Do contemporary antiretrovirals increase the risk of end‐stage liver disease? Signals from patients starting therapy in the North American AIDS Cohort Collaboration on Research and Design
Bibliographic record
Abstract
PURPOSE: Despite effective antiretroviral therapy, rates of end-stage liver disease (ESLD) remain high. It is not clear whether contemporary antiretrovirals contribute to the risk of ESLD. METHODS: We included patients from cohorts with validated ESLD data in the North American AIDS Cohort Collaboration on Research and Design. Patients had to initiate antiretroviral therapy after 1 January 2004 with a nucleos(t)ide backbone of either abacavir/lamivudine or tenofovir/emtricitabine and a contemporary third (anchor) drug. Patients were followed until a first ESLD event, death, end of a cohort's ESLD validation period, loss to follow-up or 31 December 2015. We estimated associations between cumulative exposure to each drug and ESLD using a hierarchical Bayesian survival model with weakly informative prior distributions. RESULTS: Among 10 564 patients included from 12 cohorts, 62 had an ESLD event. Of the nine anchor drugs, boosted protease inhibitors atazanavir and darunavir had the strongest signals for ESLD, with increasing hazard ratios (HR) and narrowing credible intervals (CrI), from a prior HR of 1.5 (95% CrI 0.32-7.1) per 5 year's exposure to posterior HRs respectively of 1.8 (95% CrI 0.82-3.9) and 2.0 (95% CrI 0.86-4.7). Both backbones and efavirenz showed no signal. Hepatitis C coinfection was the most important covariate risk factor (HR 4.4, 95% CrI 2.6-7.0). CONCLUSIONS: While contemporary antiretrovirals pose less risk for ESLD than hepatitis coinfection, atazanavir and darunavir had a toxicity signal. We show how hierarchical Bayesian modelling can be used to detect toxicity signals in cohort event monitoring data even with complex treatments and few events.
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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 | 0.060 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".