Current Management of Anemia in Critically Ill Patients: Analysis of a Database of 139 Hospitals
Bibliographic record
Abstract
PURPOSE: This analysis focused on three objectives: 1) to measure packed red blood cell (pRBC) use across different critical care settings; 2) to characterize transfused and nontransfused critically ill patients; and (3) to identify potential predictors of transfusion use. METHODS: A retrospective analysis of critically ill patients from 139 hospitals across the United States was conducted. Hospital administrative and laboratory data were collected for patients 18 years of age and older admitted to the intensive care unit (ICU) (including coronary care unit and intermediate care units) from January 1, 2004, to May 31, 2005. Multivariate analyses controlling for patient and hospital heterogeneity evaluated the association between pRBC transfusions and patients' ICU or hospital length of stay. RESULTS: A total of 180,221 patients met all inclusion criteria, with 29,331 (16.3%) receiving pRBCs during their ICU stay. There was differential use of pRBCs by ICU/coronary care unit setting (ie, 23% of general ICU patients versus 7% of intermediate coronary care unit patients). Increasing age [odds ratio (OR), 1.007; 95% confidence interval (CI), 1.006-1.008], declining hemoglobin concentrations (OR, 2.315; 95% CI, 2.288-2.342), mechanical ventilation (OR, 1.338; 95% CI, 1.287-1.392), dialysis (OR, 2.071; 95% CI, 1.913-2.242), and presence of acute renal failure (OR, 1.259; 95% CI, 1.193-1.329), congestive heart failure (OR, 1.156; 95% CI, 1.106-1.208), or septicemia (OR, 1.143; 95% CI, 1.071-1.221) were associated with a higher likelihood of pRBC use. Each pRBC transfusion significantly increased hospital length of stay (1.6, 0.5, and 2.7 additional days for patients with 1, 2, and 3 or more transfusions, respectively, P < 0.0001) as compared with nontransfused patients. CONCLUSIONS: Multiple factors increased the likelihood of pRBC use in ICU patients. In addition, pRBC transfusion was associated with increased length of stay. Clinicians should evaluate the risk-benefit ratio and consider interventions to limit any unnecessary pRBC use in the critically ill.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".