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Ventricular tachycardia (VT) and ventricular fibrillation remain major contributors to sudden cardiac death, with current therapies limited by our incomplete understanding of the arrhythmogenic substrate. This narrative review explores recent developments in computer-aided techniques for characterizing the arrhythmogenic substrate, focusing on post-myocardial infarction VT. High-resolution cardiac imaging now enables detailed visualization of structural abnormalities, including heterogeneous scar architecture and fatty infiltration. Sophisticated invasive mapping techniques provide insights into local electrophysiological properties, while novel non-invasive mapping approaches offer complementary views of global electrical patterns. Integration of these modalities through computational simulations allows for mechanistic insights into arrhythmia initiation and maintenance, particularly in post-myocardial infarction VT, where structural and functional substrates interact in complex ways. Emerging artificial intelligence applications enhance substrate analysis through automated feature extraction and pattern recognition, enabling more sophisticated risk stratification. These computer-aided approaches are advancing from research tools to clinical applications, with early evidence suggesting improved ablation outcomes and better risk prediction. However, significant challenges remain in validation, standardization, and clinical implementation of these innovations. This narrative review highlights recent methodological advances and clinical applications of computer-aided substrate characterization, and conceptualizes future directions towards personalized arrhythmia management, also beyond post-infarction VTs.

Aims: Concomitant pulsed field ablation (PFA) for atrial fibrillation (AF) with left atrial appendage closure (LAAC) offers a single-procedure approach for arrhythmia control and thromboembolic risk reduction. This study assessed the workflow, safety, and feasibility of combined PFA and LAAC in routine practice.

Methods and results: We prospectively analysed patients undergoing zero-fluoroscopy PFA, with low fluoroscopy for LAAC. Pre-procedural planning used CT imaging and AI-based models for device selection and landing-zone assessment. A single transseptal puncture facilitated intracardiac echocardiography, PFA catheter, and LAAC sheath. A total of 209 patients were included (56% male; mean age 76.5 ± 7.8 years), with 59.3% paroxysmal AF, 40.7% persistent AF, and 50% de novo AF. The mean CHA2DS2-VASc score was 4.5. Mean procedure and left atrial dwell times were 57.3 ± 17 and 45.1 ± 13.6 min, respectively; fluoroscopy averaged 3.4 ± 0.8 min for LAAC. A single LAAC device was used in 94% of cases, achieving adequate seal in all. No pericardial effusion, phrenic nerve injury, kidney, or oesophageal injury occurred; two patients had minor groin bleeding. All were discharged same day on oral anticoagulation for 90 days. Follow-up CT (80%) or TEE (20%) at 111.6 ± 16.5 days showed no leaks >2 mm, a 4.7% small-leak rate, and two device-related thrombi without stroke, managed with extended anticoagulation.

Conclusion: Combined PFA and LAAC is feasible and safe with favourable early outcomes. Multi-centre studies are warranted to confirm findings and standardize this workflow for broader clinical adoption.

Inherited mutations in the KCNH2 gene, which encodes the cardiac hERG potassium channel, are major contributors to arrhythmogenic syndromes such as long QT and short QT syndromes. However, clinical interpretation of the growing number of missense variants - many of which are classified as variants of uncertain significance (VUS) - remains a pressing challenge. Here, we present a semi-automated in silico pipeline for predicting hERG variant pathogenicity, acting as a binary classifier and integrating five structural metrics - residue volume, hydrophobicity, charge, steric clashes, and proximity to pathogenic hotspots - into a composite structural pathogenicity score (SPS) scaled from 1 to 5. Applied to 1727 hERG variants from ClinVar and from a French nationwide cohort, this binary classifier, termed SPARC, identified 260 variants as high risk of pathogenicity with SPS ≥3.25, of which a representative subset from the French cohort was functionally validated using high-throughput automated patch-clamp. Functional phenotyping confirmed the structural predictions, including for several VUS, demonstrating that comprehensive structural scoring can reliably stratify variant pathogenicity. This approach, benchmarked with Alpha Missense and Revel, offers a superior scalable, cost-effective pre-screening tool to guide clinical variant interpretation and prioritization for experimental validation.

Coronary artery spasm can be life-threatening. Clinically significant complications include myocardial infarction, ventricular arrhythmias and sudden cardiac arrest. Although challenging to diagnose, new international guidelines have been published to guide the diagnosis of coronary artery spasm when this is the suspected cause of cardiac arrest. The aim of this review is to consider existing knowledge for the diagnosis and management of coronary artery spasm in survivors of sudden cardiac arrest. Twenty-seven original research articles (written in English) involving a total of 1541 survivors of sudden cardiac arrest associated with coronary artery spasm form the basis of this review. Most cohorts included >75% male participants with a mean age range of 45-63 years. A positive family history or coronary risk factors of coronary artery disease are not commonly found, albeit many survivors are smokers (ranged 17%-100% across cohorts). Provocative testing for coronary spasm was reported in 25 of the evaluated papers, but the indications for testing were inconsistently specified. A high recurrence rate (up to 45%) of life-threatening ventricular arrhythmias was reported, and implantable cardioverter-defibrillator placement varied markedly. In conclusion, diagnosing coronary artery spasm as a cause of sudden cardiac arrest is challenging. The pathophysiological understanding is limited. Knowledge gaps include the incidence and prevalence, as well as the usefulness of provocative testing in survivors. More data are needed regarding patient risk stratification, indications for implantable cardioverter-defibrillator insertion, and optimal pharmacological therapy.

