Background and aims: Left atrial appendage (LAA) thrombus is the primary cause of stroke and systemic embolism in atrial fibrillation (AF). Non-vitamin-K oral anticoagulants (NOACs) effectively reduce LAA thrombus prevalence and stroke risk. However, the optimal treatment of a NOAC-resistant thrombus remains unclear. We aimed to evaluate therapeutic strategies for resolving LAA thrombus in patients on optimal NOAC therapy. Methods: We retrospectively analyzed patients scheduled for cardioversion or catheter ablation of AF between 2014 and 2023 with LAA thrombus on transesophageal echocardiography (TEE) despite being on optimal NOAC therapy. We assessed how the applied management strategy affected thrombus resolution. Results: Among the analyzed 120 patients, a change to a different NOAC occurred in 41% of cases, a transition to a VKA in 30%, and the supplementation with antiplatelet therapy in 11%. In contrast, 18% of the patients received unchanged therapy. Follow-up imaging at 65 [44 – 95] days showed successful thrombus resolution in 92 (77%) of cases, predicted by a lower CHA2DS2-VASc score ( p = 0.01). Any modification of antithrombotic therapy was an independent predictor of thrombus resolution (OR 5.28 [1.55–18], p = 0.01). Of the four strategies, there was a trend toward better thrombus resolution with switching to a VKA (OR 3.23 [1.03–10.1], p = 0.04). Conclusion: Resolution of LAA thrombus in patients already on adequate NOAC treatment may require a revision of the anticoagulation strategy. In addition, transitioning from NOAC to VKA might be considered. Graphical

Background: Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. Methods: For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. Results: We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. Conclusions: Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies.

Achievements and endurance represent distinct aspects of sports performance, influenced by different underlying factors. However, these determinants are not well characterized. To address this gap between achievements and endurance, we evaluated machine learning models to estimate the competition results-based Achievement Score and the absolute cardiorespiratory capacity-based Endurance Score in elite athletes. We analyzed these models using SHapley Additive exPlanations to identify the most influential determinants of each performance component. Altogether, 688 healthy, asymptomatic elite athletes (19.7 ± 6.9 years; males: 75%) with 1194 sports cardiology screening exams were included in our machine learning-based analysis. In a hold-out test set of randomly selected 100 athletes (21.5 ± 9.2 years, males 67%), the best-performing models predicted Achievement Score and Endurance Score with the coefficients of determination values of 0.29 (95% bootstrap CI (0.00, 0.48)) and 0.69 (95% bootstrap CI (0.56, 0.79)), respectively. For Achievement Score, the strongest determinants were training years, weekly training hours, and age. In contrast, Endurance Score was primarily driven by skeletal muscle mass, weight, and peak heart rate. These findings suggest that achievements are predominantly related to training history and experience, whereas endurance is more closely associated with body composition and sports adaptation, partly reflecting its reliance on absolute cardiorespiratory measures.

Background: Ventricular tachycardia (VT) in ischemic heart disease (IHD) is the leading cause of sudden cardiac death (SCD). Despite the well-defined recommendations, tailoring therapies individually is often a challenge owing to the great heterogeneity in this population. We hypothesized that unsupervised machine learning can uncover hidden patterns to identify patient subgroups with similar characteristics. Aims: To use unsupervised machine learning methods on a database of patients with ischemic VT, to discover subgroups with similar characteristics and compare the survival of the identified groups. Methods: We collected the data of 566 patients with IHD hospitalized for sustained monomorphic VT, excluding those with acute coronary syndrome. The 17 recorded features included medical history and echocardiography. All-cause mortality was registered with one-year follow-up. We performed dimension reduction using T-distributed Stochastic Neighbor Embedding (TSNE) to produce 3 dimensions of the data. Then, we applied spectral clustering to create three groups within the population. Each group was compared to the rest of the population for each variable. Finally, we compared the survival across the three groups using Kaplan-Meyer analysis, univariate Cox-regression, and Log-rank test. Results: We identified three groups with distinct characteristics. Group 1 represented the healthiest population; patients had no previous VT hospitalizations, and none of HD instability, LBBB, SCD, diabetes, VT storm or ICD shocks; additionally, they always had a single VT episode at presentation. They showed better left ventricular ejection fraction (LVEF) (38% vs. 33%, p<0.001) and TAPSE (20 mm vs. 18 mm, p=0.01), less severe mitral and tricuspid regurgitation (MR, TR), smaller left ventricular end-systolic diameter, and longer DT. Conversely, patients in Group 2 (n=349) had more previous VT hospitalizations (30% vs. 18%, p=0,003), more often had diabetes (46% vs. 18%, p<0.001), left bundle branch block (23% vs. 13%, p=0.006), and more often presented with HD instability (47% vs. 28%, p<0.001). Group 3 contained all seriously ill patients with VT storm or incessant VT, more commonly presenting with ICD shocks (34% vs. 21%, p=0.004), SCD (24% vs. 15%, p=0.004), hemodynamic instability (50% vs. 37%, p=0.01). This group had the poorest LVEF (30% vs. 35%, <0.001) and TAPSE (17 mm vs. 19 mm p=0.001), more severe MR and TR, and the shortest DT (140 ms vs. 173 ms p<0.001). Finally, we compared survival across the three groups. Significantly better survival was found in Group 1 (HR: 0.52 CI [0.28 - 0.98]) and Group 2 (HR: 0.61 CI [0.42 - 0.88]), while Group 3 had higher mortality (HR: 2.7 CI [1.83 - 3.97]). Conclusions: Using spectral clustering we identified three groups in our cohort of ischemic VT patients. This method delineates the group of ischemic VT patients with the highest risk of mortality who might need more aggressive treatment and closer follow-up.

Background: The beneficial effects of mineralocorticoid receptor antagonists (MRAs) regarding sudden cardiac death (SCD) risk and cardiac remodeling are well known, moreover, they also reduce episodes of ventricular tachycardia (VT). However, superiority in effectiveness between receptor selective eplerenone and conventional spironolactone is unknown. Purpose: Our aim was to assess the effect of eplerenone and spironolactone on ventricular tachycardia (VT) recurrence in patients with ischemic heart disease (IHD). Methods: We collected data of 563 patients with IHD following hospitalization due to a sustained VT episode in a structured registry. Propensity score matching was performed based on age, hypertension, diabetes, history of acute myocardial infarction, left ventricular ejection fraction (LVEF) and presentation with incessant VT. The matched population consisted of 188 patients and was further analyzed. Predictors were assessed using univariate and multivariate Cox regression models. Kaplan-Meier curves were used for time-to-event analyses. Results: The median age was 70 years with 81% being male. One third of the patients had an ICD implanted, while 43 (23%) had CRT. The median LVEF was 30%. Twenty-five (13%) patients presented with an electrical storm and 19 (10%) had incessant VT. In 86 cases (46%), the VT caused hemodynamic instability. Ablation was performed in 66 patients (35%), while 4 required mechanical circulatory support. During the median follow-up of 856 [399-1624] days, 69 patients had VT recurrence and 118 patients died. Eplerenone use significantly reduced the risk of VT recurrence compared to spironolactone (HR: 0.48 [0.29-0.78], log-rank p=0.003). This effect was consistent in the multivariate model as well (p=0.006). Conclusions: Eplerenone use may be more beneficial in terms of VT recurrence in patients with IHD who present with a sustained VT. Further analysis is required to confirm these findings.