Prognostic significance of anaemia in patients with heart failure with preserved and reduced ejection fraction: results from the MAGGIC individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Anaemia is common among patients with heart failure (HF) and is an important prognostic marker. AIM: We sought to determine the prognostic importance of anaemia in a large multinational pooled dataset of prospectively enrolled HF patients, with the specific aim to determine the prognostic role of anaemia in HF with preserved and reduced ejection fraction (HF-PEF and HF-REF, respectively). DESIGN: Individual person data meta-analysis. METHODS: Patients with haemoglobin (Hb) data from the MAGGIC dataset were used. Anaemia was defined as Hb < 120 g/l in women and <130 g/l in men. HF-PEF was defined as EF ≥ 50%; HF-REF was EF < 50%. Cox proportional hazard modelling, with adjustment for clinically relevant variables, was undertaken to investigate factors associated with 3-year all-cause mortality. RESULTS: Thirteen thousand two hundred and ninety-five patients with HF from 19 studies (9887 with HF-REF and 3408 with HF-PEF). The prevalence of anaemia was similar among those with HF-REF and HF-PEF (42.8 and 41.6% respectively). Compared with patients with normal Hb values, those with anaemia were older, were more likely to have diabetes, ischaemic aetiology, New York Heart Association class IV symptoms, lower estimated glomerular filtration rate and were more likely to be taking diuretic and less likely to be taking a beta-blocker. Patients with anaemia had higher all-cause mortality (adjusted hazard ratio [aHR] 1.38, 95% confidence interval [CI] 1.25-1.51), independent of EF group: aHR 1.67 (1.39-1.99) in HF-PEF and aHR 2.49 (2.13-2.90) in HF-REF. CONCLUSIONS: Anaemia is an adverse prognostic factor in HF irrespective of EF. The prognostic importance of anaemia was greatest in patients with HF-REF.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.040 |
| 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 | 0.002 | 0.002 |
| 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".