Atazanavir and Other Determinants of Hyperbilirubinemia in a Cohort of 1150 HIV-Positive Patients: Results from 9 Years of Follow-Up
Bibliographic record
Abstract
Hyperbilirubinemia is common among patients exposed to atazanavir (ATV), but its long-term significance is not well documented. The objective was to analyze hyperbilirubinemia (incidence, regression, determinants, and outcome) among 1150 HIV-positive patients followed-up in a prospective cohort between 2003 and 2012. Cumulative incidence of hyperbilirubinemia grades 3-4 and its probability of regression were estimated using Kaplan-Meier method. Cox proportional hazards model was used to study the determinants. Generalized estimating equation (GEE) regression was used to evaluate the association between hyperbilirubinemia grades 3-4 and adverse health outcome. Eight years cumulative incidence of hyperbilirubinemia was 83.6% (95% CI:79.0-87.7) and 6.6% (95% CI:4.7-9.2) among ATV users and non-users, respectively. This clinical outcome fluctuated considerably, as most patients exposed to ATV (91%) regressed, transiently, to lower grade at some point during follow-up. Determinants were atazanavir (HR=147.90, 95% CI: 33.64-604.18), ritonavir (HR=5.18, 95% CI:2.33-11.48), zidovudine (HR=2.62, 95% CI:1.07-6.46), and age (HR=1.04 95% CI:1.01-1.08). Alcohol consumption and others non-antiretroviral medications including hepatotoxic and recreational drugs were not available for analyses. Incidence of hyperbilirubinemia was very high among ATV users and, although regression to lower grade was frequent in the clinical follow-up of these patients, this was usually transient as the mean level of bilirubin stayed at a relatively high level. Importantly, long-term hyperbilirubinemia was not associated with adverse health outcome.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".