Modeling red blood cell survival data

Anaemia of chronic kidney disease (CKD) is a common complication in patients with renal impairment, especially in end-stage renal failure. As well as erythropoietin deficiency, decreased red blood cell survival is a contributing factor. However, it remains unclear which mechanism underlies the altered survival of red blood cells (RBCs). In this work a previously developed statistical model for RBC survival was applied to clinical data obtained from 14 patients with CKD undergoing hemodialysis as well as 14 healthy controls using radioactive chromium ( 51 Cr) as random labelling method. A classical two-stage approach and a full population analysis were applied to estimate the key parameters controlling random destruction and senescence in the model. Estimating random destruction was preferred over estimating an accelerated senescence in both approaches and both groups as it provided the better fit to the data. Due to significant nonspecific random loss of the label from the cells that cannot be quantified directly only a relative RBC survival can be obtained from data using 51 Cr as labelling method. Nevertheless, RBC survival was found to be significantly reduced in CKD patients compared to the controls with a relative reduction of 20–30% depending on the analysis method used.