Recently acquired infection among HIV-seropositive donors in the US from 2010-2018

HIV Subtypes and Drug-resistance-associated Mutations in US Blood Donors, 2015-2020

Background

Monitoring genotypes of HIV infections in blood donors may provide insights into infection trends in the general population.

Methods

HIV RNA was extracted from plasma samples of blood donors confirmed as HIV positive by blood screening nucleic acid and antibody tests. HIV genome target regions were amplified using nested real time-polymerase chain reaction followed by next-generation sequencing. Sequences were compared to those in the Los Alamos National Laboratory (LANL) database. Sequences were also assessed for drug resistance mutations (DRM) using the Stanford HIV DRM Database.

Results

From available HIV-positive donations collected between 1 September 2015 and 31 December 2020, 563 of 743 (75.8%) were successfully sequenced; 4 were subtype A, 543 subtype B, 5 subtype C, 1 subtype G, 5 circulating recombinant forms (CRF), and 2 were subtype B and D recombinants. Overall, no significant differences between blood donor and available LANL genotypes were found, and the genotypes of newly acquired versus prevalent HIV infections in donors were similar. The proportion of non-B subtypes and CRF remained a small fraction, with no other subtype or CRF representing more than 1% of the total. DRM were identified in 122 (21.6%) samples with protease inhibitor, nucleoside reverse transcriptase inhibitor and non-nucleoside reverse transcriptase inhibitor DRMs identified in 4.9%, 4.6% and 14.0% of samples, respectively.

Conclusions

HIV genetic diversity and DRM in blood donors appear representative of circulating HIV infections in the US general population and may provide more information on infection diversity than sequences reported to LANL, particularly for recently transmitted infections.

The impact of non-disclosure of HIV status and antiretroviral therapy on HIV recency testing and incidence algorithms

BACKGROUND AND OBJECTIVES

Accurate HIV incidence estimates among blood donors are necessary to assess the effectiveness of programs aimed at limiting transfusion-transmitted HIV. We assessed the impact of undisclosed HIV status and antiretroviral (ARV) use on HIV recency and incidence estimates using increasingly comprehensive recent infection testing algorithms.

MATERIALS AND METHODS

Using 2017 donation data from first-time and lapsed donors, we populated four HIV recency algorithms: (1) serology and limiting-antigen avidity testing, (2) with individual donation nucleic amplification testing (ID-NAT) added to Algorithm 1, (3) with viral load added to Algorithm 2 and (4) with ARV testing added to Algorithm 3. Algorithm-specific mean durations of recent infection (MDRI) and false recency rates (FRR) were calculated and used to derive and compare incidence estimates.

RESULTS

Compared with Algorithm 4, progressive algorithms misclassified fewer donors as recent: Algorithm 1: 61 (12.1%); Algorithm 2: 14 (2.8%) and Algorithm 3: 3 (0.6%). Algorithm-specific MDRI and FRR values resulted in marginally lower incidence estimates: Algorithm 1: 0.19% per annum (p.a.) (95% confidence interval [CI]: 0.13%-0.26%); Algorithm 2: 0.18% p.a. (95% CI: 0.13%-0.22%); Algorithm 3: 0.17% p.a. (95% CI: 0.13%-0.22%) and Algorithm 4: 0.17% p.a. (95% CI: 0.13%-0.21%).

CONCLUSION

We confirmed significant misclassification of recent HIV cases when not including viral load and ARV testing. Context-specific MDRI and FRR resulted in progressively lower incidence estimates but did not fully account for the context-specific variability in incidence modelling. The inclusion of ARV testing, in addition to viral load and ID-NAT testing, did not have a significant impact on incidence estimates.

Recent HIV-1 infection in Israel 2017-2021: Evaluation of geenius and HIV-1/2 combo assays for identifying recent infection detected by Sedia assay and assessment of factors related to recent infection: Recent HIV-1 infection in Israel

BACKGROUND

Estimating HIV-1 recency of infection for incidence and local outbreaks detection usually involves specifically designed assays. Here, we established an approach to identify recent infections, estimate their rate, and assess potential risk factors.

METHODS

Randomly selected HIV-1 positive samples (n = 382) collected in 2017-2021 were tested by Sedia and compared to the results of Geenius recency algorithm and the S/CO values of the HIV-1/2 Combo assay. Using Geenius and Combo recency verdict, we assessed all cases diagnosed in 2017-2021. Related factors were further assessed.

RESULTS

While Geenius and Combo had a sensitivity of 65.9 % and 89.30 %, respectively, and specificity of 96 % and 90 %, respectively, compared to Sedia, higher concordance (97.2 %) and kappa (>0.9) were observed when the verdict of both assays together was compared to Sedia. Using this approach, 15.3 % (238/1548) of individuals diagnosed in 2017-2021 were defined as recently infected. In multivariate analysis, recent diagnosis was mainly associated with men who have sex with men (MSM) and with birthplace in Israel, Western/Central Europe, or North America.

CONCLUSIONS

Only 15.3 % of infections in 2017-2021, mainly in MSM and Israeli/Western countries-born individuals, were diagnosed early. Regular diagnostic assays have a potential to identify and monitor trends in recent infections.

Taking a step back from testing: Preanalytical considerations in molecular infectious disease diagnostics

Recent studies evaluating the preanalytical factors that impact the outcome of nucleic-acid based methods for the confirmation of SARS-CoV-2 have illuminated the importance of identifying variables that promoted accurate testing, while using scarce resources efficiently. The majority of laboratory errors occur in the preanalytical phase. While there are many resources identifying and describing mechanisms for main laboratory testing on automated platforms, there are fewer comprehensive resources for understanding important preanalytical and environmental factors that affect accurate molecular diagnostic testing of infectious diseases. This review identifies evidence-based factors that have been documented to impact the outcome of nucleic acid-based molecular techniques for the diagnosis of infectious diseases.