Transfusion in Radical Cystectomy Increases Overall Morbidity and Mortality: A Retrospective Study Using Data from the American College of Surgeons—National Surgical Quality Improvement Program
Bibliographic record
Abstract
Background: Radical cystectomy is a complex procedure imposing significant post-operation complications. Objective: Explore the impact of peri-operative pRBC transfusion on mortality and overall morbidity in a matched cohort. Methods: The American College of Surgeons—National Surgical Quality Improvement Program’s (ACS-NSQIP) dataset was used to select patients who underwent RC in 2008–2019. Patients who witnessed pre-operative transfusion and emergency cases were excluded. Peri-operative pRBC transfusion was defined as an intra-operative or up to 24-h post-operative pRBC transfusion. We matched patients who underwent peri-operative pRBC transfusion to patients who did not receive transfusion. Length of stay, mortality, and overall morbidity were compared between the two matched cohorts. Results: The match cohort was matched on all pre-operative demographics and medical history variables and yielded 3578 matched patients. Patients who underwent peri-operative pRBC transfusion had a longer length of hospital stay (9.3 days) as compared to patients who did not undergo transfusion (8.13 days) (p < 0.001). Furthermore, patients who underwent transfusion also had higher odds of mortality (OR = 1.934) and overall morbidity (OR = 1.443) (p < 0.03). Specifically, patients who underwent transfusion had higher odds of organ space SSI, pneumonia, unplanned intubation, pulmonary embolism, failure to wean off of ventilator, renal insufficiency, urinary tract infections, stroke, myocardial infarction, cardiac arrest requiring CPR, deep vein thrombosis, and septic shock (p < 0.047). Conclusion: Peri-operative pRBC transfusion in RC was associated with longer hospital stays, significant morbidity, and mortality. For this reason, pre-operative patient optimization and possible alternatives to common pRBC practices should be considered in RC to circumvent complications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".