BMC Infectious Diseases (Apr 2012)

Diagnostic performance of line-immunoassay based algorithms for incident HIV-1 infection

  • Schüpbach Jörg,
  • Bisset Leslie R,
  • Gebhardt Martin D,
  • Regenass Stephan,
  • Bürgisser Philippe,
  • Gorgievski Meri,
  • Klimkait Thomas,
  • Andreutti Corinne,
  • Martinetti Gladys,
  • Niederhauser Christoph,
  • Yerly Sabine,
  • Pfister Stefan,
  • Schultze Detlev,
  • Brandenberger Marcel,
  • Schöni-Affolter Franziska,
  • Scherrer Alexandra U,
  • Günthard Huldrych F

DOI
https://doi.org/10.1186/1471-2334-12-88
Journal volume & issue
Vol. 12, no. 1
p. 88

Abstract

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Abstract Background Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have previously demonstrated that a patient's antibody reaction pattern in a confirmatory line immunoassay (INNO-LIA™ HIV I/II Score) provides information on the duration of infection, which is unaffected by clinical, immunological and viral variables. In this report we have set out to determine the diagnostic performance of Inno-Lia algorithms for identifying incident infections in patients with known duration of infection and evaluated the algorithms in annual cohorts of HIV notifications. Methods Diagnostic sensitivity was determined in 527 treatment-naive patients infected for up to 12 months. Specificity was determined in 740 patients infected for longer than 12 months. Plasma was tested by Inno-Lia and classified as either incident ( Results The 10 best algorithms had a mean raw sensitivity of 59.4% and a mean specificity of 95.1%. Adjustment for overrepresentation of patients in the first quarter year of infection further reduced the sensitivity. In the preferred model, the mean adjusted sensitivity was 37.4%. Application of the 10 best algorithms to four annual cohorts of HIV-1 notifications totalling 2'595 patients yielded a mean IIR of 0.35 in 2005/6 (baseline) and of 0.45, 0.42 and 0.35 in 2008, 2009 and 2010, respectively. The increase between baseline and 2008 and the ensuing decreases were highly significant. Other adjustment models yielded different absolute IIR, although the relative changes between the cohorts were identical for all models. Conclusions The method can be used for comparing IIR in annual cohorts of HIV notifications. The use of several different algorithms in combination, each with its own sensitivity and specificity to detect incident infection, is advisable as this reduces the impact of individual imperfections stemming primarily from relatively low sensitivities and sampling bias.