Definition and management of arrhythmia-induced cardiomyopathy: findings from the European Heart Rhythm Association survey.

AIMS: Arrhythmia-induced cardiomyopathy (AiCM) represents a subtype of acute heart failure (HF) in the context of sustained arrhythmia. Clear definitions and management recommendations for AiCM are lacking. The European Heart Rhythm Association Scientific Initiatives Committee (EHRA SIC) conducted a survey to explore the current definitions and management of patients with AiCM among European and non-European electrophysiologists. METHODS AND RESULTS: A 25-item online questionnaire was developed and distributed among EP specialists on the EHRA SIC website and on social media between 4 September and 5 October 2023. Of the 206 respondents, 16% were female and 61% were between 30 and 49 years old. Most of the respondents were EP specialists (81%) working at university hospitals (47%). While most participants (67%) agreed that AiCM should be defined as a left ventricular ejection fraction (LVEF) impairment after new onset of an arrhythmia, only 35% identified a specific LVEF drop to diagnose AiCM with a wide range of values (5-20% LVEF drop). Most respondents considered all available therapies: catheter ablation (93%), electrical cardioversion (83%), antiarrhythmic drugs (76%), and adjuvant HF treatment (76%). A total of 83% of respondents indicated that adjuvant HF treatment should be started at first HF diagnosis prior to antiarrhythmic treatment, and 84% agreed it should be stopped within six months after LVEF normalization. Responses for the optimal time point for the first LVEF reassessment during follow-up varied markedly (1 day-6 months after antiarrhythmic treatment). CONCLUSION: This EHRA Survey reveals varying practices regarding AiCM among physicians, highlighting a lack of consensus and heterogenous care of these patients.

Carnitine palmitoyltransferase II (CPT II) deficiency responsible for refractory cardiac arrhythmias, acute multiorgan failure and early fatal outcome.

BACKGROUND: Carnitine palmitoyltransferase II (CPT II) deficiency is a rare inborn error of mitochondrial fatty acid metabolism with autosomal recessive pattern of inheritance. Its phenotype is highly variable (neonatal, infantile, and adult onset) on the base of mutations of the CPT II gene. In affected subjects, long-chain acylcarnitines cannot be subdivided into carnitine and acyl-CoA, leading to their toxic accumulation in different organs. Neonatal form is the most severe, and all the reported patients died within a few days to 6 months after birth. Hereby, we report on a male late-preterm newborn who presented refractory cardiac arrhythmias and acute multiorgan (hepatic, renal, muscular) injury, leading to cerebral hemorrhage, hydrocephalus, cardiovascular failure and early (day 5 of life) to death. Subsequently, extended metabolic screening and target next generation sequencing (NGS) analysis allowed the CPT II deficiency diagnosis. CASE PRESENTATION: The male proband was born at 36+ 4 weeks of gestation by spontaneous vaginal delivery. Parents were healthy and nonconsanguineous, although both coming from Nigeria. Family history was unremarkable. Apgar score was 9/9. At birth, anthropometric measures were as follows: weight 2850 g (47th centile, -0.07 standard deviations, SD), length 50 cm (81st centile, + 0.89 SD) and occipitofrontal circumference (OFC) 35 cm (87th centile, + 1.14 SD). On day 2 of life our newborn showed bradycardia (heart rate around 80 bpm) and hypotonia, and was then transferred to the Neonatal Intensive Care Unit (NICU). There, he subsequently manifested many episodes of ventricular tachycardia, which were treated with pharmacological (magnesium sulfate) and electrical cardioversion. Due to the critical conditions of the baby (hepatic, renal and cardiac dysfunctions) and to guarantee optimal management of the arrythmias, he was transferred to the Pediatric Cardiology Reference Center of our region (Sicily, Italy), where he died 2 days later. Thereafter, the carnitines profile evidenced by the extended metabolic screening resulted compatible with a fatty acid oxidation defect (increased levels of acylcarnitines C16 and C18, and low of C2); afterwards, the targeted next generation sequencing (NGS) analysis revealed the known c.680 C > T p. (Pro227Leu) homozygous missense mutation of the CPTII gene, for diagnosis of CPT II deficiency. Genetic investigations have been, then, extended to the baby's parents, who were identified as heterozygous carriers of the same variant. When we meet again the parents for genetic counseling, the mother was within the first trimester of her second pregnancy. Therefore, we offered to the couple and performed the prenatal target NGS analysis on chorionic villi sample, which did not detect any alterations, excluding thus the CPT II deficiency in their second child. CONCLUSIONS: CPTII deficiency may be suspected in newborns showing cardiac arrhythmias, associated or not with hypertrophic cardiomyopathy, polycystic kidneys, brain malformations, hepatomegaly. Its diagnosis should be even more suspected and investigated in cases of increased plasmatic levels of creatine phosphokinase and acylcarnitines in addition to kidney, heart and liver dysfunctions, as occurred in the present patient. Accurate family history, extended metabolic screening, and multidisciplinary approach are necessary for diagnosis and adequate management of affected subjects. Next generation sequencing (NGS) techniques allow the identification of the CPTII gene mutation, essential to confirm the diagnosis before or after birth, as well as to calculate the recurrence risk for family members. Our report broads the knowledge of the genetic and molecular bases of such rare disease, improving its clinical characterization, and provides useful indications for the treatment of patients.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Burden of cardiac arrhythmias in patients with acute myocardial infarction and their impact on hospitalization outcomes: insights from China acute myocardial infarction (CAMI) registry.

BACKGROUND: The coexistence of cardiac arrhythmias in patients with acute myocardial infarction (AMI) usually exhibits poor prognosis. However, there are few contemporary data available on the burden of cardiac arrhythmias in AMI patients and their impact on in-hospital outcomes. METHODS: The present study analyzed data from the China Acute Myocardial Infarction (CAMI) registry involving 23,825 consecutive AMI patients admitted to 108 hospitals from January 2013 to February 2018. Cardiac arrhythmias were defined as the presence of bradyarrhythmias, sustained atrial tachyarrhythmias, and sustained ventricular tachyarrhythmias that occurred during hospitalization. In-hospital outcome was defined as a composite of all-cause mortality, cardiogenic shock, re-infarction, stroke, or heart failure. RESULTS: Cardiac arrhythmia was presented in 1991 (8.35%) AMI patients, including 3.4% ventricular tachyarrhythmias, 2.44% bradyarrhythmias, 1.78% atrial tachyarrhythmias, and 0.73% ≥2 kinds of arrhythmias. Patients with arrhythmias were more common with ST-segment elevation myocardial infarction (83.3% vs. 75.5%, P < 0.001), fibrinolysis (12.8% vs. 8.0%, P < 0.001), and previous heart failure (3.7% vs. 1.5%, P < 0.001). The incidences of in-hospital outcomes were 77.0%, 50.7%, 43.5%, and 41.4%, respectively, in patients with ≥ 2 kinds of arrhythmias, ventricular tachyarrhythmias, bradyarrhythmias, and atrial tachyarrhythmias, and were significantly higher in all patients with arrhythmias than those without arrhythmias (48.9% vs. 12.5%, P < 0.001). The presence of any kinds of arrhythmia was independently associated with an increased risk of hospitalization outcome (≥ 2 kinds of arrhythmias, OR 26.83, 95%CI 18.51-38.90; ventricular tachyarrhythmias, OR 8.56, 95%CI 7.34-9.98; bradyarrhythmias, OR 5.82, 95%CI 4.87-6.95; atrial tachyarrhythmias, OR4.15, 95%CI 3.38-5.10), and in-hospital mortality (≥ 2 kinds of arrhythmias, OR 24.44, 95%CI 17.03-35.07; ventricular tachyarrhythmias, OR 13.61, 95%CI 10.87-17.05; bradyarrhythmias, OR 7.85, 95%CI 6.0-10.26; atrial tachyarrhythmias, OR 4.28, 95%CI 2.98-6.16). CONCLUSION: Cardiac arrhythmia commonly occurred in patients with AMI might be ventricular tachyarrhythmias, followed by bradyarrhythmias, atrial tachyarrhythmias, and ≥ 2 kinds of arrhythmias. The presence of any arrhythmias could impact poor hospitalization outcomes. REGISTRATION: Clinical Trial Registration: Identifier: NCT01874691.

Long-term incidence of arrhythmias in extracardiac conduit Fontan and comparison between systemic left and right ventricle.

AIMS: The extracardiac conduit-Fontan (ECC) has become the preferred technique for univentricular heart palliation, but there are currently no data on the incidence of long-term arrhythmias. This study investigated the incidence of arrhythmias and relation to single ventricle morphology in the long-term follow-up (FU) in ECC. METHODS AND RESULTS: All patients with ECC performed in our Centre between 1987 and 2017 were included (minimum FU 5 years). Of 353 consecutive patients, 303 [57.8% males, aging 8-50 (median 20) years at last FU] were considered and divided into two groups depending on left (194 in Group 1) or right (109 in Group 2) ventricular morphology. Eighty-five (28%) experienced ≥1 arrhythmic complications, with early and late arrhythmias in 17 (5.6%) and 73 (24.1%) patients, respectively. Notably, late bradyarrhythmias occurred after 6 years in 21 (11%) patients in Group 1, and in 15 (13.8%) in Group 2 [P = 0.48]. Late tachyarrhythmias occurred in 55 (18.2%) patients after 12 years: 33 (17%) in Group 1 and 22 (20.2%) patients in Group 2 [P  = 0.5]. Ventricular tachycardias (VT) were documented after 12.5 years in 14 (7.2%) patients of Group 1 and 15 (13.8%) of Group 2 [P = 0.06] with a higher incidence in Group 2 during the FU [P = 0.005]. CONCLUSION: Extracardiac conduit is related to a significant arrhythmic risk in the long-term FU, higher than previously reported. Bradyarrhythmias occur earlier but are less frequent than tachyarrhythmias. Interestingly, patients with systemic right ventricle have a significantly higher incidence of VT, especially in a very long FU.

