Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2019
Analytical performance evaluation and enhancement of the ADVIA Centaur® HIV Ag/Ab Combo assay.
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Demey Baptiste, Usureau Cédric, Boillod Paul, Bodeau Sandra, François Catherine, Roussel Catherine, Duverlie Gilles, Castelain Sandrine, Brochot Etienne
Аннотация
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.
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.