Total and Cause-Specific Mortality After Percutaneous Coronary Intervention: Observations From the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease Registry
Bibliographic record
Abstract
Background Patients undergoing percutaneous coronary intervention (PCI) are increasingly older and have a higher comorbidity burden. This study evaluated trends in 30-day, 1-year, and 2-year total and cause-specific mortality using a large, contemporary cohort of patients who underwent PCI in Alberta, Canada. Methods We used the A lberta P rovincial Pr oject for O utcome A ssessment in C oronary H eart Disease (APPROACH) registry to identify patients aged ≥ 20 years who underwent PCI between 2005 and 2013. All patients were followed until death or being censored by August 2016. Cause of death was from the Vital Statistics database and classified as cardiac or noncardiac. Multivariable logistic regression was used to calculate predicted mortality at 30 days, 1 year, and 2 years post-PCI. Results Of the 35,602 patients who underwent PCI, 5284 (14.8%) had died. Mean (standard deviation) follow-up was 74.9 (35.1) months. Over the study period, patients were older and more likely to undergo PCI for an acute coronary syndrome indication. Thirty-day (2005: 1.3%; 2013: 3.2%; P < 0.001), 1-year (2005: 2.7%; 2013: 5.7%; P < 0.001), and 2-year (2005: 4.5%; 2013: 7.5%; P < 0.001) predicted mortality after PCI increased over the study period. Cardiac cause of death dominated in the short-term, but the proportion of noncardiac deaths increased as time from PCI to death increased (30 days = 11.5%, 1 year = 31.5%, 2 years = 39.6%; P < 0.001). Conclusions In this population-based study, we found all-cause mortality at 30 days, 1 year, and 2 years after PCI increased over time. Cardiac causes of death dominate in the short-term after PCI; however, noncardiac cause becomes a major driver of mortality in the long-term.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".