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.

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.

U-Shaped Association of the Heart Rate Variability Triangular Index and Mortality in Hemodialysis Patients With Atrial Fibrillation.

Background: Atrial fibrillation (AF) is common in hemodialysis patients and contributes to increased mortality. We aimed to examine heart rate variability triangular index (HRVI) in hemodialysis patients with AF as it has recently been reported to predict mortality in AF patients without kidney disease. Methods: A total of 88 patients on hemodialysis with a medical history of AF or newly diagnosed AF underwent 24-h electrocardiography recordings. The primary endpoint of cardiovascular mortality was recorded during a median follow up of 3.0 years. Risk prediction was assessed by Cox regression, both unadjusted and adjusted for the Charlson Comorbidity Index and the Cardiovascular Mortality Risk Score. Results: Median age was 76 years, median dialysis vintage was 27 months. Altogether, 22 and 44 patients died due to cardiovascular and non-cardiovascular causes. In 55% of patients AF was present during the recording. Kaplan-Meier plots of HRVI quartiles suggested a non-linear association between HRVI, cardiovascular, and all-cause mortality which was confirmed in non-linear Cox regression analysis. Adjusted linear Cox regression revealed a hazard ratio of 6.2 (95% CI: 2.1-17.7, p = 0.001) and 2.2 (95% CI: 1.3-3.8, p = 0.002) for the outer quartiles (combined first and fourth quartile) for cardiovascular and all-cause mortality, respectively. Patients in the first quartile were more likely to have sinus rhythm whereas patients in the fourth quartile were more likely to have AF. Conclusions: We found a U-shaped association between HRVI and mortality in hemodialysis AF patients. The results might contribute to risk stratification independent of known risk scores in hemodialysis AF patients.

Cognitive Impairment is Associated with Mortality in Hemodialysis Patients.

BACKGROUND: The prevalence of cognitive impairment in hemodialysis patients is notably high. In previous studises performed in the general population, cognitive impairment has been associated with increased mortality. OBJECTIVE: We evaluated the relationship between global cognitive function tested by a short screening instrument and mortality in hemodialysis patients. METHODS: Cognitive testing was performed in 242 maintenance hemodialysis patients under standardized conditions at baseline using the Montreal Cognitive Assessment (MoCA).Cognitive impairment was defined as a MoCA test score ≤24 points, as published previously. All-cause mortality was monitored during a median follow-up of 3.54 years. Kaplan-Meier plot and Cox regression model adjusted for known risk factors for mortality in hemodialysis patients were used to examine a possible association between global cognitive function and all-cause mortality. RESULTS: A MoCA test score ≤24 points resulted in a significant almost 3-fold higher hazard for all-cause mortality (unadjusted hazard ratio [HR]: 2.812; 95% confidence interval [95% CI]: 1.683-4.698; p < 0.001). After adjustment, this association was attenuated but remained significant (adjusted HR: 1.749; 95% CI: 1.007-3.038; p = 0.047). CONCLUSION: Impairment of global cognitive function measured by a short screening instrument was identified for the first time as an independent predictor of all-cause mortality in hemodialysis patients. Thus, implementing the MoCA test in clinical routine could contribute to a better risk stratification of this patient population.