Transfusion thresholds and other strategies for guiding allogeneic red blood cell transfusion
Bibliographic record
Abstract
BACKGROUND: Most clinical practice guidelines recommend restrictive red cell transfusion practices, with the goal of minimising exposure to allogeneic blood. The purpose of this review is to compare clinical outcomes in patients randomised to restrictive versus liberal transfusion thresholds (triggers). OBJECTIVES: To examine the evidence for the effect of transfusion thresholds on the use of allogeneic and/or autologous red cell transfusion, and the evidence for any effect on clinical outcomes. SEARCH METHODS: We identified trials by searching; The Cochrane Injuries Group Specialised Register (searched 01 Feb 2011), Cochrane Central Register of Controlled Trials 2011, issue 1 (The Cochrane Library), MEDLINE (Ovid) 1948 to January Week 3 2011, EMBASE (Ovid) 1980 to 2011 (Week 04), ISI Web of Science: Science Citation Index Expanded (1970 to Feb 2011), ISI Web of Science: Conference Proceedings Citation Index- Science (1990 to Feb 2011). We checked reference lists of other published reviews and relevant papers to identify any additional trials. SELECTION CRITERIA: Controlled trials in which patients were randomised to an intervention group or to a control group. Trials were included where intervention groups were assigned on the basis of a clear transfusion 'trigger', described as a haemoglobin (Hb) or haematocrit (Hct) level below which a red blood cell (RBC) transfusion was to be administered. DATA COLLECTION AND ANALYSIS: Risk ratios of requiring allogeneic blood transfusion, transfused blood volumes and other clinical outcomes were pooled across trials, using a random effects model. Data extraction and assessment of the risk of bias was performed by two people. MAIN RESULTS: Nineteen trials involving a total of 6264 patients were identified, and were similar enough that the results could be combined. Restrictive transfusion strategies reduced the risk of receiving a RBC transfusion by 39% (RR 0.61, 95% CI 0.52 to 0.72). This equates to an average absolute risk reduction (ARR) of 34% (95% CI 24% to 45%). The volume of RBCs transfused was reduced on average by 1.19 units (95% CI 0.53 to 1.85 units). However, heterogeneity between trials was statistically significant (P<0.00001; I(2)≥93%) for these outcomes. Restrictive transfusion strategies did not appear to impact the rate of adverse events compared to liberal transfusion strategies (i.e. mortality, cardiac events, myocardial infarction, stroke, pneumonia and thromboembolism). Restrictive transfusion strategies were associated with a statistically significant reduction in hospital mortality (RR 0.77, 95% CI 0.62-0.95) but not 30 day mortality (RR 0.85, 95% CI 0.70 to 1.03). The use of restrictive transfusion strategies did not reduce functional recovery, hospital or intensive care length of stay. The majority of patients randomised were included in good quality trials, but some items of methodological quality were unclear. There are no trials in patients with acute coronary syndrome. AUTHORS' CONCLUSIONS: The existing evidence supports the use of restrictive transfusion triggers in most patients including those with pre-existing cardiovascular disease. As there are no trials, the effects of restrictive transfusion triggers in high risk groups such as acute coronary syndrome need to be tested in further large clinical trials. In countries with inadequate screening of donor blood, the data may constitute a stronger basis for avoiding transfusion with allogeneic red cells.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".