The Dutch Idiopathic Ventricular Fibrillation Registry: progress report on the quest to identify the unidentifiable

BACKGROUND

Idiopathic ventricular fibrillation (iVF) is a rare cause of sudden cardiac arrest and, by definition, a diagnosis of exclusion. Due to the rarity of the disease, previous and current studies are limited by their retrospective design and small patient numbers. Even though the incidence of iVF has declined owing to the identification of new disease entities, an important subgroup of patients remains.

AIM

To expand the existing Dutch iVF Registry into a large nationwide cohort of patients initially diagnosed with iVF, to reveal the underlying cause of iVF in these patients, and to improve arrhythmia management.

METHODS

The Dutch iVF Registry includes sudden cardiac arrest survivors with an initial diagnosis of iVF. Clinical data and outcomes are collected. Outcomes include subsequent detection of a diagnosis other than 'idiopathic', arrhythmia recurrence and death. Non-invasive electrocardiographic imaging is used to investigate electropathological substrates and triggers of VF.

RESULTS

To date, 432 patients have been included in the registry (median age at event 40 years (interquartile range 28-52)), 61% male. During a median follow-up of 6 (2-12) years, 38 patients (9%) received a diagnosis other than 'idiopathic'. Eleven iVF patients were characterised with electrocardiographic imaging.

CONCLUSION

The Dutch iVF Registry is currently the largest of its kind worldwide. In this heterogeneous population of index patients, we aim to identify common functional denominators associated with iVF. With the implementation of non-invasive electrocardiographic imaging and other diagnostic modalities (e.g. echocardiographic deformation, cardiac magnetic resonance), we advance the possibilities to reveal pro-fibrillatory substrates.

Comparing adolescent- and adult-onset unexplained cardiac arrest: Results from the Dutch Idiopathic VF Registry

BACKGROUND

Current cohorts of patients with idiopathic ventricular fibrillation (IVF) primarily include adult-onset patients. Underlying causes of sudden cardiac arrest vary with age; therefore, underlying causes and disease course may differ for adolescent-onset vs adult-onset patients.

OBJECTIVE

The purpose of this study was to compare adolescent-onset with adult-onset patients having an initially unexplained cause of VF.

METHODS

The study included 39 patients with an index event aged ≤19 years (adolescent-onset) and 417 adult-onset patients from the Dutch Idiopathic VF Registry. Data on event circumstances, clinical characteristics, change in diagnosis, and arrhythmia recurrences were collected and compared between the 2 groups.

RESULTS

In total, 42 patients received an underlying diagnosis during follow-up (median 7 [2-12] years), with similar yields (15% adolescent-onset vs 9% adult-onset; P = .16). Among the remaining unexplained patients, adolescent-onset patients (n = 33) had their index event at a median age of 17 [16-18] years, and 72% were male. The youngest patient was aged 13 years. In comparison with adults (n = 381), adolescent-onset patients more often had their index event during exercise (P <.01). Adolescent-onset patients experienced more appropriate implantable cardioverter-defibrillator (ICD) therapy during follow-up compared with adults (44% vs 26%; P = .03). Inappropriate ICD therapy (26% vs 17%; P = .19), ICD complications (19% vs 14%; P = .41), and deaths (3% vs 4%; P = 1) did not significantly differ between adolescent-onset and adult-onset patients.

CONCLUSION

IVF may occur during adolescence. Adolescent-onset patients more often present during exercise compared with adults. Furthermore, they are more vulnerable to ventricular arrhythmias as reflected by a higher incidence of appropriate ICD therapy.

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study

Aims

Many portable electrocardiogram (ECG) devices have been developed to monitor patients at home, but the majority of these devices are single lead and only intended for rhythm disorders. We developed the miniECG, a smartphone-sized portable device with four dry electrodes capable of recording a high-quality multi-lead ECG by placing the device on the chest. The aim of our study was to investigate the ability of the miniECG to detect occlusive myocardial infarction (OMI) in patients with chest pain.

Methods and results

Patients presenting with acute chest pain at the emergency department of the University Medical Center Utrecht or Meander Medical Center, between May 2021 and February 2022, were included in the study. The clinical 12-lead ECG and the miniECG before coronary intervention were recorded. The recordings were evaluated by cardiologists and compared the outcome of the coronary angiography, if performed. A total of 369 patients were measured with the miniECG, 46 of whom had OMI. The miniECG detected OMI with a sensitivity and specificity of 65 and 92%, compared with 83 and 90% for the 12-lead ECG. Sensitivity of the miniECG was similar for different culprit vessels.

Conclusion

The miniECG can record a multi-lead ECG and rule-in ST-segment deviation in patients with occluded or near-occluded coronary arteries from different culprit vessels without many false alarms. Further research is required to add automated analysis to the recordings and to show feasibility to use the miniECG by patients at home.

Mitral Annular Disjunction in Idiopathic Ventricular Fibrillation Patients: Just a Bystander or a Potential Cause?

AIMS

Previously, we demonstrated that inferolateral mitral annular disjunction (MAD) is more prevalent in patients with idiopathic ventricular fibrillation (IVF) than in healthy controls. In the present study, we advanced the insights into the prevalence and ventricular arrhythmogenicity by inferolateral MAD in an even larger IVF cohort.

