Effect of intravenous iron use on hospitalizations in patients undergoing hemodialysis: a comparative effectiveness analysis from the DEcIDE-ESRD study
Bibliographic record
Abstract
BACKGROUND: Intravenous iron use in hemodialysis patients has greatly increased over the last decade, despite limited studies on the safety of iron. METHODS: We studied the association of receipt of intravenous iron with hospitalizations in an incident cohort of hemodialysis patients. We examined 9544 patients from Dialysis Clinic, Inc. (DCI). We ascertained intravenous iron use from DCI electronic medical record and USRDS data files, and hospitalizations through Medicare claims. We examined the association between iron exposure accumulated over 1-, 3- or 6-month time windows and incident hospitalizations in the follow-up period using marginal structural models accounting for time-dependent confounders. We performed sensitivity analyses including recurrent events models for multiple hospitalizations and models for combined outcome of hospitalization and death. RESULTS: There were 22 347 hospitalizations during a median follow-up of 23 months. Higher cumulative dose of intravenous iron was not associated with all-cause, cardiovascular or infectious hospitalizations [HR 0.97 (95% CI: 0.77-1.22) for all-cause hospitalizations comparing >2100 mg versus 0-900 mg of iron over 6 months]. Findings were similar in models examining the risk of hospitalizations in 1- and 3-month windows [HR 0.88 (95% CI: 0.79-0.99) and HR 0.88 (95% CI: 0.74-1.03), respectively] or the risk of combined outcome of hospitalization and death in the 6-month window [HR 0.98 (95% CI: 0.78-1.23)]. CONCLUSIONS: Higher cumulative dose of intravenous iron may not be associated with increased risk of hospitalizations in hemodialysis patients. While clinical trials are needed, employing higher iron doses to reduce erythropoiesis-stimulating agents does not appear to increase morbidity in routine clinical care.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".