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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".