Unstable QT interval dynamics precedes ventricular tachycardia onset in patients with acute myocardial infarction: a novel approach to detect instability in QT interval dynamics from clinical ECG

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Model Systems for Addressing Mechanism of Arrhythmogenesis in Cardiac Repair

PURPOSE OF REVIEW

Cardiac cell-based therapy represents a promising approach for cardiac repair. However, one of the main challenges is cardiac arrhythmias associated with stem cell transplantation. The current review summarizes the recent progress in model systems for addressing mechanisms of arrhythmogenesis in cardiac repair.

RECENT FINDINGS

Animal models have been extensively developed for mechanistic studies of cardiac arrhythmogenesis. Advances in human induced pluripotent stem cells (hiPSCs), patient-specific disease models, tissue engineering, and gene editing have greatly enhanced our ability to probe the mechanistic bases of cardiac arrhythmias. Additionally, recent development in multiscale computational studies and machine learning provides yet another powerful tool to quantitatively decipher the mechanisms of cardiac arrhythmias. Advancing efforts towards the integrations of experimental and computational studies are critical to gain insights into novel mitigation strategies for cardiac arrhythmias in cell-based therapy.

Autonomic Control of the Heart and Its Clinical Impact. A Personal Perspective

This essay covers several aspects of the autonomic control of the heart, all relevant to cardiovascular pathophysiology with a direct impact on clinical outcomes. Ischemic heart disease, heart failure, channelopathies, and life-threatening arrhythmias are in the picture. Beginning with an overview on some of the events that marked the oscillations in the medical interest for the autonomic nervous system, our text explores specific areas, including experimental and clinical work focused on understanding the different roles of tonic and reflex sympathetic and vagal activity. The role of the baroreceptors, not just for the direct control of circulation but also because of the clinical value of interpreting alterations (spontaneous or induced) in their function, is discussed. The importance of the autonomic nervous system for gaining insights on risk stratification and for providing specific antiarrhythmic protection is also considered. Examples are the interventions to decrease sympathetic activity and/or to increase vagal activity. The non-invasive analysis of the RR and QT intervals provides additional information. The three of us have collaborated in several studies and each of us contributes with very specific and independent areas of expertise. Here, we have focused on those areas to which we have directly contributed and hence speak with personal experience. This is not an attempt to provide a neutral and general overview on the autonomic nervous system; rather, it represents our effort to share and provide the readers with our own personal views matured after many years of research in this field.

Evaluation of index of cardiac-electrophysiological balance as arrhythmia predictor in bonsai users

BACKGROUND

Synthetic cannabinoids are part of a group of drugs called new psychoactive substances. The increase in substance use among young adults is becoming a major problem in the world. In this study we aimed to investigate the effects of synthetic cannabinoid drugs such as bonsai to electrocardiographic (ECG) parameters, in patients who were admitted to emergency service with self-reported usage of bonsai.

METHODS

Seventy-two patients (68 males; mean age 33.8±11.8) with self-reported use of bonsai and 27 (22 males; mean age 37.1±8.7) age and sex-matched healthy control group enrolled the study. ECG parameters and rhythm holter were measurements calculated in both groups.

RESULTS

Groups were age and sex matched. Glucose, potassium, white blood cell count, heart rate end smoking status was significantly different in patients compared to control group. P wave max time, P wave min. time, P wave dispersion, QT max. time QT dispersion, QT corrected time and index of cardiac-electrophysiological balance measurements (iCEB) were significantly different in groups of patients. A multivariate logistic regression analysis was used to determine independent predictors of ≥30 Ventricular premature beat (VPB)/h using parameters found to be associated with ≥30 VPB/h in a univariate analysis (potassium, QT<inf>max</inf> time, QTc, QRS time, iCEB).In a multivariate analysis, independent predictors of ≥30 VPB/h were potassium (Odds ratio [OR]: 0.107, 95% CI: 0.024-0.481;P=0.004) and iCEB (OR: 4.474, 95% CI: 1.752-11.429;P=0.002). In generalize linear model β-coefficient value of interaction terms between K*iCEB has no important effect on ventricular premature beats.

