BACKGROUND
In 1998 we estimated that 34/million infectious window period donations were entering the blood supply at the South African National Blood Service. Selective use of donations based on ...donor race‐ethnicity reduced this risk to 26/million donations but was deemed unethical. Consequently, in 2005 South African National Blood Service eliminated race‐ethnicity–based collection policies and implemented individual‐donation nucleic acid testing (ID‐NAT). We describe the change in donor base demographics, human immunodeficiency virus (HIV) detection rates, and transfusion‐transmissible HIV risk.
STUDY DESIGN AND METHODS
In ten years 7.7 million donations were tested for anti‐HIV and HIV RNA. Number of donations, HIV prevalence, ID‐NAT yield rate, serology yield rate and residual transfusion‐transmissible HIV risk were analyzed by donor type, race‐ethnicity, age, and sex. Multiple regression analysis was performed to investigate the determinants of HIV‐positive and nucleic acid testing yield donations.
RESULTS
The combined strategy of increasing donations from black donors and implementing ID‐NAT increased the proportion of donations from black donors from 6% in 2005 to 30% in 2015 (p < 0.00001), and reduced the transfusion‐transmissible risk from 24 to 13 per million transfusions. ID‐NAT interdicted 481 (1:16,100) seronegative window period donations, while one transfusion‐transmissible case (0.13 per million) was documented. Race‐ethnicity and donor type were highly significant predictors of HIV positivity, with adjusted odds ratio for first‐time donors of 12.5 (95% confidence interval, 11.9‐13.1) and for black race‐ethnicity of 31.1 (95% confidence interval, 28.9‐33.4). The proportion of serology yields among HIV‐infected donors increased from 0.27% to 2.4%.
CONCLUSION
ID‐NAT enabled the South African National Blood Service to increase the number of donations from black donors fivefold while enhancing the safety of the blood supply.
See article on page 9–11, in this issue
Summary
Viral diagnostics have shown continued innovation, with serological and molecular diagnostic assays pushing the limits of sensitivity. Technology has provided new automated shared diagnostic ...platforms that reduce hands‐on time, while with globalisation of the diagnostic market, commercial assays are applied across epidemiologically diverse settings on different patient and viral populations. However, with these novel developments, new and often unexpected sources of diagnostic error emerge. In this review we will reflect on case studies that highlight these often underappreciated or unexpected diagnostic errors spanning pre‐analytical, analytical, and post‐analytic processes. We will also suggest approaches that could help identify error and reduce the impact on patient management.
The first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in South Africa was identified on 5 March 2020, and by 26 March the country was in full lockdown (Oxford stringency ...index of 90)
. Despite the early response, by November 2020, over 785,000 people in South Africa were infected, which accounted for approximately 50% of all known African infections
. In this study, we analyzed 1,365 near whole genomes and report the identification of 16 new lineages of SARS-CoV-2 isolated between 6 March and 26 August 2020. Most of these lineages have unique mutations that have not been identified elsewhere. We also show that three lineages (B.1.1.54, B.1.1.56 and C.1) spread widely in South Africa during the first wave, comprising ~42% of all infections in the country at the time. The newly identified C lineage of SARS-CoV-2, C.1, which has 16 nucleotide mutations as compared with the original Wuhan sequence, including one amino acid change on the spike protein, D614G (ref.
), was the most geographically widespread lineage in South Africa by the end of August 2020. An early South African-specific lineage, B.1.106, which was identified in April 2020 (ref.
), became extinct after nosocomial outbreaks were controlled in KwaZulu-Natal Province. Our findings show that genomic surveillance can be implemented on a large scale in Africa to identify new lineages and inform measures to control the spread of SARS-CoV-2. Such genomic surveillance presented in this study has been shown to be crucial in the identification of the 501Y.V2 variant in South Africa in December 2020 (ref.
).
Poor adherence to combined antiretroviral therapy (cART) has been shown to be a major determinant of virologic failure, emergence of drug resistant virus, disease progression, hospitalizations, ...mortality, and health care costs. While high adherence levels can be achieved in both resource-rich and resource-limited settings following initiation of cART, long-term adherence remains a challenge regardless of available resources. Barriers to optimal adherence may originate from individual (biological, socio-cultural, behavioral), pharmacological, and societal factors. Although patients and providers should continuously strive for maximum adherence to cART, there is accumulating evidence that each class of antiretroviral therapy has specific adherence-drug resistance relationship characteristics allowing certain regimens more flexibility than others. There is not a universally accepted measure for cART adherence, since each method has distinct advantages and disadvantages including cost, complexity, accuracy, precision, intrusiveness and bias. Development of a real-time cART adherence monitoring tool will enable the development of novel, pre-emptive adherence-improving strategies. The application of these strategies may ultimately prove to be the most cost-effective method to reduce morbidity and mortality for the individual and decrease the likelihood of HIV transmission and emergence of resistance in the community.
Introduction. Antiretroviral therapy (ART) in resource-limited settings has expanded in the last decade, reaching >8 million individuals and reducing AIDS mortality and morbidity. Continued success ...of ART programs will require understanding the emergence of HIV drug resistance patterns among individuals in whom treatment has failed and managing ART from both an individual and public health perspective. We review data on the emergence of HIV drug resistance among individuals in whom first-line therapy has failed and clinical and resistance outcomes of those receiving second-line therapy in resource-limited settings. Results. Resistance surveys among patients initiating first-line nonnucleoside reverse-transcriptase inhibitor (NNRTI)—based therapy suggest that 76%—90% of living patients achieve HIV RNA suppression by 12 months after ART initiation. Among patients with detectable HIV RNA at 12 months, HIV drug resistance, primarily due to M184V and NNRTI mutations, has been identified in 60%—72%, although the antiretroviral activity of proposed second-line regimens has been preserved. Complex mutation patterns, including thymidine-analog mutations, K65R, and multinucleoside mutations, are prevalent among cases of treatment failure identified by clinical or immunologic methods. Approximately 22% of patients receiving second-line therapy do not achieve HIV RNA suppression by 6 months, with poor adherence, rather than HIV drug resistance, driving most failures. Major protease inhibitor resistance at the time of second-line failure ranges from 0% to 50%, but studies are limited. Conclusions. Resistance of HIV to first-line therapy is predictable at 12 months when evaluated by means of HIV RNA monitoring and, when detected, largely preserves second-line therapy options. Optimizing adherence, performing resistance surveillance, and improving treatment monitoring are critical for long-term prevention of drug resistance.
Objectives
Low‐capital‐layout sequencing options from Oxford Nanopore Technologies (ONT) could assist in expanding HIV drug resistance testing to resource‐limited settings. HIV drug resistance ...mutations often occur as mixtures, but current ONT pipelines provide a consensus sequence only. Moreover, there is no integrated pipeline that provides a drug resistance report from an ONT sequence file without intervention from skilled bioinformaticists. We therefore investigated Nano‐RECall, which provides seamless drug resistance interpretation while requiring low‐read coverage ONT sequence data from affordable Flongle or MinION flow cells and which provides mutation mixtures similar to Sanger Sequencing.
Methods
We compared Sanger sequencing to ONT sequencing of the same HIV‐1 subtype C polymerase chain reaction (PCR) amplicons, respectively using RECall and the novel Nano‐RECall bioinformatics pipelines. Amplicons were from separate assays: (a) Applied Biosystems HIV‐1 Genotyping Kit (ThermoFisher) spanning protease (PR) to reverse transcriptase (RT) (PR‐RT) (n = 46) and (b) homebrew integrase (IN) (n = 21). The agreement between Sanger sequences and ONT sequences was assessed at nucleotide level, and at codon level for Stanford HIV drug resistance database mutations at an optimal ONT read depth of 400 reads only.
