In-Hospital Mortality Among Patients Undergoing Percutaneous Pericardiocentesis for Pericardial Effusion with and Without Malignancy
Bibliographic record
Abstract
Background: Despite the high prevalence of malignant pericardial effusions (MPEs), the differences in mortality between those undergoing pericardiocentesis for MPE versus non-malignancy pericardial effusions (NMPEs) are not well characterized. To address this knowledge gap, we aim to compare clinical outcomes following pericardiocentesis among patients with MPE and NMPE. Methods: A retrospective analysis was conducted on the US National Inpatient Sample (NIS) to identify all hospitalizations during which pericardiocentesis was performed between 1 January 2016 and 31 December 2020 (total n = 174,776,205). This cohort was further stratified based on the presence or absence of malignancy. The primary outcome of interest was in-hospital mortality. Secondary outcomes included discharge disposition (categorized as non-home discharges), length of stay, and total hospitalization costs. Results: A total of 85,125 patients with pericardial effusions undergoing pericardiocentesis were identified. Patients with an MPE (n = 24,740) were younger and more likely to have a history of malnutrition, prior radiation, palliative care treatments, and do-not-resuscitate (DNR) orders compared to those with an NMPE (n = 60,385). Lung cancer was the most common malignancy (40.3%) in patients with an MPE requiring pericardiocentesis. The in-hospital mortality following pericardiocentesis was 11.8% in patients with malignancy and 8.2% in patients without (odds ratio (OR) for mortality 1.50 (95% confidence interval [CI]: 1.34–1.68, p < 0.001). Lung cancer, non-Hodgkin lymphoma, esophageal cancer, ovarian cancer, and leukemia were associated with a significantly increased risk of death during the same admission. Non-home discharge, length of stay, and total hospitalization cost were marginally greater in those with an MPE. Conclusions: In patients undergoing pericardiocentesis, those with an MPE had significantly higher in-hospital mortality compared to those with an NMPE. Additionally, the MPE group had a marginally longer length of stay and incurred higher total hospital costs. Further research is warranted to explore optimal treatment strategies for MPEs, particularly in patients with a limited life expectancy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".