Utilization of and perceived need for simulators in clinical electrophysiology: results from an EHRA physician survey.

AIMS: Simulator training has been recently introduced in electrophysiology (EP) programmes in order to improve catheter manipulation skills without complication risks. The aim of this study is to survey the current use of EP simulators and the perceived need for these tools in clinical training and practice. METHODS AND RESULTS: A 20-item online questionnaire developed by the Scientific Initiatives Committee of the European Heart Rhythm Association (EHRA) in collaboration with EHRA Digital Committee was disseminated through the EHRA Scientific Research Network members, national EP groups, and social media platforms. Seventy-four respondents from 22 countries (73% males; 50% under 40 years old) completed the survey. Despite being perceived as useful among EP professionals (81%), EP simulators are rarely a part of the institutional cardiology training programme (20%) and only 18% of the respondents have an EP simulator at their institution. When available, simulators are mainly used in EP to train transseptal puncture, ablation, and mapping, followed by device implantation (cardiac resynchronization therapy [CRT], leadless, and conduction system pacing [CSP]). Almost all respondents (96%) believe that simulator programmes should be a part of the routine institutional EP training, hopefully developed by EHRA, in order to improve the efficacy and safety of EP procedures and in particular CSP 58%, CRT 42%, leadless pacing 38%, or complex arrhythmia ablations (VT 58%, PVI 45%, and PVC 42%). CONCLUSION: This current EHRA survey identified a perceived need but a lack of institutional simulator programme access for electrophysiologists who could benefit from it in order to speed up the learning curve process and reduce complications of complex EP procedures.

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

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).

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

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.

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.

AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS: A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.

Five-year safety and efficacy of leadless pacemakers in a Dutch cohort.

BACKGROUND: Adequate real-world safety and efficacy of leadless pacemakers (LPs) have been demonstrated up to 3 years after implantation. Longer-term data are warranted to assess the net clinical benefit of leadless pacing. OBJECTIVE: The purpose of this study was to evaluate the long-term safety and efficacy of LP therapy in a real-world cohort. METHODS: In this retrospective cohort study, all consecutive patients with a first LP implantation from December 21, 2012, to December 13, 2016, in 6 Dutch high-volume centers were included. The primary safety endpoint was the rate of major procedure- or device-related complications (ie, requiring surgery) at 5-year follow-up. Analyses were performed with and without Nanostim battery advisory-related complications. The primary efficacy endpoint was the percentage of patients with a pacing capture threshold ≤2.0 V at implantation and without ≥1.5-V increase at the last follow-up visit. RESULTS: A total of 179 patients were included (mean age 79 ± 9 years), 93 (52%) with a Nanostim and 86 (48%) with a Micra VR LP. Mean follow-up duration was 44 ± 26 months. Forty-one major complications occurred, of which 7 were not advisory related. The 5-year major complication rate was 4% without advisory-related complications and 27% including advisory-related complications. No advisory-related major complications occurred a median 10 days (range 0-88 days) postimplantation. The pacing capture threshold was low in 163 of 167 patients (98%) and stable in 157 of 160 (98%). CONCLUSION: The long-term major complication rate without advisory-related complications was low with LPs. No complications occurred after the acute phase and no infections occurred, which may be a specific benefit of LPs. The performance was adequate with a stable pacing capture threshold.

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.

Predicted Need for Atrial and Ventricular Pacing Per Indication Group in Patients With Dual-Chamber Pacemakers.

