Probabilistic Bayesian reasoning can help identifying potentially wrong immunoassays results in clinical practice: even when they appear ‘not-unreasonable’

Abstract

Background: Immunoassays are susceptible to analytical interferences including from endogenous immunoglobulin antibodies at a rate of 0.4% to 4%. Hundreds of millions of immunoassay tests (.10 millions in the UK alone) are performed yearly worldwide for measurements of an array of large and small moieties such as proteins, hormones, tumour markers, rheumatoid factor, troponin, small peptides, steroids and drugs. Methods: Interference in these tests can lead to false results which when suspected, or surmised, can be analytically confirmed in most cases. Suspecting false laboratory data in the first place is not difficult when results are gross and without clinical correlates. However, when false results are subtle and/or plausible, it can be difficult to suspect with adverse clinical sequelae. This problem can be ameliorated by using a probabilistic Bayesian reasoning to flag up potentially suspect results even when laboratory data appear "not-unreasonable". Results: Essentially, in disorders with low prevalence, the majority of positive results caused by analytical interference are likely to be false positives. On the other hand, when the disease prevalence is high, false negative results increase and become more significant. To illustrate the scope and utility of this approach, six different examples covering wide range of analytes are given, each highlighting specific aspect/nature of interference and suggested options to reduce it. Conclusion: Bayesian reasoning would allow laboratorians and/or clinicians to extract information about potentially false results, thus seeking follow-up confirmatory tests prior to the initiation of more expensive/invasive procedures or concluding a potentially wrong diagnosis.

Negative Interference by Rheumatoid Factor of Plasma B-Type Natriuretic Peptide in Chemiluminescent Microparticle Immunoassays

“…Supporting this finding, our previous study showed that the decline in the rates of HBsAg signal/cut-off values was not associated with the serum RF concentration in an HBsAg ELISA [5]. Furthermore, our previous and present studies provided evidence that other important aspects such as binding affinity/avidity of RF affected the pattern and degree of interference in immunoassays [21]. It has been reported that RF leads to a false-positive or higher results in immunoassays [7]–[10].…”

“…These data supported our suspicion that RF causes a negative interference in BNP CMIA. The probability of a negative interference rate from RF assuming a prevalence of heart failure of 80% in symptomatic patients was calculated to be 33% according to Bayes theorem [21]), a figure not dissimilar to ∼50% found in this study. Therefore, in order to determinate BNP level in RF-positive plasma samples with better accuracy, we suggest that samples need to be pretreated with commercially available blocking reagents such as heterophil-blocking reagent [17], [21].…”

“…Plasma or serum samples suspected of containing interfering substances can be identified by three independent tests in clinical laboratory use: commercially available blocking reagents, serial dilutions, and repeated analyses using different immunoassay platforms [21]. In previous studies, we found that serial dilutions and the use of commercially available blocking reagents were two effective methods of identifying positive or negative interference from RF [6]–[7].…”

Heterophilic antibody interference in commercial immunoassays; a screening study using paired native and pre-blocked sera

“…To identify samples, Ismail et al (25) suggest a probabilistic approach, i.e., elevated results in assays known to have a low rate of true positive results should be retested for interference. We agree with this probabilistic approach, but we also think that the impact of the assay result should guide which samples to retest for interference.…”

Serum sample containing endogenous antibodies interfering with multiple hormone immunoassays. Laboratory strategies to detect interference

“…In fact, Ismail et al . [23] estimated that the probability of a raised TSH result in a young adult being a false positive is greater than 30%.…”