A Simple, Dynamic Measure of Antiretroviral Therapy Adherence Predicts Failure to Maintain HIV‐1 Suppression
Bibliographic record
Abstract
BACKGROUND: High levels of antiretroviral therapy adherence are important for human immunodeficiency virus type 1 (HIV-1) suppression, yet the magnitude of adherence required to maintain it is less well characterized. Furthermore, methods to accommodate changes in adherence over time are lacking. In the present study, our objective was to determine the magnitude of antiretroviral therapy adherence needed to maintain HIV-1 suppression by use of a time-updated adherence measure that has the potential to be of use in a clinical setting. METHODS: We examined a population-based cohort of HIV-1-infected subjects > or =18 years of age, residing in British Columbia, Canada, who started receiving antiretroviral therapy between 1 August 1996 and 30 September 2003, who had at least 2 consecutive viral loads <500 copies/mL and who had prescriptions filled at least 3 times during a follow-up period ending 30 September 2004. Virological failure was defined as the second of 2 consecutive viral loads >1000 copies/mL. Cox proportional hazards model was used to determine the relationship between virological failure and refill-based, time-updated surrogate measure of adherence. RESULTS: Among the 1634 participants > or =18 years of age who initiated triple combination therapy during the study, 606 virological failure events were identified. In multivariate analyses, subjects with < or =95% adherence were 1.66 (95% confidence interval, 1.38-2.01) times more likely to experience virological failure than those with >95% adherence. CONCLUSIONS: The highest levels of antiretroviral therapy adherence are associated with higher rates of maintained virological suppression. This simple, dynamic surrogate measure of adherence overcomes the limitation of single-point-in-time calculations of adherence and may be useful in real time to determine whether an individual is exhibiting incomplete adherence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".