CONCLUSIONS

If the results are confirmed in further studies, iCEB seems to be a simple, easily measurable and non-invasive marker to predict cannabinoid-induced ventricular arrhythmias.

Statin Use in the Early Phase of ST-Segment Elevation Myocardial Infarction Is Associated With Decreased QTc Dispersion

BACKGROUND

Although there is strong evidence supporting the use of statin therapy after myocardial infarction (MI), some mechanistic gaps exist regarding the benefits of this therapy at the very onset of MI. Among the potential beneficial mechanisms, statins may improve myocardial electrical stability and reduce life-threatening ventricular arrhythmia, as reported in stable clinical conditions. This study was designed to evaluate whether this mechanism could also occur during the acute phase of MI.

METHODS

Consecutive patients with ST-segment elevation MI were treated without statin (n = 57) or with a simvastatin dose of 20 to 80 mg (n = 87) within the first 24 hours after MI symptom onset. Patients underwent digital electrocardiography within the first 24 hours and at the third and fifth days after MI. The QTC dispersion (QTcD) was measured both with and without the U waves.

RESULTS

Although QTcD values were equivalent between the groups at the first day (80.6 ± 36.0 vs 80.0 ± 32.1; P = 0.36), they were shorter among individuals using simvastatin than in those receiving no statins on the third (90.4 ± 38.6 vs 86.5 ± 36.9; P = .036) and fifth days (73.1 ± 31 vs 69.2 ± 32.6; P = .049). We obtained similar results when analyzing the QTcD duration including the U wave. All values were adjusted by an ANCOVA model after propensity-score matching.

CONCLUSIONS

Statins administered within 24 hours of ST-segment elevation MI reduced QTc dispersion, which may potentially attenuate the substrate for life-threatening ventricular arrhythmias.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Repolarization variability independent of heart rate during sympathetic activation elicited by head-up tilt

The fraction of repolarization variability independent of RR interval variability is of clinical interest. It has been linked to direct autonomic nervous system (ANS) regulation of the ventricles in healthy subjects and seems to reflect the instability of the ventricular repolarization process in heart disease. In this study, we sought to identify repolarization measures that best reflect the sympathetic influences on the ventricles independent of the RR interval. ECG was recorded in 46 young subjects during supine and then following 45 degrees head-up tilt. RR intervals and five repolarization features (QTend, QTpeak, RTend, RTpeak, and TpTe) were extracted from the ECG recordings. Repolarization variability was separated into RR-dependent and RR-independent variability using parametric spectral analysis. Results show that LF power of TpTe is independent of RR in both supine and tilt, while the LF power of QTend and RTend independent of RR and respiration increases following tilt. We conclude that TpTe is independent of RR and is highly affected by respiration. QTend and RTend LF power might reflect the sympathetic influences on the ventricles elicited by tilt. Graphical abstract.

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.

A study on stability analysis of atrial repolarization variability using ARX model in sinus rhythm and atrial tachycardia ECGs

BACKGROUND

The interaction between the PTa and PP interval dynamics from the surface ECG is seldom explained. Mathematical modeling of these intervals is of interest in finding the relationship between the heart rate and repolarization variability.

OBJECTIVE

The goal of this paper is to assess the bounded input bounded output (BIBO) stability in PTa interval (PTaI) dynamics using autoregressive exogenous (ARX) model and to investigate the reason for causing instability in the atrial repolarization process.

METHODS

Twenty-five male subjects in normal sinus rhythm (NSR) and ten male subjects experiencing atrial tachycardia (AT) were included in this study. Five minute long, modified limb lead (MLL) ECGs were recorded with an EDAN SE-1010 PC ECG system. The number of minute ECGs with unstable segments (N_us) and the frequency of premature activation (PA) (i.e. atrial activation) were counted for each ECG recording and compared between AT and NSR subjects.

