A positive blood culture is associated with a lower haemoglobin increment in hospitalized patients after red blood cell transfusion
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Abundant clinical evidence supports the safety of red blood cell (RBC) concentrates for transfusion irrespective of storage age, but still, less is known about how recipient characteristics may affect post-transfusion RBC recovery and function. Septic patients are frequently transfused. We hypothesized that the recipient environment in patients with septicaemia would blunt the increase in post-transfusion blood haemoglobin (Hb). The main objective was to compare the post-transfusion Hb increment in hospitalized patients with or without a positive blood culture. MATERIALS AND METHODS: A retrospective cohort study using data from the Transfusion Research, Utilization, Surveillance, and Tracking database (TRUST) was performed. All adult non-trauma in-patients transfused between 2010 and 2017 with ≥1 RBC unit, and for whom both pre- and post-transfusion complete blood count and pre-transfusion blood culture data were available were included. A general linear model with binary blood culture positivity was fit for continuous Hb increment after transfusion and was adjusted for patient demographic parameters and transfusion-related covariates. RESULTS: Among 210,263 admitted patients, 6252 were transfused: 596 had positive cultures, and 5656 had negative blood cultures. A modelled Hb deficit of 1.50 g/L in blood culture-positive patients was found. All covariates had a significant effect on Hb increment, except for the age of the transfused RBC. CONCLUSION: Recipient blood culture positivity was associated with a statistically significant but modestly lower post-transfusion Hb increment in hospitalized patients. In isolation, the effect is unlikely to be clinically significant, but it could become so in combination with other recipient characteristics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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".