Time Trends in Cause-Specific Mortality in Patients with Pulmonary Embolism Aged 50 Years and Older
Bibliographic record
Abstract
Background: Patients with pulmonary embolism (PE) have high mortality rates. However, data on cause-specific mortality trends in this population are limited. Aims: To study time trends in cause-specific mortality among PE patients aged ≥50 years, analyzed across three time periods: 2006-2011, 2012-2017, and 2018-2023. The secondary aims included examining mortality trends in matched controls and subgroups of PE patients. Methods: This nationwide Swedish register study included patients with a first-time PE and matched controls. We assessed 30-day and 31- to 365-day cause-specific mortality and employed age- and sex-adjusted Poisson regression for the relative risk (RR) for annual mortality trends. Results: The study comprised 115,476 patients, with cancer as the leading cause of 30-day mortality, stable at 4.7% from 2006-2011 to 2018-2023 (RR 1.00; 95% confidence interval [CI]: 0.99-1.01). Mortality from fatal venous thromboembolism (VTE) decreased from 2.7 to 1.3% (RR 0.94; 95% CI: 0.93-0.95), and cardiovascular disease from 2.3 to 1.1% (RR 0.94; 95% CI: 0.93-0.94). The 31- to 365-day mortality from cancer was stable at 11.8% in 2006-2011 and 11.4% in 2018-2022 (RR 1.00; 95% CI: 0.99-1.00), while mortality due to cardiovascular disease decreased from 4.1 to 2.3% (RR 0.96; CI: 0.95-0.96), and fatal VTE from 0.8 to 0.5% (RR 0.95; 95%: 0.93-0.96). Subgroup analysis showed a decrease in cancer-related mortality among PE patients with known cancer, while it increased in those without known cancer. Conclusion: Cancer was the leading cause of death in PE patients aged ≥50 years, with stable rates over time due to contrasting trends in patients with and without known cancer. Fatal VTE comprised a minor percentage of overall mortality in recent years.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".