RESULTS

The instability in PTaI dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject. The unstable segments in the PTaI dynamics were associated with the frequency of PA. The presence of PA is not the only factor causing the instability in PTaI dynamics in NSR subjects, and it is found that the cause of instability is mainly due to the heart rate variability (HRV).

CONCLUSION

The ARX model showed better prediction of PTa interval dynamics in both groups. The frequency of PA is significantly higher in AT patients than NSR subjects. A more complex model is needed to better identify and characterize healthy heart dynamics.

Spatial reproducibility of complex fractionated atrial electrogram depending on the direction and configuration of bipolar electrodes: an in-silico modeling study

Although 3D-complex fractionated atrial electrogram (CFAE) mapping is useful in radiofrequency catheter ablation for persistent atrial fibrillation (AF), the directions and configuration of the bipolar electrodes may affect the electrogram. This study aimed to compare the spatial reproducibility of CFAE by changing the catheter orientations and electrode distance in an in-silico left atrium (LA). We conducted this study by importing the heart CT image of a patient with AF into a 3D-homogeneous human LA model. Electrogram morphology, CFAE-cycle lengths (CLs) were compared for 16 different orientations of a virtual bipolar conventional catheter (conv-cath: size 3.5 mm, inter-electrode distance 4.75 mm). Additionally, the spatial correlations of CFAE-CLs and the percentage of consistent sites with CFAE-CL<120 ms were analyzed. The results from the conv-cath were compared with that obtained using a mini catheter (mini-cath: size 1 mm, inter-electrode distance 2.5 mm). Depending on the catheter orientation, the electrogram morphology and CFAE-CLs varied (conv-cath: 11.5±0.7% variation, mini-cath: 7.1±1.2% variation), however the mini-cath produced less variation of CFAE-CL than conv-cath (p<0.001). There were moderate spatial correlations among CFAE-CL measured at 16 orientations (conv-cath: r=0.3055±0.2194 vs. mini-cath: 0.6074±0.0733, p<0.001). Additionally, the ratio of consistent CFAE sites was higher for mini catheter than conventional one (38.3±4.6% vs. 22.3±1.4%, p<0.05). Electrograms and CFAE distribution are affected by catheter orientation and electrode configuration in the in-silico LA model. However, there was moderate spatial consistency of CFAE areas, and narrowly spaced bipolar catheters were less influenced by catheter direction than conventional catheters.

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.

Predicting the risk of sudden cardiac death

Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.

How computer simulations of the human heart can improve anti-arrhythmia therapy

Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.

Stochastic pacing reveals the propensity to cardiac action potential alternans and uncovers its underlying dynamics

KEY POINTS

Beat-to-beat alternation (alternans) of the cardiac action potential duration is known to precipitate life-threatening arrhythmias and can be driven by the kinetics of voltage-gated membrane currents or by instabilities in intracellular calcium fluxes. To prevent alternans and associated arrhythmias, suitable markers must be developed to quantify the susceptibility to alternans; previous theoretical studies showed that the eigenvalue of the alternating eigenmode represents an ideal marker of alternans. Using rabbit ventricular myocytes, we show that this eigenvalue can be estimated in practice by pacing these cells at intervals varying stochastically. We also show that stochastic pacing permits the estimation of further markers distinguishing between voltage-driven and calcium-driven alternans. Our study opens the perspective to use stochastic pacing during clinical investigations and in patients with implanted pacing devices to determine the susceptibility to, and the type of alternans, which are both important to guide preventive or therapeutic measures.

