Association between leukoreduced red blood cell transfusions and hospital‐acquired infections in critically ill children: A secondary analysis of the <scp>TRIPICU</scp> study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hospital-acquired infections (HAIs) are an important problem in critically ill children. Studies show associations between the transfusion of non-leukoreduced red blood cell units (RBC) and increased HAI incidence rates (IRs). We hypothesize that transfusing pre-storage leukoreduced RBC is also associated with increased HAI IR. We aim to evaluate the associations between (1) a leukoreduced RBC restrictive transfusion strategy and HAI IR, (2) leukoreduced RBC transfusions and HAI IR, and (3) the number or volume of leukoreduced RBC transfusions and HAI IR in critically ill children. MATERIALS AND METHODS: This post hoc secondary analysis of the "Transfusion Requirement in Paediatric Intensive Care Units" (TRIPICU) randomized controlled trial (637 patients) used quasi-Poisson multivariable regression models to estimate HAI incidence rate ratios (IRRs) and 95% confidence intervals (CI). RESULTS: A restrictive transfusion strategy yielded an IRR of 0.88 (95% CI 0.67, 1.16). The association between transfusing leukoreduced RBCs (IRR 1.25; 95% CI 0.73, 2.13) and HAI IR was not statistically significant. However, we observed significant associations between patients who received >20 cc/kg volume of leukoreduced RBC transfusions (IRR 2.14; 95% CI 1.15, 3.99) and ≥3 leukoreduced RBC transfusions (IRR 2.40; 95% CI 1.15, 4.99) and HAI IR. CONCLUSION: Exposing critically ill children to >20 cc/kg or ≥3 leukoreduced RBC transfusions were associated with higher HAI IR, suggesting dose-response patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".