a wide range of experimental settings.

New approach for visualization of relationships between RR and JT intervals

This paper presents the concept of perfect matrices of Lagrange differences which are used to analyze relationships between RR and JT intervals during the bicycle ergometry exercise. The concept of the perfect matrix of Lagrange differences, its parameters, the construction of the load function and the corresponding optimization problem, the introduction of internal and external smoothing, embedding of the scalar parameter time series into the phase plane—all these computational techniques allow visualization of complex dynamical processes taking place in the cardiovascular system during the load and the recovery processes. Detailed analysis is performed with one person’s RR and JT records only—but the presented techniques open new possibilities for novel interpretation of the dynamics of the cardiovascular system.

Estimation of the QT-RR relation: trade-off between goodness-of-fit and extrapolation accuracy

Correction of the QT interval in the ECG for changes in heart rate (RR interval) is needed to compare groups of patients and assess the risk of sudden cardiac death. The QTc represents the QT interval at 60 bpm, although most patients typically have a faster heart rate, thus requiring extrapolation of the QT-RR relationship.

OBJECTIVE

This paper investigates the ability of QT-RR models with increasing number of parameters to fit beat-to-beat variations in the QT interval and provide a reliable estimate of the QTc.

APPROACH

One-, two- and three-parameter functions generalising the Bazett and Fridericia formulas were used in combination with hysteresis reduction (memory) obtained by time-averaging the history of RR intervals with exponentially-decaying weights. In normal men and women datasets of Holter recordings in normal subjects (24 h monitoring), two measures were computed for each model: the root mean square error (RMSE) of fitting and the difference between the estimated QTc and a reference QTc obtained by collecting data points around RR  =  1000 ms.

MAIN RESULTS

The two- and three-parameter functions all gave similar low RMSE with uncorrelated residues. An optimal memory parameter was found that still minimized the RMSE and could be used for all functions and subjects. This reduction in RMSE resulted from changes in the parameters linked to the increased steepness of the QT-RR relation after hysteresis reduction. At optimal memory, the two and three-parameter models provided poorer prediction of the QTc as compared to the Fridericia's model in subjects with fast heart rates, since accurate representation of the steeper QT-RR relation worsened the extrapolation that was then needed to determine the QTc.

SIGNIFICANCE

As a result, among all models investigated, the Fridericia formulation offered the best trade-off for QTc prediction robust to memory and fast heart rates.

Long QTc Interval and Torsade de Pointes Caused by Fluconazole

Objective:

To describe a patient who developed torsade de pointes while being treated with fluconazole.

Case Summary:

A 33-year-old woman with a 5 year history of systemic lupus erythematosus was admitted to the intensive care unit because of respiratory insufficiency due to Candida albicans pneumonia. Therapy with intravenous fluconazole 200 mg/day, with dose later adjusted according to her renal function, was started. Prolongation of the QTc interval and torsade de pointes occurred. Initially, domperidone, which had been initiated the day before fluconazole, was suspected as the possible cause and was discontinued; ultimately, both drugs were discontinued. However, torsade de pointes recurred several weeks later when the patient was treated with fluconazole for a second time and disappeared again on withdrawal of the drug. According to the Naranjo probability scale, this adverse reaction was highly probable.

Discussion:

The risk of torsade de pointes does not correlate in a linear fashion with prolongation of the QTc interval, but an interval beyond 500 msec is considered a significant risk factor. Given that both fluconazole and domperidone are metabolized by the cytochrome P450 system, they may intensify each other's proarrhythmic effects, particularly in patients with concurrent renal dysfunction. These risks are of particular concern in patients whose baseline QTc interval is prolonged for any reason.

Conclusions:

From the case history, as well as use of the Naranjo scale, we concluded that fluconazole was the highly probable cause of the development of torsade de pointes in our patient.

Influence of Oral Progesterone Administration on Drug-Induced QT Interval Lengthening: A Randomized, Double-Blind, Placebo-Controlled Crossover Study

OBJECTIVES

We tested the hypothesis that oral progesterone administration attenuates drug-induced QT interval lengthening.

BACKGROUND

Evidence from preclinical and human investigations suggests that higher serum progesterone concentrations may be protective against drug-induced QT interval lengthening.