BACKGROUND: Bradyarrhythmias are adequately treated with pacemakers. There are different pacing modes (single-chamber, dual-chamber, cardiac resynchronisation therapy [CRT] and conduction system pacing [CSP]) and a choice between leadless or transvenous pacemakers. The expected pacing need is important for determining optimal pacing mode and device type. This study aimed to evaluate atrial pacing (AP) and ventricular pacing (VP) percentages over time for the most common pacing indications. METHODS: Included patients were aged ≥18 years with a dual-chamber rate-modulated [DDD(R)] pacemaker implantation and ≥1 year of follow-up at a tertiary centre between January 2008 and January 2020. Baseline characteristics, AP and VP at yearly follow-up visits up to 6 years after implantation were retrieved from the medical records. RESULTS: A total of 381 patients were included. Primary pacing indications were incomplete atrioventricular block (AVB) in 85 (22%), complete AVB in 156 (41%) and sinus node dysfunction (SND) in 140 (37%) patients. Mean age at implantation was 71±14, 69±17 and 68±14 years, respectively (p=0.23). Median follow-up was 42 months (25-68 months). Overall, AP was highest in SND with median 37% (7%-75%) versus 7% (1%-26%) in incomplete AVB and 3% (1%-16%) in complete AVB (p<0.001); VP was highest in complete AVB with median 98% (43%-100%) versus 44% (7%-94%) in incomplete AVB and 3% (1%-14%) in SND (p<0.001). Ventricular pacing significantly increased over time in patients with incomplete AVB and SND (both p=0.001). CONCLUSIONS: These results confirm the pathophysiology of different pacing indications, causing clear differences in pacing need and expected battery longevity. They may help guide optimal pacing mode and suitability for leadless or physiological pacing.

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.

BACKGROUND: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. 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).

Reduction in long-term mortality using remote device monitoring in a large real-world population of patients with implantable defibrillators.

AIMS: Remote monitoring (RM) for implantable cardioverter-defibrillators (ICDs) is advocated for the potential of early detection of disease progression and device dysfunction. While studies have examined the effect of RM on clinical outcomes in carefully selected populations of heart failure patients implanted with ICDs from a single vendor, there is a paucity of data in real-world patients. We aimed to assess the long-term effect of RM in a representative ICD population using real-world data. METHODS AND RESULTS: This is an observational retrospective longitudinal study of 1004 patients implanted with an ICD or cardiac resynchronization therapy device (CRT-D) from all device vendors between 2010 and 2021. Patients started on RM (N = 403) within 90 days following de novo device implantation and yearly in-office visits were compared with patients with only bi-yearly in-office follow-up (non-RM, N = 601). In a propensity score matched cohort of 430 patients (mean age 61.4 ± 14.3 years, 26.7% female), all-cause mortality at 4-year was 12.6% in the RM and 27.7% in the non-RM group [hazard ratio (HR) 0.52, 95% confidence interval (CI) 0.32-0.82; P = 0.005]. No difference in inappropriate ICD-therapy (HR 1.90, 95% CI 0.86-4.21; P = 0.122) was observed. The risk of appropriate ICD-therapy (HR 1.71, 95% CI 1.07-2.74; P = 0.026) was higher in the RM group. CONCLUSION: Remote monitoring was associated with a reduction in long-term all-cause and cardiac mortality compared with traditional office visits in a real-world ICD population.

Timing and mid-term outcomes of using leadless pacemakers as replacement for infected cardiac implantable electronic devices.

BACKGROUND: Cardiac implantable electronic device (CIED) infections have a high morbidity and mortality and are an indication of device extraction. As a replacement, leadless pacemakers (LPs) may be preferable due to a low infection risk, but mid-term data on reinfections is lacking. Moreover, early LP reimplantation in pacemaker-dependent patients would circumvent the need for temporary pacemakers. METHODS: We included all patients with LP implantation as a replacement for an infected CIED, between January 2013 and December 2021. The occurrence of reinfection was assessed during standard follow-up visits. RESULTS: Twenty-nine patients (mean age 81 ± 9 years) were included, of which 21 (73%) had a pocket infection, 7 (24%) endocarditis, and 1 (3%) a systemic infection without endocarditis. All LP implantations were successful. LPs were implanted before extraction (n = 4, 13%), simultaneously with extraction (n = 5, 17%) and after extraction (n = 20, 70%). No reinfection occurred during the follow-up of median 32 months (IQR 13-66 months). Repeat blood cultures obtained in 9 (30%) patients and transthoracic echocardiography in all 7 patients with pacemaker endocarditis were negative for reinfection. In a subset of 6 LPs extracted during follow-up due to early battery depletion, prophylactically after the battery advisory or due to non-capture (median 36 months (range 0-67 months) post-implantation), histopathologic examination of tissues around the LPs showed no signs of infection. CONCLUSIONS: After replacing infected CIEDs for an LP, no reinfections occurred in over 2.5 years follow-up. These results confirm that in case of CIED infection, the LP is an appealing replacement device. LP implantation before CIED extraction is feasible.

