Impact of vessel size, lesion length and diabetes mellitus on angiographic restenosis outcomes: Insights from the NIRTOP study
Bibliographic record
Abstract
BACKGROUND: The primary objective of the current analysis was to define the impact of vessel size, lesion length, and diabetes on clinical and angiographic restenosis following implantation of the NIRFLEX stent. METHODS AND RESULTS: Clinical and angiographic restenosis outcomes and multivariate predictors were compared between patients treated in 'small' (<3 mm, n=113 pts/133 lesions) versus 'large' (> or =3 mm, n=41 pts/53 lesions) vessels; between 'tubular' (10-20 mm lesion length n=49 pts/51 lesions) versus 'discrete' (<10 mm lesion length n=103 pts/133 lesions) lesions; and between 'diabetic' (n=30/35 lesions) versus 'non-diabetic' (n=128/156 lesions) patients using the flexible closed-cell design 'bare-metal' NIRFLEX stent in patients with native coronary artery disease. At six month follow-up, target vessel revascularization (TVR) and target lesion revascularization (TLR) rates were significantly less frequent in the 'large' versus 'small' vessel group (2.4% versus 16.8% for TVR, P=0.016, 0% versus 12.4% for TLR, P=0.022). Likewise, angiographic late loss was lower in 'large' versus 'small' vessels (0.54 versus 0.70 mm, P=0.05). Lesion length affected MACE rates but not angiographic restenosis. Angiographic late loss was greater in diabetics compared to the non-diabetic group (0.89 versus 0.60 mm, P=0.003). Using a multivariate model, diabetes mellitus (odds ratio=2.65, P=0.047) and post-procedure in-stent MLD (mm) (odds ratio=0.178, P=0.0019) were major determinants of restenosis. CONCLUSION: Clinical and angiographic restenosis outcomes following NIRFLEX stent implantation were dependent upon vessel size, lesions length, post-procedural stent lumen dimensions, and the diabetic status.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".