METHODS AND RESULTS

This retrospective multicentre study included 185 IVF patients (median age 39 [27, 52] years, 40% female). Cardiac magnetic resonance images were analysed for mitral valve and annular abnormalities and late gadolinium enhancement. Clinical characteristics were compared between patients with and without MAD. MAD in any of the 4 locations was present in 112 (61%) IVF patients and inferolateral MAD was identified in 24 (13%) IVF patients. Mitral valve prolapse (MVP) was found in 13 (7%) IVF patients. MVP was more prevalent in patients with inferolateral MAD compared with patients without inferolateral MAD(42% vs. 2%, p < 0.001). Proarrhythmic characteristics in terms of a high burden of premature ventricular complexes (PVC) and non-sustained ventricular tachycardia (VT) were more prevalent in patients with inferolateral MAD compared to patients without inferolateral MAD (67% vs. 23%, p < 0.001 and 63% vs 41%, p = 0.046, respectively). Appropriate implantable cardioverter defibrillator therapy during follow-up was comparable for IVF patients with or without inferolateral MAD (13% vs. 18%, p = 0.579).

CONCLUSION

A high prevalence of inferolateral MAD and MVP is a consistent finding in this large IVF cohort. The presence of inferolateral MAD is associated with a higher PVC burden and non-sustained VTs. Further research is needed to explain this potential interplay.

Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study

Aims

Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.

Methods and results

Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%).

Conclusion

This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.

The genetic basis of apparently idiopathic ventricular fibrillation: a retrospective overview

AIMS

During the diagnostic work-up of patients with idiopathic ventricular fibrillation (VF), next-generation sequencing panels can be considered to identify genotypes associated with arrhythmias. However, consensus for gene panel testing is still lacking, and variants of uncertain significance (VUS) are often identified. The aim of this study was to evaluate genetic testing and its results in idiopathic VF patients.

METHODS AND RESULTS

We investigated 419 patients with available medical records from the Dutch Idiopathic VF Registry. Genetic testing was performed in 379 (91%) patients [median age at event 39 years (27-51), 60% male]. Single-gene testing was performed in 87 patients (23%) and was initiated more often in patients with idiopathic VF before 2010. Panel testing was performed in 292 patients (77%). The majority of causal (likely) pathogenic variants (LP/P, n = 56, 15%) entailed the DPP6 risk haplotype (n = 39, 70%). Moreover, 10 LP/P variants were found in cardiomyopathy genes (FLNC, MYL2, MYH7, PLN (two), TTN (four), RBM20), and 7 LP/P variants were identified in genes associated with cardiac arrhythmias (KCNQ1, SCN5A (2), RYR2 (four)). For eight patients (2%), identification of an LP/P variant resulted in a change of diagnosis. In 113 patients (30%), a VUS was identified. Broad panel testing resulted in a higher incidence of VUS in comparison to single-gene testing (38% vs. 3%, P < 0.001).

CONCLUSION

Almost all patients from the registry underwent, albeit not broad, genetic testing. The genetic yield of causal LP/P variants in idiopathic VF patients is 5%, increasing to 15% when including DPP6. In specific cases, the LP/P variant is the underlying diagnosis. A gene panel specifically for idiopathic VF patients is proposed.

Outcomes in Dutch DPP6 risk haplotype for familial idiopathic ventricular fibrillation: a focused update

BACKGROUND

The genetic risk haplotype DPP6 has been linked to familial idiopathic ventricular fibrillation (IVF), but the associated long-term outcomes are unknown.

METHODS

DPP6 risk haplotype-positive family members (DPP6 cases) and their risk haplotype-negative relatives (DPP6 controls) were included. Clinical follow-up data were collected through March 2023. Implantable cardioverter-defibrillator (ICD) indication was divided in primary or secondary prevention. Cumulative survival and event rates were calculated.

RESULTS

We included 327 DPP6 cases and 315 DPP6 controls. Median follow-up time was 9 years (interquartile range: 4-12). Of the DPP6 cases, 129 (39%) reached the composite endpoint of appropriate ICD shock, sudden cardiac arrest or death, at a median age of 45 years (range: 15-97). Median overall survival was 83 years and 87 years for DPP6 cases and DPP6 controls, respectively (p < 0.001). In DPP6 cases, median overall survival was shorter for males (74 years) than females (85 years) (p < 0.001). Of the DPP6 cases, 97 (30%) died, at a median age of 50 years. With a prophylactic ICD implantation advise based on risk haplotype, sex and age, 137 (42%) of DPP6 cases received an ICD, for primary prevention (n = 109) or secondary prevention (n = 28). In the primary prevention subgroup, 10 patients experienced a total of 34 appropriate ICD shocks, and there were no deaths during follow-up. DPP6 cases with a secondary prevention ICD experienced a total of 231 appropriate ICD shocks.

CONCLUSION

Patients with the DPP6 risk haplotype, particularly males, are at an increased risk of IVF and sudden cardiac death. Using a risk stratification approach based on risk haplotype, sex and age, a substantial proportion of patients with a primary prevention ICD experienced appropriate ICD shocks, showing the benefit of prophylactic ICD implantation with this strategy.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Work-Related Well-Being Among Dutch Cardiologists - A National Survey

This is the first study to provide a holistic examination of cardiologists' well-being, investigating positive and negative dimensions, and its determinants. We conducted a national, multicenter, self-administered web-based questionnaire. We used frequencies to depict scores on three well-being indicators (professional fulfillment, work exhaustion and interpersonal disengagement) and performed three multiple regression analyses to elucidate their determinants. Cardiologists' mean scores (scale 1 to 5) were 3.85 (SD = 0.62) for professional fulfillment, 2.25 (SD = 0.97) for work exhaustion and 2.04 (SD = 0.80) for interpersonal disengagement. Workload, work-home interference and team atmosphere predicted the negative dimensions of well-being. Autonomy predicted cardiologists' professional fulfillment. Physician-patient interactions, person-job fit and individual resilience affected both dimensions. Dutch cardiologists score relatively high on professional fulfillment and average on work exhaustion and interpersonal disengagement. In order to foster cardiologists' well-being it is critical to increase energy providing work- and individual aspects.

