Estimating the minimum antiretroviral adherence required for plasma HIV-1 RNA viral load suppression among people living with HIV who use unregulated drugs
Bibliographic record
Abstract
OBJECTIVES: Owing to advances in antiretroviral therapy (ART), we re-examined minimum ART adherence levels necessary to achieve sustained HIV-1 viral load (VL) suppression among people with HIV who use drugs (PHIV-PWUD). DESIGN AND METHODS: We used data from ACCESS, a community-recruited prospective cohort of PHIV-PWUD in Vancouver, Canada. We calculated adherence using the proportion of days of ART dispensed in the year before each VL measurement. We used generalized linear mixed-effects models to identify adherence- and ART regimen-related correlates of VL suppression (<200 copies/ml). We employed probit regression models and generated dose-response curves to estimate the minimum adherence level needed to produce VL suppression in 90% of measures, stratified by regimen and calendar-year. RESULTS: Among 837 ART-exposed PHIV-PWUD recruited between 1996 and 2017, the overall estimated adherence level necessary to achieve 90% VL suppression was 93% (95% confidence interval [CI]: 90-96). This differed by regimen: 69% (95% CI: 45-92) for integrase inhibitor (INSTI)-, 96% (95% CI: 92-100) for boosted protease inhibitor (bPI)-, and 98% (95% CI: 91-100) for non-nucleoside reverse transcriptase inhibitor-based regimens. In multivariable analysis, INSTI-based regimens were positively associated with VL suppression (vs. bPIs), while un-boosted PIs and other regimens were negatively associated. We observed a decreasing temporal trend of estimated adherence necessary for 90% VL suppression, dropping to 64% (95% CI: 50-77) during 2016-2017. CONCLUSION: Although high levels of ART adherence were necessary to achieve consistent VL suppression, the minimum necessary adherence levels decreased over time. Overall, INSTI-based regimens performed the best, suggesting that they should be preferentially prescribed to PHIV-PWUD.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".