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 (>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.