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review.

Background: Current implantable cardioverter-defibrillator (ICD) devices are equipped with a device-embedded accelerometer capable of capturing physical activity (PA). In contrast, wearable accelerometer-based methods enable the measurement of physical behavior (PB) that encompasses not only PA but also sleep behavior, sedentary time, and rest-activity patterns. Objective: This systematic review evaluates accelerometer-based methods used in patients carrying an ICD or at high risk of sudden cardiac death. Methods: Papers were identified via the OVID MEDLINE and OVID EMBASE databases. PB could be assessed using a wearable accelerometer or an embedded accelerometer in the ICD. Results: A total of 52 papers were deemed appropriate for this review. Out of these studies, 30 examined device-embedded accelerometry (189,811 patients), 19 examined wearable accelerometry (1601 patients), and 3 validated wearable accelerometry against device-embedded accelerometry (106 patients). The main findings were that a low level of PA after implantation of the ICD and a decline in PA were both associated with an increased risk of mortality, heart failure hospitalization, and appropriate ICD shock. Second, PA was affected by cardiac factors (eg, onset of atrial fibrillation, ICD shocks) and noncardiac factors (eg, seasonal differences, societal factors). Conclusion: This review demonstrated the potential of accelerometer-measured PA as a marker of clinical deterioration and ventricular arrhythmias. Notwithstanding that the evidence of PB assessed using wearable accelerometry was limited, there seems to be potential for accelerometers to improve early warning systems and facilitate preventative and proactive strategies.

Long-term performance of a novel communicating antitachycardia pacing-enabled leadless pacemaker and subcutaneous implantable cardioverter-defibrillator system: A comprehensive preclinical study.

BACKGROUND: Subcutaneous implantable cardioverter-defibrillators (S-ICDs) and leadless pacemakers (LPs) are intended to diminish transvenous lead-related complications. However, S-ICDs do not deliver antibradycardia pacing or antitachycardia pacing, and currently, there is no commercially available coordinated leadless option for patients with defibrillator and (expected) pacing needs. OBJECTIVE: We evaluated the performance, safety, and potential replacement strategies of a novel modular cardiac rhythm management (mCRM) system, a wirelessly communicating antitachycardia pacing-enabled LP and S-ICD in a preclinical model. METHODS: LP implantation was attempted in 68 canine subjects, and in 38 an S-ICD was implanted as well. Animals were evaluated serially up to 18 months. At all evaluations, communication thresholds (CTs) between the devices, LP electrical parameters, and system-related complications were assessed. Different replacement strategies were tested. RESULTS: The LP was successfully implanted in 67 of 68 (98.5%) and the concomitant S-ICD in 38 of 38 (100%). mCRM communication was successful in 1022 of 1024 evaluations (99.8%). The mean CT was 2.2 ± 0.7 V at implantation and stable afterward (18 months: 1.8 ± 0.7 V). In multivariable analysis, larger LP-to-S-ICD angle and dorsal posture were associated with higher CTs. At implantation, the mean pacing capture threshold, impedance, and R-wave amplitude were 0.3 ± 0.1 V, 898.4 ± 198.9 Ω, and 26.4 ± 8.2 mV. The mean pacing capture threshold remained stable and impedance and R-wave amplitudes were within acceptable ranges throughout (0.7 ± 0.4 V, 619.1 ± 90.6 Ω, and 20.1 ± 8.4 mV at 18 months). Different replacement strategies seem feasible. CONCLUSION: This first mCRM system demonstrated excellent performance up to 18 months in a preclinical model.

