Acute haemolysis, DIC and renal failure after transfusion of uncross‐matched blood during trauma resuscitation: illustrative case and literature review
Bibliographic record
Abstract
SUMMARY Aims/Objectives The aims of this study were to report a patient with acute haemolytic transfusion reaction (HTR) after transfusing uncross‐matched red blood cell (RBC) units and to identify the frequency of this complication. Background Uncross‐matched RBC units are commonly transfused in emergencies, but the frequency of acute HTR is unknown. Methods We describe a male stabbing victim who received three units of uncross‐matched RBC units complicated by acute intravascular HTR, disseminated intravascular coagulation (DIC) and renal failure. We identified 14 studies evaluating the frequency of acute HTR post‐emergency transfusion of uncross‐matched RBC units. Results Acute HTR was shown by haemoglobinuria, free‐plasma haemoglobin and methemalbumin, with anti‐K and anti‐Fya eluted from recipient red cells; acute DIC featured severe hypofibrinogenemia, thrombocytopenia, elevated fibrin D‐dimer and multiple bilateral renal infarcts. Two of the three transfused units reacted with pre‐existing RBC alloantibodies [anti‐K (titre, 128), anti‐Fya (titre, 512)], explained by transfusion 25 years earlier. Our literature review found the frequency of acute HTR following emergency transfusion of uncross‐matched RBC units to be 2/3998 [0·06% (95% CI, 0·01–0·21%)]. Conclusions Although emergency transfusion of uncross‐matched blood is commonly practiced at trauma centres worldwide, with low risk of acute HTR (<1/1000), our well‐documented patient case demonstrates the potential for acute HTR with severe complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".