Hemodialysis Procedures for Stable Incident and Prevalent Patients Optimize Hemodynamic Stability, Dialysis Dose, Electrolytes, and Fluid Balance.

The demographic profile of patients transitioning from chronic kidney disease to kidney replacement therapy is changing, with a higher prevalence of aging patients with multiple comorbidities such as diabetes mellitus and heart failure. Cardiovascular disease remains the leading cause of mortality in this population, exacerbated by the cardiovascular stress imposed by the HD procedure. The first year after transitioning to hemodialysis is associated with increased risks of hospitalization and mortality, particularly within the first 90-120 days, with greater vulnerability observed among the elderly. Based on data from clinics in Fresenius Medical Care Europe, Middle East, and Africa NephroCare, this review aims to optimize hemodialysis procedures to reduce mortality risk in stable incident and prevalent patients. It addresses critical aspects such as treatment duration, frequency, choice of dialysis membrane, dialysate composition, blood and dialysate flow rates, electrolyte composition, temperature control, target weight management, dialysis adequacy, and additional protocols, with a focus on mitigating prevalent intradialytic complications, particularly intradialytic hypotension prevention.

European Nephrologists' Attitudes toward 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 analyze 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, 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 NephroCare) toward 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.

An analysis of the impact of fluid overload and fluid depletion for all-cause and cardiovascular mortality.

BACKGROUND: Both baseline fluid overload (FO) and fluid depletion are associated with increased mortality risk and cardiovascular complications in haemodialysis patients. Fluid status may vary substantially over time, and this variability could also be associated with poor outcomes. METHODS: In our retrospective cohort study, including 4114 haemodialysis patients from 34 Romanian dialysis units, we investigated both all-cause and cardiovascular mortality risk according to baseline pre- and post-dialysis volume status, changes in pre- and post-dialysis fluid status during follow-up (time-varying survival analysis), pre-post changes in volume status during dialysis and pre-dialysis fluid status variability during the first 6 months of evaluation. RESULTS: According to their pre-dialysis fluid status, patients were stratified in the following groups: normovolaemic with an absolute FO (AFO) compartment between -1.1 and 1.1 L, fluid depletion with an AFO below -1.1 L, moderate FO with an AFO compartment >1.1 but <2.5 L and severe FO with the AFO compartment >2.5 L. Baseline pre-dialysis FO and fluid depletion patients had a significantly elevated risk of all-cause mortality risk {hazard ratio [HR] 1.53 [95% confidence interval (CI) 1.22-1.93], HR 2.04 (95% CI 1.59-2.60) and HR 1.88 (95% CI 1.07-3.39) for moderate FO, severe FO and fluid depletion, respectively}. In contrast, post-dialysis fluid depletion was associated with better survival [HR 0.71 (95% CI 0.57-0.89)]. Similar results were found when using changes in pre- or post-dialysis fluid status during follow-up (time-varying values): FO patients had an increased risk of all-cause [moderate FO: HR 1.39 (95% CI 1.11-1.75); severe FO: HR 2.29 (95% CI 2.01-3.31] and cardiovascular (CV) mortality [moderate FO: HR 1.34 (95% CI 1.05-1.70); severe FO: HR 2.34 (95% CI 1.67-3.28)] as compared with normohydrated patients. Using pre-post changes in volume status during dialysis, we categorized the patients into six groups: Group 1, AFO <-1.1 L pre- and post-dialysis; Group 2, AFO between -1.1 and 1.1 L pre-dialysis and <-1.1 L post-dialysis (the reference group); Group 3, AFO between -1.1 and 1.1 L pre- and post-dialysis; Group 4, AFO >1.1 L pre-dialysis and <-1.1 L post-dialysis; Group 5, AFO >1.1 L pre-dialysis and between -1.1 and 1.1 L post-dialysis; Group 6, AFO >1.1 L pre- and post-dialysis. Using the baseline values, only patients in Groups 1, 5 and 6 maintained an increased risk for all-cause mortality as compared with the reference group. Additionally, CV mortality risk was significantly higher for patients in Groups 5 and 6. When we applied the time-varying analysis, patients in Groups 1, 5 and 6 had a significantly higher risk for both all-cause and CV mortality risk. In the last approach, the highest risk for the all-cause mortality outcome was observed for patients with high-amplitude fluctuation during the first 6 months of evaluation [HR 2.75 (95% CI 1.29-5.84)]. CONCLUSION: We reconfirm the association between baseline pre- and post-dialysis volume status and mortality in dialysis patients; additionally, we showed that greater fluid status variability is independently associated with higher mortality.