Results
The average sequence similarity between ONT and Sanger sequences was 99.3% (95% CI: 99.1%–99.4%) for PR‐RT and 99.6% (95% CI: 99.4%–99.7%) for INT. Drug resistance mutations did not differ for 21 IN specimens; 8 mutations were detected by both ONT‐ and Sanger sequencing. For the 46 PR and RT specimens, 245 mutations were detected by either ONT or Sanger, of these 238 (97.1%) were detected by both.
Conclusions
The Nano‐RECall pipeline, freely available as a downloadable application on a Windows computer, provides Sanger‐equivalent HIV drug resistance interpretation. This novel pipeline combined with a simple workflow and multiplexing samples on ONT flow‐cells would contribute to making HIV drug resistance sequencing feasible for resource‐limited settings.
South Africa's national antiretroviral (ARV) treatment program expanded in 2010 to include the nucleoside reverse transcriptase (RT) inhibitors (NRTI) tenofovir (TDF) for adults and abacavir (ABC) ...for children. We investigated the associated changes in genotypic drug resistance patterns in patients with first-line ARV treatment failure since the introduction of these drugs, and protease inhibitor (PI) resistance patterns in patients who received ritonavir-boosted lopinavir (LPV/r)-containing therapy.
We analysed ARV treatment histories and HIV-1 RT and protease mutations in plasma samples submitted to the Tygerberg Academic Hospital National Health Service Laboratory.
Between 2006 and 2012, 1,667 plasma samples from 1,416 ARV-treated patients, including 588 children and infants, were submitted for genotypic resistance testing. Compared with 720 recipients of a d4T or AZT-containing first-line regimen, the 153 recipients of a TDF-containing first-line regimen were more likely to have the RT mutations K65R (46% vs 4.0%; p<0.001), Y115F (10% vs. 0.6%; p<0.001), L74VI (8.5% vs. 1.8%; p<0.001), and K70EGQ (7.8% vs. 0.4%) and recipients of an ABC-containing first-line regimen were more likely to have K65R (17% vs 4.0%; p<0.001), Y115F (30% vs 0.6%; p<0.001), and L74VI (56% vs 1.8%; p<0.001). Among the 490 LPV/r recipients, 55 (11%) had ≥1 LPV-resistance mutations including 45 (9.6%) with intermediate or high-level LPV resistance. Low (20 patients) and intermediate (3 patients) darunavir (DRV) cross resistance was present in 23 (4.6%) patients.
Among patients experiencing virological failure on a first-line regimen containing two NRTI plus one NNRTI, the use of TDF in adults and ABC in children was associated with an increase in four major non- thymidine analogue mutations. In a minority of patients, LPV/r-use was associated with intermediate or high-level LPV resistance with predominantly low-level DRV cross-resistance.
Automated testing of HIV serology on clinical chemistry analysers has become common. High sample throughput, high HIV prevalence and instrument design could all contribute to sample ...cross-contamination by microscopic droplet carry-over from seropositive samples to seronegative samples resulting in false positive low-reactive results. Following installation of an automated shared platform at our public health laboratory, we noted an increase in low reactive and false positive results. Subsequently, we investigated HIV serology screening test results for a period of 21 months. Of 485 initially low positive or equivocal samples 411 (85%) tested negative when retested using an independently collected sample. As creatinine is commonly requested with HIV screening, we used it as a proxy for concomitant clinical chemistry testing, indicating that a sample had likely been tested on a shared high-throughput instrument. The contamination risk was stratified between samples passing the clinical chemistry module first versus samples bypassing it. The odds ratio for a false positive HIV serology result was 4.1 (95% CI: 1.69-9.97) when creatinine level was determined first, versus not, on the same sample, suggesting contamination on the chemistry analyser. We subsequently issued a notice to obtain dedicated samples for HIV serology and added a suffix to the specimen identifier which restricted testing to a dedicated instrument. Low positive and false positive rates were determined before and after these interventions. Based on measured rates in low positive samples we estimate that before the intervention, of 44 117 HIV screening serology samples, 753 (1.71%) were false positive, declining to 48 of 7 072 samples (0.68%) post-intervention (p<0.01). Our findings showed that automated high throughput shared diagnostic platforms are at risk of generating false-positive HIV test results, due to sample contamination and that measures are required to address this. Restricting HIV serology samples to a dedicated platform resolved this problem.