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.

Sequential Defects in Cardiac Lineage Commitment and Maturation Cause Hypoplastic Left Heart Syndrome.

BACKGROUND: Complex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role. METHODS: To identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed whole-exome sequencing of 87 HLHS parent-offspring trios, nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, single cell RNA sequencing, and 3D modeling in induced pluripotent stem cells from 3 patients with HLHS and 3 controls. RESULTS: Gene set enrichment and protein network analyses of damaging de novo mutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell cycle and cardiomyocyte maturation. Single-cell and 3D modeling with induced pluripotent stem cells demonstrated intrinsic defects in the cell cycle/unfolded protein response/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte subtype differentiation/maturation in HLHS. Premature cell cycle exit of ventricular cardiomyocytes from patients with HLHS prevented normal tissue responses to developmental signals for growth, leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues. CONCLUSIONS: Our results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.

Tissues attached to retrieved leadless pacemakers: Histopathological evaluation of tissue composition in relation to implantation time and complications.

BACKGROUND: Leadless pacemakers (LPs) have proven safe and effective, but device revisions remain necessary. Either replacing the LP or implanting a new adjacent LP is feasible. Replacement seems more appealing, but encapsulation and tissue adhesions may hamper the safety and efficacy of LP retrieval. OBJECTIVE: We determined the incidence and cellular characteristics of tissue adherent to retrieved LPs and the potential implications for end-of-life strategy. METHODS: All 15 consecutive successful Nanostim LP retrievals in a tertiary center were included. We assessed the histopathology of adherent tissue and obtained clinical characteristics. RESULTS: Adherent tissue was present in 14 of 15 retrievals (93%; median implantation duration 36 months; range 0-96 months). The tissue consisted of fibrosis (n = 2), fibrosis and thrombus (n = 9), or thrombus only (n = 3). In short-term retrievals (<1 year), mostly fresh thrombi without fibrosis were seen. In later retrievals, the tissue consisted of fibrosis often with organizing or lytic thrombi. Fibrosis showed different stages of organization, notably early fibrocellular and later fibrosclerotic tissue. Inflammatory cells were seen (n = 4) without signs of infection. Tricuspid valve material was retrieved in 1 patient after 36 months, resulting in increased tricuspid regurgitation. CONCLUSION: Our results suggest that fibrosis and thrombus adherent to LPs are common and encapsulate the LP as seen in transvenous pacemakers. LPs may adhere to the tricuspid valve or subvalvular apparatus affecting retrieval safety. The end-of-life strategy should be optimized by incorporating risk stratification for excessive fibrotic encapsulation and adhesions.

Rare variants in KDR, encoding VEGF Receptor 2, are associated with tetralogy of Fallot.

PURPOSE: Rare genetic variants in KDR, encoding the vascular endothelial growth factor receptor 2 (VEGFR2), have been reported in patients with tetralogy of Fallot (TOF). However, their role in disease causality and pathogenesis remains unclear. METHODS: We conducted exome sequencing in a familial case of TOF and large-scale genetic studies, including burden testing, in >1,500 patients with TOF. We studied gene-targeted mice and conducted cell-based assays to explore the role of KDR genetic variation in the etiology of TOF. RESULTS: Exome sequencing in a family with two siblings affected by TOF revealed biallelic missense variants in KDR. Studies in knock-in mice and in HEK 293T cells identified embryonic lethality for one variant when occurring in the homozygous state, and a significantly reduced VEGFR2 phosphorylation for both variants. Rare variant burden analysis conducted in a set of 1,569 patients of European descent with TOF identified a 46-fold enrichment of protein-truncating variants (PTVs) in TOF cases compared to controls (P = 7 × 10-11). CONCLUSION: Rare KDR variants, in particular PTVs, strongly associate with TOF, likely in the setting of different inheritance patterns. Supported by genetic and in vivo and in vitro functional analysis, we propose loss-of-function of VEGFR2 as one of the mechanisms involved in the pathogenesis of TOF.