A novel tool for arrhythmic risk stratification in desmoplakin gene variant carriers.

BACKGROUND AND AIMS: Pathogenic desmoplakin (DSP) gene variants are associated with the development of a distinct form of arrhythmogenic cardiomyopathy known as DSP cardiomyopathy. Patients harbouring these variants are at high risk for sustained ventricular arrhythmia (VA), but existing tools for individualized arrhythmic risk assessment have proven unreliable in this population. METHODS: Patients from the multi-national DSP-ERADOS (Desmoplakin SPecific Effort for a RAre Disease Outcome Study) Network patient registry who had pathogenic or likely pathogenic DSP variants and no sustained VA prior to enrolment were followed longitudinally for the development of first sustained VA event. Clinically guided, step-wise Cox regression analysis was used to develop a novel clinical tool predicting the development of incident VA. Model performance was assessed by c-statistic in both the model development cohort (n = 385) and in an external validation cohort (n = 86). RESULTS: In total, 471 DSP patients [mean age 37.8 years, 65.6% women, 38.6% probands, 26% with left ventricular ejection fraction (LVEF) < 50%] were followed for a median of 4.0 (interquartile range: 1.6-7.3) years; 71 experienced first sustained VA events {2.6% [95% confidence interval (CI): 2.0, 3.5] events/year}. Within the development cohort, five readily available clinical parameters were identified as independent predictors of VA and included in a novel DSP risk score: female sex [hazard ratio (HR) 1.9 (95% CI: 1.1-3.4)], history of non-sustained ventricular tachycardia [HR 1.7 (95% CI: 1.1-2.8)], natural logarithm of 24-h premature ventricular contraction burden [HR 1.3 (95% CI: 1.1-1.4)], LVEF < 50% [HR 1.5 (95% CI: .95-2.5)], and presence of moderate to severe right ventricular systolic dysfunction [HR 6.0 (95% CI: 2.9-12.5)]. The model demonstrated good risk discrimination within both the development [c-statistic .782 (95% CI: .77-.80)] and external validation [c-statistic .791 (95% CI: .75-.83)] cohorts. The negative predictive value for DSP patients in the external validation cohort deemed to be at low risk for VA (<5% at 5 years; n = 26) was 100%. CONCLUSIONS: The DSP risk score is a novel model that leverages readily available clinical parameters to provide individualized VA risk assessment for DSP patients. This tool may help guide decision-making for primary prevention implantable cardioverter-defibrillator placement in this high-risk population and supports a gene-first risk stratification approach.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Natural Course of Electrocardiographic Features in Arrhythmogenic Right Ventricular Cardiomyopathy and Their Relation to Ventricular Arrhythmic Events.

BACKGROUND: Electrocardiographic abnormalities are common in arrhythmogenic right ventricular cardiomyopathy and are included in the 2010 Task Force Criteria. Their time course, however, remains uncertain. In this retrospective observational study, we aimed to assess the long-term evolution of electrocardiographic characteristics and their relation to ventricular arrhythmias. METHODS AND RESULTS: Three hundred fifty-three patients with arrhythmogenic right ventricular cardiomyopathy as per the 2010 Task Force Criteria with 6871 automatically processed 12-lead digital ECGs were included. The relationship between the electrocardiographic parameters and the risk of ventricular arrhythmias was assessed at 10 years from the first ECG. Electrocardiographic parameters were compared between the first contact ECG, the ECG at diagnosis, and the most recent ECG. Median time between the first and the latest ECG was 6 [interquartile range, 1-14] years. Reductions of QRS voltage, R- and T-wave amplitudes between the first, diagnostic, and the latest ECGs were observed across precordial and extremity leads. Mean QRS duration increased from 96 to 102 ms (P<0.001), terminal activation duration (V1) from 47 to 52 ms (P<0.001), and QTc from 419 to 432 ms (P<0.001). T-wave inversions in leads V3 to V6 and aVF at first ECG were associated with ventricular arrhythmias (adjusted hazard ratio [HRadj][V3], 2.03 [95% CI, 1.23-3.34] and HRadj[aVF], 1.87 [95% CI, 1.13-3.08]). CONCLUSIONS: Depolarization and repolarization parameters evolved over time in patients with arrhythmogenic right ventricular cardiomyopathy, supporting the progressive nature of arrhythmogenic right ventricular cardiomyopathy. Electrocardiographic abnormalities may be detected before diagnosis and might, although not fulfilling the 2010 Task Force Criteria, be markers of early disease. T-wave inversion in leads V3 or aVF before diagnosis was associated with ventricular arrhythmias during follow-up.

