A total of 714 stored plasma or serum samples from patients who participated in the SHCS and had been infected by HIV-1 for at least 12 months were tested by the Inno-Lia HIV I/II score assay, as described under Methods. The main epidemiological, virological and immunological characteristics of the patients are summarized in Table . Owing to the selection for non-B clade infections, which in our country are more frequent in women than in men, the two sexes were represented at about equal numbers. Roughly half of the patients were classified as CDC stage A, 22% were in stage B and 28% in stage C. Almost three quarters of the patients had received HAART for a median duration of 13.5 months, resulting in 308 patients who presented with a HIV-1 RNA concentration below 50 copies/mL. HIV-1 RNA among the 406 patients with HIV-1 RNA ≥50 copies/mL amounted to a median of 103.94 copies/mL. The majority of the patients (86.8%) were infected by non-B clades comprising a total of 15 different clades in addition to subtype B.
Influence of HAART
The group of patients receiving HAART at the time of testing had significantly lower concentrations of HIV-1 RNA than those untreated (101.59 copies/mL compared to 104.25 copies/mL; p < 0.0001, Mann-Whitney U test). They also had significantly less intense reactions in the Inno-Lia with respect to viral proteins sgp120 (p = 0.029), p31 (p < 0.0001), p24 (p = 0.0003) and p17 (p = 0.0013). In contrast, the intensity of antibodies to gp41 was similar in both treated and untreated patients (p = 0.17), and the minimal band intensity was 1.0 independent of the treatment status. There was also a strong association between viral load and band intensity (Figure ). The 308 patients (including 6 who were treatment-naïve) with HIV-1 RNA <50 copies/mL had on average significantly lower intensity of all bands than the 406 patients with ≥50 copies/mL, particularly with respect to sgp120, p31 and p17 (p < 0.0001 for all three) and somewhat less with respect to gp41 (p = 0.003) and p24 (p = 0.008). The result of the Inno-Lia algorithms, recent or older infection, was affected likewise by treatment status and viral load (not shown). Multivariate logistic regression analysis combining HIV-1 RNA, treatment status and duration of HAART as independents showed, however, that the treatment status and the duration of HAART had no significant effect on the result of any algorithm. In contrast, the viral load level - more or less than 50 copies/mL - was a significant determinant for outcome of all algorithms except Alg03, Alg03.1, Alg05 and Alg06 (data not shown).
Figure 1 Effect of concentration of HIV-1 RNA on intensity of Inno-Lia bands. The box-plots indicate the median (the "waist" of the boxes) and the quartiles (upper and lower boundary of the boxes); outliers above the 90th or, respectively, below the 10th percentile (more ...)
Based on these first findings we determined the diagnostic specificity of the 24 Inno-Lia algorithms among the 190 treatment-naïve patients (including 6 patients with HIV-1 RNA <50 copies/mL) and the 222 patients with a viral load ≥50 copies/mL despite receiving HAART. Algorithm specificity among these 412 patients extended from 92.0% to 100%, with a median of 96.5% (Table ). Perfect specificity (100%) was obtained with the single-band algorithms Alg03 and Alg03.1; Alg06 was least specific (92.0%). Specificity of the algorithms among the 190 HAART-naïve patients alone was similar (median 95.5%, range 93.2 -- 100%).
Specificity of 24 Inno-Lia algorithms among 412 patients either HAART-naïve or exhibiting HIV-1 RNA ≥50 copies/mL despite HAART
Investigation of factors that affect algorithm specificity
Using logistic regression analysis, we sought to identify the factors that affected the result of the various algorithms in the total of the 714 patients. Alg03 could not be analyzed, as it was 100% specific. Results for the remaining 23 algorithms are summarized in Figures and . There were predictors that promoted false-recent results and others which protected against these. Most of the effects were not distributed randomly, but were associated with distinct groups of algorithms.
Figure 2 Univariate logistic regression analysis of factors that promote or impair algorithm specificity in all 714 patients. The meaning of the colors is explained at the bottom of the figure. Numbers indicate the chi-square p-value of the respective variable (more ...)
Figure 3 Multivariate logistic regression analysis of factors that affect algorithm specificity in all 714 patients. The meaning of the colors is explained at the bottom of the figure. Numbers indicate the chi-square p-value of the respective variable analyzed. (more ...)
