Bioresorbable vascular scaffold to treat in‐stent restenosis: Single‐center experience
Bibliographic record
Abstract
AIMS: The management of patients with in-stent restenosis (ISR) is still a major clinical challenge even in the era of drug-eluting stents (DES). Recent studies have demonstrated acceptable clinical outcomes for the everolimus-eluting bioresorbable vascular scaffold (BVS) ABSORB™ in patients with stable coronary artery disease but data are scarce on its use in patients with ISR. We report the long-term results of our preliminary experience with this novel approach at our institution. METHODS AND RESULTS: We investigated the safety and efficacy of BVS implantation to treat ISR. 34 consecutive patients (37 lesions) underwent PCI for ISR with BVS implantation between May 2013 and June 2015 at our institution and were included in the current analysis. Follow-up was available in 91.9% of the patients. Mean follow-up period was 801.9 ± 179 days. One patient had definite scaffold thrombosis (ScT) 2 months after stent implantation which was treated with DES. Five patients (six lesions) experienced target lesion revascularization (TLR). The composite endpoint rate of TLR, ScT, myocardial infarction, and death occured in 6/37 lesions at follow-up (16.2%). CONCLUSIONS: These real-world data using BVS in patients with ISR demonstrates that ISR treatment with ABSORB™ BVS is feasible but could have slightly higher target lesion failure rates as compared to DES. This proof of concept could be hypothesis-generating for larger randomized controlled studies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".