STereotactic Arrhythmia Radioablation (STAR): the Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary consortium (STOPSTORM.eu) and review of current patterns of STAR practice in Europe

The EU Horizon 2020 Framework-funded Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary (STOPSTORM) consortium has been established as a large research network for investigating STereotactic Arrhythmia Radioablation (STAR) for ventricular tachycardia (VT). The aim is to provide a pooled treatment database to evaluate patterns of practice and outcomes of STAR and finally to harmonize STAR within Europe. The consortium comprises 31 clinical and research institutions. The project is divided into nine work packages (WPs): (i) observational cohort; (ii) standardization and harmonization of target delineation; (iii) harmonized prospective cohort; (iv) quality assurance (QA); (v) analysis and evaluation; (vi, ix) ethics and regulations; and (vii, viii) project coordination and dissemination. To provide a review of current clinical STAR practice in Europe, a comprehensive questionnaire was performed at project start. The STOPSTORM Institutions' experience in VT catheter ablation (83% ≥ 20 ann.) and stereotactic body radiotherapy (59% > 200 ann.) was adequate, and 84 STAR treatments were performed until project launch, while 8/22 centres already recruited VT patients in national clinical trials. The majority currently base their target definition on mapping during VT (96%) and/or pace mapping (75%), reduced voltage areas (63%), or late ventricular potentials (75%) during sinus rhythm. The majority currently apply a single-fraction dose of 25 Gy while planning techniques and dose prescription methods vary greatly. The current clinical STAR practice in the STOPSTORM consortium highlights potential areas of optimization and harmonization for substrate mapping, target delineation, motion management, dosimetry, and QA, which will be addressed in the various WPs.

The relationship between physicians' self-kindness and professional fulfillment and the mediating role of personal resilience and work-home interference: A cross-sectional study

BACKGROUND

Professional fulfillment is crucial for physicians' well-being and optimal patient care. Highly demanding work environments, perfectionism and self-critical attitudes jeopardize physicians' professional fulfillment.

OBJECTIVE

To explore to what extent a kinder attitude towards the self, i.e. self-kindness, was associated with physicians' professional fulfillment and whether this relationship was mediated by personal resilience and work-home interference.

METHODS

In 2020, cardiologists (n = 374) in the Netherlands participated in a web-based survey. Self-kindness was measured by the self-kindness subscale of the Self-Compassion Scale, personal resilience by the Brief Resilience Scale, work-home interference by the negative Work-Home Interference subscale of the Survey Work-Home Interaction-Nijmegen, and professional fulfillment by the corresponding subscale of the Professional Fulfillment Index. Using Hayes' SPSS macro PROCESS v3.5, the authors tested the parallel mediation model.

RESULTS

Self-kindness was not directly associated with professional fulfillment (direct effect = .042, p = .36, 95% CI: -0.048, 0.132). Self-kindness was indirectly related to professional fulfillment through individual resilience (indirect effect = .049, 95% CI: .020, 0.086) and work-home interference (indirect effect = .057, 95% CI: .023, 096).

CONCLUSIONS

This study suggests that improving physicians' self-kindness may enhance professional fulfillment through personal resilience and work-home interference. Our findings may stimulate and remind physicians to be kind towards themselves as it may benefit them and their patients.

Atrial fibrillation begets ventricular fibrillation when the AV node fails

Deterioration of atrial fibrillation into ventricular fibrillation has frequently been described in patients with pre-excitation of the ventricles. We report two cases of atrial fibrillation without pre-excitation leading to rapid ventricular tachycardias and recurrent implantable cardioverter defibrillator therapy in young idiopathic ventricular fibrillation patients.

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

AIMS

This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA.

METHODS AND RESULTS

A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66-0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58-0.64) and 0.57 (95% CI 0.54-0.60), P < 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai).

CONCLUSION

Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.

Prevalence of Mitral Annulus Disjunction and Mitral Valve Prolapse in Patients With Idiopathic Ventricular Fibrillation

Background

Idiopathic ventricular fibrillation (IVF) is diagnosed in patients with ventricular fibrillation of which the origin is not identified after extensive evaluations. Recent studies suggest an association between mitral annulus disjunction (MAD), mitral valve prolapse (MVP), and ventricular arrhythmias. The prevalence of MAD and MVP in patients with IVF in this regard is not well established. We aimed to explore the prevalence of MAD and MVP in a consecutive cohort of patients with IVF compared with matched controls.

Methods and Results

In this retrospective, multicenter cohort study, cardiac magnetic resonance images from patients with IVF (ie, negative for ischemia, cardiomyopathy, and channelopathies) and age‐ and sex‐matched control subjects were analyzed for the presence of MAD (≥2 mm) and MVP (>2 mm). In total, 72 patients (mean age 39±14 years, 42% women) and 72 control subjects (mean age 41±11 years, 42% women) were included. MAD in the inferolateral wall was more prevalent in patients with IVF versus healthy controls (7 [11%] versus 1 [1%], P=0.024). MVP was only seen in patients with IVF and not in controls (5 [7%] versus 0 [0%], P=0.016). MAD was observed in both patients with (n=4) and without (n=3) MVP.

Conclusions

Inferolateral MAD and MVP were significantly more prevalent in patients with IVF compared with healthy controls. The authors advocate that evaluation of the mitral valve region deserves extra attention in the extensive screening of patients with unexplained cardiac arrest. These findings support further exploration of the pathophysiological mechanisms underlying a subset of IVF that associates with MAD and MVP.

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders. Eur Heart J Digit Health 2022;3:390-404. [PMID: 36712164 PMCID: PMC9707974 DOI: 10.1093/ehjdh/ztac038]

Aims

Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate.

Methods and results

We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to 'black box' DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the 'black box' DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset.

Conclusions

Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.

Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks

Aims

While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their ‘black-box’ characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN.

Methods and results

In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44–62], and median left ventricular ejection fraction of 30% (IQR 23–39). A total of 115 patients (16.5%) reached the study outcome. Factors F₈ (prolonged PR-interval and P-wave duration, P < 0.005), F₁₅ (reduced P-wave height, P = 0.04), F₂₅ (increased right bundle branch delay, P = 0.02), F₂₇ (P-wave axis P < 0.005), and F₃₂ (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA.

Conclusion

Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities.

Low 30-day Mortality after Atrial Fibrillation Ablation; Results from the Netherlands Heart Registration: NHR-AF Mortality Study

This study presents the 30-day mortality of patients who underwent atrial fibrillation (AF) ablation between 2013 - 2020 and were registered in the Netherlands Heart Registration. In total, 30,197/30,238 (99.9%) patients were analyzed. Fifteen (0.05%) died within 30 days. Nine deaths were considered procedurally related, four were considered non-procedural related, and in two patients the cause of death was unknown.

Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk

Aims

Incorporation of sex in study design can lead to discoveries in medical research. Deep neural networks (DNNs) accurately predict sex based on the electrocardiogram (ECG) and we hypothesized that misclassification of sex is an important predictor for mortality. Therefore, we first developed and validated a DNN that classified sex based on the ECG and investigated the outcome. Second, we studied ECG drivers of DNN-classified sex and mortality.

Methods and results

A DNN was trained to classify sex based on 131 673 normal ECGs. The algorithm was validated on internal (68 500 ECGs) and external data sets (3303 and 4457 ECGs). The survival of sex (mis)classified groups was investigated using time-to-event analysis and sex-stratified mediation analysis of ECG features. The DNN successfully distinguished female from male ECGs {internal validation: area under the curve (AUC) 0.96 [95% confidence interval (CI): 0.96, 0.97]; external validations: AUC 0.89 (95% CI: 0.88, 0.90), 0.94 (95% CI: 0.93, 0.94)}. Sex-misclassified individuals (11%) had a 1.4 times higher mortality risk compared with correctly classified peers. The ventricular rate was the strongest mediating ECG variable (41%, 95% CI: 31%, 56%) in males, while the maximum amplitude of the ST segment was strongest in females (18%, 95% CI: 11%, 39%). Short QRS duration was associated with higher mortality risk.

Conclusion

Deep neural networks accurately classify sex based on ECGs. While the proportion of ECG-based sex misclassifications is low, it is an interesting biomarker. Investigation of the causal pathway between misclassification and mortality uncovered new ECG features that might be associated with mortality. Increased emphasis on sex as a biological variable in artificial intelligence is warranted.

Feasibility of cardiac-synchronized quantitative T1 and T2 mapping on a hybrid 1.5 Tesla magnetic resonance imaging and linear accelerator system

Background and Purpose

Materials and methods

Results

Conclusions

Noninvasive detection of spatiotemporal activation-repolarization interactions that prime idiopathic ventricular fibrillation

[Figure: see text].

Interpretable uncertainty estimation for automated triage of 12-lead electrocardiogram using deep convolutional neural networks

Background

Interpretation of electrocardiograms (ECGs) using artificial intelligence (AI) has shown very promising results recently, such as comprehensive triage of ECGs. Despite this progress, remarkably few of these AI algorithms have been implemented in clinical practice. Mayor challenges before safe widespread implementation is possible, are uncertainty estimation and interpretability. Without these, algorithms are forced to always provide a diagnosis or prediction. When the algorithm has never seen such an ECG before or when it is noisy or contains a difficult or ambiguous case, this could lead to unrecognized erroneous results.

Purpose

We propose a novel deep neural network (DNN) architecture that is able to overcome both the interpretability and uncertainty estimation challenges in automated diagnosis of ECGs.

Methods

A dataset with 320.000 raw 10s 12-lead ECGs was used to train and validate the DNN in separate 90:10 splits. All ECGs were interpreted by a physician and these free-text annotations were translated into 6 categories from normal to acute based on how quickly a cardiologist needs to be consulted.

Using a novel variational autoencoder DNN, the ECG is compressed into a latent space with 64 parameters. In the compressed space, each category has a separate distribution, instead of a single distribution for the whole dataset.

For clinical use, the ECG is represented in the compressed space and the category prior distribution that is the most likely to produce the input ECG representation is the predicted category. The smaller the probability of generating the data point under any of the known categories, the more uncertain the prediction. The compressed space is reduced using principal component analysis (PCA) to visualize where the input is represented in the compressed landscape.

Performance of the uncertainty estimation is assessed in the validation set by ranking the uncertainty in 10 bins from highest to lowest and comparing with the accuracy.

