Arrhythmias (IV). Clinical approach to atrial tachycardia and atrial flutter from an understanding of the mechanisms. Electrophysiology based on anatomy

Expert-enhanced machine learning for cardiac arrhythmia classification

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as "excellent" according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

High-density mapping of Koch's triangle during sinus rhythm and typical AV nodal reentrant tachycardia: new insight

BACKGROUND

Atrial activation during typical atrioventricular nodal reentrant tachycardia (AVNRT) exhibits anatomic variability and spatially heterogeneous propagation inside the Koch's triangle (KT). The mechanism of the reentrant circuit has not been elucidated yet. Aim of this study is to describe the distribution of Jackman and Haïssaguerre potentials within the KT and to explore the activation mode of the KT, in sinus rhythm and during the slow-fast AVNRT.

METHODS

Forty-five consecutive cases of successful slow pathway (SP) ablation of typical slow-fast AVNRT from the CHARISMA registry were included.

RESULTS

The KT geometry was obtained on the basis of the electroanatomic information using the Rhythmia mapping system (Boston Scientific) (mean number of points acquired inside the KT = 277 ± 47, mean mapping time = 11.9 ± 4 min). The postero-septal regions bounded anteriorly by the tricuspid annulus and posteriorly by the lateral wall toward the crista terminalis showed a higher prevalence of Jackman potentials than mid-postero-septal regions along the tendon of Todaro and coronary sinus (CS) (98% vs. 16%, p < 0.0001). Haïssaguerre potentials seemed to have a converse distribution across the KT (0% vs. 84%, p < 0.0001). Fast pathway insertion, as located during AVNRT, was mostly recorded in an antero-septal position (n = 36, 80%), rather than in a mid-septal (n = 6, 13.3%) or even postero-septal (n = 3, 7%) location. During typical slow-fast AVNRT, two types of propagation around the CS were discernible: anterior and posterior, n = 31 (69%), or only anterior, n = 14 (31%). During the first procedure, the SP was eliminated, and acute procedural success was achieved (median of 4 [3-5] RF ablations).

CONCLUSION

High-density mapping of KT in AVNRT patients both during sinus rhythm and during tachycardia provides new electrophysiological insights. A better understanding and a more precise definition of the arrhythmogenic substrate in AVNRT patients may have prognostic value, especially in high-risk cases.

TRIAL REGISTRATION

Catheter Ablation of Arrhythmias With High Density Mapping System in the Real World Practice (CHARISMA) URL: http://clinicaltrials.gov/ Identifier: NCT03793998.

Influence of propafenone on the anticonvulsant activity of various novel antiepileptic drugs in the mouse maximal electroshock model

BACKGROUND

The main mechanism of action of propafenone (antiarrhythmic drug) involves the inhibition of the fast inward sodium current during phase 0 of the action potential. Sodium channel-blocking activity is also characteristic for some antiepileptic drugs. Therefore, it could be assumed that propafenone may also affect seizures. In the present study, we evaluated the effect of propafenone on the protective effect of oxcarbazepine, lamotrigine, topiramate and pregabalin against the maximal electroshock-induced seizures in mice.

METHODS

Anticonvulsant activity of propafenone was assessed with the maximal electroshock seizure threshold (MEST) test. Influence of propafenone on the anticonvulsant activity of antiepileptic drugs was estimated in the mouse maximal electroshock model (MES). Drug-related adverse effects were determined in the chimney test (motor coordination) and passive-avoidance task (long-term memory). Brain concentrations of antiepileptics were assessed by fluorescence polarization immunoassay.

RESULTS

Propafenone at doses 60-90mg/kg significantly increased the threshold of seizures, in turn at doses 5-50mg/kg did not affect this parameter. Administration of propafenone at the subthreshold dose of 50mg/kg increased antielectroshock activity of oxcarbazepine, topiramate and pregabalin, but not that of lamotrigine. As regards adverse effects, propafenone alone and in combination with antiepileptic drugs did not significantly impair motor coordination or long-term memory in mice. Propafenone (50mg/kg) significantly increased the brain level of pregabalin. Brain concentrations of topiramate and oxcarbazepine were not affected.

CONCLUSION

Our findings show that propafenone has own anticonvulsant action and enhances efficacy of oxcarbazepine, topiramate and pregabalin, but not that of lamotrigine, at least in experimental condition.

Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

BACKGROUND

Few epidemiologic cohort studies have evaluated atrial flutter (flutter) as an arrhythmia distinct from atrial fibrillation (AF).

OBJECTIVE

The purpose of this study was to examine the clinical correlates of flutter and its associated outcomes to distinguish them from those associated with AF in the Framingham Heart Study.

METHODS

We reviewed and adjudicated electrocardiograms (ECGs) previously classified as flutter or AF/flutter and another 100 ECGs randomly selected from AF cases. We examined the clinical correlates of flutter by matching up to 5 AF and 5 referents to each flutter case using a nested case referent design. We determined the 10-year outcomes associated with flutter with Cox models.

