Erythropoietic Response After Intravenous Iron in Patients with Heart Failure and Reduced Ejection Fraction with and Without Background Treatment with Sodium–Glucose Cotransporter 2 Inhibitors
Bibliographic record
Abstract
AIMS: Intravenous (IV) iron increases haemoglobin/haematocrit and improves outcomes in patients with heart failure with reduced ejection fraction (HFrEF) and iron deficiency. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) also increase haemoglobin/haematocrit and improve outcomes in heart failure by mechanisms linked to nutrient deprivation signalling and reduction of inflammation and oxidative stress. The effect of IV iron among patients using SGLT2i has not yet been studied. The aim of this study was to evaluate the changes in haemoglobin, haematocrit, and iron biomarkers in HFrEF patients treated with IV iron with and without background SGLT2i treatment. Secondary outcomes included changes in natriuretic peptides, kidney function and heart failure-associated outcomes. METHODS AND RESULTS: Retrospective, single-centre analysis of HFrEF patients with iron deficiency treated with IV iron using (n = 60) and not using (n = 60) SGLT2i, matched for age and sex. Mean age was 73 ± 12 years, 48% were men, with more than 65% of patients having chronic kidney disease and anaemia. After adjustment for all baseline differences, SGLT2i users experienced a greater increase in haemoglobin and haematocrit compared to SGLT2i non-users: haemoglobin +0.57 g/dl (95% confidence interval [CI] 0.04-1.10, p = 0.036) and haematocrit +1.64% (95% CI 0.18-3.11, p = 0.029). No significant differences were noted for iron biomarkers or any of the secondary outcomes. CONCLUSION: Combined treatment with IV iron and background SGLT2i was associated with a greater increase in haemoglobin and haematocrit than IV iron without background SGLT2i. These results suggest that in HFrEF patients treated with IV iron, SGLT2i may increase the erythropoietic response. Further studies are needed to ascertain the potential benefit or harm of combining these two treatments in heart failure patients.
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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 | 0.000 | 0.000 |
| 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".