Results

We demonstrate that the accuracy of the algorithm decreases for ECGs where the model is more uncertain about its predictions (Figure 1). Prediction accuracies range from 90% for the ECGs where the algorithm is most certain, to 50% where it is most uncertain. The PCA of the latent space appears to be able to visually represent the categories in the latent space (Figure 2). Moreover, it indicates whether the new ECG belongs to a known category, the algorithm is doubting between categories or the ECG belongs to none of the categories.

Conclusion

The proposed novel method is promising in estimating uncertainty in an interpretable way, which could allow for safer implementation of AI-based ECG interpretation. Further research is needed to improve the baseline performance of the model, to compare to existing methods and to examine the detection of noisy or new ECGs. The interpretability can be further extended to highlight the uncertain regions on the ECGs.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ZonMw Figure 1. Uncertainty versus accuracyFigure 2. Representation of ECG

[Psychotropic drugs and QTc prolongation]

Assessment of the risk for arrhythmias requires knowledge of QTc interval prolonging drugs and baseline clinical risk factors for QTc prolongation. The combination of both determines whether ECG-monitoring is necessary at the start of a psychotropic drug. In this article, we summarize current literature regarding appropriate methods of calculating the QTc interval, risk factors for QTc prolongation and QTc-prolonging psychotropic drugs. The frequency of cardiac monitoring for patients receiving psychotropic drugs should be individually determined, based on the prescribed agent(s) and additional risk factors for TdP. In patients without baseline clinical risk factors for QTc prolongation or cardiac arrhythmias, starting a single psychotropic drug with a low risk profile, ECG-monitoring might not be necessary.

Prevalence of mitral annulus disjunction and mitral valve prolapse in a multicenter cohort of idiopathic ventricular fibrillation patients

Funding Acknowledgements

Type of funding sources: Foundation. Main funding source(s): Dutch Heart Foundation

Background

Idiopathic ventricular fibrillation (IVF) is diagnosed in patients with sudden onset of ventricular fibrillation of which the origin is not identified after extensive evaluations. Recent studies suggest an association between mitral annulus disjunction (MAD), mitral valve prolapse (MVP) and ventricular arrhythmias[1,2]. The prevalence of MAD and MVP in IVF patients in this regard, is not well established.

Purpose

To explore prevalence of MAD and MVP in IVF patients.

Methods

In this retrospective multicenter cohort study, Cardiac Magnetic Resonance images from IVF patients (i.e., negative for ischemia, cardiomyopathy and channelopathies) and matched control subjects were analyzed for MAD (≥2mm) and MVP (&gt;2mm).

Results

In total, 71 IVF patients (mean age 39, 59% male) and 71 controls (mean age 41, 58% male) were included. MAD in the inferolateral wall was more prevalent in IVF patients versus healthy controls (6 [10%] vs. 1 [1%], p = 0.035). MVP was only seen in IVF patients and not in controls (4 [7%] vs. 0 [0%], p = 0.037). MVP was observed both in IVF patients with (n = 3) and without (n = 1) MAD. Patients with MAD did not show papillary muscle fibrosis. Four (67%) patients with MAD showed frequent ventricular ectopy from the basal myocardial region.

Conclusion

Inferolateral MAD and MVP were significantly more prevalent in IVF patients compared to healthy controls (figure). This is in line with previous studies suggesting a correlation between mitral valve disease and IVF. Our findings support further exploration of the pathophysiological mechanisms underlying a subset of IVF that associates with MAD and MVP.

Echocardiographic deformation imaging unmasks global and regional mechanical dysfunction in patients with idiopathic ventricular fibrillation: A multicenter case-control study

BACKGROUND

Idiopathic ventricular fibrillation (IVF) is diagnosed in patients with sudden onset of ventricular fibrillation of unidentified origin. New diagnostic tools that can detect subtle abnormalities are needed to diagnose and treat patients with an underlying substrate.

OBJECTIVE

The purpose of this study was to explore echocardiographic deformation characteristics in IVF patients.

METHODS

Echocardiograms were analyzed with deformation imaging by 2-dimensional speckle tracking. Global and regional measurements of the left ventricle (LV) and right ventricle (RV) were performed. Regional LV deformation patterns were evaluated for the presence of postsystolic shortening. Regional RV deformation patterns were classified as type I (normal) or type II/III (abnormal).

RESULTS

In total, 47 IVF patients (mean age 45 years; left ventricular ejection fraction [LVEF] 56%) and 47 healthy controls (mean age 41 years; LVEF 60%) were included. IVF patients showed more global deformation abnormalities as indicated by lower LV global longitudinal strain (18.5% ± 2.6% vs 21.6% ± 1.8%; P <.001) and higher LV mechanical dispersion (41 ± 12 ms vs 26 ± 6 ms; P <.001). In addition, IVF patients showed more regional LV postsystolic shortening compared to healthy controls (50% vs 11%; P <.001). Abnormal RV deformation patterns were observed in 16% of IVF patients and in none of the control subjects (P <.001).

CONCLUSION

We were able to show both regional and global echocardiographic deformation abnormalities in IVF patients. This study provides evidence that localized myocardial disease is present in a subset of IVF patients.

Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms

Aims

Automated interpretation of electrocardiograms (ECGs) using deep neural networks (DNNs) has gained much attention recently. While the initial results have been encouraging, limited attention has been paid to whether such results can be trusted, which is paramount for their clinical implementation. This study aims to systematically investigate uncertainty estimation techniques for automated classification of ECGs using DNNs and to gain insight into its utility through a clinical simulation.