ABSTRACT

Alternans of the cardiac action potential (AP) duration (APD) is a well-known arrhythmogenic mechanism. APD depends on several preceding diastolic intervals (DIs) and APDs, which complicates the prediction of alternans. Previous theoretical studies pinpointed a marker called λalt that directly quantifies how an alternating perturbation persists over successive APs. When the propensity to alternans increases, λalt decreases from 0 to -1. Our aim was to quantify λalt experimentally using stochastic pacing and to examine whether stochastic pacing allows discriminating between voltage-driven and Ca(2+) -driven alternans. APs were recorded in rabbit ventricular myocytes paced at cycle lengths (CLs) decreasing progressively and incorporating stochastic variations. Fitting APD with a function of two previous APDs and CLs permitted us to estimate λalt along with additional markers characterizing whether the dependence of APD on previous DIs or CLs is strong (typical for voltage-driven alternans) or weak (Ca(2+) -driven alternans). During the recordings, λalt gradually decreased from around 0 towards -1. Intermittent alternans appeared when λalt reached -0.8 and was followed by sustained alternans. The additional markers detected that alternans was Ca(2+) driven in control experiments and voltage driven in the presence of ryanodine. This distinction could be made even before alternans was manifest (specificity/sensitivity >80% for -0.4 > λalt  > -0.5). These observations were confirmed in a mathematical model of a rabbit ventricular myocyte. In conclusion, stochastic pacing allows the practical estimation of λalt to reveal the onset of alternans and distinguishes between voltage-driven and Ca(2+) -driven mechanisms, which is important since these two mechanisms may precipitate arrhythmias in different manners.

Rate-dependent force, intracellular calcium, and action potential voltage alternans are modulated by sarcomere length and heart failure induced-remodeling of thin filament regulation in human heart failure: A myocyte modeling study

Microvolt T-wave alternans (MTWA) testing identifies heart failure patients at risk for lethal ventricular arrhythmias at near-resting heart rates (<110 beats per minute). Since pressure alternans occurs simultaneously with MTWA and has a higher signal to noise ratio, it may be a better predictor of arrhythmia, although the mechanism remains unknown. Therefore, we investigated the relationship between force alternans (FORCE-ALT), the cellular manifestation of pressure alternans, and action potential voltage alternans (APV-ALT), the cellular driver of MTWA. Our goal was to uncover the mechanisms linking APV-ALT and FORCE-ALT in failing human myocytes and to investigate how the link between those alternans was affected by pacing rate and by physiological conditions such as sarcomere length and heart failure induced-remodeling of mechanical parameters. To achieve this, a mechanically-based, strongly coupled human electromechanical myocyte model was constructed. Reducing the sarcoplasmic reticulum calcium uptake current (Iup) to 27% was incorporated to simulate abnormal calcium handling in human heart failure. Mechanical remodeling was incorporated to simulate altered thin filament activation and crossbridge (XB) cycling rates. A dynamical pacing protocol was used to investigate the development of intracellular calcium concentration ([Ca]i), voltage, and active force alternans at different pacing rates. FORCE-ALT only occurred in simulations incorporating reduced Iup, demonstrating that alternans in the intracellular calcium concentration (CA-ALT) induced FORCE-ALT. The magnitude of FORCE-ALT was found to be largest at clinically relevant pacing rates (<110 bpm), where APV-ALT was smallest. We found that the magnitudes of FORCE-ALT, CA-ALT and APV-ALT were altered by heart failure induced-remodeling of mechanical parameters and sarcomere length due to the presence of myofilament feedback. These findings provide important insight into the relationship between heart-failure-induced electrical and mechanical alternans and how they are altered by physiological conditions at near-resting heart rates.

Absolute beat-to-beat variability and instability parameters of ECG intervals: biomarkers for predicting ischaemia-induced ventricular fibrillation

BACKGROUND AND PURPOSE

Predicting lethal arrhythmia liability from beat-to-beat variability and instability (BVI) of the ECG intervals is a useful technique in drug assessment. Most investigators use only arrhythmia-free ECGs for this. Recently, it was shown that drug-induced torsades de pointes (TdP) liability can be predicted more accurately from BVI measured irrespective of rhythm, even during arrhythmias (absolute BVI). The present study tested the broader applicability of this assessment by examining whether absolute BVI parameters predict another potential lethal arrhythmia, ischaemia-induced ventricular fibrillation (VF).