In order to assess the level of transmitted and/or pre-treatment antiretroviral drug resistance to HIV-1, the World Health Organization (WHO) recommends that regular surveys are conducted. This ...study's objective was to assess the frequency of HIV-1 antiretroviral drug resistance in patients initiating antiretroviral treatment (ART) in the public sector throughout South Africa.
A prospective cross-sectional survey was conducted using probability proportional to size sampling. This method ensured that samples from each province were proportionally collected, based on the number of patients receiving ART in each region. Samples were collected between March 2013 and October 2014. Pol sequences were obtained using RT-PCR and Sanger sequencing and submitted to the Stanford Calibrated Population Resistance tool v6.0.
A total of 277 sequences were available for analysis. Most participants were female (58.8%) and the median age was 34 years (IQR: 29-42). The median baseline CD4-count was 149 cells/mm3 (IQR: 62-249) and, based on self-reporting, participants had been diagnosed as HIV-positive approximately 44 days prior to sample collection (IQR: 23-179). Subtyping revealed that 98.2% were infected with HIV-1 subtype C. Overall, 25 out of 277 patients presented with ≥1 surveillance drug resistance mutation (SDRM, 9.0%, 95% CI: 6.1-13.0%). Non-nucleoside reverse transcriptase inhibitor (NNRTI) mutations were the most numerous mutations detected (n = 23). Only two patients presented with a protease inhibitor (PI) mutation. In four patients ≥4 SDRMs were detected, which might indicate that these patients were not truly ART-naïve or were infected with a multi-resistant virus.
These results show that the level of antiretroviral drug resistance in ART-naïve South Africans has reached moderate levels, as per the WHO classification. Therefore, regular surveys of pre-treatment drug resistance levels in all regions of South Africa is highly recommended to monitor the changing levels of pre-treatment antiretroviral drug resistance.
Objective
CD4 count decline often triggers antiretroviral regimen switches in resource‐limited settings, even when viral load testing is available. We therefore compared CD4 failure and CD4 trends in ...patients with viraemia with or without antiretroviral resistance.
Methods
Retrospective cohort study investigating the association of HIV drug resistance with CD4 failure or CD4 trends in patients on first‐line antiretroviral regimens during viraemia. Patients with viraemia (HIV RNA >1000 copies/ml) from two HIV treatment programmes in South Africa (n = 350) were included. We investigated the association of M184V and NNRTI resistance with WHO immunological failure criteria and CD4 count trends, using chi‐square tests and linear mixed models.
Results
Fewer patients with the M184V mutation reached immunologic failure criteria than those without: 51 of 151(34%) vs. 90 of 199 (45%) (P = 0.03). Similarly, 79 of 220 (36%) patients, who had major NNRTI resistance, had immunological failure, whereas 62 of 130 (48%) without (chi‐square P = 0.03) did. The CD4 count decline among patients with the M184V mutation was 2.5 cells/mm3/year, whereas in those without M184V it was 14 cells/mm3/year (P = 0.1), but the difference in CD4 count decline with and without NNRTI resistance was marginal.
Conclusion
Our data suggest that CD4 count monitoring may lead to inappropriate delayed therapy switches for patients with HIV drug resistance. Conversely, patients with viraemia but no drug resistance are more likely to have a CD4 count decline and thus may be more likely to be switched to a second‐line regimen.
Objectif
La baisse de la numération des CD4 déclenche souvent des changements du traitement antirétroviral dans les pays à ressources limitées, même lorsque le test de détermination de la charge virale est disponible. Nous avons donc comparé l’échec basé sur les CD4 et les tendances des CD4 chez les patients présentant une virémie avec ou sans résistance aux antirétroviraux.