RESULTS

During mean follow-up of 33.0 ± 12.2 years, 112 participants (mean age 72 ± 10 years, 30% women) developed flutter. In multivariable analyses, smoking (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.54-5.23), increased PR interval (OR 1.28 per SD, 95% CI 1.03-1.60), myocardial infarction (OR 2.25, 95% CI 1.05-4.80) and heart failure (OR 5.22, 95% CI 1.26-21.64) were associated with incident flutter. In age- and sex-adjusted models, flutter (vs referents) was associated with 10-year increased risk of AF (hazard ratio [HR] 5.01, 95% CI 3.14-7.99), myocardial infarction (HR 3.05, 95% CI 1.42-6.59), heart failure (HR 4.14, 95% CI 1.90-8.99), stroke (HR 2.17, 95% CI 1.13-4.17), and mortality (HR 2.00, 95% CI 1.44-2.79).

CONCLUSION

We identified the clinical correlates associated with flutter and observed that flutter was associated with multiple adverse outcomes.

Antiarrhythmic drugs and epilepsy

For a long time it has been suspected that epilepsy and cardiac arrhythmia may have common molecular background. Furthermore, seizures can affect function of the central autonomic control centers leading to short- and long-term alterations of cardiac rhythm. Sudden unexpected death in epilepsy (SUDEP) has most likely a cardiac mechanism. Common elements of pathogenesis create a basis for the assumption that antiarrhythmic drugs (AADs) may affect seizure phenomena and interact with antiepileptic drugs (AEDs). Numerous studies have demonstrated anticonvulsant effects of AADs. Among class I AADs (sodium channel blockers), phenytoin is an established antiepileptic drug. Propafenone exerted low anti-electroshock activity in rats. Lidocaine and mexiletine showed the anticonvulsant activity not only in animal models, but also in patients with partial seizures. Among beta-blockers (class II AADs), propranolol was anticonvulsant in models for generalized tonic-clonic and complex partial seizures, but not for myoclonic convulsions. Metoprolol and pindolol antagonized tonic-clonic seizures in DBA/2 mice. Timolol reversed the epileptiform activity of pentylenetetrazol (PTZ) in the brain. Furthermore, amiodarone, the representative of class III AADs, inhibited PTZ- and caffeine-induced convulsions in mice. In the group of class IV AADs, verapamil protected mice against PTZ-induced seizures and inhibited epileptogenesis in amygdala-kindled rats. Verapamil and diltiazem showed moderate anticonvulsant activity in genetically epilepsy prone rats. Additionally, numerous AADs potentiated the anticonvulsant action of AEDs in both experimental and clinical conditions. It should be mentioned, however, that many AADs showed proconvulsant effects in overdose. Moreover, intravenous esmolol and intra-arterial verapamil induced seizures even at therapeutic dose ranges.

A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included

Existing atrial models with detailed anatomical structure and multi-variable cardiac transmembrane current models are too complex to allow to combine an investigation of long time dycal properties of the heart rhythm with the ability to effectively simulate cardiac electrical activity during arrhythmia. Other ways of modeling need to be investigated. Moreover, many state-of-the-art models of the right atrium do not include an atrioventricular node (AVN) and only rarely—the sinoatrial node (SAN). A model of the heart tissue within the right atrium including the SAN and AVN nodes was developed. Looking for a minimal model, currently we are testing our approach on chosen well-known arrhythmias, which were until now obtained only using much more complicated models, or were only observed in a clinical setting. Ultimately, the goal is to obtain a model able to generate sequences of RR intervals specific for the arrhythmias involving the AV junction as well as for other phenomena occurring within the atrium. The model should be fast enough to allow the study of heart rate variability and arrhythmias at a time scale of thousands of heart beats in real-time. In the model of the right atrium proposed here, different kinds of cardiac tissues are described by sets of different equations, with most of them belonging to the class of Liénard nonlinear dynamical systems. We have developed a series of models of the right atrium with differing anatomical simplifications, in the form of a 2D mapping of the atrium or of an idealized cylindrical geometry, including only those anatomical details required to reproduce a given physiological phenomenon. The simulations allowed to reconstruct the phase relations between the sinus rhythm and the location and properties of a parasystolic source together with the effect of this source on the resultant heart rhythm. We model the action potential conduction time alternans through the atrioventricular AVN junction observed in cardiac tissue in electrophysiological studies during the ventricular-triggered atrial tachycardia. A simulation of the atrio-ventricular nodal reentry tachycardia was performed together with an entrainment procedure in which the arrhythmia circuit was located by measuring the post-pacing interval (PPI) at simulated mapping catheters. The generation and interpretation of RR times series is the ultimate goal of our research. However, to reach that goal we need first to (1) somehow verify the validity of the model of the atrium with the nodes included and (2) include in the model the effect of the sympathetic and vagal ANS. The current paper serves as a partial solution of the 1). In particular we show, that measuring the PPI–TCL entrainment response in proximal (possibly-the slow-conducting pathway), the distal and at a mid-distance from CS could help in rapid distinction of AVNRT from other atrial tachycardias. Our simulations support the hypothesis that the alternans of the conduction time between the atria and the ventricles in the AV orthodromic reciprocating tachycardia can occur within a single pathway. In the atrial parasystole simulation, we found a mathematical condition which allows for a rough estimation of the location of the parasystolic source within the atrium, both for simplified (planar) and the cylindrical geometry of the atrium. The planar and the cylindrical geometry yielded practically the same results of simulations.