EXPERIMENTAL APPROACH

Langendorff-perfused rat hearts were subjected to regional ischaemia for 15 min. Absolute BVI parameters were derived from ECG intervals measured in 40 consecutive ventricular complexes (irrespective of rhythm) immediately preceding VF onset and compared with time-matched values in hearts not expressing VF.

KEY RESULTS

Increased frequency of non-sinus beats and 'R on T' arrhythmic beats, shortened mean RR and electrical diastolic intervals, and increased BVI of cycle length and repolarization predicted VF occurrence. Absolute BVI parameters that quantify variability of repolarization (e.g. 'short-term variability' of QT interval) had the best predictive power with high sensitivity and specificity. In contrast, VF was not predicted by any BVI parameter derived from the last arrhythmia-free interlude before VF.

CONCLUSIONS AND IMPLICATIONS

The novel absolute BVI parameters that predicted TdP in rabbit also predict ischaemia-induced VF in rat, indicating a diagnostic and mechanistic congruence. Repolarization inhomogeneity represents a pivotal biomarker of ischaemia-induced VF. The newly validated biomarkers could serve as surrogates for VF in pre-clinical drug investigations.

Advances in modeling ventricular arrhythmias: from mechanisms to the clinic

Modern cardiovascular research has increasingly recognized that heart models and simulation can help interpret an array of experimental data and dissect important mechanisms and interrelationships, with developments rooted in the iterative interaction between modeling and experimentation. This article reviews the progress made in simulating cardiac electrical behavior at the level of the organ and, specifically, in the development of models of ventricular arrhythmias and fibrillation, as well as their termination (defibrillation). The ability to construct multiscale models of ventricular arrhythmias, representing integrative behavior from the molecule to the entire organ, has enabled mechanistic inquiry into the dynamics of ventricular arrhythmias in the diseased myocardium, in understanding drug-induced proarrhythmia, and in the development of new modalities for defibrillation, to name a few. In this article, we also review the initial use of ventricular models of arrhythmia in personalized diagnosis, treatment planning, and prevention of sudden cardiac death. Implementing individualized cardiac simulations at the patient bedside is poised to become one of the most thrilling examples of computational science and engineering approaches in translational medicine.

Effect of premature activation in analyzing QT dynamics instability using QT-RR model for ventricular fibrillation and healthy subjects

Perturbations in the normal heart rate are generally represented by the presence of premature activation (PA) beats in the surface electrocardiogram (ECG). The presence of PA is one of the main reasons of instability in QT dynamics which could initiate arrhythmia. Analyzing Boundary-Input Boundary-Output (BIBO) stability of the short term linear autoregressive QT-RR model is a way of detecting instability in QT dynamics from the ECG. The aim of this paper is to investigate if PA is the only reason for instability in the ventricular repolarisation process, which is denoted by QT interval of surface ECG. Ten healthy subjects with normal sinus rhythm and seven patients with sustained ventricular tachycardia (VT) were analyzed in this study. 10 min long ECG data were collected from each subject of the healthy group and 10 min ECG before the start of VT were taken for each subject of the VT group. Autoregressive QT-RR model was derived for each non-overlapping 1 min long ECG segment of the 10 min long ECG data. Instability in QT dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject ( ). Results of this study revealed that like the VT group subjects, QT instability detected by QT-RR model is also found in healthy subjects whose ECG segments are mostly free from PA beats. This finding indicates that BIBO unstable QT characteristics might arise from other inherent factors of cardiovascular system in addition to PA.

