Managing hemolysis in serum neuron-specific enolase measurements - an automated algorithm for routine practice
Neuron-specific enolase (NSE) derived from neurons and peripheral neuroendocrine cells is a biomarker for neuroendocrine tumors and for prognostication in comatose cardiac arrest survivors. However, as platelets and erythrocytes contain NSE, hemolysis causes falsely elevated NSE. We used native seru...
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Published in: | Scandinavian journal of clinical and laboratory investigation Vol. 84; no. 4; pp. 1 - 229 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
England
01-07-2024
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Subjects: | |
Online Access: | Get full text |
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Summary: | Neuron-specific enolase (NSE) derived from neurons and peripheral neuroendocrine cells is a biomarker for neuroendocrine tumors and for prognostication in comatose cardiac arrest survivors. However, as platelets and erythrocytes contain NSE, hemolysis causes falsely elevated NSE. We used native serum and hemolysate derived from the same patients to make serial dilutions, and subsequently measured NSE (mNSE) and hemolytic index (HI) in each dilution. An algorithm suitable for the laboratory information system was developed based on the mNSE, HI and the estimated gradient of hemolytic interference from 30 patients. We estimated the associated uncertainty of the corrected NSE (cNSE) results based on the observed range of the gradient and derived an equation for cNSE for samples with limited hemolysis (i.e. 5 < HI ≤ 30): cNSE = mNSE - HI × (0.34 ± 0.23) µg/L. By semi-quantitatively grading the contribution from limited hemolysis, a texted result noting the hemolysis-associated degree of uncertainty can accompany the cNSE result. The major challenge of hemolysis when using serum NSE as a biomarker can be managed using an automated algorithm for correction of NSE results based on degree of hemolysis. However, laboratorians and clinicians should be aware of the limitations associated with
hemolysis. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0036-5513 1502-7686 1502-7686 |
DOI: | 10.1080/00365513.2024.2359091 |