OCT-based diagnosis, management, and predictors of recurrent stent failure: a cohort study
Bibliographic record
Abstract
Background Stent failure (SF) is a complication of percutaneous coronary intervention (PCI). Objectives This study aimed to assess the relationship of the optical coherence tomography (OCT) determined cause of SF with time since stent implantation, treatment, and outcome. Methods This retrospective study included patients who underwent an OCT evaluation for SF from January 2013 to July 2023. In-stent findings were evaluated on OCT including tissue proliferation, tissue type, underexpansion, thrombus, and multiple stent layers. The relationship between time to presentation, treatment, and outcome was assessed. Results Of the 309 patients who underwent an OCT-guided PCI for SF, tissue proliferation was present in 228 (74%) and absent in 81 (26%). Among patients with tissue proliferation, OCT commonly showed lipidic neointima (n = 122, 54%), thrombus (n = 81, 36%), and underexpansion (n = 71, 31%). In patients without tissue proliferation, OCT commonly identified underexpansion (n = 58, 72%), thrombus (n = 55, 68%), and uncovered struts (n = 37, 46%). The mean time to SF was 6.89 ± 5.88 years with tissue proliferation and 2.98 ± 3.75 years without (p < 0.001). Patients with tissue proliferation were more likely to be treated with repeat stenting (78% vs. 60%, p < 0.001). Lipidic neointimal tissue and >1 layer of stent were predictors of target SF recurrence during a median 3 years of follow-up. Conclusion In a large series of OCT-guided treatments of SF, tissue proliferation was more common, occurred later after stent implantation, and was more likely to be treated with repeat stenting than no-tissue proliferation. Lipidic neointimal tissue and >1 layer of stent were significant predictors of target SF during follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".