ABSTRACT
OBJECTIVE: The implications of saline flushing of the radial sheath have not been studied in terms of radial artery occlusion. We aimed to investigate radial artery patency outcomes after the saline flush of the radial sheath. METHODS: In this prospective observational study, patients were selected to receive either radial sheath flushing with 10 mL of saline after pulling the sheath to one-third of its length (Group 1) or standard care (Group 2) after removal of the catheter sheath as per physician discretion. Radial artery patency was assessed by Doppler ultrasound at 24 h and 30 days after the procedure. RESULTS: A total of 2877 patients were enrolled in the study, with 1340 receiving radial sheath flushing and 1537 receiving standard care. At 24 h after the procedure, the incidence of radial artery occlusion was significantly lower in the radial sheath flushing group compared to the standard care group (4.4% vs 12.6%, p = 0.027). This difference persisted 30 days after the procedure (6.1% vs 15.8%, p = 0.015). Radial sheath flushing was independently associated with a lower risk of radial artery occlusion 30 days after the procedure, after adjusting for potential confounders (OR 0.375, 95% CI 0.18-0.77, p = 0.008). CONCLUSION: In conclusion, this prospective study provides evidence to support the use of radial sheath flushing after coronary intervention via the radial artery as a simple and effective strategy for reducing the risk of radial artery occlusion without increasing the risk of other adverse outcomes.
ABSTRACT
Directed by 2 decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, artificial intelligence has shown potential in providing data interpretation and automated analysis from electrocardiogram, echocardiography, computed tomography angiography, magnetic resonance imaging, and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention, along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of machine learning has emerged as a deep learning algorithm for numerous cardiovascular applications. However, the impact deep learning on the future of cardiology practice is not clear. Predictive models based on deep learning have several limitations including low generalizability and decision processing in cardiac anatomy.
