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The ViroSeq human immunodeficiency virus type 1 (HIV-1) genotyping system is an integrated system for identification of drug resistance mutations in HIV-1 protease and reverse transcriptase (RT). Reagents are included for sample preparation, reverse transcription, PCR amplification, and sequencing. Software is provided to assemble and edit sequence data and to generate a drug resistance report. We determined the sensitivity and specificity of the ViroSeq system for mutation detection using an ABI PRISM 3100 genetic analyzer with a set of clinical samples and recombinant viruses. Twenty clinical plasma samples (viral loads, 1,800 to 10,500 copies/ml) were characterized by cloning and sequencing individual viral variants. Twelve recombinant-virus samples (viral loads, approximately 2,000 to 5,000 copies/ml) were also prepared. Eleven recombinant-virus samples contained drug resistance mutations as 40% mixtures. One recombinant-virus sample contained an insertion at codon 69 in RT (100% mutant). Plasma and recombinant-virus samples were analyzed using the ViroSeq system. Each sample was analyzed on three consecutive days at each of three testing laboratories. The sensitivity of mutation detection was 99.65% for the clinical plasma samples and 99.7% for the recombinant-virus preparations. The specificity of mutation detection was 99.95% for the clinical samples and 100% for the recombinant-virus mixtures. The base calling accuracy of the 3100 instrument was 99.91%. Mutations in clinical plasma samples and recombinant-virus samples were detected with high sensitivity and specificity, including mutations present as mixtures. This report supports the use of the ViroSeq system for identification of drug resistance mutations in HIV-1 protease and RT genes.

Current guidelines recommend obtaining an ankle-brachial index (ABI) to screen for peripheral artery disease (PAD) in subjects at risk. Previous work demonstrated that a combination of β(2)-microglobulin, cystatin C, high-sensitivity C-reactive protein and glucose was associated with PAD. This study evaluated the ability of these biomarkers combined with clinical parameters to predict PAD in at-risk subjects. This study enrolled 1025 subjects from 99 primary care clinics who were smokers and/or diabetics ≥ 50 years or any individual ≥ 70 years. Consented subjects underwent a clinical assessment, fasting blood draw, and an ABI measurement with PAD defined as an ABI < 0.90 in either leg. The biomarkers and their interactions were evaluated using logistic regression and performance was evaluated at a cut point of the biomarker panel selected to maximize sensitivity while minimizing the false positive rate of the test. Of the 1025 subjects enrolled, 46 did not meet the ABI or other criteria for inclusion in the analysis. Among the evaluable subjects (n = 979), PAD was detected in 83 (8.5%). The model had a C-statistic of 0.73 (95% CI 0.67-0.79). There were 20 patients with PAD who were judged to be at low to moderate risk for cardiovascular events by clinical assessment; the model correctly identified 17 of these 20 patients. The model also performed well in subjects with no prior history of PAD. Thus, a biomarker panel may have a role for identifying PAD.