Evaluating the effectiveness of IV iron dosing for anemia management in common clinical practice: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS)
Bibliographic record
Abstract
BACKGROUND: Anemia management protocols in hemodialysis (HD) units differ conspicuously regarding optimal intravenous (IV) iron dosing; consequently, patients receive markedly different cumulative exposures to IV iron and erythropoiesis-stimulating agents (ESAs). Complementary to IV iron safety studies, our goal was to gain insight into optimal IV iron dosing by estimating the effects of IV iron doses on Hgb, TSAT, ferritin, and ESA dose in common clinical practice. METHODS: 9,471 HD patients (11 countries, 2009-2011) in the DOPPS, a prospective cohort study, were analyzed. Associations of IV iron dose (3-month average, categorized as 0, <300, ≥300 mg/month) with 3-month change in Hgb, TSAT, ferritin, and ESA dose were evaluated using adjusted GEE models. RESULTS: Relative change: Monotonically positive associations between IV iron dose and Hgb, TSAT, and ferritin change, and inverse associations with ESA dose change, were observed across multiple strata of prior Hgb, TSAT, and ferritin levels. Absolute change: TSAT, ferritin, and ESA dose changes were nearest zero with IV iron <300 mg/month, rather than 0 mg/month or ≥300 mg/month by maintenance or replacement dosing. Findings were robust to numerous sensitivity analyses. CONCLUSIONS: Though residual confounding cannot be ruled out in this observational study, findings suggest that IV iron dosing <300 mg/month, as commonly seen with maintenance dosing of 100-200 mg/month, may be a more effective approach to support Hgb than the higher IV iron doses (300-400 mg/month) often given in many European and North American hemodialysis clinics. Alongside studies supporting the safety of IV iron in 100-200 mg/month dose range, these findings help guide the rational dosing of IV iron in anemia management protocols for everyday hemodialysis practice.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".