Analytical performance evaluation and enhancement of the ADVIA Centaur® HIV Ag/Ab Combo assay.
ADVIA centaur
Diagnosis
HIV
Immunoassay
Machine learning
Journal
Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology
ISSN: 1873-5967
Titre abrégé: J Clin Virol
Pays: Netherlands
ID NLM: 9815671
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
14
06
2019
revised:
22
07
2019
accepted:
24
07
2019
pubmed:
16
8
2019
medline:
17
6
2020
entrez:
16
8
2019
Statut:
ppublish
Résumé
Fourth-generation immunoassays (such as the ADVIA Centaur® HIV Ag/Ab Combo (CHIV) assay) have improved the early diagnosis of human immunodeficiency virus (HIV), and their sensitivity and specificity usually exceed 99%. In regions with a low prevalence of HIV infection, however, the regular occurrence of false positives interferes with a medical laboratory's workflow. The additional reagent and staff costs associated with false positives can nevertheless be avoided or reduced by gaining a better knowledge of the CHIV assay's performance. To improve our HIV diagnosis strategy, we retrospectively analyzed all the Centaur® CHIV assays and confirmatory tests performed at Amiens University Medical Center between 2012 and 2018. We used open-source machine learning software to process this large database, develop a predictive model, and identify a new cut-off for Centaur® CHIV index interpretation. A total of 56,682 HIV serological assay results were analyzed. The results of the CHIV assay were initially reactive or indeterminate for 449 samples. After p24 antigen and/or immunoblotting, there were 171 (38%) false positives and 278 (62%) confirmed true positives. The application of a cut-off of 2.12 led to reclassification of 130 of the 171 false positives as true negatives. Combining our predictive model with medical record analysis reduced the number of false positive CHIV assay results from 171 to 12. The efficiency of the Centaur® CHIV assay can be increased by adjusting its cut-off for positivity. This adjustment may reduce the number of unnecessary confirmatory tests and accelerate the delivery of HIV test results.
Sections du résumé
BACKGROUND
Fourth-generation immunoassays (such as the ADVIA Centaur® HIV Ag/Ab Combo (CHIV) assay) have improved the early diagnosis of human immunodeficiency virus (HIV), and their sensitivity and specificity usually exceed 99%. In regions with a low prevalence of HIV infection, however, the regular occurrence of false positives interferes with a medical laboratory's workflow. The additional reagent and staff costs associated with false positives can nevertheless be avoided or reduced by gaining a better knowledge of the CHIV assay's performance.
OBJECTIVES/STUDY DESIGN
To improve our HIV diagnosis strategy, we retrospectively analyzed all the Centaur® CHIV assays and confirmatory tests performed at Amiens University Medical Center between 2012 and 2018. We used open-source machine learning software to process this large database, develop a predictive model, and identify a new cut-off for Centaur® CHIV index interpretation.
RESULTS
A total of 56,682 HIV serological assay results were analyzed. The results of the CHIV assay were initially reactive or indeterminate for 449 samples. After p24 antigen and/or immunoblotting, there were 171 (38%) false positives and 278 (62%) confirmed true positives. The application of a cut-off of 2.12 led to reclassification of 130 of the 171 false positives as true negatives. Combining our predictive model with medical record analysis reduced the number of false positive CHIV assay results from 171 to 12.
CONCLUSIONS
The efficiency of the Centaur® CHIV assay can be increased by adjusting its cut-off for positivity. This adjustment may reduce the number of unnecessary confirmatory tests and accelerate the delivery of HIV test results.
Identifiants
pubmed: 31415958
pii: S1386-6532(19)30165-9
doi: 10.1016/j.jcv.2019.07.007
pii:
doi:
Substances chimiques
HIV Antibodies
0
HIV Antigens
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
36-40Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.