Belief is only half the truth--or why screening for heterophilic antibody interference in certain assays makes double sense

Identification of and solution for false D-dimer results

BACKGROUND

Clinically, D-dimer (DD) levels are mainly used to exclude diseases such as deep venous thrombosis (DVT). In clinical testing, DD assays can be subjected to interference that may cause false results, which directly affect the clinical diagnosis. Our hypothesis was that the 95% confidence intervals (CIs) of the fibrin degradation product (FDP)/DD and fibrinogen (Fib)/DD ratios were used to identify these false results and corrected via multiple dilutions.

METHODS

In total, 16 776 samples were divided into three groups according to the DD levels detected by Sysmex CS5100 and CA7000: Group A, DD ≥ 2.0 μg/mL fibrinogen equivalent unit (FEU); group B, 0.5 < DD < 2.0 μg/mL FEU; and group C, DD ≤ 0.5 μg/mL FEU. The 95% CIs of the FDP/DD and Fib/DD ratios were calculated. Six abnormal DD results were found according to the 95% CIs. For verification, we performed multiple dilutions, compared the results with those of other instruments, and tested the addition of heterophilic blocking reagent (HBR).

RESULTS

The median and 95% CI of the FDP/DD ratio were 3.76 and 2.25-8.15 in group A, 5.63 and 2.86-10.58 in group B, 10.23 and 0.91-47.71 in groups C, respectively. For the Fib/DD ratio, the 95% CIs was 0.02-2.21 in group A, 0.68-8.15 in group B, and 3.82-55.27 in groups C. Six abnormal results were identified after multiple dilutions, by comparison with other detection systems, and after HBR addition.

CONCLUSIONS

The FDP/DD ratio is more reliable for identifying false results. If the FDP/DD ratio falls outside the 95% CI, it should be verified by different methods.

What makes D-dimer assays suspicious-heterophilic antibodies?

BACKGROUND

Heterophilic antibodies are still an important source of interference in immunoassays, but reports of interference with D-dimers are rare. Are D-dimer level abnormalities, found in the clinic, caused by heterophilic antibodies as well, or are other mechanisms involved? We will elaborate on this issue through two different examples in this article.

METHODS

Serum from two patients with significantly elevated levels of D-dimers were measured and compared by different methods, diluted, and dealt with heterophilic antibody blockers. At the same time, to retrieve the interference, we focused on the cause of D-dimer false positives and made a systematic review of the literature.

RESULTS

The D-dimer values were normal (0.49 and 0.15 μg/mL) detected with different testing method and decreased after addition of heterophilic antibody blocking reagent. According to literature data, there were 66.7% (4/6) references showed the interference were heterophilic antibody.

CONCLUSIONS

The influence of heterophilic antibodies on the measurement of D-dimers remains a big challenge. Different measuring instruments and methods may have significant differences in the measurement of D-dimers. By using a combination of instrumental methods for measuring, incorporating heterophilic antibody blockers, and combining with clinical performance and imaging data, most of the interference can be eliminated.

A Black Swan in clinical laboratory practice: the analytical error due to interferences in immunoassay methods

It is well known that the results of immunoassay methods can be affected by specific or non-specific interferences, ranging from 0.4% to 4.0%. The presence of interference may greatly compromise the accuracy of immunoassay analyses causing an error in the measurement, producing false-positive or false-negative results. From a clinical point of view, these analytical errors may have serious implications for patient care because they can cause misdiagnosis or inappropriate treatment. Unfortunately, it is a very difficult task to identify the irregular analytical errors related to immunoassay methods because they are not detectable by normal laboratory quality control procedures, are reproducible within the test system, may be clinically plausible and are relatively rare. The first line of defense against erroneous results is to use in laboratory practice only immunoassay systems with the highest level of robustness against interference. The second line of defense is always taking into account the possibility of interference in immunoassay results. A correct approach should be addressed on identification of samples at high risk of interference. The attainment of this goal requires a critical review of the test result in relation to patient's clinical conditions and literature data, taking into account the analytical characteristics of the immunoassay system. The experts in immunoassay systems should make every effort to find some specific and reliable quality indicators for irregular analytical errors in order to better detect and monitor erroneous immunoassay results due to specific or non-specific interferences.

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

The primary role of the clinical laboratory is to report accurate results for diagnosis of disease and management of illnesses. This goal has, to a large extent been achieved for routine biochemical tests, but not for immunoassays which remained susceptible to interference from endogenous immunoglobulin antibodies, causing false, and clinically misleading results. Clinicians regard all abnormal results including false ones as "pathological" necessitating further investigations, or concluding iniquitous diagnosis. Even more seriously, "false-negative" results may wrongly exclude pathology, thus denying patients' necessary treatment. Analytical error rate in immunoassays is relatively high, ranging from 0.4% to 4.0%. Because analytical interference from endogenous antibodies is confined to individuals' sera, it can be inconspicuous, pernicious, sporadic, and insidious because it cannot be detected by internal or external quality assessment procedures. An approach based on Bayesian reasoning can enhance the robustness of clinical validation in highlighting potentially erroneous immunoassay results. When this rational clinical/statistical approach is followed by analytical affirmative follow-up tests, it can help identifying inaccurate and clinically misleading immunoassay data even when they appear plausible and "not-unreasonable." This chapter is largely based on peer reviewed articles associated with and related to this approach. The first section underlines (without mathematical equations) the dominance and misuse of conventional statistics and the underuse of Bayesian paradigm and shows that laboratorians are intuitively (albeit unwittingly) practicing Bayesians. Secondly, because interference from endogenous antibodies is method's dependent (with numerous formats and different reagents), it is almost impossible to accurately assess its incidence in all differently formulated immunoassays and for each analytes/biomarkers. However, reiterating the basic concepts underpinning interference from endogenous antibodies can highlight why interference will remain analytically pernicious, sporadic, and an inveterate problem. The following section discuses various stratagems to reduce this source of inaccuracy in current immunoassay results including the role of Bayesian reasoning. Finally, the role of three commonly used follow-up affirmative tests and their interpretation in confirming analytical interference is discussed.