The use of the hemolysis index as an indirect indicator of whole blood sample quality
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Stefano Pastori
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Rossella Panella
Abstract
Objectives
The aim of this study is to assess the discrepancies between potassium values obtained from point-of-care testing and laboratory analyzers, and to propose a straightforward method to indirectly assess the likelihood of hemolysis in whole blood samples.
Methods
The data were collected from 409 hospitalized patients (199 males, 210 females; median age 78 years, interquartile range 55–87) in whom were simultaneously requested the blood gas profile, including electrolytes (Na, K, Cl and Ca2+) on the GEM Premier 5000 blood gas analyzer and K in plasma samples on the Cobas analytical system.
Results
The Wilcoxon test showed a significant difference (p<0.0001) for K measurements, with a number of positive differences between Cobas and GEM Premier 5,000 of 110 and negative differences of 293. The median value of the hemolysis index was 7 (95 % CI for the median 6 to 8). The differences between the two K measurements obtained from plasma and whole blood samples showed a lowest and highest differences of −1.7 and 4.6 mmol/L. The median difference was 0.63 (95 % CI for the median: 0.54–0.73) and the interquartile range (IQR) was 0.48–0.9. Overall, 89 samples (21 %) displayed a difference larger than the relative change value.
Conclusions
The presence of hemolysis in plasma samples should alert laboratory professionals to carefully evaluate the corresponding whole blood results. Concordant values indicate the presence of hemolysis in the whole blood sample, while discordant values suggest that hemolysis only affects one of the two samples.
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Research ethics: Not applicable.
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Informed consent: Written informed consent was waived due to the retrospective nature of the study. All sensitive data were handled anonymously in compliance with the General Data Protection Regulation (GDPR) UE 679/2016.
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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: None declared.
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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: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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