Liberal transfusion strategies reduce sepsis risk and improve neurological recovery in acute brain injury: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose To advocate for a Liberal Transfusion Strategy (LTS) in neurocritical care patients with Acute Brain Injury (ABI) and provide updated evidence for optimizing transfusion thresholds in clinical guidelines. Background Anemia frequently complicates ABI management, often necessitating red blood cell transfusions. However, the optimal hemoglobin (Hb) threshold for transfusion remains controversial. While earlier meta-analyses indicated no significant differences between LTS and restrictive transfusion strategies (RTS), emerging randomized controlled trials (RCTs) emphasize the need for reappraisal within neurocritical care. Methods This meta-analysis included five RCTs involving 2399 patients (1,191 LTS; 1208 RTS) with ABI (subarachnoid hemorrhage, traumatic brain injury, or intracerebral hemorrhage). LTS was defined as transfusion at Hb ≤ 10–9 g/dL, and RTS as transfusion at Hb ≤ 7–8 g/dL. Outcomes assessed included sepsis or septic shock, ICU mortality, unfavorable functional outcomes at six months, venous thromboembolism (VTE), acute respiratory distress syndrome (ARDS), and in-hospital mortality. Results RTS significantly increased the risk of sepsis or septic shock (relative risk [RR]: 1.42; 95% confidence interval [CI] 1.08–1.86; p = 0.01) and unfavorable functional outcomes at six months (RR 1.13; 95% CI 1.06–1.21; p = 0.0003). No significant differences were observed in ICU mortality (RR 1.00; 95% CI 0.84–1.20; p = 0.96), VTE (RR: 0.88; 95% CI 0.56–1.38; p = 0.58), ARDS (RR 1.05; 95% CI 0.69–1.61; p = 0.81), or in-hospital mortality (RR 0.98; 95% CI 0.76–1.26; p = 0.89). Heterogeneity was minimal (I2 < 25%). Conclusion LTS demonstrates the potential to enhance safety and functional recovery in ABI patients by mitigating sepsis risk and promoting favorable neurologic outcomes. Further high-powered RCTs are warranted to validate these findings and refine transfusion protocols.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".