Iron Deficiency in Heart Failure and Effect of Dapagliflozin: Findings From DAPA-HF
Bibliographic record
Abstract
Background: Iron deficiency is common in heart failure and associated with worse outcomes. We examined the prevalence and consequences of iron deficiency in the DAPA-HF trial (Dapagliflozin and Prevention of Adverse-Outcomes in Heart Failure) and the effect of dapagliflozin on markers of iron metabolism. We also analyzed the effect of dapagliflozin on outcomes, according to iron status at baseline. Methods: Iron deficiency was defined as a ferritin level <100 ng/mL or a transferrin saturation <20% and a ferritin level 100 to 299 ng/mL. Additional biomarkers of iron metabolism, including soluble transferrin receptor, erythropoietin, and hepcidin were measured at baseline and 12 months after randomization. The primary outcome was a composite of worsening heart failure (hospitalization or urgent visit requiring intravenous therapy) or cardiovascular death. Results: Of the 4744 patients randomized in DAPA-HF, 3009 had ferritin and transferrin saturation measurements available at baseline, and 1314 of these participants (43.7%) were iron deficient. The rate of the primary outcome was higher in patients with iron deficiency (16.6 per 100 person-years) compared with those without (10.4 per 100 person-years; P <0.0001). The effect of dapagliflozin on the primary outcome was consistent in iron-deficient compared with iron-replete patients (hazard ratio, 0.74 [95% CI, 0.58–0.92] versus 0.81 [95% CI, 0.63–1.03]; P -interaction=0.59). Similar findings were observed for cardiovascular death, heart failure hospitalization, and all-cause mortality. Transferrin saturation, ferritin, and hepcidin were reduced and total iron-binding capacity and soluble transferrin receptor increased with dapagliflozin compared with placebo. Conclusions: Iron deficiency was common in DAPA-HF and associated with worse outcomes. Dapagliflozin appeared to increase iron use but improved outcomes, irrespective of iron status at baseline. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03036124.
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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) | 0.000 | 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".