Introduction: Patients with kidney failure requiring hemodialysis are at high risk for hyperkalemia between treatments, which is associated with increased cardiovascular morbidity and mortality. Early detection of hyperkalemic events may be useful to prevent adverse outcomes and their associated costs. We performed a cost-utility analysis comparing an intervention where a real-time potassium monitoring device is administered in patients on hemodialysis in comparison to usual care.
View Article and Find Full Text PDFBackground: Patients with kidney failure treated with maintenance haemodialysis (HD) require appropriate small molecule clearance. Historically, a component of measuring 'dialysis adequacy' has been quantified using urea kinetic modelling that is dependent on the HD prescription. However, the impact of dialysate flow rate on urea clearance remains poorly described and its influence on other patient-important outcomes of adequacy is uncertain.
View Article and Find Full Text PDFAim: To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression in patients with type 2 diabetes in the pooled CANVAS/CREDENCE trials.
Materials And Methods: We externally validated the Klinrisk model for prediction of CKD progression, defined as 40% or higher decline in estimated glomerular filtration rate (eGFR) or kidney failure. Model performance was assessed for prediction up to 3 years with the area under the receiver operating characteristic curve (AUC), Brier scores and calibration plots of observed and predicted risks.
Rationale & Objective: The prevalence of kidney failure is increasing globally. Most of these patients will require life-sustaining dialysis at a substantial cost to the health care system. Assisted peritoneal dialysis (PD) and assisted home hemodialysis (HD) are potential alternatives to in-center HD and have demonstrated equivalent outcomes with respect to mortality and morbidity.
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