Telemonitoring with Electronic Devices in Patients with a Single Ventricle Anatomy

Background A growing number of patients with a single ventricle anatomy, who had a Fontan palliation as a child, are now reaching adulthood. Many need an epimyocardial pacemaker system with an optional telemonitoring (TM) unit, which evaluates the collected data and sends it via Internet to the patient's physician. There are no data on the reliability and clinical relevance of these systems in this patient group. Methods We analyzed data in 48 consecutive patients (mean age 18 years, standard deviation 9 years) with a Fontan or Fontan-like palliation who received a cardiac implantable electronic device with a TM unit from Biotronik (Home Monitoring) or Medtronic (CareLink) between 2005 and 2020 with regard to the reliability and clinical relevance of the downloaded data. Results The observation period was from 4 months to 14 years (mean 7 years, standard deviation 3.9 years). A total of 2.9 event messages (EMs)/patient/month and 1.3 intracardiac electrogram recordings/patient/month were received. Two patients died during follow-up. The combination of regularly arriving statistical data and 313 clinically relevant EMs led to the modification of antiarrhythmic or diuretic medication, hospitalization with cardioversion or ablation, and cortisone therapy to avoid exit block in 21 (44%) patients. Conclusion TM is an instrument to receive functional and physiologic parameters of our Fontan patients. It provides the ability to respond early for signs of system failure, or arrhythmia, even if the patient is not experiencing any problems. It is a useful tool to manage this difficult patient population without frequent hospital visits.

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.

Using Machine Learning to Characterize Atrial Fibrotic Substrate from Intracardiac Signals using a Hybrid in silico and in vivo Dataset

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient’s fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.

Algorithm for cavo-tricuspid isthmus flutter on surface ECGs: the ACTIONS study

ObjectiveCavo-tricuspid isthmus atrial flutter (CTI-AFL) is an important arrhythmia to recognise because there is a highly effective and relatively low-risk ablation strategy. However, clinical experience has demonstrated that providers often have difficulty distinguishing AFL from atrial fibrillation.MethodsWe developed a novel ECG-based three-step algorithm to identify CTI-AFL based on established CTI flutter characteristics and verified on consecutive ablation cases of typical flutter, atypical flutter and atrial fibrillation. The algorithm assesses V1/inferior lead F-wave concordance, consistency of P-wave morphology and the presence of isoelectric intervals in the inferior leads. In this observation study, the algorithm was validated on a cohort of 50 second-year medical students. Students were paired in a control and experimental group, and each pair received 10 randomly selected ECGs (from a pool of 50 intracardiac electrogram-proven CTI-AFL and 50 AF or atypical AFL cases). The experimental group received a cover sheet with the CTI algorithm, and the control group received no additional guidance.ResultsThere was a statistically significant difference in the mean number of correctly identified ECGs among the students in the experimental and control groups (8.12 vs 5.68, p<0.001). Students who used the algorithm correctly identified 2.44 more ECGs as being CTI-AFL or not CTI-AFL. Using the electrophysiology study as the gold standard, the algorithm had an accuracy of 81%, sensitivity of 81%, specificity of 82%, positive predictive value of 78% and negative predictive value of 84% in identifying CTI-AFL.ConclusionWe developed a three-step ECG algorithm that provides a simple, sensitive, specific and accurate tool to identify CTI-AFL.

A novel cardiac signal processing system for electrophysiology procedures: early insights from the pure ep 2.0 study

Background Intracardiac electrogram data remain one of the primary diagnostic inputs guiding complex ablation procedures. However, the technology to collect, process, and display intracardiac signals has remained relatively unchanged for the past two decades. Purpose We test a new platform, the PURE EP™ 2.0 system (PEP; BioSig Technologies) for signal processing and display. Methods Identical electrocardiographic and intracardiac signal data were recorded during 15 AF ablation procedures from the PEP system, the signal recording system, and the 3D mapping system (Figure). The collected signals underwent blinded, controlled evaluation by three independent electrophysiologist reviewers to determine whether the PEP signals are a viable alternative to conventional sources and if it provides additional or clearer diagnostic information. Reviewers were asked to record the quality of each signal sample on a scale of 1–10 and select a rationale for their rating in a dropdown menu. Each paired signal rating was collected and unblinded for the analysis. If the reviewer rated the samples in the set within 1 point of each other, the PEP sample was deemed equivalent to the control. Using a 2+1 statistical method, the ratings from the three reviewers were then compared looking for at least two positive reviews for each PEP sample. Results Based on the ratings for each pair of signals, a cumulative total of 29 PEP signals out of 34 (85.3%) were rated as statistically equivalent or better for this dataset. In 35.5% of samples, the reviewers selected PEP because “more signal components were visible”. Conclusion The PURE EP 2.0 system is able to produce reliable and high-quality signals when compared to available standard of care systems. Further studies with larger dataset across multiple sites are needed to validate these results. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): BioSig Technologies

