Hybrid approach improves success of chronic total occlusion angioplasty
Bibliographic record
Abstract
OBJECTIVES: Treatment options for coronary chronic total occlusions (CTO) are limited, with low historical success rates from percutaneous coronary intervention (PCI). We report procedural outcomes of CTO PCI from 7 centres with dedicated CTO operators trained in hybrid approaches comprising antegrade/retrograde wire escalation (AWE/RWE) and dissection re-entry (ADR/RDR) techniques. METHODS: Clinical and procedural data were collected from consecutive unselected patients with CTO between 2012 and 2014. Lesion complexity was graded by the Multicentre CTO Registry of Japan (J-CTO) score, with ≥2 defined as complex. Success was defined as thrombolysis in myocardial infarction 3 flow with <30% residual stenosis, subclassified as at first attempt or overall. Inhospital complications and 30-day major adverse cardiovascular events (MACEs, death/myocardial infarction/unplanned target vessel revascularisation) were recorded. RESULTS: 1156 patients were included. Despite high complexity (mean J-CTO score 2.5±1.3), success rates were 79% (first attempt) and 90% (overall) with 30-day MACE of 1.6%. AWE was highly effective in less complex lesions (J-CTO ≤1 94% success vs 79% in J-CTO score ≥2). ADR/RDR was used more commonly in complex lesions (J-CTO≤1 15% vs J-CTO ≥2 56%). Need for multiple approaches during each attempt increased with lesion complexity (17% J-CTO ≤1 vs 48% J-CTO ≥2). Lesion modification ('investment procedures') at the end of unsuccessful first attempts increased the chance of subsequent success (96% vs 71%). CONCLUSIONS: Hybrid-trained operators can achieve overall success rates of 90% in real world practice with acceptable MACE. Use of dissection re-entry and investment procedures maintains high success rates in complex lesions. The hybrid approach represents a significant advance in CTO treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".