Clinical chemistry and laboratory medicine · 2025
A novel corrective model based on red blood cells indices and haemolysis index enables accurate unhaemolysed potassium determination in haemolysed samples - Hemokalc project.
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Lefèvre Charles R, Vigier Bérénice, Favalelli Mathilde, Garnier Jordan, Scanff Alexandre, Pawlowski Maxime, Collet Nicolas, Bendavid Claude
Аннотация
OBJECTIVES: Haemolysis is a major preanalytical issue that affects potassium measurements, often leading to sample rejection and delayed clinical management. This study proposes a novel corrective model for accurate unhaemolysed potassium prediction.
METHODS: Blood samples from 14 healthy volunteers were used to prepare a range of haemolysates via freeze-thaw method. First, the relationship between potassium variation and haemolysis variation (ΔK/ΔHI) was studied both individually and globally to assess inter-individual variability. Then, to achieve a more personalised unhaemolysed potassium prediction, a novel corrective model was developed based on: potassium levels in paired unhaemolysed and gradually haemolysed samples, measured haemolysis index, mean corpuscular haemoglobin concentration, mean corpuscular volume and intraerythrocytic potassium level. The bias between true and model-predicted unhaemolysed potassium values was calculated and compared to the reference change value (RCV%).
RESULTS: Global data showed a strong correlation between ΔK and ΔHI (Pearson r=0.97, p<0.0001), following a linear relationship: ΔK=0.33*ΔHI (p<0.0001). However, individual data revealed substantial inter-individual variation (min ΔK=0.23*ΔHI and max ΔK=0.39*ΔHI). The correction model achieved 100 % accuracy for the 116 prepared samples, with predicted unhaemolysed potassium values falling within a ± 10 % bias range (mean ± standard deviation of bias = -0.5 ± 2.8 %).
CONCLUSIONS: We propose a novel, reliable, and cost-effective corrective model to predict unhaemolysed potassium from haemolysed samples. Compared with previously published models, the integration of red blood cells indices allows for a more personalised, patient-centred approach with high efficiency.
METHODS: Blood samples from 14 healthy volunteers were used to prepare a range of haemolysates via freeze-thaw method. First, the relationship between potassium variation and haemolysis variation (ΔK/ΔHI) was studied both individually and globally to assess inter-individual variability. Then, to achieve a more personalised unhaemolysed potassium prediction, a novel corrective model was developed based on: potassium levels in paired unhaemolysed and gradually haemolysed samples, measured haemolysis index, mean corpuscular haemoglobin concentration, mean corpuscular volume and intraerythrocytic potassium level. The bias between true and model-predicted unhaemolysed potassium values was calculated and compared to the reference change value (RCV%).
RESULTS: Global data showed a strong correlation between ΔK and ΔHI (Pearson r=0.97, p<0.0001), following a linear relationship: ΔK=0.33*ΔHI (p<0.0001). However, individual data revealed substantial inter-individual variation (min ΔK=0.23*ΔHI and max ΔK=0.39*ΔHI). The correction model achieved 100 % accuracy for the 116 prepared samples, with predicted unhaemolysed potassium values falling within a ± 10 % bias range (mean ± standard deviation of bias = -0.5 ± 2.8 %).
CONCLUSIONS: We propose a novel, reliable, and cost-effective corrective model to predict unhaemolysed potassium from haemolysed samples. Compared with previously published models, the integration of red blood cells indices allows for a more personalised, patient-centred approach with high efficiency.