The subcutaneous implantable cardioverter-defibrillator (S-ICD) is developed to overcome lead-related complications and systemic infections, inherent to transvenous ICD (TV-ICD) therapy. The PRAETORIAN trial demonstrated that the S-ICD is non-inferior to the TV-ICD with regard to the combined primary endpoint of inappropriate shocks and complications. This prespecified secondary analysis evaluates all complications in the PRAETORIAN trial. The PRAETORIAN trial is an international, multicentre, randomized trial in which 849 patients with an indication for ICD therapy were randomized to receive an S- ICD (N = 426) or TV-ICD (N = 423) and followed for a median of 49 months. Endpoints were device-related complications, lead-related complications, systemic infections, and the need for invasive interventions. Thirty-six device-related complications occurred in 31 patients in the S-ICD group of which bleedings were the most frequent. In the TV-ICD group, 49 complications occurred in 44 patients of which lead dysfunction was most frequent (HR: 0.69; P = 0.11). In both groups, half of all complications were within 30 days after implantation. Lead-related complications and systemic infections occurred significantly less in the S-ICD group compared with the TV-ICD group (P < 0.001, P = 0.03, respectively). Significantly more complications required invasive interventions in the TV-ICD group compared with the S-ICD group (8.3% vs. 4.3%, HR: 0.59; P = 0.047). This secondary analysis shows that lead-related complications and systemic infections are more prevalent in the TV-ICD group compared with the S-ICD group. In addition, complications in the TV-ICD group were more severe as they required significantly more invasive interventions. This data contributes to shared decision-making in clinical practice. Shown is the summary of the main endpoints of the manuscript: device-related complications in the subcutaneous and transvenous ICD: a secondary analysis of the PRAETORIAN trial. ICD, implantable cardioverter-defibrillator.

Atrial fibrillation (AF) is associated with profound structural and functional changes in the atria. In the present study, we investigated the association between left atrial (LA) phasic function and the extent of LA fibrosis using advanced cardiovascular magnetic resonance (CMR) imaging techniques, including 3-dimensional (3D) late gadolinium enhancement (LGE) and feature tracking. Patients with paroxysmal and persistent AF (n = 105) underwent CMR in sinus rhythm. LA global reservoir strain, conduit strain and contractile strain were derived from cine CMR images using CMR feature tracking. The extent of LA fibrosis was assessed from 3D LGE images. Healthy subjects underwent CMR and served as controls (n = 19). Significantly lower LA reservoir strain, conduit strain and contractile strain were found in AF patients, as compared to healthy controls (− 15.9 ± 3.8% vs. − 21.1 ± 3.6% P < 0.001, − 8.7 ± 2.7% vs. − 12.6 ± 2.5% P < 0.001 and − 7.2 ± 2.3% vs. − 8.6 ± 2.2% P = 0.02, respectively). Patients with a high degree of LA fibrosis (dichotomized by the median value) had lower reservoir strain and conduit strain compared to patients with a low degree of LA fibrosis (− 15.0 ± 3.9% vs. − 16.9 ± 3.3%, P = 0.02 and − 7.9 ± 2.7% vs. − 9.5 ± 2.6%, P = 0.01, respectively). In contrast, no difference was found for LA contractile strain (− 7.1 ± 2.4% vs. − 7.4 ± 2.3%, P = 0.55). Impaired LA reservoir and conduit strain are present in AF patients with extensive atrial fibrosis. Future studies are needed to examine the biologic nature of this association and possible therapeutic implications.

A significant number of patients experience recurrent atrial fibrillation (AF) after ablation. Various risk scores have been described that may predict outcomes after AF ablation. In this study, we aimed to compare ten previously described risk scores with regard to their predictive value for post-ablation AF recurrence and procedural complications. A total of 482 AF patients (63% paroxysmal AF, 66% male, mean age 62 ± 9 years) undergoing initial radiofrequency pulmonary vein isolation (PVI) were included in the present analysis. Prior to ablation, all patients underwent both transthoracic echocardiography (TTE) and either cardiac CT imaging or CMR imaging. The following risk scores were calculated for each patient: APPLE, ATLAS, BASE-AF₂, CAAP-AF, CHADS₂, CHA₂DS₂-VASc, DR-FLASH, HATCH, LAGO and MB-LATER. Median follow-up was 16 (12-31) months. AF recurrence after a 90-day blanking period was observed in 199 patients (41%), occurring after a median of 183 (124-360) days. AF recurrence was less frequent in paroxysmal AF patients compared to non-paroxysmal AF patients (34% vs. 54%, p < 0.001). Overall periprocedural complication rate was 6%. All scores, except the HATCH score, demonstrated statistically significant but poor predictive value for recurrent AF after ablation (area under curve [AUC] 0.553-0.669). CHA₂DS₂-VASc and CAAP-AF were the only risk scores with predictive value for procedural complications (AUC 0.616, p = 0.043; AUC 0.615, p = 0.044; respectively). Currently available risk scores perform poorly in predicting outcomes after AF ablation. These data suggest that the utility of these scores for clinical decision-making is limited.

