Heart failure with preserved ejection fraction is the most frequent but commonly overlooked phenotype in patients on chronic hemodialysis.

Introduction: Heart failure (HF) is a serious complication of end-stage kidney disease (ESKD). However, most data come from retrospective studies that included patients on chronic hemodialysis at the time of its initiation. These patients are frequently overhydrated, which significantly influences the echocardiogram findings.

Novel Algorithm for the Differential Diagnosis of Hyponatraemia in Anuric Patients Undergoing Maintenance Haemodialysis.

Introduction: Hyponatraemia is associated with increased mortality in patients undergoing maintenance haemodialysis. In anuric patients, hyponatraemia development depends on the water-sodium ratio in retained fluid within the interdialysis interval (IDI).

Objective: This study aimed to calculate the retained sodium-retained water ratio in patients on maintenance haemodialysis and make a differential diagnosis of hyponatraemia according to these data.

Combination of sofosbuvir and daclatasvir in the treatment of genotype 3 chronic hepatitis C virus infection in patients on maintenance hemodialysis.

Chronic hepatitis C virus infection (HCV) has a negative impact on the long-term survival of recipients of kidney transplants. HCV should be treated in hemodialyzed patients before their enlistment for kidney transplantation in order to avoid the reactivation of virus after transplantation. Direct-acting antivirals represent the current standard of care in hemodialyzed patients with HCV genotypes 1 and 4; in patients with genotypes 2 or 3, the optimal regimen is yet to be established.

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.