Machine learning based detection of T-wave alternans in real ambulatory conditions.

BACKGROUND AND OBJECTIVE: T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. METHODS: In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. RESULTS: We train ML methods to detect a wide variety of alternant voltage from 20 to 100 µV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. CONCLUSIONS: We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.

Arrhythmias and cardiac MRI associations in patients with established cardiac dystrophinopathy.

AIMS: Some patients with cardiac dystrophinopathy die suddenly. Whether such deaths are preventable by specific antiarrhythmic management or simply indicate heart failure overwhelming medical therapies is uncertain. The aim of this prospective, cohort study was to describe the occurrence and nature of cardiac arrhythmias recorded during prolonged continuous ECG rhythm surveillance in patients with established cardiac dystrophinopathy and relate them to abnormalities on cardiac MRI. METHODS AND RESULTS: A cohort of 10 patients (36.3 years; 3 female) with LVEF<40% due to Duchenne (3) or Becker muscular (4) dystrophy or Duchenne muscular dystrophy-gene carrying effects in females (3) were recruited, had cardiac MRI, ECG signal-averaging and ECG loop-recorder implants. All were on standard of care heart medications and none had prior history of arrhythmias.No deaths or brady arrhythmias occurred during median follow-up 30 months (range 13-35). Self-limiting episodes of asymptomatic tachyarrhythmia (range 1-29) were confirmed in 8 (80%) patients (ventricular only 2; ventricular and atrial 6). Higher ventricular arrhythmia burden correlated with extent of myocardial fibrosis (extracellular volume%, p=0.029; native T1, p=0.49; late gadolinium enhancement, p=0.49), but not with LVEF% (p=1.0) on MRI and atrial arrhythmias with left atrial dilatation. Features of VT episodes suggested various underlying arrhythmia mechanisms. CONCLUSIONS: The overall prevalence of arrhythmias was low. Even in such a small sample size, higher arrhythmia counts occurred in those with larger scar burden and greater ventricular volume, suggesting key roles for myocardial stretch as well as disease progression in arrhythmogenesis. These features overlap with the stage of left ventricular dysfunction when heart failure also becomes overt. The findings of this pilot study should help inform the design of a definitive study of specific antiarrhythmic management in dystrophinopathy. TRIAL REGISTRATION NUMBER: ISRCTN15622536.

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia: a landmark study.

AIMS: Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. METHODS AND RESULTS: We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6-3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79-0.87) and calibration slope of 0.97. CONCLUSION: The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)-positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.

Hemodynamic gain index and risk of ventricular arrhythmias: a prospective cohort study.

Objectives: Hemodynamic gain index (HGI), a novel hemodynamic index obtained from cardiopulmonary exercise testing (CPX), is associated with adverse cardiovascular outcomes. However, its specific relationship with ventricular arrhythmias (VAs) is unknown. We aimed to assess the association of HGI with risk of VAs in a prospective study. Design: Hemodynamic gain index was estimated using heart rate and systolic blood pressure (SBP) responses ascertained in 1945 men aged 42-61 years during CPX from rest to maximum exercise, using the formula: [(Heart ratemax x SBPmax) - (Heart raterest x SBPrest)]/(Heart raterest x SBPrest). Cardiorespiratory fitness (CRF) was measured using respiratory gas exchange analysis. Hazard ratios (HRs) (95% confidence intervals, CIs) were estimated for VAs. Results: Over a median follow-up duration of 28.2 years, 75 cases of VA were recorded. In analysis adjusted for established risk factors, a unit (bpm/mmHg) higher HGI was associated with a decreased risk of VA (HR 0.72, 95% CI: 0.55-0.95). The results remained consistent on adjustment for lifestyle factors and comorbidities (HR 0.72, 95% CI: 0.55-0.93). Comparing the top versus bottom tertiles of HGI, the corresponding adjusted HRs (95% CIs) were 0.51 (0.27-0.96) and 0.52 (0.28-0.94), respectively. The associations were attenuated on addition of CRF to the model. HGI improved risk discrimination beyond established risk factors but not CRF. Conclusions: Higher HGI is associated with a reduced risk of VAs in middle-aged and older Caucasian men, but dependent on CRF levels. Furthermore, HGI improves the prediction of the long-term risk for VAs beyond established risk factors but not CRF.