METHODS

In this prospective, double-blind, crossover study, 19 healthy female volunteers (21-40 years) were randomized to receive progesterone 400 mg or matching placebo orally once daily for 7 days timed to the menses phase of the menstrual cycle (between-phase washout period = 49 days). On day 7, ibutilide 0.003 mg/kg was infused over 10 minutes, after which QT intervals were recorded and blood samples collected for 12 hours. Prior to the treatment phases, subjects underwent ECG monitoring for 12 hours to calculate individualized heart rate-corrected QT intervals (QT_cI).

RESULTS

Fifteen subjects completed all study phases. Maximum serum ibutilide concentrations in the progesterone and placebo phases were similar (1247±770 vs 1172±709 pg/mL, p=0.43). Serum progesterone concentrations were higher during the progesterone phase (16.2±11.0 vs 1.2±1.0 ng/mL, p<0.0001), while serum estradiol concentrations in the two phases were similar (89.3±62.8 vs 71.8±31.7 pg/mL, p=0.36). Pre-ibutilide lead II QT_cI was significantly lower in the progesterone phase (412±15 vs 419±14 ms, p=0.04). Maximum ibutilide-associated QT_cI (443±17 vs 458±19 ms, p=0.003), maximum percent increase in QT_cI from pretreatment value (7.5±2.4 vs 9.3±3.4%, p=0.02) and area under the effect (QT_cI) curve during the first hour post-ibutilide (497±13 vs 510±16 ms-hr, p=0.002) were lower during the progesterone phase. Progesterone-associated adverse effects included fatigue/malaise and vertigo.

CONCLUSIONS

Oral progesterone administration attenuates drug-induced QT_cI lengthening.

Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety?

Safety pharmacology aims to predict rare side effects of new drugs. We explored whether rare pro-arrhythmic effects could be linked to the variability of the effects of these drugs on ion currents and whether taking into consideration this variability in computational models could help to better detect and predict cardiac side effects. For this purpose, we evaluated how intra- and inter-individual variability influences the effect of hERG inhibition on both the action potential duration and the occurrence of arrhythmias. Using two computer simulation models of human action potentials (endocardial and Purkinje cells), we analyzed the contribution of two biological parameters on the pro-arrhythmic effects of several hERG channel blockers: (i) spermine concentration, which varies with metabolic status, and (ii) L-type calcium conductance, which varies due to single nucleotide polymorphisms or mutations. By varying these parameters, we were able to induce arrhythmias in 1 out of 16 simulations although conventional modeling methods to detect pro-arrhythmic molecules failed. On the basis of our results, taking into consideration only 2 parameters subjected to intra- and inter-individual variability, we propose that in silico computer modeling may help to better define the risks of new drug candidates at early stages of pre-clinical development.

Recording duration and short-term reproducibility of heart rate and QT interval variability in patients with myocardial infarction

Beat-to-beat variability of the QT interval (QTV) measured on surface ECG has emerged as a potential marker for ventricular repolarization instability and has been used along with heart rate variability (HRV) to predict arrhythmic risk. Since measurement modalities of QTV have not been standardized, the objective of this study was to investigate the effect of ECG recording duration on QTV as well as HRV. Using a database of 30 min ECG recorded from 500 patients with acute myocardial infraction during rest, we extracted RR and QT interval time series and estimated different HRV and QTV metrics over windows of varying length. Analysis of variance (ANOVA) and intra-class correlation analyses were computed to investigate the effect of recording length on consistency and short-term reproducibility of HRV and QTV variables. Good consistency (non-significant ANOVA results) and short-term reproducibility (intra-class correlation coefficients  >0.8) were demonstrated for all but standard deviation based metrics when at least 200 beats were included in the estimation. In conclusion, QTV can be quantified from resting ECG with good short-term consistency and reproducibility that is comparable to that of HRV.

