Perioperative complications and in-hospital mortality in radical cystectomy patients with heart-valve replacement
Bibliographic record
Abstract
PURPOSE: To assess in-hospital mortality and complication rates after radical cystectomy (RC) in patients with history of heart-valve replacement. MATERIALS AND METHODS: Using the National Inpatient Sample (2000-2019), non-metastatic bladder cancer patients undergoing RC were stratified according to history of heart-valve replacement. Regression models (RM) predicted hospital outcomes. RESULTS: Of 25,535 RC patients, 250 (1.0%) harbored history of heart-valve replacement. Heart-valve replacement patients were older (median 74 vs. 70 years), more frequently male (87.2 vs. 80.6%), and more frequently had Charlson comorbidity index ≥3 (26.8 vs. 18.9%). In RC patients with history of heart-valve replacement vs. others, 62 vs. 2634 (24.8 vs. 10.4%) experienced cardiac complications, 28 vs. 3092 (11.2 vs. 12.2%) intraoperative complications, 11 vs. 1046 (4.4 vs. 4.1%) infections, <11 vs. 594 (<4.4 vs. 2.3%) perioperative bleeding, <11 vs. 699 (<4.4 vs. 2.8%) vascular complications, 74 vs. 6225 (29.6 vs. 24.7%) received blood transfusions, 37 vs. 3054 (14.8 vs. 12.1%) critical care therapy (CCT), and in-hospital mortality was recorded in <11 vs. 463 (<4.4 vs. 1.8%) patients. In multivariable RM, history of heart-valve replacement independently predicted cardiac complications (odds ratio 2.20, 95% confidence interval 1.62-2.99; p < 0.001). Conversely, no statically significant association was recorded between history of heart-valve replacement and length of stay, estimated hospital cost, intraoperative complications, perioperative bleeding, vascular complications, infections, blood transfusions, CCT use, and in-hospital mortality. CONCLUSIONS: Radical cystectomy patients with history of heart-valve replacement exhibited a 2.2-fold higher risk of cardiac complications, but no other complications, including no significantly higher in-hospital mortality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".