Prolonged Blood Storage and Risk of Posttransfusion Acute Kidney Injury
Bibliographic record
Abstract
BACKGROUND: Erythrocyte transfusions are independently associated with acute kidney injury. Kidney injury may be consequent to the progressive hematologic changes that develop during storage. This study therefore tested the hypothesis that prolonged erythrocyte storage increases posttransfusion acute kidney injury. METHODS: The Informing Fresh versus Old Red Cell Management (INFORM) trial randomized 31,497 patients to receive either the freshest or oldest available matching erythrocyte units and showed comparable mortality with both. This a priori substudy compared the incidence of posttransfusion acute kidney injury in the randomized groups. Acute kidney injury was defined by the creatinine component of the Kidney Disease: Improving Global Outcomes criteria. RESULTS: The 14,461 patients included in this substudy received 40,077 erythrocyte units. For patients who received more than one unit, the mean age of the blood units was used as the exposure. The median of the mean age of blood units transfused per patient was 11 days [interquartile range, 8, 15] in the freshest available blood group and 23 days [interquartile range, 17, 30] in the oldest available blood group. In the primary analysis, posttransfusion acute kidney injury was observed in 688 of 4,777 (14.4%) patients given the freshest available blood and 1,487 of 9,684 (15.4%) patients given the oldest available blood, with an estimated relative risk (95% CI) of 0.94 (0.86 to 1.02; P = 0.132). The secondary analysis treated blood age as a continuous variable (defined as duration of storage in days), with an estimated relative risk (95% CI) of 1.00 (0.96 to 1.04; P = 0.978) for a 10-day increase in the mean age of erythrocyte units. CONCLUSIONS: In a population of patients without severely impaired baseline renal function receiving fewer than 10 erythrocyte units, duration of blood storage had no effect on the incidence of posttransfusion acute kidney injury.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".