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

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 (< = 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. 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.

Simple Estimation of Incident HIV Infection Rates in Notification Cohorts Based on Window Periods of Algorithms for Evaluation of Line-Immunoassay Result Patterns

“…In order to enable an optimal comparison of window-based and diagnostic-performance-based estimation of the incident infection rate (IIR), the patients and specimens were exactly as used in a previous study [19]. For determination of the window periods of the Inno-Lia algorithms, we used a group of 527 patients with HIV-1 infection of up to 12.0 months duration ( =  incident infection).…”

“…The ten best-performing algorithms had an unadjusted mean sensitivity of 59.4% to recognize infections of up to 12 months duration and a mean specificity of 95.1% among patients infected for longer than 12 months. Using these ten algorithms in combination, we were able to identify distinct changes between the incident infection rates (IIR) of four successive annual cohorts of HIV-1 notifications [19].…”

“…based on their window periods. We determined the window periods of all hitherto published Inno-Lia algorithms and compared window-based and performance-based incident infection rates in the four previously studied annual cohorts of HIV-1 notifications [19].…”

How can we better identify early HIV infections?

“…However, the Western blot is no longer being performed in most clinical laboratories. Among confirmatory tests replacing the Western blot are the INNO-LIA HIV I/II Score (Innogenetics, Gent, Belgium) [62] and a newer rapid test, the Bio-Rad™ Geenius HIV 1/2 Assay (Bio-Rad Laboratories, Inc.). These diagnostics provide similar semiquantitative information and can distinguish EHI from long-standing infections [55,63•] if such information is sought.…”

Performance of the Bio-Rad Geenius HIV1/2 Supplemental Assay in Detecting “Recent” HIV Infection and Calculating Population Incidence

“…21 Other diagnostic tests detect early infection but do not help distinguish those with ongoing seroconversion from those with established infection. 22 A diagnostic test providing this information could improve clinical decision making, help target public health interventions, and enhance case-based surveillance.…”