Premedication for the prevention of nonhemolytic transfusion reactions: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The efficacy of premedication for the prevention of nonhemolytic transfusion reactions remains controversial. This systematic review and meta-analysis assessed the effect of premedication on the rate of nonhemolytic transfusion reactions after allogeneic blood transfusion. STUDY DESIGN AND METHODS: We searched the literature using CENTRAL, MEDLINE, EMBASE, ISI Web of Science, and clinicaltrials.gov databases from inception until October 31, 2018. We included all randomized controlled trials comparing premedication to placebo or no treatment in patients receiving any labile blood product. Outcome measures were reported as relative risks (RRs) with 95% confidence intervals (CIs). Data were combined for similar outcomes where appropriate using a random-effects model. Analyses were done at both the patient and transfusion level. RESULTS: Three randomized trials using acetaminophen and antihistamine as premedication met the inclusion criteria. A total of 517 patients received 4444 red blood cell or platelet transfusions. Pooled patient-level estimates with premedication for all nonhemolytic, febrile nonhemolytic, and minor allergic reactions were RR, 0.92 (95% CI, 0.63-1.35); RR, 0.54 (95% CI, 0.26-1.1); and RR, 1.37 (95% CI 0.81-2.31), respectively. Transfusion-level analyses also showed no benefit with premedication. Of 517 patients randomized, only 27 (5.2%) had a history of transfusion reactions. CONCLUSION: Routine premedication with acetaminophen and antihistamines did not prevent nonhemolytic transfusion reactions; however, the estimate of effect was greatest for febrile reactions. The impact of premedication in patients with a prior history of transfusion reactions remains unknown and requires further evaluation in future clinical trials.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".