Perioperative Complications and In-Hospital Mortality After Radical Prostatectomy in Prostate Cancer Patients with a History of Heart Valve Replacement
Bibliographic record
Abstract
Objective: To test for in-hospital mortality and complication rates in a population-based group of patients with vs. without a history of heart valve replacement undergoing radical prostatectomy (RP). Methods: Relying on the National Inpatient Sample (2000–2019), prostate cancer patients undergoing RP were stratified according to the presence or absence of heart-valve replacement. Multivariable logistics and Poisson regression models addressed adverse hospital outcomes. Results: Within the NIS, 220,358 patients underwent RP. Of those, 694 (0.3%) had a history of heart valve replacement. The patients undergoing heart valve replacement were older (median age 66 vs. 62 years). The proportion of patients with a history of heart valve replacement increases with the Charlson Comorbidity Index (CCI): CCI 0–0.3%, CCI 1–0.4%, and CCI ≥ 2–0.7%. Patients with a history of heart valve replacement exhibited higher rates of postoperative bleeding (<1.5% vs. <0.1%; odds ratio (OR) 16.2; p < 0.001), cardiac complications (7.5% vs. 1.2%; OR 3.9; p < 0.001), infections (<1.5% vs. 0.1%; OR 3.7; p = 0.01), critical care therapy (CCT) use (<1.5% vs. 0.4%; OR 2.5; p = 0.003), intraoperative complications (8.8% vs. 4.1%; OR 1.9; p < 0.001), transfusions (11% vs. 7.2%; OR 1.5; p < 0.001), longer hospital stay (mean 3.39 vs. 2.37 days; rates ratio [RR] 1.4; p < 0.001), and higher estimated hospital cost (median 33,539 vs. 30,716 $USD; RR 1.1; p < 0.001). Conversely, no statistically significant differences were observed in vascular complications (p = 0.3) or concerning in-hospital mortality (p = 0.1). Conclusions: After RP, patients with a history of heart valve replacement exhibited a higher rate of eight out of nine adverse in-hospital outcomes. However, these differences did not translate into higher in-hospital mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".