Culprit Vessel Revascularization Prior to Diagnostic Angiography as a Strategy to Reduce Delays in Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Delays are important markers of quality of care in primary percutaneous coronary intervention. There is scarce data on the impact of obtaining a complete diagnostic angiography before primary percutaneous coronary intervention. METHODS AND RESULTS: Consecutive patients treated with primary percutaneous coronary intervention at our institution between January 2012 and December 2014 were studied. After excluding patients with prior coronary artery bypass surgery, 925 patients were included in the analysis. Patients were classified into 3 groups according to the as-treated revascularization strategy: culprit-vessel revascularization first, contralateral angiography first, or complete angiography first. Propensity score matching was used to minimize difference in clinical characteristics between groups. Predictors of culprit-vessel first revascularization were anterior/lateral infarct location and absence of diabetes mellitus. After propensity score matching, the median vascular access-to-balloon time was 4 to 6 minutes shorter with a culprit-vessel revascularization first strategy. This reduction in time to reperfusion increased the proportion of patients treated within recommended delays. However, there was no significant difference in 30-day clinical outcomes associated with these delays reduction. CONCLUSIONS: Performing culprit-vessel primary percutaneous coronary intervention before contralateral or complete diagnostic angiography is associated with a statistically significant reduction in vascular access-to-balloon time, although the 4- to 6-minute difference is unlikely to be clinically relevant. This small but significant reduction could translate in an augmentation in the proportion of patients treated within recommended delays.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".