Double-Kissing Nanocrush for Bifurcation Lesions: Development, Bioengineering, Fluid Dynamics, and Initial Clinical Testing
Bibliographic record
Abstract
BACKGROUND: When possible, a single-stent technique to treat coronary bifurcation disease is preferable. However, when 2 stents are required, there is scope to improve on existing techniques. The crush technique has already been improved with the introduction of double-kissing (DK) and minicrush. We sought to refine and simplify the minicrush technique, retaining its advantages while avoiding its disadvantages, by developing a DK nanocrush technique. METHODS: The DK nanocrush method allows complete lesion coverage of a bifurcation lesion without excessive metal layers. This is achieved by positioning the side branch (SB) stent with minimal protrusion into the main branch (MB), implantation of the SB stent with an undeployed balloon in the MB, immediate kissing-balloon inflation with formation of a minimal neocarina, stenting the MB, recrossing the proximal part of the SB without crossing a double metal layer, and final kissing. We demonstrate this technique with benchtop implantation, microscopic computed tomographic reconstruction, computational fluid dynamics (CFD) modelling, and clinically with the use of angiographic and intravascular imaging. RESULTS: The DK nanocrush was practically feasible and resulted in full ostial coverage. CFD analysis demonstrated minimally disturbed blood flow. The technique was successfully utilised in 9 patients with bifurcation lesions with excellent angiographic outcomes and no adverse events over 12 months. CONCLUSIONS: The DK nanocrush technique may represent the ultimate refinement of the original crush technique with a number of practical and theoretical advantages. It remains to be tested against other bifurcation techniques in prospective trials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".