Methods and results

On a total of 526 656 ECGs from three different datasets, six different methods for estimation of aleatoric and epistemic uncertainty were systematically investigated. The methods were evaluated based on ranking, calibration, and robustness against out-of-distribution data. Furthermore, a clinical simulation was performed where increasing uncertainty thresholds were applied to achieve a clinically acceptable performance. Finally, the correspondence between the uncertainty of ECGs and the lack of interpretational agreement between cardiologists was estimated. Results demonstrated the largest benefit when modelling both epistemic and aleatoric uncertainty. Notably, the combination of variational inference with Bayesian decomposition and ensemble with auxiliary output outperformed the other methods. The clinical simulation showed that the accuracy of the algorithm increased as uncertain predictions were referred to the physician. Moreover, high uncertainty in DNN-based ECG classification strongly corresponded with a lower diagnostic agreement in cardiologist's interpretation (P < 0.001).

Conclusion

Uncertainty estimation is warranted in automated DNN-based ECG classification and its accurate estimation enables intermediate quality control in the clinical implementation of deep learning. This is an important step towards the clinical applicability of automated ECG diagnosis using DNNs.

Sarcoma of the Heart Treated with Stereotactic MR-Guided Online Adaptive Radiation Therapy

We present the first case in the literature of a patient with a histology-proven intimal sarcoma of the heart, recurrent after surgery, treated with stereotactic MR-guided online adaptive radiation therapy on an MR-Linac machine. The treatment was feasible and well tolerated. The CT scan 6 months after the last treatment showed stable disease.

Familial Evaluation in Idiopathic Ventricular Fibrillation: Diagnostic Yield and Significance of J Wave Syndromes

[Figure: see text].

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers

BACKGROUND

ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover, we aimed to expand our knowledge on novel ECG features in these patients.

METHODS

A DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Gradient Class Activation Mapping++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features.

RESULTS

The DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% CI, 0.91-0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (eg, R- and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (eg, increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features (P<0.001).

CONCLUSIONS

A DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.

Follow-up after hemodynamically not tolerated ventricular tachycardia in patients with midrange reduced to normal ejection fraction: A retrospective single-centre case series

INTRODUCTION

The benefit of implantable cardioverter-defibrillator (ICD) implantation in patients with hemodynamically not tolerated ventricular tachycardia (VT) and midrange reduced to normal ejection fraction (LVEF >35%) is currently unclear. The purpose of this study was to investigate follow-up after hemodynamically not tolerated VT in patients with LVEF >35%. In addition, we aimed to find possible predictive factors to identify who will benefit from ICD implantation.

METHODS

In a retrospective single-centre case series, all patients with hemodynamically not tolerated VT and LVEF >35% that underwent electrophysiological study (EPS) and/or radiofrequency VT ablation were included.

RESULTS

Forty-two patients (5 women, median age 68 years) with hemodynamically not tolerated VT and LVEF >35% underwent EPS. VT ablation was performed in thirty-one patients, which was considered successful in twenty-three patients. Nineteen patients had an ICD at discharge while 23 patients were discharged without an ICD. The severity of hemodynamic compromise, LVEF and ablation success played an important role in the decision-making for ICD implantation. Six patients (14.3%) had recurrence of VT, all hemodynamically tolerated.

CONCLUSIONS

In this small case series, patients with hemodynamically not tolerated VT and LVEF >35% had a relatively low recurrence rate and all recurrences were nonfatal. Based on our results, we hypothesize that the severity of hemodynamic compromise, LVEF and ablation success might modify the risk for VA recurrence. A prospective study to determine the prognostic value of these factors in patients with hemodynamically not tolerated VT and LVEF >35% is necessary.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Complications in pulmonary vein isolation in the Netherlands Heart Registration differ with sex and ablation technique

Aims 

Pulmonary vein isolation (PVI) has become a cornerstone of the invasive treatment of atrial fibrillation. Severe complications are reported in 1–3% of patients. This study aims to compare complications and follow-up outcome of PVI in patients with atrial fibrillation.

Methods and results 

The data were extracted from the Netherlands Heart Registration. Procedural and follow-up outcomes in patients treated with conventional radiofrequency (C-RF), multielectrode phased RF (Ph-RF), or cryoballoon (CB) ablation from 2012 to 2017 were compared. Subgroup analysis was performed to identify variables associated with complications and repeat ablations. In total, 13 823 patients (69% male) were included. The reported complication incidence was 3.6%. Patients treated with C-RF developed more cardiac tamponades (C-RF 0.8% vs. Ph-RF 0.3% vs. CB 0.3%, P ≤ 0.001) and vascular complications (C-RF 1.7% vs. Ph-RF 1.2% vs. CB 1.3%, P ≤ 0.001). Ph-RF was associated with fewer bleeding complications (C-RF: 1.0% vs. Ph-RF: 0.4% vs. CB: 0.7%, P = 0.020). Phrenic nerve palsy mainly occurred in patients treated with CB (C-RF: 0.1% vs. Ph-RF: 0.2% vs. CB: 1.5%, P ≤ 0.001). In total, 18.4% of patients were referred for repeat ablation within 1 year. Female sex, age, and CHA2DS2-VASc were independent risk factors for cardiac tamponade and bleeding complications, with an adjusted OR for female patients of 2.97 (95% CI 1.98–4.45) and 2.02 (95% CI 1.03–4.00) respectively.