Aims: Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using ECG and clinical variables in 37,534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP).

Methods and results: We analyzed 12-lead ECGs of 48,835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%) and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants' median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1,423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition and CMR labelling as important factors.

Conclusion: Our findings support the feasibility of scalable DL-based screening tools for prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

Aims: Intracardiac echocardiography (ICE) is increasingly incorporated into atrial fibrillation (AF) ablation workflows, enabling real-time anatomic guidance and procedural precision. Nevertheless, ICE utilization shows substantial geographic variability, and its clinical benefit remains incompletely understood. This meta-analysis evaluated the efficacy, safety, and procedural performance of ICE-guided versus non-ICE-guided AF ablation.

Methods and results: MEDLINE, the Cochrane Library, and Scopus were systematically searched through 3 August 2025. Three reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Random-effects models were used to pool data from 44 AF ablation studies comprising 482,043 patients. ICE guidance was associated with lower odds of any complication (OR 0.69; 95% CI 0.53-0.89), including significant reductions in cardiac tamponade (OR 0.58; 95% CI 0.53-0.62) and mortality (OR 0.21; 95% CI 0.16-0.27). ICE-guided ablation was also associated with shorter total procedure and fluoroscopy times, reduced radiation exposure, and lower contrast agent utilization. Atrial tachyarrhythmia (AT) recurrence did not differ between groups (OR 0.92; 95% CI 0.79-1.06). However, ICE use was associated with higher odds of first-pass pulmonary vein isolation (OR 1.62; 95% CI 1.09-2.41) and successful isolation of all pulmonary veins at the end of the procedure (OR 2.12; 95% CI 1.37-3.27), and lower odds of repeat ablation (OR 0.65; 95% CI 0.59-0.72).

Conclusion: ICE-guided AF ablation is associated with superior procedural safety and efficiency and a similar risk of AT recurrence compared to non-ICE-guided approaches.

Background and aims: In the PRAGUE-25 study, the effect of lifestyle modification (LFM) in combination with antiarrhythmic drugs (AAD) in patients with atrial fibrillation (AF) and BMI 30-40 was smaller than that of catheter ablation (CA). The presented sub-analysis aims to assess the relationship between the actual weight reduction and the AF burden 12 months after randomization.

Methods: Per-protocol analysis of the PRAGUE-25 trial. Patients in the LFM+AAD group were stratified into tertiles based on the extent of relative weight reduction, and a comparison of AF burden was performed between the LFM+AAD tertiles and the CA group.

Results: The study analyzed 83 LFM+AAD patients (age 60 ±9 years, 31% women, 54% paroxysmal AF, mean weight 109±17 kg) and 99 CA patients (age 60±9 years, 32% women, 56% paroxysmal AF, weight 109±15 kg). Within the LFM+AAD group, 28 patients achieved a weight reduction of >8.3% (-15.46±6.16 kg), 27 patients were between 3.1-8.1% (-6.56±2.28 kg), and 28 patients were <3.1% (+1.38±4.03 kg). Compared to the CA group, the AF burden at 12 months (0 [IQR 0-0]) was significantly higher in the second (3.5% [0; 52] and third (1[0; 47] LFM-ADD tertiles, but did not differ from CA in the first tertile (0% [0; 11.], p=0.17).

Conclusion: In obese patients, the maintenance of SR on AADs is related to the level of weight reduction. The efficacy of AADs in obese patients with a significant weight loss at 12-months follow-up is close to catheter ablation, although the overall AF burden is still non-significantly higher.

Background: Intracardiac echocardiography (ICE)-based electroanatomical mapping (EAM) improves procedural efficiency and safety in atrial fibrillation (AF) ablation and remains the standard of care. The CARTOSOUND FAM (AI FAM) module uses a deep-learning algorithm that automates left atrial reconstruction without manual contouring.

Objective: This study aims to evaluate the one-year outcomes of AI FAM compared to standard-of-care EAM in AF ablation.

Methods: This study included 298 patients undergoing radiofrequency AF ablation between Jan 2021 to Dec 2023. Patients treated before Jan 2023 underwent standard-of-care EAM, while those in 2023 utilized AI FAM-based reconstruction. Baseline demographics, comorbidities, AF type, and medication use were recorded. Procedural characteristics, acute success, complications, and AF recurrence at one-year follow up were analyzed.

Results: Of the 298 patients, 115 underwent mapping with AI FAM and 183 with EAM. Baseline characteristics were comparable. AI FAM reduced mean total procedure time (122.5±23.5 vs 129.0±30.4min, P=0.046) and left atrial (LA) dwell time (78.3±21.45 vs 87.5±28.2min, P=0.001). Acute procedural success was 98.3% in AI FAM vs 98.9% in EAM with fewer complications observed in the AI FAM group (1 vs 4). At one year, freedom from AF recurrence was comparable (80.0% AI FAM vs 81.4% EAM at 1yr, LogRank P=0.610).

Conclusion: AI FAM was associated with incremental but significant procedural advantages over conventional contouring via reduced total procedure time and LA dwell time, without compromising acute and long-term safety and rhythm control efficacy. AI FAM integration with PFA will mark another step towards making AF ablation more streamlined and accessible.