Evidence map›Paper›PMID 37106580›Full record

ArticleJournal of clinical laboratory analysis2023

Evaluating the utility of a two-assay serological algorithm for hepatitis C screening in a low prevalence population.

Kai J Rogers, Tracy S Halvorson, Matthew D Krasowski, Anna E Merrill

Abstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kai J RogersDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.ORCID https://orcid.org/0000-0003-3331-5933
Tracy S HalvorsonDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.
Matthew D KrasowskiDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.
Anna E MerrillDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.ORCID https://orcid.org/0000-0002-5945-5937

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionScreening for hepatitis C virus (HCV) is performed by testing for anti-HCV antibodies, which may yield false-positive results leading to additional testing and other downstream consequences for the patient. We report our experience in a low prevalence population (<0.05%) using a two-assay algorithm aimed at testing specimens with borderline or weak positive anti-HCV reactivity in the screening assay by a second anti-HCV assay prior to confirming positive anti-HCV results with RT-PCR. MATERIALS AND

methodsRetrospective analysis of 58,908 plasma samples was obtained over a 5-year period. Samples were initially tested using the Elecsys Anti-HCV II assay (Roche Diagnostics), with borderline or weakly positive results (defined in our algorithm as a Roche cutoff index of 0.9-19.99) reflexively analyzed using the Architect Anti-HCV assay (Abbott Diagnostics). The Abbott anti-HCV results dictated the final anti-HCV interpretation for reflexed samples.

resultsOur testing algorithm resulted in 180 samples requiring second-line testing, with final anti-HCV results interpreted as 9% positive, 87% negative, and 4% indeterminate. The positive predictive value (PPV) of a weakly positive Roche result was 12%, which was significantly lower than the PPV using our two-assay approach (65%).

conclusionsThe incorporation of a two-assay serological testing algorithm in a low prevalence population provides a cost-effective method of improving the PPV of HCV screening in specimens with borderline or weakly positive anti-HCV results.

Indexed as

Hepatitis CRNA, ViralAlgorithmsHepacivirusHepatitis C AntibodiesHumansPrevalenceRetrospective StudiesSensitivity and SpecificityHepatitis C AntibodiesRNA, Viralhepatitis Cprevalenceserology

Identifiers

PMID37106580
PMCPMC10220299

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