Failure Mechanisms and Neoatherosclerosis Patterns in Very Late Drug-Eluting and Bare-Metal Stent Thrombosis
Bibliographic record
Abstract
BACKGROUND: There are few clinical studies on the pathophysiological mechanisms of very late stent thrombosis (VLST). We report optical coherence tomography findings in patients with VLST and compare the findings between bare-metal stents (BMS) and drug-eluting stents (DES). METHODS AND RESULTS: We conducted a registry of stent thrombosis at 4 North American centers with optical coherence tomography imaging programs SAFE registry (The Study of Late Stent Failure Evaluated by OCT). Images were acquired in 61 patients (42 DES and 19 BMS) presenting with definite VLST. The median duration from implantation to VLST presentation was 51.4 months in the DES and 69.9 months in the BMS group (P=0.011). Uncovered and malapposed struts were observed in 70.5% (43/61) and 62.3% (38/61) of patients, respectively, whereas neoatherosclerosis was revealed in 49.2% (30/61). Stent underexpansion was observed in 42.4% of patients. Malapposed struts and stent underexpansion were more frequently demonstrated in DES than in BMS patients, whereas neoatherosclerosis was frequently observed in BMS (40.5% in DES and 68.4% in BMS; P=0.056). The percentage of frames with neoatherosclerosis was lower in DES than in BMS (15.56% [12.24-28.57] versus, 56.41% [40.74-70.00], respectively; P<0.001). Maximum consecutive lipid neointima length was shorter in DES than in BMS (2.4 [1.2-3.6] and 5.3 [3.0-7.0] mm; P=0.011). CONCLUSIONS: Optical coherence tomography imaging demonstrated that VLST in DES and BMS had a wide variety of abnormal findings, such as neoatherosclerosis, uncovered strut, and malapposed strut. Neoatherosclerosis and lipid neointima were more frequently observed and had more longitudinal extension in BMS compared with DES.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".