Supplementary Material for: Effects of Vitamin E-Coated versus Conventional Membranes in Chronic Hemodialysis Patients: A Systematic Review and Meta-Analysis

<strong><em>Introduction:</em></strong> Accruing evidence suggests that vitamin E-coated membranes (ViE-m) might improve the clinical management of chronic hemodialysis (HD) patients. <b><i>Methods:</i></b> We conducted a systematic review and meta-analysis of RCTs comparing ViE-m to conventional HD. Endpoints of interest were a series of biomarkers pertaining to anemia status, inflammation, oxidative stress and dialysis efficacy/status. <b><i>Results:</i></b> Sixty studies were included. ViE-m significantly improved the Erythropoietin Resistance Index but had no impact on other anemia parameters. As for oxidative stress and inflammation, ViE-m produced a significant decrease in interleukin-6 levels, thiobarbituric acid reactive substances, plasma and red blood cell (RBC) malonylaldehyde and a significant increase in blood and RBC vitamin E. Conversely, ViE-m use had no impact on lipid profile, dialysis adequacy, blood pressure, albumin and uric acid. <b><i>Conclusions:</i></b> ViE-m might ameliorate anemia management by reducing oxidative stress and inflammation. Benefits of these bio-membranes on harder clinical outcomes are uncertain and need to be investigated by future, targeted trials

Supplementary Material for: European Nephrologists' Attitudes Towards the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey

Introduction: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyse large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients’ treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, and reducing healthcare costs, and consequently creating values for all the involved stakeholders. Objectives: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care) towards the application of Artificial intelligence in clinical practice. Methods: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. Results: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. Conclusion: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice