Medical Microbiology and Immunology

, Volume 208, Issue 5, pp 693–702 | Cite as

Discrimination between recent and non-recent HIV infections using routine diagnostic serological assays

  • Jaythoon HassanEmail author
  • Joanne Moran
  • Gary Murphy
  • Olivia Mason
  • Jeff Connell
  • Cillian De Gascun
Original Investigation


The suitability of routine diagnostic HIV assays to accurately discriminate between recent and non-recent HIV infections has not been fully investigated. The aim of this study was to compare an established HIV recency assay, the Sedia limiting antigen HIV avidity assay (LAg), with the diagnostic assays; Abbott ARCHITECT HIV Ag/Ab Combo and INNO-LIA HIV line assays. Samples from all new HIV diagnoses in Ireland from January to December 2016 (n = 455) were tested. An extended logistic regression model, the Spiegelhalter–Knill–Jones method, was utilised to establish a scoring system to predict recency of HIV infection. As proof of concept, 50 well-characterised samples were obtained from the CEPHIA repository whose stage of infection was blinded to the authors, which were tested and analysed. The proportion of samples that were determined as recent was 18.1% for LAg, 6.4% with the ARCHITECT, and 14.5% in the INNO-LIA assay. There was a significant correlation between the ARCHITECT S/CO values and the LAg results, r = 0.717, p < 0.001. ROC analysis revealed that an ARCHITECT S/CO < 250 had a sensitivity and specificity of 90.32% and 89.83%, respectively. Combining the Abbott ARCHITECT HIV Ag/Ab Combo assay and INNO-LIA HIV assays resulted in an observed risk of being recent of 100%. Analysis of the CEPHIA samples revealed a strong agreement between the LAg assay and the combination of routine assays (κ = 0.908, p < 0.001). Our findings provide evidence that assays routinely employed to diagnose and confirm HIV infection may be utilised to determine the recency of HIV infection.


HIV infection Recency Routine diagnostic assays 


Author contributions

Concept and design of the study: JH and JM; acquisition of data: JH and JM, statistical analysis: JH and OM; interpretation of data and drafting the manuscript: JH and JC; critical revision of the manuscript: CDG and GM.

Compliance with ethical standards

Conflict of interest

The authors declare they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required.


  1. 1.
    Moyo S, Wilkinson E, Novitsky V et al (2015) Identifying recent HIV infections: from serological assays to genomics. Viruses 7(10):5508–5524. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Schüpbach J, Gebhardt MD, Tomasik Z et al (2007) Assessment of recent HIV-1 infection by a line immunoassay for HIV-1/2 confirmation. PLoS Med 4(12):e343CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Schüpbach J, Gebhardt MD, Scherrer AU et al (2013) Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns. PLoS One 8(8):e71662. CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Schupbach J, Bisset LR, Regenass S et al (2011) High specificity of line-immunoassay based algorithms for recent HIV-1 infection independent of viral subtype and stage of disease. BMC Infect Dis 11:254CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Schupbach J, Bisset LR, Gebhardt MD et al (2012) Diagnostic performance of line-immunoassay based algorithms for incident HIV-1 infection. BMC Infect Dis 12:88CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Schüpbach J, Niederhauser C, Yerly S et al (2015) Decreasing proportion of recent infections among newly diagnosed HIV-1 cases in Switzerland, 2008 to 2013 based on line-immunoassay-based algorithms. PLoS One 10(7):e0131828. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Wei X, Liu X, Dobbs T, Kuehl et al (2010) Development of two avidity-based assays to detect recent HIV type 1 seroconversion using a multisubtype gp41 recombinant protein. AIDS Res Hum Retrovirus 26:1–11CrossRefGoogle Scholar
  8. 8.
    Duong YT, Qiu M, De AK et al (2012) Detection of recent HIV-1 infection using a new limiting-antigen avidity assay: potential for HIV-1 incidence estimates and avidity maturation studies. PLoS One 7:e33328CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Parekh B, Duong Y, Mavengere Y et al (2012) Performance of new LAg-Avidity EIA to measure HIV-1 incidence in a cross-sectional population: Swaziland HIV Incidence Measurement Survey (SHIMS). In: XIX international AIDS conference. Washington DC. July 22–27, Abstract #LBPE27Google Scholar
  10. 10.
    Schwarcz S, Kellogg T, McFarland W et al (2001) Differences in the temporal trends of HIV seroincidence and seroprevalence among sexually transmitted disease clinic patients, 1989–1998: application of the serological testing algorithm for recent HIV seroconversion. Am J Epidemiol 153:925–934CrossRefPubMedGoogle Scholar
  11. 11.
    Machado DM, Delwart EL, Diza RS et al (2002) Use of the sensitive/less-sensitive (detuned) EIA strategy for targeting genetic analysis of HIV-1 recently infected blood donors. AIDS 16:113–119CrossRefPubMedGoogle Scholar
  12. 12.
    Duong YT, Kassanjee R, Welte A et al (2015) Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes. PLoS One 10(2):e0114947. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Seymour DG, Green M, Vaz FG (1990) Making better decisions: construction of clinical scoring systems by the Spiegelhalter–Knill–Jones approach. BMJ 300:223–226CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Chan SF, Deeks JJ, Macaskill P, Irwig L (2008) Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results. J Clin Epidemiol 1:52–63CrossRefGoogle Scholar
  15. 15.
    Suligoi B, Rodella A, Raimondo M et al (2011) Avidity index for anti-HIV antibodies: comparison between third- and fourth-generation automated immunoassays. J Clin Micro 49:2610–2613CrossRefGoogle Scholar
  16. 16.
    Glese C, Igoe D, Gibbons Z, Hurley C, Stokes S, McNamara S, Ennis O, O’Donnell K, Keenan E, De Gascun C, Lyons F, Ward M, Danis K, Glynn R, Waters A, Fitzgerald M, On behalf of the outbreak control team (2015). Injection of new psychoactive substance snow blow associated with recently acquired HIV infections among homeless people who inject drugs in Dublin, Ireland. Eurosurveillance 20(40):30036CrossRefGoogle Scholar
  17. 17.
    Keating SM, Pilcher CD, Jain V et al (2017) HIV antibody level as a marker of HIV persistence and low level viral replication. J Infect Dis 216:72–81CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wendel SK, Longosz AF, Eshleman SH et al (2017) Short communication: the impact of viral suppression and viral breakthrough on limited-antigen avidity assay results in individuals with clade B HIV infection. AIDS Res Hum Retrovirus 33(4):325–327. CrossRefGoogle Scholar
  19. 19.
    Manns A, Miley WJ, Wilks RJ et al (1999) Quantitative proviral DNA and antibody levels in the natural history of HTLV-I infection. J Infect Dis 180(5):1487–1493CrossRefPubMedGoogle Scholar
  20. 20.
    Grebe E, Welte A, Hall J et al (2017) Infection staging and incidence surveillance applications of high dynamic range diagnostic immune-assay platforms. J Acquir Immune Defic Syndromes 76(5):547–555CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Virus Reference LaboratoryUniversity College DublinDublin 4Ireland
  2. 2.Health Protection Surveillance CentreDublin 1Ireland
  3. 3.Public Health England, London on Behalf of the Consortium for Performance and Evaluation of HIV Incidence Assays (CEPHIA)LondonUK
  4. 4.Department of Public Health, Physiotherapy and Population Science, Centre for Support and Training in Analysis and ResearchUniversity College DublinDublin 4Ireland

Personalised recommendations