High specificity of line-immunoassay based algorithms for recent HIV-1 infection independent of viral subtype and stage of disease

Schüpbach, Jörg; Bisset, Leslie R.; Regenass, Stephan; Bürgisser, Philippe; Gorgievski, Meri; Steffen, Ingrid; Andreutti, Corinne; Martinetti, Gladys; Shah, Cyril; Yerly, Sabine; Klimkait, Thomas; Gebhardt, Martin; Schöni‐Affolter, Franziska; Rickenbach, Martin

doi:10.1186/1471-2334-11-254

Cited by 9 publications

(32 citation statements)

References 23 publications

(21 reference statements)

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“…In a recent study of patients known to have been infected for at least one year, we have demonstrated a high diagnostic specificity of the algorithms, which was not affected by the HIV-1 clade or the disease stage [36]. The present work also confirms the high specificity of the algorithms (Table 2).…”

Section: Discussionsupporting

confidence: 84%

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

et al. 2012

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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.

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Section: Discussionsupporting

confidence: 84%

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

et al. 2012

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“…Linking Inno-Lia based IIR estimation to the context of prospective, individual confirmation of an HIV diagnosis is of advantage, because newly diagnosed patients are generally ART-naive. Prolonged aviremia due to long-term ART, which has been shown to lower the specificity of Inno-Lia based incident infection algorithms [18], will thus not be present. We do not recommend the Inno-Lia for IIR estimation outside of the context of prospective individual confirmation of newly diagnosed HIV infections.…”

Section: Discussionmentioning

confidence: 99%

“…Twelve of the algorithms, Alg2 to Alg13, were published in that paper. The other 14 were developed more recently in the same way and based on the same dataset; they were used in two further studies [18], [19]. All 26 algorithms were applied to the collected Inno-Lia data of the present study.…”

Section: Methodsmentioning

confidence: 99%

“…A study of 714 patients selected from the Swiss HIV Cohort Study (SHCS), who had been infected for at least 12 months and represented all clinical stages and major clades of HIV-1, showed that none of these variables affected the incident infection algorithms [18]. Of the 714 patients investigated in that study, only 94 were infected by HIV-1 subtype B, while 620 patients were infected by one of 15 different non-B clades.…”

Section: Introductionmentioning

confidence: 99%

“…The study showed that none of these non-B clades impaired the diagnostic specificity of the method in comparison to subtype B. Although a viral RNA load below 50 copies/mL significantly reduced the specificity among patients receiving antiretroviral treatment (ART), age was the sole factor which weakly impaired the test specificity in untreated patients [18]. In another study, we assessed the diagnostic performance of the algorithms based on 527 incident and 740 older infections.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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BackgroundTests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods.MethodsWe classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship ‘Prevalence = Incidence x Duration’ in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship ‘incident = true incident + false incident’ and also to the IIR derived from the BED incidence assay.ResultsWindow periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R2 = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods.ConclusionsIIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

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

Hassan

Moran

Murphy

et al. 2019

Med Microbiol Immunol

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High specificity of line-immunoassay based algorithms for recent HIV-1 infection independent of viral subtype and stage of disease

Cited by 9 publications

References 23 publications

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

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

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

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

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