Beat-to-beat QT interval variability and T-wave amplitude in patients with myocardial infarction

The purpose of this study was to investigate the effects of T-wave amplitude and ECG lead on beat-to-beat QT interval variability (QTV) in patients with myocardial infarction (MI) compared to healthy subjects. Standard resting 12-lead ECGs of 79 MI patients and 69 healthy subjects were investigated. Beat-to-beat QT intervals were measured separately for each lead using a template matching algorithm. In addition, we extracted the beat-to-beat T-wave amplitude in each lead. We computed the standard deviation of beat-to-beat QT intervals as a marker of QTV for both healthy subjects and MI patients. Significant QTV differences were observed between the 12 ECG leads as well as between the groups of healthy subjects and MI patients. Beat-to-beat QTV was significantly higher in MI patients than in healthy subjects for half of the leads. Furthermore, significant T-wave amplitude differences across leads and between groups were observed. A significant inverse relation between beat-to-beat QTV and T-wave amplitude was demonstrated. The group differences in QTV remained significant after co-varying for the T-wave amplitude. In conclusion, the increase in beat-to-beat QTV that has been repeatedly reported in patients with MI is partly due to the lower T-wave amplitudes. However, QTV remains significantly increased in MI patients after covarying for this effect.

Autonomic modulation of repolarization instability in patients with heart failure prone to ventricular tachycardia

QT variability (QTV) signifies repolarization lability, and increased QTV is a risk predictor for sudden cardiac death. The aim of the present study was to investigate the role of autonomic nervous system activity on QTV. This study was performed in 29 subjects: 10 heart failure (HF) patients with spontaneous ventricular tachycardia [HFVT(+)], 10 HF patients without spontaneous VT [HFVT(-)], and 9 subjects with structurally normal hearts (HNorm). The beat-to-beat QT interval was measured on 3-min records of surface ECGs at baseline and during interventions (atrial pacing and esmolol, isoprenaline, and atropine infusion). Variability in QT intervals was expressed as the SD of all QT intervals (SDQT). The ratio of the SDQT to SD of RR intervals (SDRR) was calculated as an index of QTV normalized to heart rate variability. There was a trend toward a higher baseline SDQT-to-SDRR ratio in the HFVT(+) group compared with the HFVT(-) and HNorm groups (P = 0.09). SDQT increased significantly in the HFVT(+) and HFVT(-) groups compared with the HNorm group during fixed-rate atrial pacing (P = 0.008). Compared with baseline, isoprenaline infusion increased SDQT in HNorm subjects (P = 0.02) but not in HF patients. SDQT remained elevated in the HFVT(+) group relative to the HNorm group despite acute β-adrenoceptor blockade with esmolol (P = 0.02). In conclusion, patients with HF and spontaneous VT have larger fluctuations in beat-to-beat QT intervals. This appears to be a genuine effect that is not solely a consequence of heart rate variation. The effect of acute autonomic nervous system modulation on QTV appears to be limited in HF patients.

Arrhythmia risk stratification based on QT interval instability: an intracardiac electrocardiogram study

BACKGROUND

Experimental studies have demonstrated that unstable repolarization dynamics is a risk factor of arrhythmia. We have recently developed an algorithm to detect QT interval (QTI) instability from the clinical electrocardiogram (ECG).

OBJECTIVE

To develop a clinical arrhythmia risk stratification index based on the detection of QTI instability.

METHODS

Intracardiac ECGs were recorded at rest in 114 patients with implanted implantable cardioverter-defibrillators (ICDs). Patients were followed up until appropriate implantable cardioverter-defibrillator therapy or death occurred, whichever came first. Each ECG recording was divided into 1-minute episodes (minECGs); the instability in QTI dynamics, if any, of each minECG was detected with our algorithm. An arrhythmia risk index termed QTI instability index (QTII) was defined as the number of minECGs with unstable QTI dynamics normalized by the number of minECGs with premature activations. The performance of QTII in arrhythmia risk stratification was examined with survival analysis and was compared with other risk indices, such as the mean RR interval (RRI), the standard deviation of the RRI and the QTI, and the frequency of premature activation. We hypothesized that the index QTII, which accounts for multiple risk factors and their interdependence, performs better than indices quantifying individual arrhythmia risk factors in the stratification of arrhythmia risk.