Catheter Ablation for Ventricular Tachycardia in Patients With Desmoplakin Cardiomyopathy.

BACKGROUND: Desmoplakin (DSP) pathogenic/likely pathogenic (P/LP) variants are associated with malignant phenotypes of arrhythmogenic cardiomyopathy (DSP-ACM). Reports of outcomes after ventricular tachycardia (VT) ablation in DSP-ACM are scarce. OBJECTIVES: In this study, the authors sought to report on long-term outcomes of VT ablation in DSP-ACM. METHODS: Patients with P/LP DSP variants at 9 institutions undergoing VT ablation were included. Demographic, clinical, and instrumental data as well as all ventricular arrhythmia (VA) events were collected. Sustained VAs after the index procedure were the primary outcome. A per-patient before and after ablation comparison of rates of VA episodes per year was performed as well. RESULTS: Twenty-four DSP-ACM patients (39.3 ± 12.1 years of age, 62.5% male, median 6,116 [Q1-Q3: 3,362-7,760] premature ventricular complexes [PVCs] per 24 hours, median 4 [Q1-Q3: 2-11] previous VA episodes per patient at ablation) were included. Index procedure was most commonly endocardial/epicardial (19/24) The endocardium of the right ventricle (RV), the left ventricle (LV), or both ventricles were mapped in 8 (33.3%), 9 (37.5%), and 7 (29.2%) cases, respectively. Low voltage potentials were found in 10 of 15 patients in the RV and 11 of 16 in the LV. Endocardial ablation was performed in 18 patients (75.0%). Epicardial mapping in 19 patients (79.2%) identified low voltage potentials in 17, and 16 received epicardial ablation. Over the following 2.9 years (Q1-Q3: 1.8-5.5 years), 13 patients (54.2%) experienced VA recurrences. A significant reduction in per-patient event/year before and after ablation was observed (1.4 [Q1-Q3: 0.5-2.4] to 0.1 [Q1-Q3: 0.0-0.4]; P = 0.009). Two patients needed heart transplantation, and 4 died (3 of heart failure and 1 noncardiac death). CONCLUSIONS: VT ablation in DSP-ACM is effective in reducing the VA burden of the disease, but recurrences are common. Most VT circuits are epicardial, with both LV and RV low voltage abnormalities. Heart failure complicates clinical course and is an important cause of mortality.

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. Design, Setting, and Participants: This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Exposures: Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. Main Outcomes and Measures: The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. Results: A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). Conclusions and Relevance: The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

Long-Term Arrhythmic Follow-Up and Risk Stratification of Patients With Desmoplakin-Associated Arrhythmogenic Right Ventricular Cardiomyopathy.

Background: Patients with likely pathogenic/pathogenic desmoplakin (DSP) variants are poorly characterized. Some of them meet diagnostic criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC), but it is unclear how risk stratification strategies for ARVC perform in this setting. Objectives: The purpose of this study was to characterize arrhythmic outcomes and to test the performance of the recently validated ARVC risk calculator in patients with DSP likely pathogenic/pathogenic variants fulfilling definite 2010 ARVC Task Force Criteria (DSP-TFC+). Methods: DSP-TFC+ patients were enrolled from 20 institutions across 3 continents. Ventricular arrhythmias (VA), defined as a composite of sustained ventricular tachycardia (VT), appropriate implantable cardioverter defibrillator therapies, and ventricular fibrillation/sudden cardiac death events in follow-up, were reported as the primary outcome. We tested the performance of the ARVC risk calculator for VA prediction, reporting c-statistics. Results: Among 252 DSP-TFC+ patients (age 39.6 ± 16.9 years, 35.3% male), 94 (37.3%) experienced VA over 44.5 [IQR: 19.6-78.3] months. Patients with left ventricle involvement (n = 194) were at higher VA risk (log-rank P = 0.0239). History of nonsustained VT (aHR 2.097; P = 0.004) showed the strongest association with VA occurrence during the first 5-year follow-up. Neither age (P = 0.723) nor male sex (P = 0.200) was associated with VAs at follow-up. In 204 patients without VA at diagnosis, incident VA rate was high (32.8%; 7.37%/y). The ARVC risk calculator performed poorly overall (c-statistic 0.604 [0.594-0.614]) and very poorly in patients with left ventricular disease (c-statistic 0.558 [0.556-0.560]). Conclusions: DSP-TFC+ patients are at substantial risk for VAs. The ARVC risk calculator performs poorly in DSP-TFC+ patients suggesting need for a gene-specific risk algorithm. Meanwhile, DSP-TFC+ patients with nonsustained VT should be considered as high-risk.