Percutaneous management of coronary bifurcation lesions: current perspective
Bibliographic record
Abstract
PURPOSE OF REVIEW: Coronary artery disease affecting bifurcations poses a challenge for percutaneous intervention. Several techniques and strategies for percutaneous management of bifurcation lesions have been described in the literature with variable data available for outcomes. In this review, we provide an overview of the strategies and techniques used for percutaneous intervention of bifurcation lesions with an update of recent technical developments and clinical evidence. RECENT FINDINGS: The coronary bifurcation lesions of both left main and other coronary segments are best treated with a provisional stenting strategy where main branch is treated with a stent placement and side branch intervention reserved for angiographically or physiologically determined hemodynamically significant residual stenosis despite application of a proximal stent optimization technique. When a provisional stent strategy is not likely to be successful due to anatomic or morphologic lesion characteristics and a large side branch or distal left main bifurcation is involved, an upfront bifurcation stenting strategy with double kissing crush technique may provide the optimum results. Coronary imaging and fractional flow determination may guide lesion specific management, facilitate device selection and improve clinical outcomes for percutaneous therapy for bifurcation lesions. SUMMARY: Despite advances in technology and procedural techniques, percutaneous intervention of coronary bifurcation lesions remains challenging and associated with higher adverse outcomes compared to non bifurcation lesions. Among the several bifurcation strategies, a provisional stenting approach is preferred for technical simplicity and better long term outcomes. Double kissing crush technique provides superior clinical results and should be preferred when a two stent strategy is indicated. Use of coronary imaging and physiology assessment should be incorporated in the algorithm of bifurcation interventions for greater technical and clinical success.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".