In the univariate analysis (Figure ), the strongest and most consistent predictors of algorithm result included the HIV-1 RNA level, the CD4+ T cell percentage (CD4%) or count, sex, HAART status, age, and CDC stage. HIV-1 RNA <50 copies/mL, CD4% or count, age, and receiving HAART promoted a recent infection result. Other promoting factors included, in decreasing order, testing in certain laboratories compared to the one taken as reference, a long duration of sample storage, or being infected with the circulating recombinant form (CRF) CRF01_AE. Conversely, HIV-1 RNA concentration (in log(copies/mL), female sex or, for some algorithms, being in CDC stages B or C compared to A were factors that protected against a recent infection result. Other protective factors for some algorithms included being infected with CRF02_AG or subtypes A, C or D, or manual Inno-Lia testing. There were also some sporadic associations with the type or volume of the stored specimen or lot number of test kit. No associations were seen for duration of HAART, time since registration into the SHCS and mode of result scoring (visual versus automated).
The multivariate analysis of factors that affected algorithm specificity (Figure ) was performed with all parameters that had shown at least one significant association in the univariate analysis. There was strong co-linearity between CD4 count and CD4%, the two parameters for HIV-1 RNA, as well as testing laboratory and mode of testing. We therefore excluded CD4 count and testing laboratory from the analysis. Regarding HIV-1 RNA, we excluded log(copies/mL) in favor of the statistically stronger level.
The multivariate analysis confirmed the importance of HIV-1 RNA, CD4%, sex and age. Specifically, for the 20 algorithms for which an effect of HIV-1 RNA was demonstrated, <50 copies/mL was associated with a roughly fivefold increase in false-recent results compared to a concentration ≥50 copies/mL (odds ratio [OR]; mean, 4.85; range, 3.1 - 45.5). For the 18 algorithms affected by CD4%, there was a mean 1.046fold (range 1.025 - 1.083) increase in false-recent results for each additional CD4%, i.e. by 4.6%. Women had a mean 2.4fold lower risk than men for the 13 affected algorithms (mean OR, 0.412; range, 0.203 - 0.620). For age, there was a 3.2% increase of false-recent results per additional year with respect to the 11 marked algorithms (mean OR, 1.032; range 1.021 - 1.043).
Furthermore, for those algorithms in which age promoted a false-recent result, the testing of serum stored at -20°C instead of plasma stored at -70°C appeared to be a further promoting factor. There were only 12 serum samples, however, thus relativizing this finding. Advanced clinical stage lost the protective effect seen in univariate analysis in all algorithms but one. Sample size, duration of sample storage, and modes of testing and result evaluation retained no significance. Test kit lot #3 was again associated with a lower specificity when using Alg07 or, as a trend, Alg13.1. Close inspection of the data showed, however, that the great majority of the samples, namely 671 (86.4%), had been tested with lot #1. Only 32 specimens (4.5%) had been tested with lot #3, too few to permit any conclusions regarding possible variations in lot quality.
No influence by HIV clade
Compared to HIV-1 subtype B, infection with CRF01_AE remained significantly associated with an increased proportion of false-recent results by Alg02, Alg03.2, Alg07 and, as a trend, Alg17. Closer inspection of the data showed that these associations were largely restricted to patients receiving HAART. For example, with Alg02, among treated patients, there were 17 false-recent results among 71 patients infected with CRF01_AE (24%) compared to 2 of 62 (3.2%) infected with subtype B (p = 0.0008, Fisher's exact test). In contrast, among untreated patients, the respective figures were 3/21 for CRF01_AE compared to 2/32 for subtype B (p = 0.37). Thus, only 3 of the 20 false-recent results were among treatment-naïve patients, too few to permit any safe conclusion.
Similarly, the apparent protective effects of infections by CRF02_AG or subtypes A, C and D also turned out to be associated with HAART. With Alg04, e.g., there were 13 false-recent results among 62 treated patients infected with subtype B (21%), while the respective numbers for CRF02_AG were 4/72 (5.6%). Thus, among treated patients, those infected with CRF02_AG had a significantly lower risk for false-recent results than those infected with subtype B (p = 0.009). In contrast, among untreated patients, the proportions of false-recent results between CRF02_AG (1/24, 4%) and subtype B (4/32, 12.5%) differed less (p = 0.38). Again, only 5 of the false-recent results occurred among HAART-naïve patients. Similar relationships were found with respect to the apparent protective effects of subtypes A, C and D compared to B (not shown).
