Does first-line antiretroviral regimen impact risk for chronic kidney disease whatever the risk group?
Bibliographic record
Abstract
OBJECTIVES: We used the D:A:D risk score for chronic kidney disease (CKD) for patients starting antiretroviral therapy (ART) in the recent years, and investigated whether specific regimens enhanced the risk of CKD in the different risk groups. DESIGN: Retrospective analysis of a prospectively collected cohort of French HIV-infected patients. METHODS: Patients who started their first ART after January the 1st, 2004 with a baseline estimated glomerular filtration rate (eGFR) greater than 60 ml/min per 1.73 m were analyzed. CKD was defined by confirmed eGFR less than 60 ml/min per 1.73 m. Incidence of CKD was estimated by Kaplan-Meier method, and Poisson regression models were used to quantify the relationship between CKD, exposure to the initial ART regimens and the D:A:D score. RESULTS: We included 6301 patients representing 21 936 person-years of follow-up (PYFU), median eGFR at baseline was 101 ml/min per 1.73 m (inter-quartile range 86; 118) and CKD incidence 9.6/1000 PYFU. Five years probabilities of CKD were 0.65, 4.6 and 15.9% in the low, medium and high-risk groups, respectively. In patients treated with a boosted protease inhibitor, incidences rates were 7.1/1000 and 9.0/1000 PYFU in the absence or presence of tenofovir, respectively, and markedly increased with increasing risk score. In the low-risk group the treatment choice had no impact on CKD incidence. CONCLUSION: When choosing the ideal first antiretroviral regimen for one given patient, clinicians should rely on the D:A:D score and avoid some drugs in high-risk patients, whereas in low-risk patients classic regimens may be safely prescribed, with an economic benefit due to soon available generic formulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".