Erythropoietic agents, iron and hemoglobin—What happens beyond the trial setting: Observational data from the ANZDATA Registry
Bibliographic record
Abstract
BACKGROUND: The issues surrounding anemia management in patients receiving dialysis therapy are complex and widely debated. Although numerous trials have been published, clinical practice patterns may differ, particularly in the presence of uncertainty about the optimal management of anemia in this setting. METHODS: We examined data from the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) regarding use of erythropoietic agents (EA), hemoglobin, and ferritin concentrations and transferrin saturation in 8476 prevalent dialysis patients in Australia and New Zealand during the 6 months preceding March 31, 2001. From this cross-sectional survey, we examined the distribution of reported hemoglobin concentration, transferrin saturation, and ferritin concentration. Among hemodialysis patients, other predictors of hemoglobin examined included urea reduction ratio (URR), age, sex, and the presence of comorbidities. RESULTS: In Australia, 87% of dialysis patients received an EA; in contrast, only 42% of New Zealand patients received an EA. Hemoglobin concentrations were significantly higher in Australia, where 16% of reported values were <100 g/L, compared to New Zealand where 37% reported values were <100 g/L. Transferrin saturation and serum ferritin concentrations were significantly correlated, but less strongly among those receiving EA than those not receiving these agents. Both transferrin saturation and serum ferritin were significantly and independently associated with hemoglobin concentration, as were age and sex. The association with ferritin was inverse: higher serum ferritin concentrations were associated with lower hemoglobin concentrations. There was poor agreement (kappa = 0.15) between categories of low transferrin saturation (<20%) and low ferritin concentrations (<200 ng/mL). Among the Australian hemodialysis patients, there was no significant variation in Hb between categories where reported URR was >/=65%, whereas the group with a reported URR <65% had a significantly lower hemoglobin concentration. CONCLUSIONS: There was a wide variation in reported hemoglobin concentrations in this population. Potential contributing factors include variable patient responsiveness to EA and iron, differing regulations in Australia and New Zealand regarding government subsidy of EA, and the lack of consensus among physicians regarding hemoglobin target values. Although a cross-sectional study cannot directly address the predictive value of iron indices for iron deficiency, it appears likely that transferrin and ferritin have different relationships with hemoglobin, and measurement of both may have greater clinical utility than either parameter alone.
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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 | 0.094 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".