Performance of HIV-1 Drug Resistance Testing at Low-Level Viremia and Its Ability to Predict Future Virologic Outcomes and Viral Evolution in Treatment-Naive Individuals
Bibliographic record
Abstract
BACKGROUND: Low-level viremia (LLV; human immunodeficiency virus [HIV-1] RNA 50-999 copies/mL) occurs frequently in patients receiving antiretroviral therapy (ART), but there are few or no data available demonstrating that HIV-1 drug resistance testing at a plasma viral load (pVL) <1000 copies/mL provides potentially clinically useful information. Here, we assess the ability to perform resistance testing by genotyping at LLV and whether it is predictive of future virologic outcomes in patients beginning ART. METHODS: Resistance testing by genotyping at LLV was attempted on 4915 plasma samples from 2492 patients. A subset of previously ART-naive patients was analyzed who achieved undetectable pVL and subsequently rebounded with LLV (n = 212). A genotypic sensitivity score (GSS) was calculated based on therapy and resistance testing results by genotyping, and stratified according to number of active drugs. RESULTS: Eighty-eight percent of LLV resistance assays produced useable sequences, with higher success at higher pVL. Overall, 16 of 212 (8%) patients had pretherapy resistance. Thirty-eight of 196 (19%) patients without pretherapy resistance evolved resistance to 1 or more drug classes, primarily the nucleoside reverse transcriptase (14%) and/or nonnucleoside reverse transcriptase (9%) inhibitors. Patients with resistance at LLV (GSS <3) had a 2.1-fold higher risk of virologic failure (95% confidence interval, 1.2- to 3.7-fold) than those without resistance (P = .007). Progressively lower GSS scores at LLV were associated with a higher increase in pVL over time (P < .001). Acquisition of additional resistance mutations to a new class of antiretroviral drugs during LLV was not found in a subset of patients. CONCLUSIONS: Routine HIV-1 genotyping of LLV samples can be performed with a reasonably high success rate, and the results appear predictive of future virologic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".