Automated prozone effect detection in ferritin homogeneous immunoassays using neural network classifiers.

Abstract:

:The application of turbidimetric homogeneous immunoassays made the determination of several plasma components widely available. The sensitivity and accuracy of these assays are appropriate enough for routine laboratory use; however, in the case of many pathologically high concentration samples, prozone effect (high dose hook effect) can be observed, that leads to false-negative determination. Up to the present there are no cost-effective algorithms available for the safe detection of the prozone effect. Pathological serum ferritin values can be elevated up to 5000 ng/ml, while the measuring range covers only the 0-440 ng/ml range by a commercial assay. The determination of samples with ferritin concentration higher than 1500 ng/ml results in false-negative values because of the overlapping measuring range and prozone effect range. The prozone effect can be recognised by analysis of reaction kinetics after measurement. We have developed a neural network classifier system to analyse reaction kinetics of the measurements and check the prozone effect. One thousand five hundred determinations and 77 patient samples were used for neural network training and test. Using the trained neural networks, false-negative results can be filtered immediately after the determination, without re-run; thus, the sensitivity of plasma ferritin determination may become reliable enough, even in the case of high concentration samples. Applying this new technology, false-negative serum ferritin determinations can be avoided, thus even a relatively high hook effect rate (5-12% in different patient groups) can be handled safely.

journal_name

Clin Chem Lab Med

authors

Papik K,Molnar B,Fedorcsak P,Schaefer R,Lang F,Sreter L,Feher J,Tulassay Z

doi

10.1515/CCLM.1999.076

keywords:

subject

Has Abstract

pub_date

1999-04-01 00:00:00

pages

471-6

issue

4

eissn

1434-6621

issn

1437-4331

journal_volume

37

pub_type

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