The impact of coronary perforation in percutaneous interventions involving the left main stem coronary artery in the United Kingdom 2007–2014: Insights from the British Cardiovascular Intervention Society database
Bibliographic record
Abstract
BACKGROUND: Percutaneous coronary intervention (PCI) is increasingly utilized for treatment of coronary disease involving the unprotected left main stem (ULMS). However, no studies to date have examined the outcomes of such interventions when complicated by coronary perforation (CP). METHODS: Using the British Cardiovascular Intervention society (BCIS) database, data were analyzed on all ULMS-PCI procedures complicated by CP in England and Wales between 2007 and 2014. Multivariate logistic regressions were used to identify predictors of ULMS CP and to evaluate the association between this complication and outcomes. RESULTS: During 10,373 ULMS-PCI procedures, CP occurred more frequently than in non-ULMS-PCI (0.9 vs. 0.4%, p < .001) with a stable annual incidence. Covariates associated with CP included number of stents used, female gender, use of rotational atherectomy and chronic total occlusion (CTO) intervention. Adjusted odds of adverse outcomes for ULMS-PCI complicated by CP were higher for peri-procedural complications including cardiogenic shock, tamponade, side-branch loss, DC cardioversion, in-hospital major bleeding, transfusion requirement, and peri-procedural myocardial infarction. There were also significantly increased odds for in-hospital major adverse cardiac events (MACCE, OR 8.961, 95% CI [4.902-16.383]) and 30-day mortality (OR 5.301, 95% CI [2.741-10.251]). CONCLUSIONS: CP is an infrequent event during ULMS-PCI and is predicted by female gender, rotational atherectomy, CTO interventions or number of stents used. CP was associated with significantly higher odds of mortality and morbidity, but at rates similar to previously published all-comer PCI complicated by CP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".