Vascular responses to coronary calcification following implantation of newer-generation drug-eluting stents in humans: impact on healing
Bibliographic record
Abstract
AIMS: Vascular calcification is routinely encountered in percutaneous coronary intervention (PCI) and severe coronary calcification is a known predictor of in-stent restenosis and stent thrombosis. However, the histopathologic mechanisms behind such events have not been systematically described. METHODS AND RESULTS: From our registry of 1211 stents, a total of 134 newer-generation drug-eluting stents (DES) (Xience, Resolute-Integrity, PROMUS-Element, and Synergy) with duration of implant ≥30 days were histologically analysed. The extent of calcification of the stented lesions was evaluated radiographically and divided into severe (SC, n = 46) and non-severely calcified lesions (NC, n = 88). The percent-uncovered struts per section {SC vs. NC; median 2.4 [interquartile range (IQR) 0.0-19.0] % vs. 0.0 (IQR 0.0-4.6) %, P = 0.02} and the presence of severe medial tears (MTs) (59% vs. 44%, respectively, P = 0.03) were greater in SC than NC. In addition, SC had a higher prevalence of ≥3 consecutive struts lying directly in contact with surface calcified area (3SC) (52% vs. 8%, respectively, P < 0.0001). Multivariate analysis demonstrated that sections with duration of implantation ≤6 months [odds ratio (OR): 7.7, P < 0.0001], 3SC (OR: 6.5, P < 0.0001), strut malapposition (OR: 5.0, P < 0.0001), and lack of MTs (OR: 2.5, P = 0.0005) were independent predictors of uncovered struts. Prevalence of neoatherosclerosis was significantly lower in SC than that of NC (24% vs. 44%, P = 0.02). CONCLUSION: Severe calcification, especially surface calcified area is an independent predictor of uncovered struts and delayed healing after newer-generation DES implantation. These data expand of knowledge of the vascular responses of stenting of calcified arteries and suggests further understand of how best to deal with calcification in patients undergoing PCI.
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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 | 0.000 |
| 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 | 0.000 | 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".