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Updated: May 1, 2026

Procedure and Key Optimization Strategies for an Automated Capillary Electrophoretic-based Immunoassay Method
Published on: September 10, 2017
Optimizing the Elecsys® HCV assay threshold to improve hepatitis C diagnosis algorithm and reduce healthcare costs by
Beverly Buffart1, Sara Cherkaoui2, Hafid Dahma2
1Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB, Bruxelles, Rue Haute, 322 - 1000, Belgium. beverly.buffart@lhub-ulb.be.
Purpose:
Viral hepatitis remains a leading cause of infectious mortality worldwide. This study aimed to improve the serological screening process by reducing unnecessary testing and healthcare costs by optimizing the assay threshold.
Methods:
Three methods (Elecsys® Anti-HCV II, Alinity s Anti-HCV and Ortho® HCV 3.0) were compared against a composite gold standard on 260 samples. The best-performing was used to establish an optimal S/CO cut-off for our screening method (Elecsys® Anti-HCV II), predictive of true-positivity. A confirmatory assay (Vidas® anti-HCV) was used to differentiate true from false positives. A simulation of the use of this cut-off was conducted on a retrospective cohort to assess its effectiveness in terms of diagnosis and healthcare costs.
Results:
The Alinity s Anti-HCV II assay showed the highest sensitivity (100%) and specificity (98.2%). This assay was used to establish the cut-off threshold for our method. The best Youden's index was obtained with a value of 17.8 (sensitivity (95.6%), specificity (96.4%)). In a retrospective cohort of 667 patients with a positive initial HCV screening, 124 had an S/CO ratio of ≤ 17.8. Of these, Vidas® anti-HCV confirmed 105 negative results (15.74% of the cohort), eliminating the need for PCR testing for these patients. The estimated theoretical savings for a 7-month assessment period is € 7186,2.
Conclusions:
An S/CO cut-off of 17.8 was established for the Elecsys® Anti-HCV II assay, accurately predicting true positives in 97% of cases and reduced the need for unnecessary PCR confirmatory testing, resulting in cost savings.
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