Open Access Open Badges Research article

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

Jörg Schüpbach1*, Leslie R Bisset1, Martin D Gebhardt2, Stephan Regenass3, Philippe Bürgisser4, Meri Gorgievski5, Thomas Klimkait6, Corinne Andreutti7, Gladys Martinetti8, Christoph Niederhauser9, Sabine Yerly10, Stefan Pfister11, Detlev Schultze12, Marcel Brandenberger13, Franziska Schöni-Affolter14, Alexandra U Scherrer15, Huldrych F Günthard15 and Swiss HIV Cohort Study

Author Affiliations

1 Swiss National Center for Retroviruses, Institute of Medical Virology, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland

2 Swiss Federal Office of Public Health, Berne CH-3003, Switzerland

3 Clinic for Immunology, University Hospital, Zurich CH-8044, Switzerland

4 Service d'Immunologie et d'Allergie, University Hospital, Lausanne CH-1011, Switzerland

5 Institute of Infectious Diseases, University of Berne, Berne CH-3010, Switzerland

6 Institute for Medical Microbiology, University of Basel, Basel CH-4003, Switzerland

7 Clinique de la Source, Laboratoire, Lausanne CH-1004, Switzerland

8 Istituto Cantonale di Microbiologia, Bellinzona CH-6501, Switzerland

9 Blood Transfusion Service, Swiss Red Cross Berne, Berne CH-3001, Switzerland

10 Laboratory of Virology, Geneva University Hospitals, Genève 14 CH-1211, Switzerland

11 Institut Dr. Viollier AG, Basel CH-4002, Switzerland

12 Center of Laboratory Medicine, St. Gallen, Switzerland

13 Labor Dr. Güntert, Lucerne CH-6002, Switzerland

14 Swiss HIV Cohort Study (SHCS) Data Center, University Hospital, Lausanne CH-1011, Switzerland

15 Division of Infectious Diseases and and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich CH-8091, Switzerland

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BMC Infectious Diseases 2012, 12:88  doi:10.1186/1471-2334-12-88

Published: 12 April 2012



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.


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 (< = 12 m) or older infection by 26 different algorithms. Incident infection rates (IIR) were calculated based on diagnostic sensitivity and specificity of each algorithm and the rule that the total of incident results is the sum of true-incident and false-incident results, which can be calculated by means of the pre-determined sensitivity and specificity.


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.


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.