Impact of stent strut thickness on arterial healing after drug-eluting stents implantation assessed by optical coherence tomography
Bibliographic record
Abstract
Purpose: We have previously shown that stents with thinner-struts are associated with a reduced rate of restenosis after bare metal stenting. The aim of this study was to evaluate the impact of strut thickness on arterial healing after drug-eluting stent (DES) implantation as assessed by optical coherence tomography (OCT) at 6-8 months follow-up. Methods: We included 72 patients with 80 de novo lesions undergoing DES implantation and OCT follow-up at 6-8 months after stent implantation at 2 centers in Munich, Germany. Patients were stratified according to total strut thickness (strut thickness plus coating thickness) as thin-strut DES (≥100μm thickness; n=37; Orsiro sirolimus-eluting stents, Xience everolimus-eluting stents) or thick-strut DES (> 100μm thickness; n=43; Yukon PC Choice sirolimus-eluting stents, Resolute-zotarolimus-eluting stents, Nobori-biolimus-eluting stents). The primary endpoint was the rate of uncovered struts at follow-up. To account for clustering of the data, strut-level data in both groups were compared using a generalized linear mixed model approach. Results: The rate of uncovered struts was 10.7% [95% confidence interval (CI): 2.6 – 35.1%] with thin-strut DES versus 19.0% [95% CI: 4.2 – 55.7%] with thick-strut DES (Odds ratio (OR) 0.38 [95% CI: 0.19 – 0.76], p=0.006). No differences in neointimal thickness above the struts were observed between groups (thin-strut DES: 99 μm [95% CI: -120 – 318] versus thick-strut DES: 82 μm [95% CI: 136 - 300] OR 1.02 [95% CI: 0.99 – 1.06], p=0.23), Figure. Conclusions: As compared to thick-strut DES, thin-strut DES were associated with improved rates of stent strut coverage as assessed by OCT at 6-8 months follow-up. In struts with coverage, there was no difference in neointimal hyperplasia between thin- and thick-strut DES.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".