Intracoronary imaging in PCI for acute coronary syndrome: Insights from British Cardiovascular Intervention Society registry
Bibliographic record
Abstract
BACKGROUND: While previous studies have demonstrated the superiority of ICI-guided PCI over an angiography-based approach, there are limited data on all-comer ACS patients. This study aimed to identify the characteristics and in-hospital outcomes of patients undergoing intracoronary imaging (ICI) guided percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS). METHODS: All patient undergoing PCI for ACS in England and Wales between 2006 and 2019 were retrospectively analyzed and stratified according to ICI utilization. The outcomes assessed were in-hospital all-cause mortality and major adverse cardiovascular and cerebrovascular events (MACCE) using multivariable logistic regression models. RESULTS: 598,921 patients underwent PCI for ACS, of which 41,716 (7.0 %) had ICI which was predominantly driven by IVUS use (5.6 %). ICI use steadily increased from 1.4 % in 2006 to 13.5 % in 2019. Adjusted odds of mortality (OR 0.69, 95%CI 0.58-0.83) and MACCE (OR 0.77, 95%CI 0.73-0.83) were significantly lower in the ICI group. The association between ICI and improved outcomes varied according to vessel treated with both left main stem (LMS) and LMS/left anterior descending (LAD) PCI associated with significantly lower odds of mortality (OR 0.34, 95%CI 0.27-0.44, OR 0.51 95%CI 0.45-0.56) and MACCE (OR 0.44 95%CI 0.35-0.54, OR 0.67 95%CI 0.62-0.72) respectively. CONCLUSIONS: Although ICI use has steadily increased, less than one in seven patients underwent ICI-guided PCI. The association between ICI use and improved in-hospital outcomes was mainly observed in PCI procedures involving LMS and LAD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".