Hybrid Strategies for CTO PCI: A Systematic Review and Meta-Analysis of Antegrade and Retrograde Techniques
Bibliographic record
Abstract
Background: Chronic total occlusion percutaneous coronary intervention (CTO PCI) is a complex revascularization procedure requiring advanced techniques to ensure procedural success and safety. Hybrid strategies combining antegrade dissection/re-entry (ADR) and retrograde approaches have become increasingly adopted in contemporary practice. Objectives: To systematically review and synthesize evidence comparing outcomes of ADR and retrograde CTO PCI techniques, with pooled estimates of success rates and adverse events. Methods: This review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane CENTRAL, and Google Scholar for studies published between January 2015 and June 2025. Eligible studies included randomized controlled trials and observational studies reporting outcomes of ADR and/or retrograde CTO PCI. Data extraction was performed by two independent reviewers. Risk of bias was assessed using the Newcastle–Ottawa Scale and the Cochrane RoB 2.0 tool. A random-effects meta-analysis was conducted for consistently reported outcomes. Results: Twenty studies encompassing over 87,000 CTO PCI procedures were included. Pooled analysis of 16 studies demonstrated a technical success rate of 83.4% and a procedural success rate of 84.6%. The in-hospital major adverse cardiac event (MACE) rate was 3.3%. Hybrid strategies integrating ADR and retrograde approaches yielded the highest success rates (86–91%) with acceptable safety profiles. Use of adjunctive tools such as IVUS, dual arterial access, and re-entry devices was associated with improved outcomes. Discussion: Hybrid CTO PCI techniques are safe, effective, and reproducible across diverse clinical settings. When performed by experienced operators using modern adjuncts, these strategies provide durable benefits and should be considered standard for complex occlusions. Limitations include variation in study quality, heterogeneous procedural definitions, and lack of long-term data in several cohorts.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".