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.

Left bundle branch pacing improved heart function in a 10-year-old child after a 3-month follow-up

Aims As a physiological pacing strategy, left bundle branch pacing (LBBP) were used to correct left bundle branch block (LBBB), however, there is no relevant report in children. We aimed to evaluate the feasibility of LBBP in children. Methods and results Left bundle branch pacing was performed in a 10-year-old girl with a second-degree atrioventricular and LBBB. Under the guide of fluoroscopy, the pacing lead was deeply screwed into the interventricular septum to gain right bundle branch block (RBBB) pattern of paced QRS. Selective LBBP was achieved with a typical RBBB pattern of paced morphology and a discrete component between stimulus and ventricular activation in intracardiac electrogram and reached the standard of the stimulus to left ventricular activation time of 56 ms. At a 3-month follow-up, the LBBP acquired the reduction of left ventricular size and enhancement of left ventricular ejection fraction. Conclusion The application of LBBP in a child was first achieved with inspiring preliminary results. The LBBP can be carried out in children by cautiousness under the premise of strict grasp of indications.

P478Cardiac response to live music performance: effect of large-scale musical structure on action potential duration

Funding Acknowledgements This work has received funding from the ERC under the EU’s Horizon 2020 R&I programme (Grant agreement No. 788960) Background Strong emotions can trigger cardiac arrhythmias, but the heart-brain mechanism by which they do so is not well understood. Music induces strong emotions, precipitated by musical changes and intensified during live performance; it thus serves as a powerful tool through which to investigate heart-brain interaction. However, existing studies use short, artificial or pre-recorded music excerpts, out of context and classified into singular, simple emotion classes over which aggregate response are reported, ignoring the range of responses possible for the same music stimulus. None has considered electrical response to music as measured from the heart muscles. Purpose To evaluate the impact on action potential duration due to musical changes at large-scale structural boundaries in live music performance. Methods Patients implanted with biventricular pacemakers/ICDs are invited to a live classical piano concert. Prior to the concert, the patients’ pacemakers are programmed from CRT to dual chamber pacing at 80 bpm or ten above their intrinsic heart rate. Following a ten-minute adjustment period, they listen to three pieces lasting 15 minutes; this was subsequently expanded to five lasting 30 minutes. Continuous recordings of the intracardiac electrogram (EGM) signals are downloaded from the ICD lead connected to the left ventricle whilst the patients listen to the music. The pacemakers are returned to their original settings after the concert. The patients further provide annotations for perceived change boundaries and tension, as well as information on their music training/experience. We approximate the action potential duration (APD) using the action recovery interval (ARI) extracted from the EGM signal, and compare the ARIs before and after each structural boundary indicated in the music score. Results We analyze the ARI data surrounding 24 music structural boundaries. The first results are for the three patients (two male; one female) from the initial study day. We perform a two-sample t-test to assess the population means in ARI values before and after each of the 24 structural boundaries. The figure attached shows the statistically significant changes across structural boundaries for α = 0.05; the bar plots show the sample means and 95% confidence intervals (CI) for the 80 ARIs before and after a boundary, and report the p-values of the t-tests. Patients 1 and 3 each reacted significantly to three out of the 24 boundaries (12.5%), sometimes in opposite directions, and Patient 2 to 15 out of the 24 boundaries (62.5%). The CIs for the significant differences spanned the range (–4.4896,4.8745). Conclusions We show that structural boundaries, where music features change or transition, can produce significant changes in APD. A range of significant responses are observed, including contradictory ones, that span a nearly 10ms range, which could play a contributory role to clinical understanding of arrhythmias and emotion responses. Abstract Figure.