Patterns of Transmitted Drug Resistance and Virological Response to First-line Antiretroviral Treatment Among Human Immunodeficiency Virus–Infected People Who Use Illicit Drugs in a Canadian Setting
Bibliographic record
Abstract
BACKGROUND: Transmitted drug resistance (TDR) may compromise response to antiretroviral therapy (ART). However, there are limited data on TDR patterns and impacts among people who use illicit drugs (PWUD). METHODS: Data were drawn from 2 prospective cohorts of PWUD in Vancouver, Canada. We characterized patterns of TDR among human immunodeficiency virus (HIV)-infected PWUD, and assessed its impacts on first-line ART virological outcomes. RESULTS: Between 1996 and 2015, among 573 ART-naive PWUD (18% with recent HIV infection), the overall TDR prevalence was 9.8% (95% confidence interval [CI], 7.3%-12.2%), with an increasing trend over time, from 8.5% in 1996-1999 to 21.1% in 2010-2015 (P = .003), mainly driven by resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs). TDR-associated mutations were more common for NNRTIs (5.4%), followed by nucleoside reverse transcriptase inhibitors (3.0%) and protease inhibitors (1.9%). TDR prevalence was lower among recently infected PWUD (adjusted odds ratio, 0.39 [95% CI, .15-.87]). Participants with TDR had higher risk of virological failure than those without TDR (log-rank P = .037) in the first year of ART. CONCLUSIONS: Between 1996 and 2015, TDR prevalence increased significantly among PWUD in Vancouver. Higher risk of virological failure among PWUD with TDR may be explained by some inappropriate ART prescribing, as well as undetected minority resistant variants in participants with chronic HIV infection. Our findings support baseline resistance testing early in the course of HIV infection to guide ART selection among PWUD in our setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".