BACKGROUND: It is well known that the difference between prescribed and delivered dialysis doses greatly affects the morbidity and mortality of dialysed patients. The on-line monitoring of delivered dialysis is therefore of paramount importance. Recently, a conductivity-based method for determining Kt/V on routine basis has been proposed. METHODS: The study was performed using a specially designed module (Biofeedback Module, COT, Hospal) which, when connected to a dialysis monitor, automatically determines effective ionic dialysance (ID). During three consecutive dialysis sessions, administered to each of eight patients at the same depurative efficiency, we determined Kt/V by using mean effective ionic dialysance and by assuming, as suggested, that urea distribution volume corresponded to 55% of body weight. This method was compared with the gold standard of the direct quantification method. The Kt/V was also calculated by using mean effective ionic dialysance and the volume of urea distribution derived from anthropometric parameters. RESULTS: The Kt/V determined by using mean effective ionic dialysance and by assuming that urea distribution volume corresponded to 55% of body weight was heavily underestimated (-22%). This difference was due to both the overestimate of urea distribution volume (+17%) and underestimate of effective urea clearance (KUeff) (-11%). The mean Kt/V calculated on the basis of ionic dialysance and anthropometric volume was also underestimated (-23%) since this volume was overestimated (+17%). Nevertheless, ionic dialysance and urea clearance proved to be closely correlated (r2 = 0.89) so that effective urea clearance can be derived according to: Kueff = ID x 0.865 + 39.89. CONCLUSIONS: In steady-state patients, once urea distribution volume has been correctly determined by means of direct quantification, effective urea clearance can be easily derived from ionic dialysance and Kt/V calculated on-line at each session, without blood sampling or any additional costs.
BACKGROUND: It is well known that the difference between prescribed and delivered dialysis doses greatly affects the morbidity and mortality of dialysed patients. The on-line monitoring of delivered dialysis is therefore of paramount importance. Recently, a conductivity-based method for determining Kt/V on routine basis has been proposed. METHODS: The study was performed using a specially designed module (Biofeedback Module, COT, Hospal) which, when connected to a dialysis monitor, automatically determines effective ionic dialysance (ID). During three consecutive dialysis sessions, administered to each of eight patients at the same depurative efficiency, we determined Kt/V by using mean effective ionic dialysance and by assuming, as suggested, that urea distribution volume corresponded to 55% of body weight. This method was compared with the gold standard of the direct quantification method. The Kt/V was also calculated by using mean effective ionic dialysance and the volume of urea distribution derived from anthropometric parameters. RESULTS: The Kt/V determined by using mean effective ionic dialysance and by assuming that urea distribution volume corresponded to 55% of body weight was heavily underestimated (-22%). This difference was due to both the overestimate of urea distribution volume (+17%) and underestimate of effective urea clearance (KUeff) (-11%). The mean Kt/V calculated on the basis of ionic dialysance and anthropometric volume was also underestimated (-23%) since this volume was overestimated (+17%). Nevertheless, ionic dialysance and urea clearance proved to be closely correlated (r2 = 0.89) so that effective urea clearance can be derived according to: Kueff = ID x 0.865 + 39.89. CONCLUSIONS: In steady-state patients, once urea distribution volume has been correctly determined by means of direct quantification, effective urea clearance can be easily derived from ionic dialysance and Kt/V calculated on-line at each session, without blood sampling or any additional costs.
Authors: Marta Albalate; Rafael Pérez-García; Patricia de Sequera; Elena Corchete; Roberto Alcazar; Mayra Ortega; Marta Puerta Journal: BMC Nephrol Date: 2015-02-14 Impact factor: 2.388
Authors: Franklin G Mora-Bravo; Alfonso Mariscal; Juan P Herrera-Felix; Salvador Magaña; Guadalupe De-La-Cruz; Nelly Flores; Laura Rosales; Martha Franco; Héctor Pérez-Grovas Journal: BMC Nephrol Date: 2008-11-24 Impact factor: 2.388
Authors: Wihib Gebregeorgis; Zeenat Yousuf Bhat; Nishigandha Pradhan; Stephen D Migdal; Lakshminarayanan Nandagopal; Reddy Singasani; Tehmina Mushtaq; Ronald Thomas; Yahya M Osman Malik Journal: Clin Kidney J Date: 2018-01-31