Erythropoiesis‐stimulating agents as replacement therapy for blood transfusions in critically ill patients with anaemia: A systematic review with meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: The primary objectives of this meta-analysis in critically ill adult patients admitted to the intensive care unit (ICU) were to analyse whether erythropoiesis-stimulating agents (ESAs) reduced the number of patients receiving red blood cell (RBC) transfusion and resulted in a change in haemoglobin (Hb) concentration. Our secondary objectives were adverse events and mortality. BACKGROUND: Anaemia is common in ICU patients, and currently, the standard therapy is RBC transfusion, which is known to be associated with adverse events. ESA could potentially reduce the need for RBC transfusion. METHODS: EMBASE, Cochrane and PubMed were searched up to January 2020. RESULTS: A total of 1357 articles were identified, of which 18 articles met the inclusion criteria for the qualitative synthesis. Eight of these studies were used in the meta-analyses. Comparing ESA vs control group, there was a small reduction in the proportion of patients who received one or more RBC transfusions (relative risk [RR] 0.88; confidence interval [CI] 0.78-1.00, moderate certainty). The change in Hb concentration was trivial (mean difference -0.31 g/dL; CI -0.51 to -0.05, high certainty). The number of serious adverse events (RR 1.02; 0.90-1.15, low certainty) and the overall short-term mortality were similar (RR 0.80; CI 0.61-1.05, low certainty) between the groups. CONCLUSION: ESA resulted in a small reduction in the proportion of patients transfused and a trivial increase in haemoglobin concentration, both of questionable clinical relevance, without impacting adverse events or mortality. These results do not support the routine use of ESA to treat anaemia in critically ill adults.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".