Characteristics and predictors of clinical outcome in patients with pleural effusions caused by heart, liver and renal failure: results from the ERS International Multicentre Pleural Research Collaborative (IMPACT) registry
Bibliographic record
Abstract
Introduction Pleural effusions caused by organ dysfunction are the commonest pleural disease and account for a huge healthcare burden. Previous work has demonstrated poor survival rates, but there is still uncertainty about determinants of prognosis. This study describes the characteristics and risk factors for poor outcomes in patients with pleural effusion secondary to organ failure in an international cohort. Methods The European Respiratory Society International Multicentre Pleural Research Collaborative (IMPACT) registry includes an international retrospective study of patients with effusions secondary to heart, liver or renal failure, collected from 10 countries in Europe and North and South America between 2019 and 2021. The data were analysed for associations between baseline patient characteristics and key clinical outcomes. Descriptive data were collected on treatments and complications. Results A total of 755 patients contributed data. Overall, 85.2% of effusions were classified as transudates by Light's criteria. 42% of effusions were bilateral. One-year mortality rates were 46% in renal, 35% in hepatic and 33% in cardiac effusions. Increased mortality was observed in neutrophil-predominant effusions (HR 2.001, 95% CI 1.202–3.349, p=0.008), with age (HR 1.013, 95 CI 1.002–1.024, p=0.02) and with N-terminal pro-brain natriuretic peptide >450 pg·mL −1 (HR 1.508, 95% CI 1.191–1.911) in patients with cardiac failure. Therapeutic thoracentesis was the most frequently employed pleural intervention; indwelling pleural catheter use was rare and associated with higher pleural infection rates than thoracentesis. Conclusion This study identifies prognostic factors in an international cohort of patients with transudative pleural effusions. Identification of these risk factors may support treatment approaches in a global population.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".