The reduction of morbidity and mortality in patients undergoing hemo- or peritoneal dialysis is strongly related to an efficient and selective clearance of uremic toxins. We used proteomics methods to analyze and further characterize the dialytic removal of still undefined middle and high molecular weigh proteins as a basis for further improvement of dialysis assessment. Dialysates from 26 hemodialysis patients treated with different types of low- (F6HPS®) and high-flux (FX80®, APS650®, FX60®) filters as well as peritoneal fluids from 10 continuous ambulatory peritoneal dialysis (CAPD) patients were analyzed by SELDI-TOF and 2-DE. The protein patterns showed selective differences in the proteins cleared depending on the dialysis method used and the filter membrane. While SELDI analyses of dialysates from the F6HPS revealed almost no protein clearance, high-flux filters and CAPD dialysates showed protein release of different molecular weight ranges. Furthermore, 2-DE and MS analysis identified 48 different proteins from the dialysate of high-flux filters and 21 from peritoneal dialysis fluids. In F6HPS dialysates, however, only few proteins could be identified.
Backgrounds: Criteria that may guide early renal replacement therapy (RRT) initiation in patients with acute kidney injury (AKI) currently do not exist. Methods: In 120 consecutive patients with AKI, clinical and laboratory data were analyzed on admittance. The prognostic power of those parameters which were significantly different between the two groups was analyzed by receiver operator characteristic curves and by leave-1-out cross validation. Results: Six parameters (urine albumin, plasma creatinine, blood urea nitrogen, daily urine output, fluid balance and plasma sodium) were combined in a logistic regression model that estimates the probability that a particular patient will need RRT. Additionally, a second model without daily urine output was established. Both models yielded a higher accuracy (89 and 88% correct classification rate, respectively) than the best single parameter, cystatin C (correct classification rate 74%). Conclusions: The combined models may help to better predict the necessity of RRT using clinical and routine laboratory data in patients with AKI.
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