Méthodes
Etude de cohorte rétrospective investiguant l'association entre la pharmacorésistance du VIH avec l’échec basé sur les CD4 ou avec les tendances des CD4 chez les patients sous traitement avec des antirétroviraux de première ligne pendant la virémie. Les patients présentant une virémie (ARN du VIH > 1000 copies/ml) dans deux programmes de traitement du VIH en Afrique du Sud (n = 350) ont été inclus. Nous avons investigué l'association de la résistance M184V et INNTI avec les critères d’échec immunologique de l’OMS et avec les tendances de la numération des CD4, en utilisant des tests de chi‐carré et des modèles linéaires mixtes.
Résultats
Moins de patients avec la mutation M184V répondaient aux critères d’échec immunologique que ceux sans la mutation: 51/151 (34%) contre 90/199 (45%) (p = 0,03). De même, 79 des 220 (36%) patients avec une résistance majeure INNTI avaient un échec immunologique, alors que 62 des 130 (48%) patients sans cette mutation avaient cet échec (Chi carré p = 0,03). La baisse de la numération des CD4 chez les patients possédant la mutation M184V était de 2,5 cellules/mm3/an, alors que chez ceux sans cette mutation, elle était de 14 cellules/mm3/an (p = 0,1) mais la différence dans la baisse de la numération des CD4 avec et sans résistance INNTI était marginale.
Conclusion
Nos données suggèrent que la surveillance de la numération des CD4 peut conduire à des changements inappropriés de traitement tardif pour les patients atteints de la pharmacorésistance du VIH. Par contre, les patients présentant une virémie mais sans résistance aux médicaments sont plus susceptibles d'avoir une baisse de la numération des CD4 et sont peut‐être plus susceptibles à mettre sous un traitement de deuxième ligne.
Objetivo
La caída en el conteo de CD4 a menudo determina cambios en el régimen antirretroviral en lugares con recursos limitados, aun cuando las pruebas de carga viral estén disponibles. Por lo tanto, hemos comparado el fallo en CD4 y la tendencia de CD4 en pacientes con viremia con o sin resistencia a los antirretrovirales.
Métodos
Estudio retrospectivo de cohortes que investiga la asociación entre la resistencia a medicamentos para el VIH con el fallo de CD4 o las tendencias de CD4 en pacientes en primeras líneas de tratamiento antirretroviral durante viremia. Se incluyeron pacientes (n=350) con viremia (ARN VIH > 1000 copias/mL) pertenecientes a dos programas de tratamiento del VIH en Sudáfrica. Hemos investigado la asociación entre las resistencias M184V y NNRTI con los criterios de fallo inmunológico de la OMS y las tendencias en el conteo de CD4, utilizando las pruebas de chi‐cuadrado y modelos lineares mixtos.
Resultados
Un menor número de pacientes con la mutación M184V alcanzaron criterios de fallo inmunológico que aquellos sin: 51/151(34%) versus 90/199 (45%) (p=0.03). De forma similar, 79 de 220 (36%) pacientes que tenían una resistencia NNRTI tenían fallo inmunológico, comparados con 62 de 130 (48%) sin la resistencia (Chi cuadrado p= 0.03). La disminución en el conteo de CD4 entre pacientes con la mutación M184V era 2.5 células/mm3/año, mientras que entre aquellos sin M184V era de 14 células/mm3/año (p=0.1) pero la diferencia en la disminución del conteo de CD4 con y sin resistencia NNRTI era marginal.
Conclusión
Nuestros datos sugieren que la monitorización del conteo de CD4 podría llevar a cambios tardíos e inapropiados para pacientes con resistencia a medicamentos para el VIH. Por otro lado, los pacientes con viremia pero sin resistencia a medicamentos tienen mayor probabilidad de tener una disminución en el conteo de CD4 y por lo tanto tienen mayor probabilidad de ser cambiados a regímenes de segunda línea.
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