Ultrathin-strut versus thin-strut stent healing and outcomes in preclinical and clinical subjects
Bibliographic record
Abstract
BACKGROUND: Compared with thin-strut durable-polymer drug-eluting stents (DP-DES), ultrathin-strut biodegradable-polymer sirolimus-eluting stents (BP-SES) improve stent-related clinical outcomes in patients undergoing percutaneous coronary intervention (PCI). Reduced stent strut thickness is hypothesised to underlie these benefits, but this conjecture remains unproven. AIMS: We aimed to assess the impact of strut thickness on stent healing and clinical outcomes between ultrathin-strut and thin-strut BP-SES. METHODS: First, we performed a preclinical study of 8 rabbits implanted with non-overlapping thin-strut (diameter/thickness 3.5 mm/80 μm) and ultrathin-strut (diameter/thickness 3.0 mm/60 μm) BP-SES in the infrarenal aorta. On day 7, the rabbits underwent intravascular near-infrared fluorescence optical coherence tomography (NIRF-OCT) molecular-structural imaging of fibrin deposition and stent tissue coverage, followed by histopathological analysis. Second, we conducted an individual data pooled analysis of patients enrolled in the BIOSCIENCE and BIOSTEMI randomised PCI trials treated with ultrathin-strut (n=282) or thin-strut (n=222) BP-SES. The primary endpoint was target lesion failure (TLF) at 1-year follow-up, with a landmark analysis at 30 days. RESULTS: NIRF-OCT image analyses revealed that ultrathin-strut and thin-strut BP-SES exhibited similar stent fibrin deposition (p=0.49) and percentage of uncovered stent struts (p=0.63). Histopathological assessments corroÂborated these findings. In 504 pooled randomised trial patients, TLF rates were similar for those treated with ultrathin-strut or thin-strut BP-SES at 30-day (2.5% vs 1.8%; p=0.62) and 1-year follow-up (4.3% vs 4.7%; p=0.88). CONCLUSIONS: Ultrathin-strut and thin-strut BP-SES demonstrate similar early arterial healing profiles and 30-day and 1-year clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".