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.

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.

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.

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.

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.

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.

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.

An Overview of Arrhythmias in Pregnancy.

Cardiovascular disease significantly jeopardizes pregnancies in the United States, impacting 1% to 4% of pregnancies annually. Among complications, cardiac arrhythmias are prevalent, posing concerns for maternal and fetal health. The incidence of arrhythmias during pregnancy is rising, partly due to advances in congenital heart surgery and a growing population of women with structural heart disease. While most arrhythmias are benign, the increasing prevalence of more serious arrhythmias warrants a proactive approach. Guidance and reassurance suffice in many cases, but persistent symptoms require cautious use of antiarrhythmic drugs or other therapies for a safe outcome. Managing more serious arrhythmias requires a comprehensive, multidisciplinary approach involving specialists, including maternal-fetal medicine physicians, cardiologists, electrophysiologists, and anesthesiologists.

ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range dependency features.

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN-LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN-LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.

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.

Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs.

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios. Our approach employs a cascade of CNNEDs in conjunction with an AED shock advisory algorithm to process the ECG data for shock decisions. Initially, a CNNED trained on an equal number of shockable and non-shockable rhythms is used to filter the CPR-contaminated data. The resulting filtered signal is then fed into a second CNNED, which is trained on imbalanced data more tilted toward the specific rhythm being analyzed. A reliable shock versus non-shock decision is made when both classifiers from the cascade structure agree, while segments with conflicting classifications are labeled as indeterminate, indicating the need for additional segments to analyze. To evaluate our approach, we generated CPR-contaminated ECG data by combining clean ECG data with 52 CPR samples. We used clean ECG data from the CUDB, AFDB, SDDB, and VFDB databases, to which 52 CPR artifact cases were added, while a separate test set provided by the AED manufacturer Defibtech LLC was used for performance evaluation. The test set comprised 20,384 non-shockable CPR-contaminated segments from 392 subjects, as well as 3744 shockable CPR-contaminated samples from 41 subjects with coarse ventricular fibrillation (VF) and 31 subjects with rapid ventricular tachycardia (rapid VT). We observed improvements in rhythm analysis using our proposed cascading CNNED structure when compared to using a single CNNED structure. Specifically, the specificity of the proposed cascade of CNNED structure increased from 99.14% to 99.35% for normal sinus rhythm and from 96.45% to 97.22% for other non-shockable rhythms. Moreover, the sensitivity for shockable rhythm detection increased from 90.90% to 95.41% for ventricular fibrillation and from 82.26% to 87.66% for rapid ventricular tachycardia. These results meet the performance thresholds set by the American Heart Association and demonstrate the reliable and accurate analysis of heart rhythms during CPR using only ECG data without the need for CPR interruptions or a reference signal.

Continuous stellate ganglion block for ventricular arrhythmias: case series, systematic review, and differences from thoracic epidural anaesthesia.

AIMS: Percutaneous stellate ganglion block (PSGB) through single-bolus injection and thoracic epidural anaesthesia (TEA) have been proposed for the acute management of refractory ventricular arrhythmias (VAs). However, data on continuous PSGB (C-PSGB) are scant. The aim of this study is to report our dual-centre experience with C-PSGB and to perform a systematic review on C-PSGB and TEA. METHODS AND RESULTS: Consecutive patients receiving C-PSGB at two centres were enrolled. The systematic literature review follows the latest Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Our case series (26 patients, 88% male, 60 ± 16 years, all with advanced structural heart disease, left ventricular ejection fraction 23 ± 11%, 32 C-PSGBs performed, with a median duration of 3 days) shows that C-PSGB is feasible and safe and leads to complete VAs suppression in 59% and to overall clinical benefit in 94% of cases. Overall, 61 patients received 68 C-PSGBs and 22 TEA, with complete VA suppression in 63% of C-PSGBs (61% of patients). Most TEA procedures (55%) were performed on intubated patients, as opposed to 28% of C-PSGBs (P = 0.02); 63% of cases were on full anticoagulation at C-PSGB, none at TEA (P < 0.001). Ropivacaine and lidocaine were the most used drugs for C-PSGB, and the available data support a starting dose of 12 and 100 mg/h, respectively. No major complications occurred, yet TEA discontinuation rate due to side effects was higher than C-PSGB (18 vs. 1%, P = 0.01). CONCLUSION: Continuous PSGB seems feasible, safe, and effective for the acute management of refractory VAs. The antiarrhythmic effect may be accomplished with less concerns for concomitant anticoagulation compared with TEA and with a lower side-effect related discontinuation rate.

Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to evaluate the hypothesis that AI ECG mapping may reduce time to ablation, procedural duration, and fluoroscopy. MATERIALS AND METHODS: Cases in which system output was used were retrospectively enrolled according to IRB-approved protocols at each site. Matched control cases were enrolled in reverse chronological order beginning on the last day for which the technology was unavailable. Controls were matched based upon physician, institution, arrhythmia, and a predetermined complexity rating. Procedural metrics, fluoroscopy data, and clinical outcomes were assessed from time-stamped medical records. RESULTS: The study group consisted of 28 patients (age 65 ± 11 years, 46% female, left atrial dimension 4.1 ± 0.9 cm, LVEF 50 ± 18%) and was similar to 28 controls. The most common arrhythmia types were atrial fibrillation (n = 10), premature ventricular complexes (n = 8), and ventricular tachycardia (n = 6). Use of the system was associated with a 19.0% reduction in time to ablation (133 ± 48 vs. 165 ± 49 min, p = 0.02), a 22.6% reduction in procedure duration (233 ± 51 vs. 301 ± 83 min, p < 0.001), and a 43.7% reduction in fluoroscopy (18.7 ± 13.3 vs. 33.2 ± 18.0 min, p < 0.001) versus controls. At 6 months follow-up, arrhythmia-free survival was 73.5% in the study group and 63.3% in the control group (p = 0.56). CONCLUSION: Use of forward-solution AI ECG mapping is associated with reductions in time to first ablation, procedure duration, and fluoroscopy without an adverse impact on procedure outcomes or complications.

Implantable loop recorders in patients with Brugada syndrome: the BruLoop study.

BACKGROUND AND AIMS: Available data on continuous rhythm monitoring by implantable loop recorders (ILRs) in patients with Brugada syndrome (BrS) are scarce. The aim of this multi-centre study was to evaluate the diagnostic yield and clinical implication of a continuous rhythm monitoring strategy by ILRs in a large cohort of BrS patients and to assess the precise arrhythmic cause of syncopal episodes. METHODS: A total of 370 patients with BrS and ILRs (mean age 43.5 ± 15.9, 33.8% female, 74.1% symptomatic) from 18 international centers were included. Patients were followed with continuous rhythm monitoring for a median follow-up of 3 years. RESULTS: During follow-up, an arrhythmic event was recorded in 30.7% of symptomatic patients [18.6% atrial arrhythmias (AAs), 10.2% bradyarrhythmias (BAs), and 7.3% ventricular arrhythmias (VAs)]. In patients with recurrent syncope, the aetiology was arrhythmic in 22.4% (59.3% BAs, 25.0% VAs, and 15.6% AAs). The ILR led to drug therapy initiation in 11.4%, ablation procedure in 10.9%, implantation of a pacemaker in 2.5%, and a cardioverter-defibrillator in 8%. At multivariate analysis, the presence of symptoms [hazard ratio (HR) 2.5, P = .001] and age >50 years (HR 1.7, P = .016) were independent predictors of arrhythmic events, while inducibility of ventricular fibrillation at the electrophysiological study (HR 9.0, P < .001) was a predictor of VAs. CONCLUSIONS: ILR detects arrhythmic events in nearly 30% of symptomatic BrS patients, leading to appropriate therapy in 70% of them. The most commonly detected arrhythmias are AAs and BAs, while VAs are detected only in 7% of cases. Symptom status can be used to guide ILR implantation.

Safety of ventricular arrhythmia radiofrequency ablation with half-normal saline irrigation.

AIMS: Failure of radiofrequency (RF) ablation of ventricular arrhythmias is often due to inadequate lesion size. Irrigated RF ablation with half-normal saline (HNS) has the potential to increase lesion size and reduce sodium delivery to the patient if the same volume of RF irrigant were used for normal saline (NS) and HNS but could increase risks related to steam pops and lesion size. This study aims to assess periprocedural complications and acute ablation outcome of ventricular arrhythmias ablation with HNS. METHODS AND RESULTS: Prospective assessment of outcomes was performed in 1024 endocardial and/or epicardial RF ablation procedures in 935 consecutive patients (median age 64 years, 71.2% men, 73.4% cardiomyopathy, 47.2% sustained ventricular tachycardia). Half-normal saline was selected at the discretion of the treating physician. Radiofrequency ablation power was generally titrated to a ≤15â€…Ω impedance fall with intracardiac echocardiography monitoring. Half-normal saline was used in 900 (87.9%) and NS in 124 (12.1%) procedures. Any adverse event within 30 days occurred in 13.0% of patients treated with HNS RF ablation including 4 (0.4%) strokes/transient ischaemic attacks and 34 (3.8%) pericardial effusions requiring treatment (mostly related to epicardial access). Two steam pops with perforation required surgical repair (0.2%). Patients who received NS irrigation had less severe disease and arrhythmias. In multivariable models, adverse events and acute success of the procedure were not related to the type of irrigation. CONCLUSION: Half-normal saline irrigation RF ablation with power guided by impedance fall and intracardiac echocardiography has an acceptable rate of complications and acute ablation success while administering half of the saline load expected for NS irrigation.