Coronary lithotripsy for the treatment of underexpanded stents: the international multicentre CRUNCH registry
Bibliographic record
Abstract
<b>BACKGROUND: </b> Stent underexpansion increases the risk of cardiac adverse events. At present, there are limited options to treat refractory stent underexpansion. In this context, the intravascular lithotripsy (IVL) system might be a safe and effective strategy. AIMS: We aimed to evaluate the safety and efficacy of IVL in addressing resistant stent underexpansion due to heavy underlying calcification. <b>METHODS: </b> This was an international multicentre registry including patients receiving IVL therapy to treat stent underexpansion from December 2017 to August 2020. Angiographic and intracoronary imaging data were collected. The efficacy endpoint was device success (technical success with a final percentage diameter stenosis <50%). The safety endpoint was in-hospital major adverse cardiac events (MACE). <b>RESULTS: </b> Seventy patients were included, the mean age was 73±9.2 years and 76% were male. The median time from stent implantation to IVL therapy was 49 days (0-2,537). Adjuvant treatment with non-compliant balloon dilatations pre- and post-IVL was performed in 72.3% and 76.8% of patients, respectively, and additional stenting was performed in 22.4%. Device success was 92.3%. Minimum lumen diameter increased from 1.49±0.73 mm to 2.41±0.67 mm (p<0.001) and stent expansion increased by 124.93±138.19% (p=0.016). No IVL-related procedural complications or MACE were observed. The use of bailout IVL therapy directly after stenting and the presence of ostial underexpanded lesions negatively predicted lumen diameter gain. <b>CONCLUSIONS: </b> Coronary lithotripsy is safe and effective in increasing lumen and stent dimensions in underexpanded stents secondary to heavily calcified lesions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".