The Use of Anemia Control Model Is Associated with Improved Hemoglobin Target Achievement, Lower Rates of Inappropriate Erythropoietin Stimulating Agents, and Severe Anemia among Dialysis Patients.

INTRODUCTION: The Anemia Control Model (ACM) is a certified medical device suggesting the optimal ESA and iron dosage for patients on hemodialysis. We sought to assess the effectiveness and safety of ACM in a large cohort of hemodialysis patients. METHODS: This is a retrospective study of dialysis patients treated in NephroCare centers between June 1, 2013 and December 31, 2019. We compared patients treated according to ACM suggestions and patients treated in clinics where ACM was not activated. We stratified patients belonging to the reference group by historical target achievement rates in their referral centers (tier 1: <70%; tier 2: 70-80%; tier 3: >80%). Groups were matched by propensity score. RESULTS: After matching, we obtained four groups with 85,512 patient-months each. ACM had 18% higher target achievement rate, 63% smaller inappropriate ESA administration rate, and 59% smaller severe anemia risk compared to Tier 1 centers (all p < 0.01). The corresponding risk ratios for ACM compared to Tier 2 centers were 1.08 (95% CI: 1.08-1.09), 0.49 (95% CI: 0.47-0.51), and 0.64 (95% CI: 0.61-0.68); for ACM compared to Tier 3 centers, 1.01 (95% CI: 1.01-1.02), 0.66 (95% CI: 0.63-0.69), and 0.94 (95% CI: 0.88-1.00), respectively. ACM was associated with statistically significant reductions in ESA dose administration. CONCLUSION: ACM was associated with increased hemoglobin target achievement rate, decreased inappropriate ESA usage and a decreased incidence of severe anemia among patients treated according to ACM suggestion.

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.

Effectiveness of cold HD for the prevention of HD hypotension and mortality in the general HD population.

BACKGROUND: Cold hemodialysis (HD) prevented intradialysis hypotension (IDH) in small, short-term, randomized trials in selected patients with IDH. Whether this treatments prevents IDH and mortality in the HD population at large is unknown. METHODS: We investigated the relationship between dialysate temperature and the risk of IDH, i.e. nadir blood pressure <90 mmHg (generalized estimating equation model) and all-cause mortality (Cox's regression) in an incident cohort of HD patients (n = 8071). To control for confounding by bias by indication and other factors we applied instrumental variables adjusting for case mix at facility level. RESULTS: Twenty-seven percent of patients in the study cohort were systematically treated with a dialysate temperature ≤35.5°C. Over a median follow-up of 13.6 months (interquartile range 5.2-26.1 months), a 0.5°C reduction of the dialysate temperature was associated with a small (-2.4%) reduction of the risk of IDH [odds ratio (OR) 0.976, 95% confidence interval (CI) 0.957-0.995, P = .013]. In case-mix, facility-level adjusted analysis, the association became much stronger (OR 0.67, 95% CI 0.63-0.72, risk reduction = 33%, P < .001). In contrast, colder dialysate temperature had no effect on mortality both in the unadjusted [hazard ratio (HR) (0.5°C decrease) 1.074, 95% CI 0.972-1.187, P = .16] and case-mix-adjusted analysis at facility level (HR 1.01, 95% CI 0.88-1.16, P = .84). Similar results were registered in additional analyses by instrumental variables applying the median dialysate temperature or the facility percentage of patients prescribed a dialysate temperature <36°C. Further analyses restricted to patients with recurrent IDH fully confirmed these findings. CONCLUSIONS: Cold HD was associated with IDH in the HD population but had no association with all-cause mortality.