Factors Associated With Pericardial Effusion and Mortality in Renal Cancer Patients
Bibliographic record
Abstract
Purpose: This study aims to identify determinants of pericardial effusion (PCE) in patients with renal cancer and predictors of in-hospital mortality in patients with renal cancer and PCE.Materials and Methods: We conducted a retrospective cohort study using National Inpatient Sample of patients with primary malignant renal cancer data from 2016–2020. A total of 85,288 patients were included in the analysis, of which 763 (0.90%) presented with PCE. The main outcomes of interest were the presence of PCE and the in-hospital mortality rate in patients with renal cancer and PCE.Results: The majority of our cohort are aged ≥40 years (96.79%) and males (64.57%). In the bivariate analysis, patients <40 years had higher odds of PCE (adjusted odds ratio [aOR], 1.82; 95% confidence interval [CI], 1.23–2.63). Non-Hispanic Black (aOR, 1.59; 95% CI ,1.30–1.94) and Hispanic patients were associated with an increased odd of PCE odds ratio. Higher Elixhauser comorbidity index (≥4: aOR, 2.27; 95% CI, 1.66–3.08), hypoproteinemia (aOR, 1.49; 95% CI, 1.22–1.81), heart failure (aOR, 2.15; 95% CI 1.83–2.55), metastatic disease (aOR, 1.36; 95% CI, 1.15–1.62), and severe disease presentation (aOR, 6.35; 95% CI, 4.82–8.36) were also significant predictors of PCE. Private insurance was associated with higher PCE odds (aOR, 1.43; 95% CI, 1.20–1.70). Among PCE patients, the in-hospital mortality rate was 13.37%. Self-pay insurance status (aOR, 3.82; 95% CI, 1.05–13.95), metastatic disease (aOR, 1.76; 95% CI, 1.07–2.96), and severe disease presentation (aOR, 3.46; 95% CI, 1.05–8.58) significantly predicted increased mortality.Conclusion: This study identifies crucial demographic, clinical, and healthcare system factors associated with PCE and in-hospital mortality in renal cancer patients. These findings highlight the need for increased vigilance and tailored surveillance for vulnerable groups and those with higher comorbidity burdens.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".