Diagnostic accuracy of HIV test kits, Genscreen Ultra and Bioelisa
Received 23 August 2018
Accepted for publication 24 November 2018
Published 11 February 2019 Volume 2019:11 Pages 17—22
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 3
Editor who approved publication: Professor Bassel Sawaya
Saro Abdella Abrahim, Mulu Girma, Abebe Habteselassie, Nigussie Gezahegn, Altaye Feleke, Tezera Moshago Berheto, Minilik Demissie, Wudnesh Belete, Tekalign Deressa
HIV/TB Research and Reference Laboratory Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia
Purpose: Genetic diversities in different countries affect the performance of HIV test kits. Therefore, WHO recommends evaluation of every HIV test kit in countries’ context before its use. Therefore, this study aimed to evaluate the performance of Genscreen ULTRA HIV Ag–Ab and Bioelisa.
Materials and methods: The study had used 400 characterized plasma samples obtained from CDC Atlanta bio-bank derived from Africa, USA, and Thailand.
Results: Diagnostic performance of both test kits under evaluation was assessed at 95% CI. Genscreen ULTRA HIV Ag–Ab had sensitivity and negative predictive value of 99.5% [95% CI, 97.2–99.9] and the specificity and positive predictive value of 98.5% [95% CI, 95.7–99.7]. Bioelisa HIV test kit had exhibited sensitivity and negative predictive value of 99% [95% CI, 96.4–99.7] and specificity and positive predictive value of 98.5% [95% CI, 95.7–99.7]. Both test kits were able to detect almost all samples with HIV-2, dual infections, and seroconversion.
Conclusion: Both the test kits were highly sensitive and specific in detecting HIV. However, there are still few samples containing HIV antibody which were not identified by both kits. Therefore, additional screening measures should be done in using these assays for blood transfusion and organ transplantation. In addition, the study can be used as a reference by other African countries.
Keywords: ELISA, sensitivity, positive predictive, specificity, validation
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