Perioperative Blood Transfusion Is Associated with Worse Survival in Patients Undergoing Radical Cystectomy after Neoadjuvant Chemotherapy for Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Objectives: Perioperative blood transfusion (PBT) has been associated with worse survival after radical cystectomy (RC) in patients with muscle-invasive bladder cancer (MIBC). Here, we evaluated the association between PBT and survival after RC that was preceded by neoadjuvant chemotherapy (NAC). Methods: A retrospective analysis was performed on 949 patients with cT2-4aN0M0 bladder cancer who received NAC prior to RC between 2000 and 2013 at 19 centers. Kaplan–Meier estimates of overall survival (OS) were made. Presumed risk factors for OS were analyzed using Cox regression analysis. PBT was defined by the administration of any packed red blood cells during surgery or during the post-operative hospital stay. Results: A transfusion was given to 608 patients (64%). Transfused patients were more likely to have adverse clinical and pathologic parameters, including clinical stage and performance status. Transfused patients had worse OS (p = 0.01). On multivariable Cox regression, PBT was found to be independently associated with worse OS (HR 1.53 (95% CI 1.13–2.08), p = 0.007). Conclusions: PBT is common after NAC and RC, which may be linked, in part, to the anemia induced by NAC. PBT was associated with several adverse risk factors that correlate with poor outcomes after NAC and RC, and it was an independent predictor of adverse OS on multivariable analysis. Further study should determine if measures to avoid blood loss can reduce the need for PBT and thereby improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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 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".