Change in Hemoglobin Trajectory and Darbepoetin Dose Approaching End-Stage Renal Disease: Data from the Trial to Reduce Cardiovascular Events with Aranesp Therapy Trial
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of chronic kidney disease associated anemia is multifactorial and includes decreased production of erythropoietin (EPO), iron deficiency, inflammation, and EPO resistance. To better understand the trajectory of these parameters, we described temporal trends in hemoglobin (Hb), ferritin, transferrin saturation, C-reactive protein (CRP), and darbepoetin dosing in the Trial to Reduce cardiovascular Events with Aranesp Therapy (TREAT). METHODS: We performed a post hoc analysis of 4,038 participants in TREAT. Mixed effects linear regression models were used to determine the trajectory of parameters of interest prior to end-stage renal disease (ESRD). Likelihood ratio tests were used to determine the overall differences in biomarker values and differences in trajectories between those who did and did not develop ESRD. RESULTS: Hb declined precipitously in the year prior to the development of ESRD (irrespective of treatment assignment), and was on average 1.15 g/dL (95% CI -1.26 to -1.04) lower in those who developed ESRD versus those who did not, at the time of ESRD/end of follow-up. Simultaneously, the mean darbepoetin dose and CRP concentration increased, while serum ferritin and transferrin saturations were >140 μg/L and 20%, respectively. CONCLUSIONS: Our analyses provide descriptive insights regarding the temporal changes of Hb, darbepoetin dose, and related parameters as ESRD approaches in participants of TREAT. Hb declined as much as 1-2 years prior to the development of ESRD, without biochemical evidence of iron deficiency. The most precipitous decline occurred in the months immediately prior to ESRD, despite administration of escalating doses of darbepoetin and in parallel with an increase in CRP.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".