Pulmonary vein isolation (PVI) by radiofrequency (RF) ablation is an important alternative to antiarrhythmic drugs in the treatment of symptomatic atrial fibrillation. However, the inability to consistently achieve durable isolation of the pulmonary veins hampers the long-term efficacy of PVI procedures. The large number of factors involved in RF lesion formation and the complex interplay of these factors complicate reliable creation of durable and transmural ablation lesions. Various surrogate markers of ablation lesion formation have been proposed that may provide information on RF lesion completeness. Real-time assessment of these surrogates may aid in the creation of transmural ablation lesions, and therefore, holds potential to decrease the risk of PV reconnection and consequent post-PVI arrhythmia recurrence. Moreover, titration of energy delivery until lesions is transmural may prevent unnecessary ablation and subsequent adverse events. Whereas several surrogate markers of ablation lesion formation have been described over the past decades, a ‘gold standard’ is currently lacking. This review provides a state-of-the-art overview of ablation strategies that aim to enhance durability of RF-PVI, with special focus on real-time available surrogates of RF lesion formation in light of the biophysical basis of RF ablation.

Background: The pathogenic missense variant p.G125R in TBX5 (T-box transcription factor 5) causes Holt–Oram syndrome (also known as hand–heart syndrome) and early onset of atrial fibrillation. Revealing how an altered key developmental transcription factor modulates cardiac physiology in vivo will provide unique insights into the mechanisms underlying atrial fibrillation in these patients. Methods: We analyzed ECGs of an extended family pedigree of Holt–Oram syndrome patients. Next, we introduced the TBX5-p.G125R variant in the mouse genome ( Tbx5 ^G125R ) and performed electrophysiologic analyses (ECG, optical mapping, patch clamp, intracellular calcium measurements), transcriptomics (single-nuclei and tissue RNA sequencing), and epigenetic profiling (assay for transposase-accessible chromatin using sequencing, H3K27ac [histone H3 lysine 27 acetylation] CUT&RUN [cleavage under targets and release under nuclease sequencing]). Results: We discovered high incidence of atrial extra systoles and atrioventricular conduction disturbances in Holt–Oram syndrome patients. Tbx5 ^G125R/+ mice were morphologically unaffected and displayed variable RR intervals, atrial extra systoles, and susceptibility to atrial fibrillation, reminiscent of TBX5-p.G125R patients. Atrial conduction velocity was not affected but systolic and diastolic intracellular calcium concentrations were decreased and action potentials were prolonged in isolated cardiomyocytes of Tbx5 ^G125R/+ mice compared with controls. Transcriptional profiling of atria revealed the most profound transcriptional changes in cardiomyocytes versus other cell types, and identified over a thousand coding and noncoding transcripts that were differentially expressed. Epigenetic profiling uncovered thousands of TBX5-p.G125R-sensitive, putative regulatory elements (including enhancers) that gained accessibility in atrial cardiomyocytes. The majority of sites with increased accessibility were occupied by Tbx5. The small group of sites with reduced accessibility was enriched for DNA-binding motifs of members of the SP (specificity protein) and KLF (Krüppel-like factor) families of transcription factors. These data show that Tbx5-p.G125R induces changes in regulatory element activity, alters transcriptional regulation, and changes cardiomyocyte behavior, possibly caused by altered DNA binding and cooperativity properties. Conclusions: Our data reveal that a disease-causing missense variant in TBX5 induces profound changes in the atrial transcriptional regulatory network and epigenetic state in vivo, leading to arrhythmia reminiscent of those seen in human TBX5-p.G125R variant carriers.

Aims: Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods and results: PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC’s ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion: AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.