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.

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.

From electronic health records to clinical management systems: how the digital transformation can support healthcare services.

Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations.

How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device.

Background: The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as a medical device (MD), which is important to assess the associated risks.

Methods: An anemia control model (ACM), certified as MD, may face adverse events as a result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard.

Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one.

Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment.

Background: Fluid volume and blood pressure (BP) management are crucial endpoints for end-stage kidney disease patients. BP control in clinical practice mainly relies on reducing extracellular fluid volume overload by diminishing targeted postdialysis weight. This approach exposes dialysis patients to intradialytic hypotensive episodes.

The anaemia control model: Does it help nephrologists in therapeutic decision-making in the management of anaemia?

Introduction: Anaemia is common in haemodialysis patients and treating it with erythropoiesis-stimulating agents (ESAs) is complex due to many factors.

Objectives: To assess the usefulness of the Anaemia Control Model (ACM) in the treatment of anaemia in haemodialysis.

Methods: ACM is a software that predicts the optimal dose of darbepoetin and iron sucrose to achieve target haemoglobin (Hb) and ferritin levels, and makes prescription suggestions.

Patients' Characteristics Affect the Survival Benefit of Warfarin Treatment for Hemodialysis Patients with Atrial Fibrillation. A Historical Cohort Study.

Background: Stroke prevention in dialysis-dependent patients with atrial fibrillation (AF) is an unresolved clinical dilemma. Indeed, no randomized controlled trial evaluating the efficacy and safety of oral anticoagulants in this population, has been conducted so far. Observational research on the use of warfarin in patients on dialysis has shown conflicting results.

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.

Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses.

Atrial fibrillation in dialysis patients: time to abandon warfarin?

Atrial fibrillation (AF) is a frequent clinical complication in dialysis patients, and warfarin therapy represents the most common approach for reducing the risk of stroke in this population. However, current evidence based on observational studies, offer conflicting results, whereas no randomized controlled trials have been carried out so far. Additionally, many clinicians are wary of the possible role of warfarin as vascular calcification inducer and its potential to increase the high risk of bleeding among patients on dialysis.

Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.

Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.

A New Data Analysis System to Quantify Associations between Biochemical Parameters of Chronic Kidney Disease-Mineral Bone Disease.

Background: In hemodialysis patients, deviations from KDIGO recommended values of individual parameters, phosphate, calcium or parathyroid hormone (PTH), are associated with increased mortality. However, it is widely accepted that these parameters are not regulated independently of each other and that therapy aimed to correct one parameter often modifies the others. The aim of the present study is to quantify the degree of association between parameters of chronic kidney disease and mineral bone disease (CKD-MBD).

Optimal convection volume for improving patient outcomes in an international incident dialysis cohort treated with online hemodiafiltration.

Online hemodiafiltration (OL-HDF), the most efficient renal replacement therapy, enables enhanced removal of small and large uremic toxins by combining diffusive and convective solute transport. Randomized controlled trials on prevalent chronic kidney disease (CKD) patients showed improved patient survival with high-volume OL-HDF, underlining the effect of convection volume (CV). This retrospective international study was conducted in a large cohort of incident CKD patients to determine the CV threshold and range associated with survival advantage.

A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on average behaviors, and thus, they do not take into account the particular response to those drugs by different patients, although that response may vary enormously from one patient to another and even for the same patient in different stages of the anemia.

Optimization of anemia treatment in hemodialysis patients via reinforcement learning.

Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects.

Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.

Objectives: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it.

Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics.