Heart Failure With Anemia
Bibliographic record
Abstract
BACKGROUND: Anemia is a highly prevalent and strong independent prognostic marker in heart failure (HF), yet this association is not completely understood. Whether anemia is simply a marker of disease severity and concomitant chronic kidney disease or represents the activation of other detrimental pathways remains uncertain. We sought to determine which pathophysiological pathways are exacerbated in patients with HF, reduced ejection fraction (HFrEF) and anemia in comparison with those without anemia. METHODS AND RESULTS: In a prospective study involving 151 patients, selected biomarkers were analyzed, each representing proposed contributive mechanisms in the pathophysiology of anemia in HF. We compared clinical, echocardiographic, and circulating biomarkers profiles among patients with HFrEF and anemia (group 1), HFrEF without anemia (group 2), and chronic kidney disease with preserved EF, without established HF (chronic kidney disease control group 3). We demonstrate here that many processes other than those related to chronic kidney disease are involved in the anemia-HF relationship. These are linked to the pathophysiological mechanisms pertaining to left ventricular systolic dysfunction and remodeling, systemic inflammation and volume overload. We found that levels of interleukin-6 and interleukin-10, specific markers of cardiac remodeling (procollagen type III N-terminal peptide, matrix metalloproteinase-2, tissue inhibitor of matrix metalloproteinase 1, left atrial volume), myocardial stretch (NT-proBNP [N-terminal probrain natriuretic peptide]), and myocyte death (troponin T) are related to anemia in HFrEF. CONCLUSIONS: Anemia is strongly associated not only with markers of more advanced and active heart disease but also with the level of renal dysfunction in HFrEF. Increased myocardial remodeling, inflammation, and volume overload are the hallmarks of patients with anemia and HF. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00834691.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 0.001 |
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".