The DOI matches its Crossref record.
Low-Abundance Drug-Resistant Viral Variants in Chronically HIV-Infected, Antiretroviral Treatment–Naive Patients Significantly Impact Treatment Outcomes
Abstract
BACKGROUND: Minor (i.e., <20% prevalence) drug-resistant human immunodeficiency virus (HIV) variants may go undetected, yet be clinically important. OBJECTIVES: To compare the prevalence of drug-resistant variants detected with standard and ultra-deep sequencing (detection down to 1% prevalence) and to determine the impact of minor resistant variants on virologic failure (VF). METHODS: The Flexible Initial Retrovirus Suppressive Therapies (FIRST) Study (N = 1397) compared 3 initial antiretroviral therapy (ART) strategies. A random subset (n = 491) had baseline testing for drug-resistance mutations performed by use of standard sequencing methods. Ultra-deep sequencing was performed on samples that had sufficient viral content (N = 264). Proportional hazards models were used to compare rates of VF for those who did and did not have mutations identified. RESULTS: Mutations were detected by standard and ultra-deep sequencing (in 14% and 28% of participants, respectively; P < .001). Among individuals who initiated treatment with an ART regimen that combined nucleoside and nonnucleoside reverse-transcriptase inhibitors (hereafter, "NNRTI strategy"), all individuals who had an NNRTI-resistance mutation identified by ultra-deep sequencing experienced VF. When these individuals were compared with individuals who initiated treatment with the NNRTI strategy but who had no NNRTI-resistance mutations, the risk of VF was higher for those who had an NNRTI-resistance mutation detected by both methods (hazard ratio [HR], 12.40 [95% confidence interval {CI}, 3.41-45.10]) and those who had mutation(s) detected only with ultra-deep sequencing (HR, 2.50 [95% CI, 1.17-5.36]). CONCLUSIONS: Ultra-deep sequencing identified a significantly larger proportion of HIV-infected, treatment-naive persons as harboring drug-resistant viral variants. Among participants who initiated treatment with the NNRTI strategy, the risk of VF was significantly greater for participants who had low- and high-prevalence NNRTI-resistant variants.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Domingo, E., Sheldon, J., & Perales, C. (2012) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.05023-11 · Cited by 1,009
Hajibabaei, M., Shokralla, S., Zhou, X., Singer, G. A. C., & Baird, D. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017497 · Cited by 584
Clinical and biological insights from viral genome sequencing
Houldcroft, C. J., Beale, M. A., & Breuer, J. (2017) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2016.182 · Cited by 334
HIV-1 Antiretroviral Resistance: Scientific Principles and Clinical Applications
Tang, M. W., & Shafer, R. W. (2012) · Drugs · doi:10.2165/11633630-000000000-00000 · Cited by 326
Henn, M. R., Boutwell, C. L., Charlebois, P., Lennon, N. J., Power, K. A., Macalalad, A. R., Berlin, A. M., Malboeuf, C. M., Ryan, E. M., Gnerre, S., et al. (2012) · PLoS Pathogens · doi:10.1371/journal.ppat.1002529 · Cited by 317
Metadata
- Authors
- Simen, B. B., Simons, J. F., Hullsiek, K. H., Novak, R. M., MacArthur, R. D., Baxter, J. D., Huang, C., Lubeski, C., Turenchalk, G. S., Braverman, M. S., Desany, B., Rothberg, J. M., Egholm, M., Kozal, M. J., & Terry Beirn Community Programs for Clinical Research on, A. I. D. S.
- Venue
- Journal of Infectious Diseases
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-03-01
- Volume
- 199
- Issue
- 5
- Pages
- 693-701
- ISSN
- 0022-1899 (ISSN-L)
- 1537-6613
- OpenAlex
- W1966063036
- Cited by
- 392
- References
- 0
- FWCI
- 34.14
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Low-Abundance Drug-Resistant Viral Variants in Chronically HIV-Infected, Antiretroviral Treatment–Naive Patients Significantly Impact Treatment Outcomes. https://app.cochat.ai/papers/10.1086/596736
- The paper
- Simen, B. B., Simons, J. F., Hullsiek, K. H., Novak, R. M., MacArthur, R. D., Baxter, J. D., Huang, C., Lubeski, C., Turenchalk, G. S., Braverman, M. S., Desany, B., Rothberg, J. M., Egholm, M., Kozal, M. J., & Terry Beirn Community Programs for Clinical Research on, A. I. D. S. (2009). Low-Abundance Drug-Resistant Viral Variants in Chronically HIV-Infected, Antiretroviral Treatment–Naive Patients Significantly Impact Treatment Outcomes. Journal of Infectious Diseases. https://doi.org/10.1086/596736
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.