Influence of iron deficiency definition on the efficacy of intravenous iron in heart failure: a meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND: Intravenous iron improves symptoms in heart failure (HF) with iron deficiency (ID) but failed to consistently show a benefit in cardiovascular outcomes. The ID definition used may influence the response to intravenous iron. The aim of this meta-analysis is to assess the influence of ID definition on the intravenous iron effect in HF. METHODS/RESULTS: We performed a random-effects meta-analysis of randomized controlled trials (RCT) on intravenous iron (vs. placebo or standard of care) in patients with HF and ID that provided data on transferrin saturation (TSAT) and ferritin subgroups on the composite outcome of cardiovascular death (CVD) or HF hospitalizations (HFH). The risk ratio (RR) and 95% confidence intervals (95% CI) were extracted on the TSAT (< 20% and ≥ 20%) and ferritin (< 100 ng/mL and ≥ 100 ng/mL) subgroups. Data from four major RCT was collected including a total of more than 5500 patients. In patients with a TSAT < 20%, intravenous iron reduced the composite outcome of CVD or HFH: RR 0.81, 95%CI 0.69-0.94, while in patients with a TSAT ≥ 20% the effect was neutral: RR 0.98, 95%CI 0.79-1.21, interaction, P = 0.05. On the other hand, ferritin levels did not modify the effect of IV iron: ferritin ≥ 100 ng/mL RR 0.84, 95%CI 0.65-1.09, and ferritin < 100 ng/mL RR 0.85, 95%CI 0.74-0.97; interaction, P = 0.96. CONCLUSIONS: Our meta-analysis suggests that the benefit of intravenous iron may be restricted to patients with TSAT < 20% regardless of ferritin levels and supports the single use of TSAT < 20% to identify patients with ID who may benefit from intravenous iron therapy.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".