Background: The lifespan of red blood cells (RBCs) is unknown. All current labelling methods contain significant flaws including loss of label from viable RBCs or reincorporation of the label into new RBCs after death of the originally labelled cells. Previously proposed models for the lifespan of RBCs either assume a fixed lifespan for all cells  or a continuous distribution of lifespans where the cells are thought to die solely due to old age, also known as senescence [2,3]. Recently, Kalicki et al. have shown that combining a finite lifespan with random destruction improves the performance of these models . Our work combines all known physiological processes for RBC destruction in a single continuous lifespan distribution, which includes death of unviable cells shortly after their release into circulation, random destruction during the entire lifetime as well as death due to senescence and delayed failure.
Aim: To develop a model for RBC survival based on statistical theory and RBC physiology. To optimise a design for RBC survival studies that accounts for RBC labelling techniques.
Methods: A statistical model for the survival time of RBCs with respect to the physiology of RBC destruction was developed. The model was modified from established models that were developed to describe the lifespan of humans and is described by a combination of flexible and reduced additive Weibull distributions. The underlying distribution of RBC lifespans accounts for the known processes of RBC destruction, including death due to senescence, random loss during circulation, as well as death due to early or delayed failures. The model was furthermore modified to account for the possible ways that the most commonly used random label, radioactive chromium, might be lost from viable cells during their circulation in the body. This includes decay of the label, loss due to dissociation of the chromium-haemoglobin complex and loss due to vesiculation of haemoglobin. An optimal design was constructed that accounts for cohort labelling of a fraction of RBCs born on the same day as well as random labelling, where RBCs of all ages present at one point in time are labelled.
Results: The resulting survival model was used to simulate in vivo RBC survival studies using different RBC labelling techniques. Predictions from the model agreed well with models from the literature. For random labelling studies using radioactive chromium the results for optimal sampling times are pending at the moment.
Conclusions: The model accounts for all plausible processes of RBC destruction in the body, as well as for the known shortcomings of radioactive chromium as a random label for RBCs. The model and design are intended to be used for setting up and interpretation of current in vivo studies of RBC survival.
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