Perioperative Complications and In-Hospital Mortality in Partial and Radical Nephrectomy Patients with Heart-Valve Replacement
Bibliographic record
Abstract
BACKGROUND: In-hospital mortality and complication rates after partial and radical nephrectomy in patients with history of heart-valve replacement are unknown. PATIENTS AND METHODS: Relying on the National Inpatient Sample (2000-2019), kidney cancer patients undergoing partial or radical nephrectomy were stratified according to presence or absence of heart-valve replacement. Multivariable logistic and Poisson regression models addressed adverse hospital outcomes. RESULTS: Overall, 39,673 patients underwent partial nephrectomy versus 94,890 radical nephrectomy. Of those, 248 (0.6%) and 676 (0.7%) had a history of heart-valve replacement. Heart-valve replacement patients were older (median partial nephrectomy 69 versus 60 years; radical nephrectomy 71 versus 63 years), and more frequently exhibited Charlson comorbidity index ≥ 3 (partial nephrectomy 22 versus 12%; radical nephrectomy 32 versus 23%). In partial nephrectomy patients, history of heart-valve replacement increased the risk of cardiac complications [odds ratio (OR) 4.33; p < 0.001), blood transfusions (OR 2.00; p < 0.001), intraoperative complications (OR 1.53; p = 0.03), and longer hospital stay [rate ratio (RR) 1.25; p < 0.001], but not in-hospital mortality (p = 0.5). In radical nephrectomy patients, history of heart-valve replacement increased risk of postoperative bleeding (OR 4.13; p < 0.001), cardiac complications (OR 2.72; p < 0.001), intraoperative complications (OR 1.53; p < 0.001), blood transfusions (OR 1.27; p = 0.02), and longer hospital stay (RR 1.12; p < 0.001), but not in-hospital mortality (p = 0.5). CONCLUSIONS: History of heart-valve replacement independently predicted four of twelve adverse outcomes in partial nephrectomy and five of twelve adverse outcomes in radical nephrectomy patients including intraoperative and cardiac complications, blood transfusions, and longer hospital stay. Conversely, no statistically significant differences were observed in in-hospital mortality.
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 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".