RBC transfusion is associated with increased risk of respiratory failure after pneumonectomy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pneumonectomy is associated with high risk of respiratory complications. Our objective was to determine if transfusions are associated with increased rate of ARDS and respiratory failure in adults undergoing elective pneumonectomy. METHODS: Retrospective cohort study of consecutive pneumonectomies undertaken at a tertiary hospital (2003-2013). Multivariable logistic regression was performed to adjust for confounding factors. RESULTS: ARDS and respiratory failure occurred in 12.4% (n = 20) and 19.2% (n = 31) of 161 pneumonectomy patients, respectively, and were more likely to occur in transfused patients (P = 0.03, P < 0.001). pRBCs, FFP and platelets were transfused in 27% (n = 43), 6% (n = 9), and 2% (n = 3), respectively. On multivariable analyses utilizing blood products as continuous and binary variables, pRBC use was the only independent predictor of ARDS with odds ratio (OR) = 1.23 (95%CI:1.08-1.39, P = 0.002) and OR = 2.45 (95%CI:1.10-5.49, P = 0.03), respectively. On multivariable analyses utilizing blood products as continuous and binary variables, pRBCs were the only independent predictor of respiratory failure with OR = 1.37 (95%CI:1.16-1.60, P < 0.001) and OR = 3.17 (95%CI:1.25-8.02, P = 0.02), respectively. CONCLUSIONS: Peri-operative pRBC use appears to be an independent risk factor for ARDS and respiratory failure after pneumonectomy. There is a significant dose-response relationship. Platelets and FFP did not appear to increase ARDS risk but this may be due to low utilization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".