Impact of haemoglobin and erythropoietin dose changes on mortality: a secondary analysis of results from a randomized anaemia management trial
Bibliographic record
Abstract
BACKGROUND: Anaemia is a common complication of chronic kidney disease. A number of studies have identified an adverse association between haemoglobin (Hgb) variability and mortality. To date, no study has evaluated the impact of Hgb variability on mortality in the setting of a uniform Hgb target and erythropoiesis-stimulating agents (ESA) dosing strategy. METHODS: One hundred and fifty-four haemodialysis (HD) patients from a previous randomized anaemia management study were followed up for up to 6 years. The impact of Hgb variability and ESA dosing parameters on subsequent mortality risk were evaluated. RESULTS: More rapid rises in Hgb (Hgb deflect(pos)) and ESA dose increases were independently associated with mortality in multivariate analysis, whereas more rapid Hgb declines (Hgb deflect(neg)) and ESA dose decreases were not. Each gram per litre per week increase in Hgb deflect(pos) was associated with an adjusted hazard ratio (HR) of 1.23 (1.03-1.48), while for every 1000-unit increase in ESA dose, the adjusted HR was 1.12 (1.01-1.24). Factors associated with positive Hgb deflections included frequency and magnitude of ESA dose changes, baseline Hgb, patient weight and presence of an HD catheter. CONCLUSIONS: Rapid Hgb rises and greater average Eprex dose increases were independently associated with a higher mortality risk in HD patients after adjustment for baseline Hgb and Eprex dose. A randomized controlled trial evaluating different ESA dosing strategies in response to individual patient ESA responsiveness is needed.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".