Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model.

Ventricular arrhythmias increase cardiovascular morbidity and mortality. Recurrent PVCs and IVT are generally considered benign in the absence of structural heart abnormalities. Artificial intelligence is a rapidly growing field. In recent years, medical professionals have shown great interest in the potential use of ML, an integral part of AI, in various disciplines, including diagnostic applications, decision-making, prognostic stratification, and solving complex pathophysiological aspects of diseases from these data at extraordinary complexity, scale, and acquisition rate. The aim of this study was to design an ML model to predict the probability of PVC and IVT recurrence after RF ablation. Data of patients were collected and manipulated using traditional analysis and various artificial intelligence models, namely MLP, Gradient Boosting Machines, Random Forest, and Logistic Regression. Hypertension, male sex, and the use of non-irrigate catheters were associated with less freedom from arrhythmia. All these results were obtained through traditional analytic methods, and according to AI, none of the variables had a clear effect on the recurrence of arrhythmia. Each AI model presents unique strengths and weaknesses, and further optimization and fine-tuning of these models are necessary to increase their clinical utility. By expanding the dataset, improved predictions can be fostered to ultimately increase the clinical utility of AI in predicting PVC erosion outcomes.

Risk factor profile and outcomes of premature acute coronary syndrome after percutaneous coronary intervention: A 1-year prospective design.

BACKGROUND: The prevalence of acute coronary syndrome (ACS) among young adults (premature ACS) has dramatically increased in recent years, especially in developing countries. Yet, the data on these patients' attributed risk factors and outcomes are inconsistent. In this study, we aimed to investigate these data in a cohort of premature ACS cases who underwent percutaneous coronary intervention (PCI) compared to older patients. HYPOTHESIS: We hypothesize that premature ACS patients undergoing PCI will exhibit different risk factor profiles and outcomes compared to non-premature patients. specifically, we anticipate that premature patients do not necessarily have better outcomes than non-premature. METHODS: Overall, 3142 and 10 399 patients were included in premature and non-premature groups, respectively. Patients' pre-operative, post-operative, and follow-up data were retrieved retrospectively from the Tehran Heart Center PCI databank. RESULTS: The mean age of premature and non-premature cohorts was 48.39 and 67 years, respectively. Patients were predominantly male in both groups. Family history of coronary artery disease (CAD), dyslipidemia, smoking, and opium addiction were more prevalent among the younger cohort. After adjustment, in-hospital mortality in younger patients was considerably higher, with all-cause mortality and major cardiovascular and cerebrovascular events (MACCE) exhibiting no noticeable difference among the two groups. CONCLUSIONS: Risk factor profile is different in young patients, and traditional cardiovascular risk factors, such as hypertension and diabetes mellitus, are more prevalent among older adults. Younger age is not equivalent to a better prognosis; hence, similar or even more caution should be taken into consideration regarding secondary prevention for these patients.