Comparison of atazanavir/ritonavir and darunavir/ritonavir based antiretroviral therapy for antiretroviral naïve patients
Bibliographic record
Abstract
BACKGROUND: Atazanavir/ritonavir and darunavir/ritonavir are common protease inhibitor-based regimens for treating patients with HIV. Studies comparing these drugs in clinical practice are lacking. METHODS: We conducted a retrospective cohort study of antiretroviral naïve participants in the Canadian Observational Cohort (CANOC) collaboration initiating atazanavir/ritonavir- or darunavir/ritonavir-based treatment. We used separate Fine and Gray competing risk regression models to compare times to regimen failure (composite of virologic failure or discontinuation for any reason). Additional endpoints included virologic failure, discontinuation due to virologic failure, discontinuation for other reasons, and virologic suppression. RESULTS: We studied 222 patients treated with darunavir/ritonavir and 1791 patients treated with atazanavir/ritonavir. Following multivariable adjustment, there was no difference between darunavir/ritonavir and atazanavir-ritonavir in the risk of regimen failure (adjusted hazard ratio 0.76, 95% CI 0.56 to 1.03) Darunavir/ritonavir-treated patients were at lower risk of virologic failure relative to atazanavir/ritonavir treated patients (aHR 0.50, 95% CI 0.28 to 0.91), findings driven largely by high rates of virologic failure among atazanavir/ritonavir-treated patients in the province of British Columbia. Of 108 discontinuations due to virologic failure, all occurred in patients starting atazanavir/ritonavir. There was no difference between regimens in time to discontinuation for reasons other than virologic failure (aHR 0.93; 95% CI 0.65 to 1.33) or virologic suppression (aHR 0.99, 95% CI 0.82 to 1.21). CONCLUSIONS: The risk of regimen failure was similar between patients treated with darunavir/ritonavir and atazanavir/ritonavir. Although darunavir/ritonavir was associated with a lower risk of virologic failure relative to atazanavir/ritonavir, this difference varied substantially by Canadian province and likely reflects regional variation in prescribing practices and patient characteristics.
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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) | 0.001 | 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".