Anemia Is Common in Heart Failure and Is Associated With Poor Outcomes
Bibliographic record
Abstract
BACKGROUND: Although previous work has suggested that anemia is associated with an increased mortality in selected patients with congestive heart failure (CHF), little is known about the prevalence and predictors of anemia, or whether anemia is an independent prognostic factor in unselected, community-based patients with CHF. METHODS AND RESULTS: We analyzed a population-based cohort of patients with new-onset CHF from a database of patients discharged from 138 acute-care hospitals in Alberta, Canada, between April 1993 and March 2001. Logistic regression, Kaplan-Meier survival analyses, and Cox proportional hazards model were used. Among the 12 065 patients with CHF (median age 78 years), 17% had anemia, 58% of whom had anemia of chronic disease. After adjustment for clinical and demographic variables, patients with anemia were more likely to be older (odds ratio [OR] 1.01 per year) and female (OR 1.2 [95% confidence interval 1.1 to 1.3]) and to have a history of chronic renal insufficiency (OR=3.2 [95% confidence interval 2.8 to 3.6]), or hypertension (OR 1.3 [95% confidence interval 1.2 to 1.5]). Hazard ratios for mortality, adjusting for covariates, were 1.34 (1.24 to 1.46) in anemic patients, and 1.36 (1.23 to 1.50) in those patients with anemia of chronic disease. CONCLUSIONS: In this large cohort of community-dwelling patients with CHF, anemia is common and an independent prognostic factor for mortality. Further research into the mechanisms of anemia in CHF and randomized controlled trials to test whether correction of anemia improves prognosis in CHF are needed.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".