Complex High-Risk Percutaneous Coronary Intervention Types, Trends, and Outcomes in Nonsurgical Centres
Bibliographic record
Abstract
BACKGROUND: Limited data are available on complex high-risk percutaneous coronary intervention (CHiP) trends and outcomes in nonsurgical centres (NSCs), particularly in health care systems where most centres are NSCs. METHODS: Using data from a national registry, we studied the characteristics and outcomes of CHiP procedures performed for stable angina from 2006 to 2017 according to the presence or absence of on-site surgical cover. Multivariate regression analyses and propensity score matching were used to determine risks for in-hospital death, major bleeding, and major cardiovascular or cerebral events (MACCE). RESULTS: Out of 134,730 CHiP procedures, 42,433 (31.5%) were performed in NSCs, increasing from 12.5% in 2006 to 42% in 2017. Compared with surgical centres (SCs), patients who had a CHiP procedure undertaken in NSCs were, on average, 2.4 years older and had a greater prevalence of cardiovascular risks. Common CHiP procedures performed in NSCs included poor left ventricular function (41.6%), chronic renal failure (38.8%), and chronic total occlusion percutaneous coronary intervention (31.1%). NSC-based CHiP is associated with lower odds of mortality (adjusted odds ratio [aOR] 0.7, 95% confidence interval [CI] 0.5-0.8) and major bleeding (aOR 0.7, 95% CI 0.6-0.8). In both groups, MACCE odds were similar (aOR 1.0, 95% CI 0.9-1.1). CONCLUSIONS: CHiP numbers have steadily increased in NSCs. NSC patients were older and had a higher prevalence of cardiovascular risks than SC patients. Mortality and major bleeding odds were significantly lower in those cases undertaken in NSCs, although MACCE odds were not different between the groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".