Effects of Ferric Carboxymaltose on Hospitalisations and Mortality Rates in Iron-Deficient Heart Failure Patients: An Individual Patient data Meta-Analysis
Bibliographic record
Abstract
AIMS: Iron deficiency (ID) is a common co-morbidity in patients with heart failure (HF) and has been suggested to be associated with poor prognosis. Recently completed double-blind randomised controlled trials (RCTs) studying HF patients with ID have shown improvements in functional capacity, symptoms and quality of life when treated with i.v. ferric carboxymaltose (FCM). This individual patient data meta-analysis investigates the effect of FCM vs. placebo on recurrent hospitalisations and mortality in HF patients with ID. METHODS AND RESULTS: Individual patient data were extracted from four RCTs comparing FCM with placebo in patients with systolic HF and ID. The main outcome measures were recurrent cardiovascular (CV) hospitalisations and CV mortality. Other outcomes included cause-specific hospitalisations and death. The main analyses of recurrent events were backed up by time-to-first-event analyses. In total, 839 patients, of whom 504 were randomised to FCM, were included. Compared with those taking placebo, patients on FCM had lower rates of recurrent CV hospitalisations and CV mortality [rate ratio 0.59, 95% confidence interval (CI) 0.40-0.88; P = 0.009]. Treatment with FCM also reduced recurrent HF hospitalisations and CV mortality (rate ratio 0.53, 95% CI 0.33-0.86; P = 0.011) and recurrent CV hospitalisations and all-cause mortality (rate ratio 0.60, 95% CI 0.41-0.88; P = 0.009). Time-to-first-event analyses showed similar findings, with somewhat attenuated treatment effects. The administration of i.v. FCM was not associated with an increased risk for adverse events. CONCLUSIONS: Treatment with i.v. FCM was associated with a reduction in recurrent CV hospitalisations in systolic HF patients with ID.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.059 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".