In a next step to determine the relevance of the factors leading to false-recent results, we narrowed the analysis to those 412 patients who were either HAART-naïve or exhibited HIV-1 RNA ≥50 copies/mL despite receiving HAART (Figure ). The analysis was further restricted to those independents which in Figure had shown significant effects with at least two algorithms. Thus, CDC stage, HAART, sample volume, storage duration, modes of testing and scoring, and kit lot were no longer in the model. Alg03.1 had no false-recent result and could not be analyzed.
Figure 4 Multivariate logistic regression analysis of factors that affect algorithm specificity among the 412 patients that are either HAART-naïve or exhibit HIV-1 RNA ≥50 copies/mL despite HAART. The meaning of the colors and numbers is as in (more ...)
Age impairs algorithm specificity
The only variable that retained broad significance in this setting was age, which significantly promoted false-recent results in 6 algorithms and showed a trend in a further 8. On average, the rate of false-recent results among these 14 algorithms increased by 5.2% for each additional year (range, 2.6 - 8.8%). CD4% lost all significance. HIV-1 RNA retained trends for significance with Alg 4.1 (OR, 0.74; 95% CI 0.52 - 1.04) and Alg16 (OR, 0.63; 95% CI 0.37 - 1.07). Although HIV-1 RNA was far away from significance in all other algorithms, the respective OR were usually below 1.0, particularly for all 16 combined algorithms, where the average OR per additional log RNA was 0.75. Thus, a certain influence of this parameter remains possible despite the lack of individual statistical significance. HIV clade also lost significance - note that the effect of CRF11_CPX with Algs 10 to 13.1, 15 and 16 is based on only 2 cases. Even more than in Figure , the remaining weak trends for either promoting or protective effects are based on too few cases to be of any relevance.
When finally focusing the investigation on the 190 HAART-naïve patients, univariate analysis revealed age as a factor, which significantly promoted false-recent results in four algorithms and showed a trend in two further ones (Figure , top panel). CD4% and HIV-1 RNA had no clear effects; a higher viral load even promoted false-recent results in Alg06. All other factors had no significance and are not represented in the figure. Multivariate analysis confirmed most effects of the three independents (lower panel). CD4% showed additional weak protective effects with Alg07 and Alg09, while HIV-1 RNA showed further protective effects with Algs 07, 09, 10, 16, and 17. Age lost its effect with Alg06, but gained a new one with Alg 10. Exclusion of the 6 cases with HIV-1 RNA <50 copies/mL led to the loss of all protective effects of HIV-1 RNA, while the effects of age and CD4% remained. This suggested that HIV-1 RNA <50 copies/mL promoted false-recent results also among untreated patients, while there was no effect among the higher concentrations. With regard to CD4%, close inspection of the data revealed no evidence for an association of low CD4% with low antibody intensities, and antibody intensities among patients in CDC stage C were on average higher than in stage A. Therefore, the weak effects of CD4% were not attributable to patients in advanced stage of disease.
Figure 5 Uni- and multivariate logistic regression analysis of factors that affect algorithm specificity among the 190 HAART-naïve patients. The meaning of the colors and numbers is as in the preceding figures. Odds ratios of variables of particular interest (more ...)
In cross-comparison of Figures , and , age clearly promoted false-recent results in all groups. Independent of the statistical significance in individual algorithms, the mean odds ratio for age among the algorithms differed little between the analyses of Figures , , and and amounted to 1.021, 1.037 and respectively 1.032, thus suggesting a relative increase in false-recent results of about 3% per additional year of life. An HIV-1 RNA below 50 copies/mL promoted false-recent results in both treated and untreated patients; above this level there was, however, no effect of the concentration. With respect to CD4%, the strongly promoting effect in Figure was strictly associated with long-term, successful HAART, as it was no longer present when HIV-1 RNA was above 50 copies/mL or when patients were HAART-naïve (Figure ). If the weak protective effects in this latter group are real, they were not attributable to patients in the most advanced stage of disease. All other factors, including HIV-1 clade, had no effect.