Spurious test results as a diagnostic clue – pseudohyponatremia revealing multiple myeloma
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Francesca Pons-Vidal
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Macarena Calvente de Rávena
Abstract
Objectives
To describe a case of a pseudohyponatremia secondary to hyperproteinemia in multiple myeloma and to emphasize the importance of analytical verification and laboratory-clinical collaboration to prevent diagnostic errors.
Case presentation
A 55 year-old male presented with severe hyponatremia (119 mmol/L) but paradoxically elevated serum osmolarity, suggesting that the hyponatremia might represent a laboratory artifact. Repeat serum sodium measurement by direct potentiometry with ion-specific electrodes yielded a normal value of 137 mmol/L, confirming artifactual hyponatremia. Additional tests revealed marked hyperproteinemia due to an IgG-kappa monoclonal component, leading to the diagnosis of multiple myeloma.
Conclusions
This case highlights the importance of artifacts in laboratory measurement, in this case recognizing analytical interferences when discordant hyponatremia is found. Confirmatory testing by direct potentiometry ion-specific electrodes prevents misdiagnosis and inappropriate therapy. Early identification of pseudohyponatremia further emphasizes the crucial role of the laboratory in ensuring accurate interpretation and optimizing patient management.
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Research ethics: Not applicable. This paper focuses on a patient who comes to the emergency department, where a blood sample is taken for a basic emergency profile, and who arrives without a diagnosis. A series of further tests are carried out in the laboratory in order to clarify the cause of false hyponatremia and assist in the patient’s final diagnosis. The patient was not asked to participate in any study; this is a description of a clinical case in which an interference was observed and its cause was identified, leading to the diagnosis of an unknown pathology in the patient.
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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 have 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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