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the Graph Convolution Network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman's 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the Graph Convolution Network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with an accuracy equal to 92.68%. Therefore, utilizing this network, the types of arrhythmia were recognized and classified. Also, the new network proposed by the Graph Convolution Network-Weighted Mutual Information (GCN-WMI) was more accurate than those conducted in other studies on the same data set (Chapman). Based on the obtained results, the structure proposed in this study increased the accuracy of cardiac arrhythmia diagnosis and classification on the Chapman data set. Achieving such accuracy for arrhythmia diagnosis is a great achievement in clinical sciences.

Management of patients with an electrical storm or clustered ventricular arrhythmias: a clinical consensus statement of the European Heart Rhythm Association of the ESC-endorsed by the Asia-Pacific Heart Rhythm Society, Heart Rhythm Society, and Latin-American Heart Rhythm Society.

Electrical storm (ES) is a state of electrical instability, manifesting as recurrent ventricular arrhythmias (VAs) over a short period of time (three or more episodes of sustained VA within 24 h, separated by at least 5 min, requiring termination by an intervention). The clinical presentation can vary, but ES is usually a cardiac emergency. Electrical storm mainly affects patients with structural or primary electrical heart disease, often with an implantable cardioverter-defibrillator (ICD). Management of ES requires a multi-faceted approach and the involvement of multi-disciplinary teams, but despite advanced treatment and often invasive procedures, it is associated with high morbidity and mortality. With an ageing population, longer survival of heart failure patients, and an increasing number of patients with ICD, the incidence of ES is expected to increase. This European Heart Rhythm Association clinical consensus statement focuses on pathophysiology, clinical presentation, diagnostic evaluation, and acute and long-term management of patients presenting with ES or clustered VA.

External Validation of a Risk Score Model for Predicting Major Clinical Events in Adults After Atrial Switch.

BACKGROUND: A risk model has been proposed to provide a patient individualized estimation of risk for major clinical events (heart failure events, ventricular arrhythmia, all-cause mortality) in patients with transposition of the great arteries and atrial switch surgery. We aimed to externally validate the model. METHODS AND RESULTS: A retrospective, multicentric, longitudinal cohort of 417 patients with transposition of the great arteries (median age, 24 years at baseline [interquartile range, 18-30]; 63% men) independent of the model development and internal validation cohort was studied. The performance of the prediction model in predicting risk at 5 years was assessed, and additional predictors of major clinical events were evaluated separately in our cohort. Twenty-five patients (5.9%) met the major clinical events end point within 5 years. Model validation showed good discrimination between high and low 5-year risk patients (Harrell C index of 0.73 [95% CI, 0.65-0.81]) but tended to overestimate this risk (calibration slope of 0.20 [95% CI, 0.03-0.36]). In our population, the strongest independent predictors of major clinical events were a history of heart failure and at least mild impairment of the subpulmonary left ventricle function. CONCLUSIONS: We reported the first external validation of a major clinical events risk model in a large cohort of adults with transposition of the great arteries. The model allows for distinguishing patients at low risk from those at intermediate to high risk. Previous episode of heart failure and subpulmonary left ventricle dysfunction appear to be key markers in the prognosis of patients. Further optimizing risk models are needed to individualize risk predictions in patients with transposition of the great arteries.

Electrophysiological phenotyping of left ventricular noncompaction cardiomyopathy in pediatric populations: A systematic review.

Left ventricular noncompaction cardiomyopathy (LVNC) is a structural heart defect that has been associated with generation of arrhythmias in the population and is a cause of sudden cardiac death with severe systolic dysfunction and fatal arrhythmias. LVNC has gained increasing acknowledgment with increased prevalence. We conducted a systematic review of reported electrocardiogram (ECG) results for pediatric LVNC patients. EMBASE database query was performed, yielding 4531 articles related to LVNC between 1990 and December 2023. Patient age ranged from prenatal to 18 years of age. Qualitative analyses were performed to characterize individual arrhythmias, and summative interpretation of ECG evaluations was gathered for the entire cohort. Systematic review of 57 LVNC cases and ECG presentation revealed many waveform consistencies, including abnormal left ventricular, atrioventricular node, and interventricular septal patterns, and specifically a high incidence of Mobitz type II and Wolff-Parkinson-White waveforms. This review of ECG analysis reinforces the clinical and etiologic significance of pediatric LVNC. While LVNC in pediatric populations may not always present as acute clinical cases, further investigation into the electrophysiology of the disease supports the need for further evaluation and risk stratification for patients with suspected LVNC and/or ventricular arrhythmia.