Real-world effectiveness of hemodialysis modalities: a retrospective cohort study.

Background: Results from the CONVINCE clinical trial suggest a 23% mortality risk reduction among patients receiving high-volume (> 23 L) hemodiafiltration. We assessed the real-world effectiveness of blood-based kidney replacement therapy (KRT) with hemodiafiltration vs. hemodialysis in a large, unselected patient population treated prior to and during the COVID-19 pandemic.

CKD-Associated Pruritus is Associated with Greater Use of Antidepressants and Anti-pruritus Medications.

Introduction: Chronic kidney disease-associated pruritus (CKD-aP) is a common, yet underdiagnosed condition among patients on hemodialysis. Considering the lack of established treatment pathways, we sought to evaluate the use of antidepressant, systemic antihistamines, or gabapentinoid medications among patients with CKD-aP in the year following pruritus assessment.

Methods: We included 6209 patients on hemodialysis in the analysis.

Fluid overload trajectories and mortality in hemodialysis patients.

Background: Fluid overload remains critical in managing patients with end-stage kidney disease. However, there is limited empirical understanding of fluid overload's impact on mortality. This study analyzes fluid overload trajectories and their association with mortality in hemodialysis patients.

Usage of the Anemia Control Model Is Associated with Reduced Hospitalization Risk in Hemodialysis.

Introduction: The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESAs), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed to personalize anemia treatment, which has shown improvements in achieving Hb targets, reducing ESA doses, and maintaining Hb stability. This study aimed to evaluate the association between ACM-guided anemia management with hospitalizations and survival in a large cohort of hemodialysis patients.

A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19.

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals.

Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.

COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g.

Household Water Lead and Hematologic Toxic Effects in Chronic Kidney Disease.

Importance: The consequences of low levels of environmental lead exposure, as found commonly in US household water, have not been established.

Objective: To examine whether commonly encountered levels of lead in household water are associated with hematologic toxicity among individuals with advanced kidney disease, a group known to have disproportionate susceptibility to environmental toxicants.

Design, Setting, And Participants: Cross-sectional analysis of household water lead concentrations and hematologic outcomes was performed among patients beginning dialysis at a Fresenius Medical Care outpatient facility between January 1, 2017, and December 20, 2021.

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.

Development and validation of AI-based triage support algorithms for prevention of intradialytic hypotension.

Background: Intradialytic hypotension remains one of the most recurrent complications of dialysis sessions. Inadequate management can lead to adverse outcomes, highlighting the need to develop personalized approaches for the prevention of intradialytic hypotension. Here, we sought to develop and validate two AI-based risk models predicting the occurrence of symptomatic intradialytic hypotension at different time points.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.

Effectiveness of COVID-19 vaccines in a large European hemodialysis cohort.

Background: Hemodialysis patients have high-risk of severe SARS-CoV-2 infection but were unrepresented in randomized controlled trials evaluating the safety and efficacy of COVID-19 vaccines. We estimated the real-world effectiveness of COVID-19 vaccines in a large international cohort of hemodialysis patients.

Methods: In this historical, 1:1 matched cohort study, we included adult hemodialysis patients receiving treatment from December 1, 2020, to May 31, 2021.

Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.

Background: In maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates.

Methods: We developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance.

Variability of Serum Phosphate in Incident Hemodialysis Patients: Association with All-Cause Mortality.

Key Points: An increase in serum phosphate variability is an independent risk factor of mortality. The effects of a positive directional range (DR) is most pronounced in patients with high serum phosphate levels whereas the effects of a negative DR is most pronounced in patients with low serum phosphate and/or serum albumin.

Background: In maintenance hemodialysis (HD) patients, previous studies have shown that serum phosphate levels have a bidirectional relation to outcome.

Machine Learning Case Study: Patterns of Kidney Function Decline and Their Association With Clinical Outcomes Within 90 Days After the Initiation of Renal Dialysis.

A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis.

Biphasic Dynamics of Inflammatory Markers Following Hemodialysis Initiation: Results From the International MONitoring Dialysis Outcome Initiative.

Introduction: Inflammation is highly prevalent among patients with end-stage kidney disease and is associated with adverse outcomes. We aimed to investigate longitudinal changes in inflammatory markers in a diverse international incident hemodialysis patient population.

Methods: The MONitoring Dialysis Outcomes (MONDO) Consortium encompasses hemodialysis databases from 31 countries in Europe, North America, South America, and Asia.

Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative.

Introduction: Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the risk factors for mortality prediction model in hemodialysis (HD) patients.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

Background: We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas.

Methods: We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors.

Modifiable Risk Factors Are Important Predictors of COVID-19-Related Mortality in Patients on Hemodialysis.

Introduction: Patients with end-stage kidney disease face a higher risk of severe outcomes from SARS-CoV-2 infection. Moreover, it is not well known to what extent potentially modifiable risk factors contribute to mortality risk. In this historical cohort study, we investigated the incidence and risk factors for 30-day mortality among hemodialysis patients with SARS-CoV-2 infection treated in the European Fresenius Medical Care NephroCare network using conventional and machine learning techniques.

Fatigue in incident peritoneal dialysis and mortality: A real-world side-by-side study in Brazil and the United States.

Background: We tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders.

Methods: We conducted a side-by-side study from two of incident PD patients in Brazil and the United States. We used the same code to independently analyze data in both countries during 2004 to 2011.

Cephalic arch restenosis rates in hemodialysis patients with brachiocephalic fistulae: a retrospective multicenter analysis of 3301 patients.

Background: We evaluated restenosis rates at the cephalic arch after percutaneous angioplasty and stenting procedures in patients with brachial artery to cephalic vein arteriovenous fistula (BCAVF) hemodialysis access.

Methods: We used data from adult hemodialysis patients treated at a national network of 44 outpatient interventional facilities during Oct 2011-2015. We included data from patients with BCAVF who received an exclusive angioplasty, or stent with angioplasty, for treatment of cephalic arch stenosis and had ≥1 subsequent evaluation of the cephalic arch.

Transmission of SARS-CoV-2 considering shared chairs in outpatient dialysis: a real-world case-control study.

Background: SARS-CoV-2 can remain transiently viable on surfaces. We examined if use of shared chairs in outpatient hemodialysis associates with a risk for indirect patient-to-patient transmission of SARS-CoV-2.

Methods: We used data from adults treated at 2,600 hemodialysis facilities in United States between February 1st and June 8th, 2020.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival.

Introduction: The clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients.

Methods: We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 2020.