Trends and outcomes in multicomponent blood transfusion: an 11‐year cohort study of a large multisite academic center
Bibliographic record
Abstract
BACKGROUND: Most studies reporting on blood component utilization overlook patients transfused with more than one type of blood product (multicomponent transfusion). These patients are of importance, as they are large consumers of blood products and likely have different characteristics and outcomes than nontransfused patients and patients transfused with only one blood component type. Our study aimed to determine the prevalence of multicomponent transfusion at a large multisite academic center, as well as the patient characteristics and outcomes associated with multicomponent transfusion. METHODS: A retrospective cohort study of transfused adult inpatients at the Ottawa Hospital between 2007 and 2017 was performed. Eligible transfusions were red blood cells (RBCs), platelets, plasma, cryoprecipitate, and/or fibrinogen concentrate. Descriptive analyses were done to determine multicomponent transfusion prevalence. Patient characteristics and outcomes associated with multicomponent transfusion were assessed using multivariable regressions. RESULTS: Of 55,719 adult transfused inpatient admissions, 25% received a multicomponent transfusion. Multicomponent transfusion prevalence was highest in hematology (51%), cardiac surgery (45%), and critical care (40%) patients. Multivariable regression analysis showed that compared to RBC-only transfusion, multicomponent transfusion was associated with increased odds of in-hospital mortality (odds ratio, 3.48; 95% confidence interval [CI], 3.26-3.73), greater odds of institutional discharge as opposed to discharge home (odds ratio, 1.22; 95% CI, 1.15-1.30), and a 1.58 time increase in duration of hospitalization (95% CI, 1.54-1.62). CONCLUSION: Multicomponent transfusion recipients make up a large proportion of transfused patients and have poorer outcomes. It is necessary to continue studying these patients, including outcomes and transfusion appropriateness, to inform best practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".