Identifying and reducing potentially wrong immunoassay results even when plausible and "not-unreasonable"

Area of Science:

• Clinical laboratory science
• Analytical chemistry
• Statistics in medicine

Background:

• Routine biochemical tests achieve high accuracy, but immunoassays are prone to interference from endogenous immunoglobulin antibodies.
• This interference causes false positive or false negative results, leading to incorrect diagnoses and unnecessary treatments.
• Analytical error rates in immunoassays range from 0.4% to 4.0% and are difficult to detect through standard quality assessments.

Purpose of the Study:

• To explore the use of Bayesian reasoning to enhance clinical validation of immunoassay results.
• To discuss strategies for reducing inaccuracies caused by endogenous antibody interference in immunoassays.
• To evaluate the role of affirmative follow-up tests in confirming analytical interference.

Main Methods:

• Review of peer-reviewed literature on Bayesian reasoning and statistical approaches in clinical diagnostics.
• Analysis of the nature and impact of endogenous antibody interference in various immunoassay formats.
• Discussion of stratagems to mitigate interference, including Bayesian methods and specific follow-up tests.

Main Results:

• Conventional statistics are often misused, while Bayesian paradigms are underutilized in clinical laboratories.
• Interference from endogenous antibodies is method-dependent and challenging to quantify across all immunoassays and biomarkers.
• Bayesian reasoning, when combined with affirmative follow-up tests, can identify misleading immunoassay data.

Conclusions:

• Endogenous antibody interference remains a persistent challenge in immunoassay accuracy.
• Bayesian reasoning offers a robust approach to clinical validation, improving the detection of erroneous immunoassay results.
• Affirmative follow-up tests are crucial for confirming analytical interference and ensuring reliable patient diagnosis.