Potassium-based algorithm allows correction for the hematocrit bias in quantitative analysis of caffeine and its...

Although dried blood spot (DBS) sampling is increasingly receiving interest as a potential alternative to traditional blood sampling, the impact of hematocrit (Hct) on DBS results is limiting its final breakthrough in routine bioanalysis. To predict the Hct of a given DBS, potassium (K(+)) proved to be a reliable marker. The aim of this study was to evaluate whether application of an algorithm, based upon predicted Hct or K(+) concentrations as such, allowed correction for the Hct bias. Using validated LC-MS/MS methods, caffeine, chosen as a model compound, was determined in whole blood and corresponding DBS samples with a broad Hct range (0.18-0.47). A reference subset (n = 50) was used to generate an algorithm based on K(+) concentrations in DBS. Application of the developed algorithm on an independent test set (n = 50) alleviated the assay bias, especially at lower Hct values. Before correction, differences between DBS and whole blood concentrations ranged from -29.1 to 21.1%. The mean difference, as obtained by Bland-Altman comparison, was -6.6% (95% confidence interval (CI), -9.7 to -3.4%). After application of the algorithm, differences between corrected and whole blood concentrations lay between -19.9 and 13.9% with a mean difference of -2.1% (95% CI, -4.5 to 0.3%). The same algorithm was applied to a separate compound, paraxanthine, which was determined in 103 samples (Hct range, 0.17-0.47), yielding similar results. In conclusion, a K(+)-based algorithm allows correction for the Hct bias in the quantitative analysis of caffeine and its metabolite paraxanthine.