Literature DB >> 12517853

Quality assessment program for genotypic antiretroviral testing improves detection of drug resistance mutations.

D C Sayer1, S Land, L Gizzarelli, M French, G Hales, S Emery, F T Christiansen, E M Dax.   

Abstract

Genotypic antiretroviral testing is now widely used for the management of patients who are undergoing antiretroviral therapy for human immunodeficiency virus infection. The assays are complex, and there is considerable potential for variation between laboratories. Informative and ongoing quality assessment programs (QAPs) which address all aspects of testing are required. The panel distribution of clinical material is a critical component of QAPs. We report on the results and data from a recent panel. Four cryopreserved plasma samples from treated donors were distributed to nine laboratories. Three laboratories performed testing by commercial assays, and six laboratories used in-house assays, with one laboratory reporting results from two in-house assays. There was complete concordance between results for 95.9% of the nucleotide sequence and 94.5% of the amino acid sequence. Despite this overall high level of concordance, the degree of concordance at drug resistance mutation (DRM) sites when DRMs were present was considerably less (38% of DRM sites). Consequently, only 3 of the 10 methods reported 100% of DRMs as present. This elevated discrepancy rate is almost certainly a result of variability in the identification of mixtures of nucleotides (mixtures) at any site within the sequence. In addition, laboratories differed in the number of codons in the reverse transcriptase gene that were sequenced and their ability to amplify all samples. This panel distribution demonstrated a requirement for laboratory participation in ongoing QAPs and the optimization of assays with standards that contain mixtures.

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Year:  2003        PMID: 12517853      PMCID: PMC149552          DOI: 10.1128/JCM.41.1.227-236.2003

Source DB:  PubMed          Journal:  J Clin Microbiol        ISSN: 0095-1137            Impact factor:   5.948


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