Rapid prediction of stem cell mobilization using volume and conductivity data from automated hematology analyzers

Rapid analytics to predict circulating hematopoietic stem cells are valuable for optimal management of mobilization, particularly for the use of newer and costly mobilization agents such as plerixafor.

We used stepwise, linear multiple regression modeling applied to cell population data collected by routine hematology analyzers (Beckman Coulter DxH 800) on patients undergoing autologous stem cell collection (n = 131). Beta coefficients were used to derive a formula for a stem cell index (SCI). We then tested the correlation of SCI with stem cell counts and performance of the SCI as a predictor of poor mobilization with external validation in a separate cohort (n = 183).

The SCI correlated strongly with CD34 counts by flow cytometry (r = 0.8372 in the development cohort, r = 0.8332 in the external validation cohort) and compares favorably with other rapid stem cell enumerating technologies. In the external validation cohort, the SCI performed well as a predictor (receiver operating characteristic area under the curve, 0.9336) of poor mobilization (CD34 count < 10), with a sensitivity of 72% and a specificity of 93%. When prevalence of poor mobilization was 33%, this resulted in a positive predictive value of 83% and a negative predictive value of 87%. The SCI also showed promise in tracking responses to plerixafor administration.

The findings demonstrate the utility of the cell population data collected by hematology analyzers to provide rapid data beyond standard complete blood counts, particularly for stem cell count prediction, requiring no additional reagents, specimen, or instrumentation.