Modeling the Lifespan of Red Blood Cells

The subject of red blood cell (RBC) survival has been discussed in the medical literature for nearly a hundred years. There has been a large amount of experimental work on RBC survival, but the supporting analysis consisted mostly of a number of more or less ad hoc models for the RBC lifespan distribution. In this context, this dissertation makes four key contributions based on the biotin-tagged RBC survival data from healthy subjects: 1. We provide a theory of RBC survival supported by appropriate analysis. Specifically, we apply non-linear mixed effects (NLME) analysis to study the population level and individual level variation in several characteristics of RBC survival, based on random sample survival data. The general approach can be used for data obtained by several different experimental methods. 2. We present a unified analysis of RBC survival data obtained using RBCs labeled at multiple densities of biotin, thus exhibiting, for the first time, the dependence of the estimated RBC survival characteristics as a function of the biotin labeling density. Our results suggest that low-density biotinylation of RBCs does not have a significant effect on RBC survival. 3. We show that, using NLME analysis results from a reference population database, good accuracy in the estimation of clinically relevant parameters from random sample survival data can be achieved with only 2-point or 3-point optimized measurement schedules. 4. We present an argument that RBC survival results obtained from radioactive chromium labeling of RBCs may not be reliable with currently used analysis methods. The analysis presented in the dissertation can potentially be used to study RBC survival in broad range of clinical applications such as drug efficacy, quality of stored blood, and the development of protocols for the management of anemia