A thorough QT study to evaluate the QTc prolongation potential of two neuropsychiatric drugs, quetiapine and escitalopram, in healthy volunteers

Prolongation of the QT interval on an ECG is a surrogate marker for predicting the proarrhythmic potential of a drug under development. The aim of this study was to evaluate the QTc prolongation potential of two neuropsychiatric drugs, quetiapine immediate release (IR) and escitalopram, in healthy individuals. This was a randomized, open-label, 4×4 Williams crossover study, with four single-dose treatments [placebo, 400 mg moxifloxacin (positive control), 20 mg escitalopram, and 100 mg quetiapine IR], conducted in 40 healthy volunteers. Serial blood samples for pharmacokinetics and ECG were collected. Individually, RR-corrected QTc intervals (QTcI) and placebo-adjusted changes from baseline values of QTcI (ΔΔQTcI) were evaluated. Lower-bound values of the one-sided 95% confidence interval for ΔΔQTcI of moxifloxacin with more than 5 ms confirmed the sensitivity of the assay. The maximum upper bound 95% confidence interval for the ΔΔQTcI of quetiapine IR and escitalopram was 13.7 and 10.5 ms, with mean estimates of 10.2 and 6.9 ms, respectively. Peak effects of moxifloxacin and quetiapine IR on ΔΔQTcI were observed at approximately time to maximum concentration (Tmax), whereas that of escitalopram was observed 3 h after Tmax. The concentration-ΔΔQTcI relationships of quetiapine IR and escitalopram were relatively flat, as compared with that of moxifloxacin. The results demonstrated the validity of trial methodology and that quetiapine IR and escitalopram caused QT prolongation in healthy individuals. In addition, hysteresis of escitalopram-induced QTc prolongation. These results indicate that higher doses of these drugs could lead to greater QT prolongation in a dose-response manner.

Which QT Correction Formulae to Use for QT Monitoring?

Background

Drug safety precautions recommend monitoring of the corrected QT interval. To determine which QT correction formula to use in an automated QT‐monitoring algorithm in our electronic medical record, we studied rate correction performance of different QT correction formulae and their impact on risk assessment for mortality.

Methods and Results

All electrocardiograms (ECGs) in patients >18 years with sinus rhythm, normal QRS duration and rate <90 beats per minute (bpm) in the University Hospitals of Leuven (Leuven, Belgium) during a 2‐month period were included. QT correction was performed with Bazett, Fridericia, Framingham, Hodges, and Rautaharju formulae. In total, 6609 patients were included (age, 59.8±16.2 years; 53.6% male and heart rate 68.8±10.6 bpm). Optimal rate correction was observed using Fridericia and Framingham; Bazett performed worst. A healthy subset showed 99% upper limits of normal for Bazett above current clinical standards: men 472 ms (95% CI, 464–478 ms) and women 482 ms (95% CI 474–490 ms). Multivariate Cox regression, including age, heart rate, and prolonged QTc, identified Framingham (hazard ratio [HR], 7.31; 95% CI, 4.10–13.05) and Fridericia (HR, 5.95; 95% CI, 3.34–10.60) as significantly better predictors of 30‐day all‐cause mortality than Bazett (HR, 4.49; 95% CI, 2.31–8.74). In a point‐prevalence study with haloperidol, the number of patients classified to be at risk for possibly harmful QT prolongation could be reduced by 50% using optimal QT rate correction.

Conclusions

Fridericia and Framingham correction formulae showed the best rate correction and significantly improved prediction of 30‐day and 1‐year mortality. With current clinical standards, Bazett overestimated the number of patients with potential dangerous QTc prolongation, which could lead to unnecessary safety measurements as withholding the patient of first‐choice medication.

