Influence of Serum Transferrin Concentration on Diagnostic Criteria for Iron Deficiency in Chronic Heart Failure
Bibliographic record
Abstract
AIMS: Transferrin saturation (TSAT), a marker of iron deficiency, reflects both serum concentrations of iron (SIC) and transferrin (STC). TSAT is susceptible to changes in each of these biomarkers. Little is known about determinants of STC and its influence on TSAT and mortality in patients with heart failure. Accordingly, we studied the relationship of STC to clinical characteristics, to markers of iron deficiency and inflammation and to mortality in chronic heart failure (CHF). METHODS AND RESULTS: Prospective cohort of patients with CHF attending a clinic serving a large local population. A total of 4422 patients were included (median age 75 (68-82) years; 40% women; 32% with left ventricular ejection fraction ≤40%). STC ≤ 2.3 g/L (lowest quartile) was associated with older age, lower SIC and haemoglobin and higher high-sensitivity C-reactive protein, ferritin and N-terminal pro-brain natriuretic peptide compared with those with STC > 2.3 g/L. In the lowest STC quartile, 624 (52%) patients had SIC ≤13 μmol/L, of whom 38% had TSAT ≥20%. For patients in the highest STC quartile, TSAT was <20% when SIC was >13 μmol/L in 185 (17%) patients. STC correlated inversely with ferritin (r = -0.52) and high-sensitivity C-reactive protein (r = -0.17) and directly with albumin (r = 0.29); all P < 0.001. In models adjusted for age, N-terminal pro-brain natriuretic peptide and haemoglobin, both higher SIC (hazard ratio 0.87 [95% CI: 0.81-0.95]) and STC (hazard ratio 0.82 [95% CI: 0.73-0.91]) were associated with lower mortality. SIC was more strongly associated with both anaemia and mortality than either STC or TSAT. CONCLUSIONS: Many patients with CHF and a low STC have low SIC even when TSAT is >20% and serum ferritin >100 μg/L; such patients have a high prevalence of anaemia and a poor prognosis and might have iron deficiency but are currently excluded from clinical trials of iron repletion.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".