Background: Numerical methods have recently allowed quantitative interpretation of in vivo murine platelet consumption data in terms of values for the random destruction rate constant (RD), intrinsic lifespan (LS), and the standard deviation of ln LS (SD), as well as the platelet production rate (PR) and age distribution (AD). But application of these methods to data obtained in thrombocytopenic patients is problematic for two reasons. First, such data has in all cases been obtained with radiolabeled platelets, and uptake of the radio-isotope by long lived cells complicates the analysis. Second, inferred values of the platelet production rate (PR) and random destruction rate (RD) are difficult to interpret, since increased RD can occur either as a cause or a consequence of thrombocytopenia.
Methods: We used a numerical method to analyze in vivo platelet consumption data from a series of 41 patients with immune thrombocytopenic purpura (ITP). An additional parameter, the fraction of labeled long-lived cells (LL), was evaluated concurrently with RD, LS, and SD. To provide a basis for interpreting these values, we used an iterative interpolation process to predict their response to different pathophysiologic mechanisms. The process also generates predicted effects on the widely used immature platelet fraction (IPF).
Results: Optimal parameter value sets were identified in 76 % (31 of 41) of the data sets. 27 of 31 ITP patients showed no substantial homeostatic increase in platelet production, with the remaining 4 showing both augmented platelet consumption and a compensatory increase in PR. Up to 1/3 of the patients showed the degree of increased RD expected to result from reduced thrombopoiesis only. "Jacknife" resampling yielded CV values of <0.5 in over 75 % of the evaluable data sets. Predicted platelet age distributions indicate that interpretation of the IPF and absolute IPF (aIPF) is a complex function of platelet count. We found, counter-intuitively, that reduced PR can increase the IPF, and increased RD can reduce the aIPF.
Conclusions: Our findings support the feasibility of using numerical analysis to quantitatively interpret in vivo platelet consumption data, to identify likely etiologies of thrombocytopenias, and to assess the utility of IPF measurements in that context.
Keywords: Immune thrombocytopenic purpura; Numerical analysis; Platelets; Thrombocytopenia.