Latent pathogenicity of the G38S polymorphism of KCNE1 K+ channel modulator

KCNE1 encodes a modulator of KCNQ1 and KCNH2 channels. Although KCNE1(G38S), a single-nucleotide polymorphism (SNP) causing a G38S substitution in KCNE1, is found frequently, whether and how this SNP causes long QT syndrome (LQTS) remains unclear. We evaluated rate-dependent repolarization dynamics using Holter electrocardiogram (ECG) to assess the pathogenicity of KCNE1(G38S). Forty-five patients exhibiting long QT intervals, as assessed by their baseline ECGs, and 16 control subjects were enrolled. KCNE1(G38S) carriers were identified using genome sequencing. LQTS patients were classified into LQT1 or LQT2 using genetic analysis or epinephrine test. QT-RR relations were determined using 24-h Holter ECG recordings. Among the 15 patients (33.3 %) with KCNE1(G38S), four patients without any mutations or amino acid changes in other major cardiac ion channels were categorized as KCNE1(G38S) carriers. In the QT-RR regression lines, the QT-RR slope was greater in the KCNE1(G38S) carriers and the LQT2 patients (0.215 ± 0.021 and 0.207 ± 0.032, respectively) than in the LQT1 patients (0.163 ± 0.014, P < 0.05) and the control subjects (0.135 ± 0.025, P < 0.001). The calculated QT intervals at an RR interval of 1200 ms were longer in the KCNE1(G38S) carriers and LQT1 and LQT2 patients than in the control subjects. Patients with KCNE1(G38S) had a rate-dependent repolarization abnormality similar to patients with LQT2 and, therefore, may have a potential risk to develop lethal arrhythmias.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

ECG patterns in early pulseless electrical activity-Associations with aetiology and survival of in-hospital cardiac arrest

INTRODUCTION

Pulseless electrical activity (PEA) is an increasingly common presentation in cardiac arrest. The aim of this study was to investigate possible associations between early ECG patterns in PEA and the underlying causes and survival of in-hospital cardiac arrest (IHCA).

METHODS

Prospectively observed episodes of IHCA presenting as PEA between January 2009 and august 2013, with a reliable cause of arrest and corresponding defibrillator ECG recordings, were analysed. QRS width, QT interval, Bazett's corrected QT interval, presence of P waves and heart rate (HR) was determined. QRS width and HR were considered to be normal below 120ms and within 60-100 cardiac cycles per minute, respectively.

RESULTS

Fifty-one episodes fulfilled the inclusion criteria. The defibrillator was attached after a median of one minute (75th percentile; 3min) after the onset of arrest. Ninety percent (46/51) had widened QRS complexes, 63% (32/51) were defined as 'wide-slow' due to QRS-widened bradycardia, and only 6% (3/51) episodes were categorized as normal. No unique cause-specific ECG pattern could be identified. Further 7 episodes with a corresponding defibrillator file, but without a reliable cause, were included in analysis of survival. Abnormal ECG patterns were seen in all survivors. None of the patients with 'normal' PEA survived.

CONCLUSION

Abnormal ECG patterns were frequent at the early stage of in-hospital PEA. No unique patterns were associated with the underlying causes or survival.

Major rapid weight loss induces changes in cardiac repolarization

INTRODUCTION

Obesity is associated with increased all-cause mortality, but weight loss may not decrease cardiovascular events. In fact, very low calorie diets have been linked to arrhythmias and sudden death. The QT interval is the standard marker for cardiac repolarization, but T-wave morphology analysis has been suggested as a more sensitive method to identify changes in cardiac repolarization. We examined the effect of a major and rapid weight loss on T-wave morphology.

METHODS AND RESULTS

Twenty-six individuals had electrocardiograms (ECG) taken before and after eight weeks of weight loss intervention along with plasma measurements of fasting glucose, HbA1c, and potassium. For assessment of cardiac repolarization changes, T-wave Morphology Combination Score (MCS) and ECG intervals: RR, PR, QT, QTcF (Fridericia-corrected QT-interval), and QRS duration were derived. The participants lost on average 13.4% of their bodyweight. MCS, QRS, and RR intervals increased at week 8 (p<0.01), while QTcF and PR intervals were unaffected. Fasting plasma glucose (p<0.001) and HbA1c both decreased at week 8 (p<10(-5)), while plasma potassium was unchanged. MCS but not QTcF was negatively correlated with HbA1c (p<0.001) and fasting plasma glucose (p<0.01).

CONCLUSION

Rapid weight loss induces changes in cardiac repolarization. Monitoring of MCS during calorie restriction makes it possible to detect repolarization changes with higher discriminative power than the QT-interval during major rapid weight loss interventions. MCS was correlated with decreased HbA1c. Thus, sustained low blood glucose levels may contribute to repolarization changes.

