Mechanical complications of everolimus-eluting stents associated with adverse events: an intravascular ultrasound study
Bibliographic record
Abstract
AIMS: Mechanical complications contribute to bare metal and first-generation drug-eluting stent (DES) failure. However, the importance of the mechanical complications of second-generation DES remains unclear. We report mechanical complications associated with everolimus-eluting stent (EES) failures. METHODS AND RESULTS: We retrospectively analysed 177 consecutive EES-treated lesions in 136 patients who underwent intravascular ultrasound (IVUS) at follow-up. Mechanical complications were identified in 17 patients (five stable angina, 10 unstable angina, two non-ST-elevation myocardial infarction [NSTEMI] without angiographic thrombus). Fifteen (88.2%) were treated with repeat revascularisation. By IVUS, there were 16 focal (94.1%) and one diffuse (5.9%) in-stent restenoses. Complete stent fracture with separation was seen in only one, partial stent fracture with separation was seen in three, and in 13 there was longitudinal deformation (n=2) or stent strut fracture (n=11) with overlapping of the proximal and distal stent fragments. In 13 EES with evidence of overlapping in the setting of either fracture or deformation, there was a 35.5±12.2% smaller stent area compared to the adjacent proximal and distal stent fragments, and >50% neointimal hyperplasia in 12 (92.3%). CONCLUSIONS: We found EES mechanical complications, often followed by longitudinal deformation or fracture leading to excessive neointimal hyperplasia, in-stent restenosis, and repeat revascularisation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".