Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries

Rationale: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. Objective: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. Methods and Results: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10-10, OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10-5). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A, which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart. Conclusions: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A. Genomic and functional data support a causal role of WNT5A at the locus

Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries

RATIONALE: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. OBJECTIVE: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. METHODS AND RESULTS: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10-10, OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10-5). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A, which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart. CONCLUSIONS: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A. Genomic and functional data support a causal role of WNT5A at the locus

Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy

Quantification of cardiac motion with cine Cardiac Magnetic Resonance Imaging (CMRI) is an integral part of arrhythmogenic right ventricular cardiomyopathy (ARVC) diagnosis. Yet, the expert evaluation of motion abnormalities with CMRI is a challenging task. To automatically assess cardiac motion, we register CMRIs from different time points of the cardiac cycle using Implicit Neural Representations (INRs) and perform a biomechanically informed regularization inspired by the myocardial incompressibility assumption. To enhance the registration performance, our method first rectifies the inter-slice misalignment inherent to CMRI by performing a rigid registration guided by the long-axis views, and then increases the through-plane resolution using an unsupervised deep learning super-resolution approach. Finally, we propose to synergically combine information from short-axis and 4-chamber long-axis views, along with an initialization to incorporate information from multiple cardiac time points. Thereafter, to quantify cardiac motion, we calculate global and segmental strain over a cardiac cycle and compute the peak strain. The evaluation of the method is performed on a dataset of cine CMRI scans from 47 ARVC patients and 67 controls. Our results show that inter-slice alignment and generation of super-resolved volumes combined with joint analysis of the two cardiac views, notably improves registration performance. Furthermore, the proposed initialization yields more physiologically plausible registrations. The significant differences in the peak strain, discerned between the ARVC patients and healthy controls suggest that automated motion quantification methods may assist in diagnosis and provide further understanding of disease-specific alterations of cardiac motion

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding

[EN] Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N=6 digital hearts. The model, termed ¿virtual dissection,¿ was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%¿97.7%) and 93.5% in external (IQR: 91.9%¿94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%¿94.6%) vs. 94.4% (IQR: 92.8%¿95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.Research reported in this publication was supported by grants from the National Institutes of Health under award numbers R01 HL149134 and R01 HL83359.Feng, R.; Deb, B.; Ganesan, P.; Tjong, FV..; Rogers, AJ.; Ruiperez-Campillo, S.; Somani, S.... (2023). Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding. Frontiers in Cardiovascular Medicine. 10. https://doi.org/10.3389/fcvm.2023.11892931

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy

BACKGROUND: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence–based methods can be used to develop personalized prediction models and improve early-warning systems. OBJECTIVE: The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy. METHODS: This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring: 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing). The feasibility of the prediction engine as a clinical tool, the SafeHeart Platform, is assessed during the feasibility study. RESULTS: Development study recruitment commenced in 2021. The feasibility study starts in 2022. CONCLUSION: SafeHeart is the first study to prospectively collect a multimodal data set to construct a personalized prediction engine for ICD therapy. Moreover, SafeHeart explores the integration and added value of detailed objective accelerometer data in the prediction of clinical events. The translation of the SafeHeart Platform to clinical practice is examined during the feasibility study

Acute and 3-Month Performance of a Communicating Leadless Antitachycardia Pacemaker and Subcutaneous Implantable Defibrillator

Objectives The primary objective was to assess the acute and 3-month performance of the modular antitachycardia pacing (ATP)-enabled leadless pacemaker (LP) and subcutaneous implantable cardioverter-defibrillator (S-ICD) system, particularly device–device communication and ATP delivery. Background Transvenous pacemakers and implantable cardioverter-defibrillators (ICDs) have considerable rates of lead complications. We examined the next step in multicomponent leadless cardiac rhythm management: feasibility of pacing (including ATP) by a LP, commanded by an implanted S-ICD through wireless, intrabody, device–device communication. Methods The combined modular cardiac rhythm management therapy system of the LP and S-ICD prototypes was evaluated in 3 animal models (ovine, porcine, and canine) both in acute and chronic (90 days) experiments. LP performance, S-ICD to LP communication, S-ICD and LP rhythm discrimination, and ATP delivery triggered by the S-ICD were tested. Results The LP and S-ICD were successfully implanted in 98% of the animals (39 of 40). Of the 39 animals, 23 were followed up for 90 days post-implant. LP performance was adequate and exhibited appropriate VVI behavior during the 90 days of follow-up in all tested animals. Unidirectional communication between the S-ICD and LP was successful in 99% (398 of 401) of attempts, resulting in 100% ATP delivery by the LP (10 beats at 81% of the coupling interval). Adequate S-ICD sensing was observed during normal sinus rhythm, LP pacing, and ventricular tachycardia/ventricular fibrillation. Conclusions This study presents the preclinical acute and chronic performance of the combined function of an ATP-enabled LP and S-ICD. Appropriate VVI functionality, successful wireless device–device communication, and ATP delivery were demonstrated by the LP. Clinical studies on safety and performance are needed

NL-EVDR: Netherlands—ExtraVascular Device Registry

Cardiac implantable electronic device (CIED) therapy is an essential element in treating cardiac arrhythmias. Despite their benefits, conventional transvenous CIEDs are associated with a significant risk of mainly pocket- and lead-related complications. To overcome these complications, extravascular devices (EVDs), such as the subcutaneous implantable cardioverter-defibrillator and intracardiac leadless pacemaker, have been developed. In the near future, several other innovative EVDs will become available. However, it is difficult to evaluate EVDs in large studies because of high costs, lack of long-term follow-up, imprecise data or selected patient populations. To improve evaluation of these technologies, real-world, large-scale, long-term data are of utmost importance. A Dutch registry-based study seems to be a unique possibility for this goal due to early involvement of Dutch hospitals in novel CIEDs and an existing quality control infrastructure, the Netherlands Heart Registration (NHR). Therefore, we will soon start the Netherlands—ExtraVascular Device Registry (NL-EVDR), a Dutch nationwide registry with long-term follow-up of EVDs. The NL-EVDR will be incorporated in NHR’s device registry. Additional EVD-specific variables will be collected both retrospectively and prospectively. Hence, combining Dutch EVD data will provide highly relevant information on safety and efficacy. As a first step, a pilot project has started in selected centres in October 2022 to optimise data collection

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillatorResearch in context

Summary: Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)