Differences in the Slope of the QT-RR Relation Based on 24-Hour Holter ECG Recordings between Cardioembolic and Atherosclerotic Stroke

Objective Detecting paroxysmal atrial fibrillation in patients with ischemic stroke presenting in sinus rhythm is difficult because such episodes are often short, and they are also frequently asymptomatic. It is possible that the ventricular repolarization dynamics may reflect atrial vulnerability and cardioembolic stroke. Hence, we compared the QT-RR relation between cardioembolic stroke and atherosclerotic stroke during sinus rhythm. Methods The subjects comprised 62 consecutive ischemic stroke patients including 31 with cardioembolic strokes (71.8±12.7 years, 17 men) and 31 with atherosclerotic strokes (74.8±10.8 years, 23 men). The QT and RR intervals were measured from ECG waves based on a 15-sec averaged ECG during 24-hour Holter recording using an automatic QT analyzing system. The QT interval dependence on the RR interval was analyzed using a linear regression line for each subject ([QT]=A[RR]+B; where A is the slope and B is the y-intercept). Results The mean slope of the QT-RR relation was significantly greater in cardioembolic stroke than in atherosclerotic stroke (0.187±0.044 vs. 0.142±0.045, p<0.001). The mean QT, RR, or QTc during 24-hour Holter recordings did not differ between them. An increased slope (≥0.14) of the QT-RR regression line could predict cardioembolic stroke with 97% sensitivity, 55% specificity and a positive predictive value of 64%. Conclusion The increased slope of the QT-RR linear regression line based on 24-hour Holter ECG in patients with ischemic stroke presenting in sinus rhythm may therefore be a simple and useful marker for cardioembolic stroke.

QT Interval Variability Index and QT Interval Duration in Different Sleep Stages: Analysis of Polysomnographic Recordings in Nonapneic Male Patients

The aim of the study was to determine whether different sleep stages, especially REM sleep, affect QT interval duration and variability in male patients without obstructive sleep apnea (OSA). Polysomnographic recordings of 30 patients were analyzed. Beat-to-beat QT interval variability was calculated using QTV index (QTVI) formula. For QTc interval calculation, in addition to Bazett's formula, linear and parabolic heart rate correction formulas with two separate α values were used. QTVI and QTc values were calculated as means of 2 awake, 3 NREM, and 3 REM sleep episodes; the duration of each episode was 300 sec. Mean QTVI values were not statistically different between sleep stages. Therefore, elevated QTVI values found in patients with OSA cannot be interpreted as physiological sympathetic impact during REM sleep and should be considered as a risk factor for potentially life-threatening ventricular arrhythmias. The absence of difference of the mean QTc interval values between NREM and REM stages seems to confirm our conclusion that sympathetic surges during REM stage do not induce repolarization variability. In patients without notable structural and electrical remodeling of myocardium, physiological elevation in sympathetic activity during REM sleep remains subthreshold concerning clinically significant increase of myocardial electrical instability.

QT dynamics evaluated on fully automated QT measurement in children

BACKGROUND

The association between QT interval and heart rate (HR), QT dynamics, has been reported to vary greatly between individuals. We investigated QT dynamics using fully automated QT interval measurement with a commercially available multifunctional electrocardiogram (ECG) recorder.

METHODS

The study group included 17 otherwise healthy subjects (mean age, 12.7 years; 11 girls, 6 boys), who were undergoing exercise test for arrhythmia evaluation. The subjects had single premature ventricular contraction.

RESULTS

Mean corrected QT interval (Fridericia) at rest was 412 ± 19 ms (male, 408 ± 20 ms; female, 414 ± 19 ms), manually measured with a digitizer, using the tangent method. We assessed QT interval/HR slope during the whole exercise test, the exercise phase, and the recovery from exercise phase; the slopes were calculated offline after excluding wide QRS complex and were -1.15 ± 0.26 (r(2) = 0.65), -1.18 ± 0.30 (r(2) = 0.62), and -1.11 ± 0.25 (r(2) = 0.70), respectively. Estimated QT interval at HR 60 beats/min (QT60) was calculated using the regression line formula and was 383 ± 24 ms, 387 ± 28 ms, and 375 ± 21 ms, respectively. QT60 of the total exercise test correlated with the corrected QT interval at rest (P = 0.04, r(2) = 0.25).

