Futela, Pragyat

Abstract Background Radiofrequency ablation (RFA) has been effective in managing premature ventricular contractions (PVCs). Despite improvement in ablation techniques, recurrence remains high for LV summit PVCs. Epicardial ablation here is often precluded due to fat and proximity to coronaries, thus, aggressive but safe endocardial options are needed. Purpose To evaluate the safety and effectiveness of ultra-long ablation (>240 seconds) duration for refractory LV summit PVCs. Methods Patients with deep intramural LV summit PVCs were identified by lack of PVC elimination or late (>60 s suppression) and recurrence post-lesion under the left aortic sinus of Valsalva. We included patients who received the longest RFA of at least 240 seconds (maximum 15 minutes). RFA power was titrated slowly from 20 to 35W, carefully ensuring that the impedance drop did not exceed 12 ohms. Acute procedure success was defined as clinical PVC suppression for ≥60 minutes after the last lesion. Short-term success was defined as symptom improvement, ejection fraction (EF) recovery, and drop in PVC burden at 3 months. Results Twenty patients (59.5 ± 16.4 years, 55% male) were included. Baseline features are shown in Table 1. All RFA were delivered endocardially. No steam pops were noted. PVC burden was significantly lower at 3-month follow-up (P< 0.01), Figure 1A. Most patients had a normal ejection fraction (EF) at baseline; thus, no significant difference change in EF was observed (Figure 1B). One complication was recorded, which was tamponade likely caused by coronary sinus dissection requiring pericardiocentesis. Acute success was achieved in 95.0% of the cases, and all cases followed at 3 months (18/18) achieved short-term success. Conclusion Ultra-long duration RFA with lesions ≥ 240 seconds at 35W per lesion for LV summit PVCs appeared to be safe and effective in achieving acute and short-term success. The lack of acute suppression should not discourage operators, as there seems to be delayed suppression in some cases. Careful monitoring of biophysics is paramount when attempting this technique.

Abstract Background Ventricular tachycardia (VT) is a life-threatening arrhythmia that can complicate structural heart disease. While catheter ablation is effective in treating this condition, the recurrence of VT after the procedure continues to be a concern. Consequently, there is a need for a reliable system to evaluate the likelihood of arrhythmia recurrence following an ablation. Objective To predict recurrence or repeat ablation after a VT catheter ablation procedure using machine learning (ML) models. Methods We conducted a single-center, retrospective study of all patients undergoing catheter ablation for scar-related VT between 2012 and 2022. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Python version 3.8.5 for exploratory data analysis (EDA) and visualization. Using six different ML models from the training set (90:10 split), we used 34 variables to predict the primary outcome, including VT recurrence or repeat catheter ablation. The accuracy of these models was compared using ROC curves based on their performance on the test set. Results Out of 508 VT ablation procedures, 261 experienced a recurrence. Support Vector Classifier (SVM) performed the best, having an AUC of 0.73, followed by logistic regression at 0.69 (Figure A). SVM yielded sensitivity, specificity, positive and negative predictive values of 0.73, 0.73, 0.79, and 0.67, respectively. The F1 score was 0.70. Among the factors with the highest feature importance in the model, the most influential variable was the proceduralist’s experience level, followed by QRS width at baseline, LVEF, and patient’s age (Figure B). Conclusion Our model can predict recurrent VT after ablation with reasonable accuracy, outperforming the existing clinical risk scores significantly. Larger training sample sizes will help achieve more robust ML models and improve predictive accuracy further.Figure A: ROC curve depicting the AUROCB. Feature importance graph

Background and Objectives: Proteomics encompasses the exploration of protein composition, regulation, function, and pathways. Its influence spans diverse clinical fields and holds promise in addressing various women’s health conditions, including cancers, osteoporosis, and cardiovascular disorders. However, no comprehensive summary of proteomics and menopausal health exists. Our objective was to summarize proteomic profiles associated with diseases and disorders in peri- and postmenopausal women. Materials and Methods: We conducted a comprehensive search of databases including PubMed, Google Scholar, the Cochrane database, Elsevier, and ScienceDirect until 2022. A total of 253 studies were identified, and 41 studies met the inclusion criteria to identify data of interest. These included the study design, disease, and proteomics/proteins of significance, as described by the authors. Results: The 41 studies covered diverse areas, including bone disorders (10 studies), cardiovascular diseases (5 studies), oncological malignancies (10 studies), and various conditions, such as obesity, nonalcoholic liver disease, the effects of hormone replacement therapy, and neurological diseases (16 studies). The results of our study indicate that proteomic profiles correlate with heart disease in peri- and postmenopausal women, with distinct sex differences. Furthermore, proteomic profiles significantly differ between women with and without osteoporosis. Additionally, patients with breast, ovarian, and endometrial cancer exhibit notable variations in proteomic profiles compared to those without these conditions. Conclusions: Proteomics has the potential to enhance risk assessment and disease monitoring in peri- and postmenopausal women. By analyzing unique protein profiles, clinicians can identify individuals with heightened susceptibility to specific diseases or those already affected by established conditions. This review suggests that there is sufficient preliminary data related to proteomics in peri- and postmenopausal women for early identification of cardiovascular disease, osteoporosis, and cancers, disease monitoring, and tailoring individualized therapies. Rigorous validation studies involving large populations are essential before drawing definitive conclusions regarding the clinical applicability of proteomic findings.