Background and Objectives: Aortic stenosis (AS) is a widespread valvular disease in developed countries, primarily among the elderly. Transcatheter aortic valve replacement (TAVR) has become a viable alternative to aortic valve surgery for patients with severe AS who are deemed a high surgical risk or for whom the AS is found to be inoperable. Predicting outcomes after TAVR is essential. The Naples Prognostic Score (NPS) is a new scoring method that evaluates nutritional status and inflammation. Our study is aims to examine the relationship between the NPS and outcomes for patients receiving TAVR. Material and Methods: We conducted a retrospective study of 370 patients who underwent TAVR across three tertiary medical centres from March 2019 to March 2023. The patients were divided into two groups based on their NPS, namely, low (0, 1, and 2) and high (3 and 4). Our study is primarily aimed to determine the one-year mortality rate. Results: Within one year, the mortality rate for the entire group was 8.6%. Nonetheless, the low-NPS group had a rate of 5.0%, whereas the high-NPS group had a rate of 13%. The difference between the two groups was statistically significant, with a p-value of 0.06. Conclusions: Our results show that NPS is an independent predictor of one-year mortality in patients undergoing TAVR.

Introduction Atrial septal defect (ASD) is the third most common congenital cardiac malformation.(1) Although surgical repair of ASD is fairly safe and a routine procedure very rarely it may be complicated to diversion of inferior vena cava (IVC) into left atrium. Diversion of inferior vena cava into left atrium;

OBJECTIVES: Accurate differentiation between ventricular tachycardia (VT) and supraventricular tachycardia (SVT) with aberrant conduction in wide complex tachyarrhythmias (WCT) remains a significant challenge in emergency medicine. This study aimed to evaluate the efficacy of deep learning (DL) models, specifically pretrained residual network (ResNet) architectures, in classifying these arrhythmias using electrocardiography (ECG) data. METHODS: A retrospective cross-sectional study was conducted, analysing 652 WCT ECGs and 248 normal sinus rhythm ECGs from an emergency medicine clinic. Three ResNet models ResNet-18, ResNet-34, and ResNet-50 were fine-tuned using transfer learning. Model performance was assessed via 10-fold cross-validation, evaluating accuracy, sensitivity, and precision. RESULTS: All ResNet models demonstrated high and consistent performance, achieving 95% accuracy, precision in distinguishing VT from SVT with aberrant conduction. The models exhibited robust generalization across validation folds. CONCLUSION: DL models, particularly ResNet architectures, show promise in enhancing ECG-based diagnosis of WCT. Their integration into emergency care could improve diagnostic accuracy, especially in settings with limited access to specialized cardiac expertise.