CONCLUSIONS

Assessment of QT dynamics is feasible on fully automated QT interval measurement with this ECG recorder. Further studies are required in larger populations to confirm the accuracy and precision of QT measurement and QT dynamics using this new multifunctional ECG.

Abnormal repolarization dynamics in a patient with KCNE1(G38S) who presented with torsades de pointes

Risk of G38S, major KCNE1 polymorphism [KCNE1(G38S)], for long QT syndrome (LQTS) remains unclear. A 72-year-old woman was admitted with recurrent torsades de pointes (TdP). She had remarkable QT prolongation (corrected QT interval 568 ms) under conditions of hypokalemia and hypomagnesemia. After correction of this electrolytic imbalance, TdP was suppressed and metoprolol was started. The QT-RR slope in 24-hour Holter electrocardiogram was steep and this enhanced bradycardia-dependent QT prolongation was similar to that in LQTS. She carried KCNE1(G38S). Patients with KCNE1(G38S) could have similar potential risk of ventricular arrhythmia as with LQTS. Analysis of QT-RR relationship could also evaluate the latent arrhythmogenicity of KCNE1(G38S).

Time-Dependent Changes in QT Dynamics after Initiation and Termination of Paroxysmal Atrial Fibrillation

BACKGROUND

Little is known about time-dependent changes in QT dynamics after initiation of atrial fibrillation (AF) and after restoration of sinus rhythm (SR) in patients with paroxysmal AF.

METHODS

Beat-to-beat QT and RR intervals in CM5 lead were measured automatically in 13 patients with both AF and SR on the single 24-hour Holter electrocardiology recording. QT-RR relation was analyzed at six periods of time: 1 hour before AF onset (Pre(0-1h)), 0-1 hour and 4-5 hours after AF onset (AF(0-1h) and AF(4-5h)), and 0-1 hour, 2-3 hours, and 4-5 hours after the restoration of SR (SR(0-1h), SR(2-3h), and SR(4-5h)).

RESULTS

QT-RR slope was gradually decreased after AF onset and gradually returned to the baseline level after restoration of SR. The slope became greater at SR(4-5h) than at AF(4-5h) and AF(0-1h). In patients receiving antiarrhythmic drugs (AADs; n = 5), QT-RR slope was greater at SR(4-5h) than in those not receiving AADs (n = 8).

CONCLUSION

In patients with paroxysmal AF, bradycardia-dependent QT prolongation was attenuated during AF, and was corrected and gradually augmented along with continuation of SR, especially in patients receiving AADs. This could increase the risk of developing torsade de pointes.

Evaluation of Index of Cardio-Electrophysiological Balance (iCEB) as a New Biomarker for the Identification of Patients at Increased Arrhythmic Risk

BACKGROUND

Recently a new risk marker for drug-induced arrhythmias called index of cardio-electrophysiological balance (iCEB), measured as QT interval divided by QRS duration, was evaluated in an animal model. It was hypothesized that iCEB is equivalent to the cardiac wavelength λ (λ = effective refractory period (ERP) x conduction velocity) and that an increased or decreased value of iCEB would potentially predict an increased susceptibility to TdP or non-TdP mediated VT/VF, respectively.

METHODS

First, the correlation between QT interval and ERP was evaluated by invasively measuring ERP during a ventricular stimulation protocol in humans (N = 40). Then the effect of administration of sotalol and flecainide on iCEB was measured in 40 patients with supraventricular tachycardias. Finally iCEB was assessed in carriers of a long QT syndrome (LQTS, N = 70) or Brugada syndrome (BrS, N = 57) mutation and compared them with genotype negative family members (N = 65).

RESULTS

The correlation between QT interval and ERP was established (Pearson R(2) = 0.25) which suggests that iCEB≈ERPxCV≈QT/QRS. Sotalol administration increased iCEB (+ 0.23; P = 0.01), while it decreased with the administration of flecainide (-0.21, P = 0.03). In the LQTS group iCEB was increased (5.22 ± 0.93, P < 0.0001) compared to genotype negative family members (4.24 ± 0.5), while it was decreased in the BrS group (3.52 ± 0.43, P < 0.0001).

