B-12 | Approaches to and Outcomes of Chronic Total Occlusion Percutaneous Coronary Intervention of the Left Anterior Descending Artery: Insights from the PROGRESS-CTO Registry
Bibliographic record
Abstract
Approaches to and outcomes of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) of the left anterior descending artery (LAD) have received limited study. We examined the procedural characteristics and outcomes of patients who underwent LAD CTO between 2012 and 2022 in a large multicenter CTO PCI registry. The LAD was the CTO target vessel in 3,059 of 11,753 CTO PCIs (26%). Overall technical and procedural success rates were 89% and 88%, respectively. Technical success was most frequently achieved using antegrade wire escalation (AWE) (77% of successful procedures), followed by antegrade dissection and re-entry (ADR) (12%) and retrograde approach (11%). The majority of the study lesions were located within the proximal (38%) and mid (55%) LAD segment and 4.6% of the lesions were located in the first or second diagonal. The distribution of crossing strategies used in the LAD segments was, as follows: proximal LAD (AWE 92%, ADR 16%, and retrograde 24%), mid LAD (AWE 97%, ADR 17%, and retrograde 14%), distal LAD (AWE 95%, ADR 17%, and retrograde 8%), and first/second diagonal (AWE 94%, ADR 11%, and retrograde 17%). Technical success was similar between various locations of LAD CTOs (p=0.683). The incidence of major adverse cardiovascular events was 2% (n=60) and was similar between various locations of LAD CTOs (p=0.106). LAD CTOs were treated with high success and acceptable complication rates, and were more often crossed with AWE, but required frequent use of the retrograde approach, especially for more proximal lesions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".