Conclusion 

The reported complication rate during PVI was low. Patients treated with C-RF ablation were more likely to develop cardiac tamponades and vascular complications. Female sex was associated with more cardiac tamponade and bleeding complications.

[Sudden death in the young: the importance of autopsy and DNA testing]

When a young person suddenly dies, there is a real chance that this was caused by genetic heart disease. Autopsy plays an important role in determining the cause of death, but the autopsy rate in the Netherlands is relatively low. Practical problems and a lack of information on autopsy play a role. It is important to inform the family on the importance of autopsy and DNA testing. If the family refuses autopsy, consent can be given for removal of material for DNA testing. If no autopsy and no DNA test were done, cardiological screening of family members remains highly recommended. New guidelines and procedures for diagnostics after sudden death are very important. For that reason, we developed a step-by-step plan to support healthcare providers. Early detection of genetic heart disease may prevent sudden death of family members, for example, by using preventive medication or internal defibrillators (ICD).

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks

BACKGROUND

The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician‐level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice.

METHODS AND RESULTS

We developed a 37‐layer convolutional residual deep neural network on a data set of free‐text physician‐annotated 12‐lead ECGs. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12‐lead ECGs were acquired in all noncardiology departments of the University Medical Center Utrecht. The algorithm learned to classify these ECGs into the following 4 triage categories: normal, abnormal not acute, subacute, and acute. Discriminative performance is presented with overall and category‐specific concordance statistics, polytomous discrimination indexes, sensitivities, specificities, and positive and negative predictive values. The patients in the validation data set had a mean age of 60.4 years and 54.3% were men. The deep neural network showed excellent overall discrimination with an overall concordance statistic of 0.93 (95% CI, 0.92–0.95) and a polytomous discriminatory index of 0.83 (95% CI, 0.79–0.87).

CONCLUSIONS

This study demonstrates that an end‐to‐end deep neural network can be accurately trained on unstructured free‐text physician annotations and used to consistently triage 12‐lead ECGs. When further fine‐tuned with other clinical outcomes and externally validated in clinical practice, the demonstrated deep learning–based ECG interpretation can potentially improve time to treatment and decrease healthcare burden.

Novel use of repolarization parameters in electrocardiographic imaging to uncover arrhythmogenic substrate

BACKGROUND

Measuring repolarization characteristics is challenging and has been reserved for experienced physicians. In electrocardiographic imaging (ECGI), activation-recovery interval (ARI) is used as a measure of local cardiac repolarization duration. We hypothesized that repolarization characteristics, such as local electrogram morphology and local and global dispersion of repolarization timing and duration could be of significance in ECGI.

OBJECTIVE

To further explore their potential in arrhythmic risk stratification we investigated the use of novel repolarization parameters in ECGI.

MATERIALS AND METHODS

We developed and compared methods for T-peak and T-end detection in reconstructed potentials. All methods were validated on annotated reconstructed electrograms (EGMs). Characteristics of the reconstructed EGMs and epicardial substrate maps in IVF patients were analyzed by using data recorded during sinus rhythm. The ECGI data were analyzed for EGM morphology, conduction, and repolarization.

RESULTS

We acquired ECGI data from 8 subjects for this study. In all patients we evaluated four repolarization parameters: Repolarization time, T-wave area, Tpeak-Tend interval, and T-wave alternans. Most prominent findings were steep repolarization time gradients in regions with flat EGMs. These regions were also characterized by low T-wave area and large differences in Tpeak-Tend interval.

CONCLUSIONS

Measuring novel repolarization parameters in reconstructed electrograms acquired with ECGI is feasible, can be done in a fully automated manner and may provide additional information on underlying arrhythmogenic substrate for risk stratification. Further studies are needed to investigate their potential use and clinical application.

Incidence and predictors of implantable cardioverter-defibrillator therapy and its complications in idiopathic ventricular fibrillation patients

Aims

Idiopathic ventricular fibrillation (IVF) is a rare cause of sudden cardiac arrest. Implantable cardioverter-defibrillator (ICD) implantation is currently the only treatment option. Limited data are available on the prevalence and complications of ICD therapy in these patients. We sought to investigate ICD therapy and its complications in patients with IVF.

Methods and results

Patients were selected from a national registry of IVF patients. Patients in whom no underlying diagnosis was found during follow-up were eligible for inclusion. Recurrence of ventricular arrhythmia (VA) was derived from medical and ICD records, electrogram records of ICD therapies were used to differentiate between appropriate or inappropriate interventions. Independent predictors for appropriate ICD shock were calculated using cox regression. In 217 IVF patients, recurrence of sustained VAs occurred in 66 patients (30%) during a median follow-up period of 6.1 years. Ten patients died (4.6%). Thirty-eight patients (17.5%) experienced inappropriate ICD therapy, and 32 patients (14.7%) had device-related complications. Symptoms before cardiac arrest [hazard ratio (HR): 2.51, 95% confidence interval (CI): 1.48–4.24], signs of conduction disease (HR: 2.27, 95% CI: 1.15–4.47), and carrier of the DPP6 risk haplotype (HR: 3.24, 1.70–6.17) were identified as independent predictors of appropriate shock occurrence.

Conclusion

Implantable cardioverter-defibrillator therapy is an effective treatment in IVF, treating recurrences of potentially lethal VAs in approximately one-third of patients during long-term follow-up. However, device-related complications and inappropriate shocks were also frequent. We found significant predictors for appropriate ICD therapy. This may imply that these patients require additional management to prevent recurrent events.

