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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Charles R. Lefèvre
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Bérénice Vigier
Abstract
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.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: ChatGPT was used to improve language of the manuscript.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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