Potassium-based algorithm allows correction for the hematocrit bias in quantitative analysis of caffeine and its major metabolite in dried blood spots.

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.