CONCLUSIONS

Our data suggest that iCEB (QT/QRS) is a simple but effective ECG surrogate of cardiac wavelength. iCEB is increased in situations that predispose to TdP and is decreased in situations that predispose to non-TdP mediated VT/VF. Therefore, iCEB might serve as a noninvasive and readily measurable marker to detect increased arrhythmic risk.

Integrated nonclinical and clinical risk assessment of the investigational proteasome inhibitor ixazomib on the QTc interval in cancer patients

BACKGROUND

Ixazomib is the first oral, proteasome inhibitor to reach phase III trials. Here, we present an integrated nonclinical and clinical assessment of ixazomib's effect on QTc intervals.

METHODS

Nonclinical studies assessed (1) the in vitro binding of ixazomib to the hERG channel and (2) its effect on QT/QTc in dogs (N = 4) via telemetry. Pharmacokinetic-matched triplicate electrocardiograms were collected in four clinical phase I studies of intravenous (0.125-3.11 mg/m(2), N = 125, solid tumors/lymphoma) or oral (0.24-3.95 mg/m(2), N = 120, multiple myeloma) ixazomib. The relationship between ixazomib plasma concentration and heart rate (HR)-corrected QT using Fridericia (QTcF) or population (QTcP) methods was analyzed using linear mixed-effects models with fixed effects for day and time.

RESULTS

In vitro binding potency for ixazomib to the hERG channel was weak (K i 24.9 μM; IC50 59.6 μM), and nonclinical telemetry studies showed no QT/QTc prolongation at doses up to 4.2 mg/m(2). In cancer patients, ixazomib, when evaluated at doses yielding various plasma concentrations (with 26 % of data greater than mean C max for the 4 mg phase 3 dose), had no meaningful effect on QTc based on model-predicted mean change in QTcF/QTcP from baseline. There was no relationship between ixazomib concentration and RR, suggesting no effect on HR.

CONCLUSIONS

Ixazomib has no clinically meaningful effects on QTc or HR. Integrating preclinical data and concentration-QTc modeling of phase 1 data may obviate the need for a dedicated QTc study in oncology. A framework for QT assessment in oncology drug development is proposed.

Fusing Continuous-Valued Medical Labels Using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator (BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78 ± 0.63 ms, significantly outperforming the best Challenge entry (15.37 ± 2.13 ms) as well as the EM, mean, and median voting strategies (14.76 ± 0.52, 17.61 ± 0.55, and 14.43 ± 0.57 ms respectively with p < 0.0001). The BCLA could therefore provide accurate estimation for medical continuous-valued label tasks in an unsupervised manner even when the ground truth is not available.

Assessing Prolongation of the Heart Rate Corrected QT Interval in Users of Tricyclic Antidepressants: Advice to Use Fridericia Rather Than Bazett's Correction

A prolonged heart rate corrected QT interval (QTc) increases the risk of sudden cardiac death. Some methods of heart rate correction (notably Bazett) overestimate QTc in people with high heart rates. Studies suggest that tricyclic antidepressants (TCAs) can prolong the QTc and increase heart rate. Therefore, we aimed to study whether TCA-induced QTc prolongation is a false-positive observation due to overestimation at high heart rates. For this, we included 12,734 participants from the prospective population-based Rotterdam Study, with a total of 27,068 electrocardiograms (ECGs), of which, 331 during TCA use. Associations between use of TCAs, QTc, and heart rate were studied with linear repeated measurement analyses. QT was corrected for heart rate according to Bazett (QTcBazett), Fridericia (QTcFridericia), or a correction based on regression coefficients obtained from the Rotterdam Study data (QTcStatistical). On ECGs recorded during TCA use, QTcBazett was 6.5 milliseconds (95% confidence interval, 4.0-9.0) longer, and heart rate was 5.8 beats per minute (95% confidence interval, 4.7-6.9) faster than during nonuse. QTcFridericia and QTcStatistical were not statistically significantly longer during TCA use than during nonuse. Furthermore, QTcBazett was similar for ECGs recorded during TCA use and nonuse after statistical adjustment for heart rate. According to our results, TCA use does not seem to be associated with QTc prolongation. Therefore, the current advice of regulatory authorities to restrict the use of these drugs and to do regular checkups of the QTc may need to be revised. Other formulas, like Fridericia's, might be preferred.