Life-long tailoring of diagnosis and management of patients with idiopathic ventricular fibrillation-future perspectives in research

The diagnosis and management of idiopathic ventricular fibrillation is challenging, as it requires extensive diagnostic testing and offers few curative options due to unknown underlying disease. The resulting population is a heterogeneous group of patients with a largely unknown natural history. Structural patient characterisation, follow-up and innovations in diagnostic testing can improve our understanding of the disease mechanisms of idiopathic ventricular fibrillation, detect underlying disease during follow-up and aid in therapeutic management. Recently, initiatives have been launched in the Netherlands to investigate the role of high-resolution non-invasive electrocardiographic imaging and genetic and familial screening in idiopathic ventricular fibrillation.

Impaired Mental Health-Related Quality of Life in Patients with Idiopathic Ventricular Fibrillation

BACKGROUND

Idiopathic ventricular fibrillation (iVF) is diagnosed in cardiac arrest survivors without an identifiable cause. Data regarding the health-related quality of life (HRQoL) in iVF patients are lacking. The purpose of this study was to investigate the HRQoL of iVF patients and to compare it to patients with an implantable cardioverter defibrillator (ICD) diagnosed with an underlying disease and healthy subjects.

METHODS

In 61 iVF patients with an ICD (iVF-ICD) and 59 ICD patients with a diagnosis (diagnosis-ICD), HRQoL was assessed using the 12-item Short-Form Health Survey (SF-12), the EuroQoL-5 dimensions (EQ-5D), the 9-item Patient Health Questionnaire, and the ICD Patient Concerns (ICDC) Questionnaire. In addition, 860 healthy subjects completed the SF-12.

RESULTS

IVF-ICD showed similar SF-12 physical summary scores compared with diagnosis-ICD patients (50.8 [interquartile range (IQR) = 42.1-53.9] vs 54.1 [IQR = 46.5-58.3]; P = 0.080) and healthy subjects (51.8 [IQR = 45.9-54.1]; P = 0.691). The mental summary score was impaired in iVF-ICD patients compared with diagnosis-ICD patients (45.9 [IQR = 40.7-49.4] vs 54.6 [IQR = 46.0-57.9]; P < 0.001) and healthy subjects (47.7 [IQR = 43.0-50.4]; P = 0.027). Scores on all five EQ-5D domains were similar between iVF-ICD patients and diagnosis-ICD patients, as well as symptoms of severe depression (19% vs 12%; P = 0.101). ICD concerns were similar between iVF-ICD and diagnosis-ICD patients (ICDC-scores 2 vs 2; P = 0.494).

CONCLUSIONS

Data suggest that there is a reduced mental HRQoL in patients with iVF compared to other cardiac arrest survivors. Screening and treatment of psychological distress should therefore be considered in iVF patients.

Next-generation sequencing of a large gene panel in patients initially diagnosed with idiopathic ventricular fibrillation

BACKGROUND

Idiopathic ventricular fibrillation (IVF) is a rare primary cardiac arrhythmia syndrome that is diagnosed in a resuscitated cardiac arrest victim, with documented ventricular fibrillation, in whom no underlying cause is identified after comprehensive clinical evaluation. In some patients, causative genetic mutations are detected that facilitate patient treatment and follow-up. The feasibility of next-generation sequencing (NGS) has increased with its greater availability and decreasing costs.

OBJECTIVE

The aim of this study was to assess the diagnostic yield of NGS in patients with IVF.

METHODS

A total of 33 patients initially diagnosed with IVF were included (mean age 53 ± 15 years; 14(42%) men). In all included patients, NGS of 33 genes and the DPP6 haplotype revealed no pathogenic mutations. Genetic screening comprised NGS of a panel of 179 additional genes. Variants with a minor allele frequency of <0.05% were assessed for pathogenicity by using existing mutation databases and in silico predictive algorithms.

RESULTS

In 1 of 33 patients, a likely pathogenic mutation was detected. The added yield of genetic testing with NGS of 179 additional genes is 3% in patients with IVF. In 15% of patients, 1 or multiple variants of uncertain clinical significance were detected.

CONCLUSION

The added yield of genetic screening of extended NGS panels in patients initially diagnosed with IVF is minimal. Routine analysis of large diagnostic NGS panels is therefore not recommended.

Long-Term Outcome of Patients Initially Diagnosed With Idiopathic Ventricular Fibrillation

Background—

Idiopathic ventricular fibrillation (IVF) is a rare cause of sudden cardiac arrest. Limited data are available on the long-term outcome of IVF patients.

Methods and Results—

In this retrospective cohort study, 107 consecutive patients with an initial diagnosis of IVF were analyzed (age at index event 40.4 years, 60% male). Missing diagnostic data were acquired during follow-up, including genetic testing, to exclude underlying disease. A specific diagnosis was revealed in 22 of 107 patients (21%) during a median follow-up of 10.2 years. Mortality rate was 9% in IVF patients (8/85). Appropriate implantable cardioverter–defibrillator therapy was delivered in 23 patients (29%) of 78 IVF patients with an implantable cardioverter–defibrillator, with a median of 3 appropriate shocks per patient.

Conclusions—

One fifth of the patients initially diagnosed with IVF reveal a specific diagnosis during long-term follow-up. Additional diagnostic testing, including genetic testing, contributes to the detection of specific diseases. The recurrence rate of ventricular arrhythmias in IVF patients is high. Our data show the importance of thorough follow-up and reassessment of diagnosis in IVF patients.