Overview of Contemporary Chronic Total Occlusion Percutaneous Coronary Intervention Techniques: A Narrative Systematic Review
Bibliographic record
Abstract
BACKGROUND: Despite the abundance of coronary chronic total occlusions (CTO) percutaneous coronary intervention (PCI) studies, the literature is not easy to digest for both general PCI operators and CTO PCI specialists because of the many varied terms used for approaches and inconsistency in terminology. This inconsistency makes it challenging to understand the advantages and disadvantages of these different approaches and, most importantly, their downstream clinical outcomes. Accordingly, we conducted a systematic review of all published studies on CTO PCI to describe techniques and algorithms used in the last decade to provide an overview on the efficacy and safety of contemporary CTO PCI techniques. METHODS: We performed a comprehensive search of the PubMed, EMBASE, and the Cochrane library databases for manuscripts about PCI of CTOs. We included studies published between the years 2005 and 2019. We categorized studies into those using a single approach (antegrade, retrograde) and those with a prespecified algorithm (ie, hybrid approach). RESULTS: Fifty-five observational studies including 28,907 patients who underwent CTO were included in this review. CTO PCI generally carries low risk of major procedural complications, with angiographic success rates being higher in studies that used an algorithmic vs single technical approach. CONCLUSIONS: This systematic review highlights the wide variation in definitions and practices in CTO PCI and calls for standardization in terminology and practice.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".