Abstract 4360332: Impact of adherence to the global algorithm for initial crossing strategy selection in chronic total occlusion percutaneous coronary intervention
Bibliographic record
Abstract
Background: The effect of selecting the initial crossing strategy using the global chronic total occlusion (CTO) crossing algorithm on the outcomes of CTO percutaneous coronary intervention (PCI) has not been studied. Methods: We examined the clinical and angiographic characteristics and procedural outcomes of 13,852 CTO PCIs at 43 US and non-US centers between 2012 and 2025. Adherence to the global CTO crossing algorithm was defined using three key case characteristics - proximal cap ambiguity, poor distal vessel quality, and use of antegrade dissection/re-entry (ADR) as the primary strategy (Figure 1). Results: Among 13,852 CTO PCIs, 70% (n=9,693) adhered to the global CTO crossing algorithm. Adherence remained consistent over time. Patients in the discordant group (non-adherent) were younger and more likely to have a history of myocardial infarction (MI) rates, while the concordant group (adherent) had more unstable angina presentations and ad hoc procedures. Discordant cases more frequently targeted the right coronary artery (61.5% vs 49.4%, p<0.001) and exhibited greater complexity: longer occlusions, proximal cap ambiguity, blunt/no stump, poor distal vessel quality, and calcification (all p<0.001). Discordant lesions also had higher J-CTO (2.8±1.2 vs 2.1±1.1; p=0.001) and PROGRESS-CTO complication scores. The retrograde approach was utilized more often as the primary crossing strategy in concordant cases (15.3% vs 4.4%; p<0.001) but was less often the successful crossing strategy (14.9% vs 28.1%; p<0.001). Discordant procedures required more stents, longer duration, higher contrast volume, fluoroscopy time, and radiation dose (all p<0.001). Algorithm adherence was associated with higher crossing success with the initially selected technique (72.5% vs 49.4%), technical (87.9% vs 85.6%), and procedural success (86.7% vs 84.2%) (all p<0.001). The incidence of perforation was lower in concordant cases (4.1% vs 6.1%; p<0.001), although major adverse cardiovascular events (MACE) were comparable. On multivariable analysis algorithm adherence was independently associated with technical success (odds ratio 1.24; 95% confidence interval 1.06 – 1.44; p=0.007). Conclusions: Adhering to the global CTO crossing algorithm for initial crossing strategy selection is associated with higher likelihood of success with the initial strategy, better technical success rates, and similar in-hospital MACE.
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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 | 0.006 | 0.021 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".