Hemolysis index. Can we uncritically trust manufacturer declarations?
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Alen Vrtaric
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Marijana Miler
Abstract
Objectives
Hemolysis is a common preanalytical interference in clinical biochemistry, affecting up to 3.3 % of routine samples and contributing to approximately 60 % of rejected specimens. It results from the release of intracellular constituents, such as hemoglobin and potassium, into the plasma due to cell membrane disruption. This interference is often evaluated through hemolysis index (HI) thresholds defined by in vitro diagnostic (IVD) analyzers, but these thresholds may not align with clinical needs or biological variation.
Methods
A descriptive study was conducted from February to April 2025, comparing the hemolysis interference data of 72 clinical chemistry analytes across five major manufacturers: Abbott, Beckman Coulter, BioSystems, Roche, and Siemens. Manufacturer package inserts were reviewed and compared to the CLSI C56-A guideline for hemolysis interference testing.
Results
The compliance of manufacturers with the CLSI guideline varied significantly. Siemens had the highest compliance, reporting hemolysis interference at two analyte levels in 46/71 of cases, while BioSystems did not report analyte concentrations for any tested parameters. Criteria for significant interference was often missing, and in many cases, manufacturers used arbitrary thresholds, such as 10 % bias. Additionally, acceptance criteria and reported bias values were inconsistent across manufacturers, with only Abbott and Siemens providing detailed bias data.
Conclusions
Manufacturer instructions often lack completeness and consistency, making it crucial for clinical laboratories to independently validate hemolysis thresholds. A harmonized approach, incorporating manufacturer data, research, and local validation, is essential to improve laboratory quality, minimize misinterpretations, and ensure compliance with regulatory standards.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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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: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-1302).
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