RESULTS

The results of survival analysis show that QTII outperformed all other studied indices in arrhythmia risk stratification and was the only independent indicator of arrhythmia propensity in a multivariate survival model.

CONCLUSION

QTII is a promising arrhythmia risk stratification index.

Computational cardiology: how computer simulations could be used to develop new therapies and advance existing ones

This article reviews the latest developments in computational cardiology. It focuses on the contribution of cardiac modelling to the development of new therapies as well as the advancement of existing ones for cardiac arrhythmias and pump dysfunction. Reviewed are cardiac modelling efforts aimed at advancing and optimizing existent therapies for cardiac disease (defibrillation, ablation of ventricular tachycardia, and cardiac resynchronization therapy) and at suggesting novel treatments, including novel molecular targets, as well as efforts to use cardiac models in stratification of patients likely to benefit from a given therapy, and the use of models in diagnostic procedures.

Computational cardiology: the heart of the matter

This paper reviews the newest developments in computational cardiology. It focuses on the contribution of cardiac modeling to the development of new therapies as well as the advancement of existing ones for cardiac arrhythmias and pump dysfunction. Reviewed are cardiac modeling efforts aimed at advancing and optimizing existent therapies for cardiac disease (defibrillation, ablation of ventricular tachycardia, and cardiac resynchronization therapy) and at suggesting novel treatments, including novel molecular targets, as well as efforts to use cardiac models in stratification of patients likely to benefit from a given therapy, and the use of models in diagnostic procedures.

Computational approaches to understand cardiac electrophysiology and arrhythmias

Cardiac rhythms arise from electrical activity generated by precisely timed opening and closing of ion channels in individual cardiac myocytes. These impulses spread throughout the cardiac muscle to manifest as electrical waves in the whole heart. Regularity of electrical waves is critically important since they signal the heart muscle to contract, driving the primary function of the heart to act as a pump and deliver blood to the brain and vital organs. When electrical activity goes awry during a cardiac arrhythmia, the pump does not function, the brain does not receive oxygenated blood, and death ensues. For more than 50 years, mathematically based models of cardiac electrical activity have been used to improve understanding of basic mechanisms of normal and abnormal cardiac electrical function. Computer-based modeling approaches to understand cardiac activity are uniquely helpful because they allow for distillation of complex emergent behaviors into the key contributing components underlying them. Here we review the latest advances and novel concepts in the field as they relate to understanding the complex interplay between electrical, mechanical, structural, and genetic mechanisms during arrhythmia development at the level of ion channels, cells, and tissues. We also discuss the latest computational approaches to guiding arrhythmia therapy.

A novel methodology for assessing the bounded-input bounded-output instability in QT interval dynamics: application to clinical ECG with ventricular tachycardia

The goal of this paper is to present a new methodology for assessing the bounded-input bounded-output (BIBO) stability in QT interval (QTI) dynamics from clinical ECG. The ECG recordings were collected from 15 patients who experienced ventricular tachycardia (VT). Ten-minute-long ECG recordings extracted immediately before the onset of a chosen VT, one per patient, were assembled into a VT group, while the control group comprised 10-min-long ECGs extracted 1 h before VT onset and at least 1 h after any prior arrhythmic event. Each 10-min recording was subdivided into 1-min ECG recordings (minECGs). The QTI dynamics of each minECG was defined as a function of several prior QTIs and RR intervals; the BIBO stability of this function was then assessed in the z -domain. The number of minECGs with unstable QTI dynamics (N (us)) and the frequency of premature activations (PA),  f (PA) , were counted for each ECG recording and were compared between the VT and control groups. The results show that the present methodology successfully captured the instability in QTI dynamics leading to VT onset in the studied population. Significantly larger N (us) was found in the VT group compared against the control and a positive correlation between N (us) and f (PA) was identified in both groups.