Hypotensive Reactions Associated with Platelet Transfusion Through Leukocyte Reduction Filters
Bibliographic record
Abstract
This article reports a case of hypotensive reaction following platelet transfusion (PT) and presents a possible etiologic mechanism implicating negatively charged leukocyte reduction filters (LRFs) and angiotensin converting enzyme (ACE) inhibitors. A 14-year-old boy with acute lymphoblastic leukemia was admitted to the pediatric intensive care unit (PICU) for respiratory failure following bone marrow transplantation. He was being treated with ACE inhibitors and was hemodynamically stable. He received a PT with a negatively charged bedside LRF the day his ACE inhibitor dose was doubled. His blood pressure (BP) dropped from 106/65 to 75/45. The PT was stopped and his BP was stabilized with a bolus of cristalloid. The same PT was restarted using a macroaggregate filter and his BP remained stable. This reaction was characterized by severe and isolated hypotension. It occurred while using a negatively charged bedside LRF in a patient who had a recent increase in ACE inhibitor therapy. The reaction did not recur when the LRF was replaced by a macroaggregate filter. This case provides further evidence to support the hypothesis that the use of negatively charged LRF may lead to hypotensive transfusion reactions in some patients. Bradykinin, which is generated when plasma is exposed to a negatively charged surface, and whose metabolism is decreased by ACE inhibitors, may play a role in these reactions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".