Facilitation of I Kr current by some hERG channel blockers suppresses early afterdepolarizations.

Drug-induced block of the cardiac rapid delayed rectifying potassium current (I Kr), carried by the human ether-a-go-go-related gene (hERG) channel, is the most common cause of acquired long QT syndrome. Indeed, some, but not all, drugs that block hERG channels cause fatal cardiac arrhythmias. However, there is no clear method to distinguish between drugs that cause deadly arrhythmias and those that are clinically safe. Here we propose a mechanism that could explain why certain clinically used hERG blockers are less proarrhythmic than others. We demonstrate that several drugs that block hERG channels, but have favorable cardiac safety profiles, also evoke another effect; they facilitate the hERG current amplitude in response to low-voltage depolarization. To investigate how hERG facilitation impacts cardiac safety, we develop computational models of I Kr block with and without this facilitation. We constrain the models using data from voltage clamp recordings of hERG block and facilitation by nifekalant, a safe class III antiarrhythmic agent. Human ventricular action potential simulations demonstrate the ability of nifekalant to suppress ectopic excitations, with or without facilitation. Without facilitation, excessive I Kr block evokes early afterdepolarizations, which cause lethal arrhythmias. When facilitation is introduced, early afterdepolarizations are prevented at the same degree of block. Facilitation appears to prevent early afterdepolarizations by increasing I Kr during the repolarization phase of action potentials. We empirically test this prediction in isolated rabbit ventricular myocytes and find that action potential prolongation with nifekalant is less likely to induce early afterdepolarization than action potential prolongation with dofetilide, a hERG channel blocker that does not induce facilitation. Our data suggest that hERG channel blockers that induce facilitation increase the repolarization reserve of cardiac myocytes, rendering them less likely to trigger lethal ventricular arrhythmias

Arrhythmic risk in elderly patients candidates to transcatheter aortic valve replacement. predicative role of repolarization temporal dispersion

Degenerative aortic valve stenosis (AS) is associated to ventricular arrhythmias and sudden cardiac death, as well as mental stress in specific patients. In such a context, substrate, autonomic imbalance as well as repolarization dispersion abnormalities play an undoubted role. Aim of the study was to evaluate the increase of premature ventricular contractions (PVC) and complex ventricular arrhythmias during mental stress in elderly patients candidate to the transcatheter aortic valve replacement (TAVR). In eighty-one elderly patients with AS we calculated several short-period RRand QT-derived variables at rest, during controlled breathing and during mild mental stress, the latter being represented by a mini-mental state evaluation (MMSE). All the myocardial repolarization dispersion markers worsened during mental stress (p < 0.05). Furthermore, during MMSE, low frequency component of the RR variability increased significantly both as absolute power (LFRR) and normalized units (LFRRNU) (p < 0.05) as well as the low-high frequency ratio (LFRR/HFRR) (p < 0.05). Eventually, twenty-four (30%) and twelve (15%) patients increased significantly PVC and, respectively, complex ventricular arrhythmias during the MMSE administration. At multivariate logistic regression analysis, the standard deviation of QTend (QTesd), obtained at rest, was predictive of increased PVC (odd ratio: 1.54, 95% CI 1.14–2.08; p = 0.005) and complex ventricular arrhythmias (odd ratio: 2.31, 95% CI 1.40–3.83; p = 0.001) during MMSE. The QTesd showed the widest sensitive-specificity area under the curve for the increase of PVC (AUC: 0.699, 95% CI: 0.576–0.822, p < 0.05) and complex ventricular arrhythmias (AUC: 0.801, 95% CI: 0.648–0.954, p < 0.05). In elderly with AS ventricular arrhythmias worsened during a simple cognitive assessment, this events being a possible further burden on the outcome of TAVR. QTesd might be useful to identify those patients with the highest risk of ventricular arrhythmias. Whether the TAVR could led to a QTesd reduction and, hence, to a reductionof thearrhythmicburdenin thissettingofpatients isworthytobe investigated

Development of High Resolution Tools for Investigating Cardiac Arrhythmia Dynamics

Every year 300,000 Americans die due to sudden cardiac death. There are many pathologies, acquired and genetic, that can lead to sudden cardiac death. Regardless of the underlying pathology, death is frequently the result of ventricular tachycardia and/or fibrillation (VT/VF). Despite decades of research, the mechanisms of ventricular arrhythmia initiation and maintenance are still incompletely understood. A contributing factor to this lack of understanding is the limitations of the investigative tools used to study VT/VF. Arrhythmias are organ level phenomena that are governed by cellular interactions and as such, near cellular levels of resolution are needed to tease out their intricacies. They are also behaviors that are not limited by region, but dynamically affect the entirety of the heart. For these reasons, high-resolution methodologies capable of measuring electrophysiology of the whole entirety of the ventricles will play an important role in gaining a complete understanding of the principles that govern ventricular arrhythmia dynamics. They will also be essential in the development of novel therapies for arrhythmia management. In this dissertation, I first present the validation and characterization of a novel capacitive electrode design that overcomes the key limitations faced by modern implantable cardiac devices. I then outline the construction, methodologies, and open-source tools of an improved optical panoramic mapping system for small mammalian cardiac electrophysiology studies. I conclude with a small mammal study of the relationship between action potential duration restitution dynamics and the mechanisms of maintenance in ventricular arrhythmias

A Time-Varying Non-Parametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE: To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in non-stationary conditions. METHODS: Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the non-stationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging non-stationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed. RESULTS: In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the non-stationary spectra. The correlation coefficient between theoretical and estimated patterns was >0.92 even for extremely noisy recordings (SNR in QTV =-10dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands (P<0.05 for most pairwise comparisons). CONCLUSION: The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV. SIGNIFICANCE: The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Exposure to Secondhand Smoke and Arrhythmogenic Cardiac Alternans in a Mouse Model.

BackgroundEpidemiological evidence suggests that a majority of deaths attributed to secondhand smoke (SHS) exposure are cardiovascular related. However, to our knowledge, the impact of SHS on cardiac electrophysiology, [Formula: see text] handling, and arrhythmia risk has not been studied.ObjectivesThe purpose of this study was to investigate the impact of an environmentally relevant concentration of SHS on cardiac electrophysiology and indicators of arrhythmia.MethodsMale C57BL/6 mice were exposed to SHS [total suspended particles (THS): [Formula: see text], nicotine: [Formula: see text], carbon monoxide: [Formula: see text], or filtered air (FA) for 4, 8, or 12 wk ([Formula: see text]]. Hearts were excised and Langendorff perfused for dual optical mapping with voltage- and [Formula: see text]-sensitive dyes.ResultsAt slow pacing rates, SHS exposure did not alter baseline electrophysiological parameters. With increasing pacing frequency, action potential duration (APD), and intracellular [Formula: see text] alternans magnitude progressively increased in all groups. At 4 and 8 wk, there were no statistical differences in APD or [Formula: see text] alternans magnitude between SHS and FA groups. At 12 wk, both APD and [Formula: see text] alternans magnitude were significantly increased in the SHS compared to FA group ([Formula: see text]). SHS exposure did not impact the time constant of [Formula: see text] transient decay ([Formula: see text]) at any exposure time point. At 12 wk exposure, the recovery of [Formula: see text] transient amplitude with premature stimuli was slightly (but nonsignificantly) delayed in SHS compared to FA hearts, suggesting that [Formula: see text] release via ryanodine receptors may be impaired.ConclusionsIn male mice, chronic exposure to SHS at levels relevant to social situations in humans increased their susceptibility to cardiac alternans, a known precursor to ventricular arrhythmia. https://doi.org/10.1289/EHP3664

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal