Interferences with thyroid function immunoassays: Clinical implications and detection algorithm

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Abstract

Automated immunoassays used to evaluate thyroid function are vulnerable to different types of interference that can affect clinical decisions. This review provides a detailed overview of the six main types of interference known to affect measurements of thyroid stimulating hormone (TSH), free thyroxine (T4) and free triiodothyronine (T3): Macro-TSH, biotin, antistreptavidin antibodies, antiruthenium antibodies, thyroid hormone autoantibodies, and heterophilic antibodies. Because the prevalence of some of these conditions has been reported to approach 1% and the frequency of testing for thyroid dysfunction is important, the scale of the problem might be tremendous. Potential interferences in thyroid function testing should always be suspected whenever clinical or biochemical discrepancies arise. Their identification usually relies on additional laboratory tests, including assay method comparison, dilution procedures, blocking reagents studies, and polyethylene glycol precipitation. Based on the pattern of thyroid function test alterations, to screen for the six aforementioned types of interference, we propose a detection algorithm, which should facilitate their identification in clinical practice. The review also evaluates the clinical impact of thyroid interference on immunoassays. On review of reported data from more than 150 patients, we found that ≥50% of documented thyroid interferences led to misdiagnosis and/or inappropriate management, including prescription of an unnecessary treatment (with adverse effects in some situations), inappropriate suppression or modification of an ongoing treatment, or use of unnecessary complementary tests such as an I123 thyroid scan. Strong interaction between the clinician and the laboratory is necessary to avoid such pitfalls.

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Favresse, J., Burlacu, M. C., Maiter, D., & Gruson, D. (2018, October 1). Interferences with thyroid function immunoassays: Clinical implications and detection algorithm. Endocrine Reviews. Oxford University Press. https://doi.org/10.1210/er.2018-00119

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