Multicenter Evaluation of the Cepheid Xpert® HBV Viral Load Test

Accurate measurement of the hepatitis B virus (HBV) DNA is important for the management of patients with chronic HBV infection. Here, the performance of the Xpert® HBV Viral Load test (Xpert HBV Viral Load) versus the Roche COBAS® Ampliprep/COBAS® TaqMan® system (CAP/CTM HBV) HBV test v2.0 was evaluated. From September 2017 to December 2017, a total of 876 prospectively collected or archived serum or EDTA plasma specimens from subjects chronically infected with HBV were tested using the Xpert HBV Viral Load and the CAP/CTM HBV v2.0 assays. Of the 876 specimens tested, 560 were within the quantitative range of both assays. The agreement between the two methods was 90.0%. No difference in plasma or serum samples was observed. Deming regression analysis showed a good correlation of the Xpert HBV Viral Load assay with the CAP/CTM HBV v2.0 assay. The Bland–Altman analysis showed a good agreement between the results of the Xpert HBV Viral Load assay and the CAP/CTM HBV assay, with a mean difference (±1.96 standard deviation) of 0.0091 ± 0.3852 Log IU/mL. Comparing the two assays, only nineteen specimens (2.1%) had a difference greater than 1.96 times the standard deviation. The Xpert® HBV Viral Load test is suitable for monitoring patients with HBV infection and is useful in diagnostic settings.

Novel RT-ddPCR Assays for determining the transcriptional profile of SARS-CoV-2

The exact mechanism of coronavirus replication and transcription is not fully understood; however, a hallmark of coronavirus transcription is the generation of negative-sense RNA intermediates that serve as the templates for the synthesis of positive-sense genomic RNA (gRNA) and an array of subgenomic mRNAs (sgRNAs) encompassing sequences arising from discontinuous transcription.Existing PCR-based diagnostic assays for SAR-CoV-2 are qualitative or semi-quantitative and do not provide the resolution needed to assess the complex transcription dynamics of SARS-CoV-2 over the course of infection. We developed and validated a novel panel of specially designed SARS-CoV-2 ddPCR-based assays to map the viral transcription profile. Application of these assays to clinically relevant samples will enhance our understanding of SARS-CoV-2 replication and transcription and may also inform the development of improved diagnostic tools and therapeutics.HighlightsWe developed a novel panel of 7 quantitative RT-ddPCRs assays for SARS-Cov-2Our panel targets nongenic and genic regions in genomic and subgenomic RNAsAll assays detect 1-10 copies and are linear over 3-4 orders of magnitudeAll assays correlated with the clinical Abbott SARS-CoV-2 Viral Load AssayClinical samples showed higher copy numbers for targets at the 3’ end of the genome

Utility of CD4 Cell Count and Viral Load Assay in Hospitalized Patients with Known HIV Infection: High Value Care Exercise

Purpose: Healthcare spending as a percentage of Gross domestic product (GDP) is at all-time high and continues to rise in the United States. The Centers for Medicare and Medicaid Services estimate that 33% of resources spent on healthcare goes to waste. As part of a ‘high value care’ exercise, we studied if estimating CD4 cell counts and HIV viral load in hospitalized patients with a known diagnosis of HIV led to any meaningful change in HAART regimen and discharge diagnosis. Methods: Retrospective chart review for all patients admitted with a known diagnosis of HIV from January 1, through December 31, 2017. Results: A total of 83 patient encounters were reviewed during the period. The mean age was 54.1 ± 16.4 years, 64.1 % of patients were males. 75 patients (90.3%) were already on highly active antiretroviral therapy (HAART). The median hospital length of stay (LOS) was 3 days (IQR 2.0 - 5.0). The mean turnaround time for CD4 counts and HIV viral load assay was 2.9 days (95% CI 2.1 – 3.7) and 3.9 days (95% CI, 3.2 – 4.6), respectively. A CD4 count estimation led to no change in HAART regimen. HIV viral load assay testing had no impact on a change in treatment or a change in diagnosis. Conclusions: In our study, testing CD4 counts and HIV viral load for inpatients did not confer any benefit in altering the diagnosis or HAART regimen. We believe that our study identifies a systems level opportunity to add to the concept of ‘Choosing Wisely.’

Performance of a high-throughput next-generation sequencing method for analysis of HIV drug resistance and viral load

Objectives To evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load. Methods Plasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV). Results HIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with >5000 copies/mL; 50.0% for 26 samples with 1000–5000 copies/mL; 0% for 23 samples with <1000 copies/mL). For samples with results from both methods, drug resistance mutations (DRMs) were detected in 33 samples using ViroSeq and 42 samples using veSEQ-HIV (detection threshold: 5.0%). Overall, 146 major DRMs were detected; 107 were detected by both methods, 37 were detected by veSEQ-HIV only (frequency range: 5.0%–30.6%) and two were detected by ViroSeq only. HIV viral loads estimated by veSEQ-HIV strongly correlated with results from the Abbott RealTime Viral Load assay (R2 = 0.85; n = 142). Conclusions The NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.