Factors Determining Periprocedural and Long-term Complications of High Risk Carotid Artery Stenting
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid artery stenting (CAS) has been, historically, an alternative to open endarterectomy (CEA) for stroke prevention in high risk patients with carotid atherosclerosis. We sought to determine the rates of periprocedural and long-term stroke or death and the risk factors for complications after CAS in our high risk patient population. METHODS: Clinical and treatment variables of consecutive CAS procedures performed between 2002 and 2011 were analyzed. Using univariate and multivariate logistic regression analyses we examined how patient characteristics influenced outcomes and changes in modified Rankin Score (mRS). RESULTS: In 152 patients, the composite total of periprocedural death, stroke, transient ischemic attack (TIA) and myocardial infarction (MI) rate was 3.95% (6/152). Chronic kidney disease (CKD) was strongly associated with periprocedural complications (p<0.001). Coronary artery disease/peripheral vascular disease (CAD/PVD) (p=0.03), dyslipidemia (p=0.02), CKD (p=0.01), and contralateral internal carotid artery stenosis (p=0.02) were non-modifiable risk factors for mRS increase. There were 25 deaths, 8 strokes, 11 TIAs, and 1 MI (mean follow-up 38.4 months, range 0-116 months). The presence of CAD/PVD (p=0.009) and dyslipidemia (p=0.002) were significantly associated with long-term complications. CONCLUSION: CAS was performed with low periprocedural complications in high-risk patients. Our rates compare very favorably to large-scale trials that have ideal patients. This data encourages the consideration of CAS in patients considered high risk for CEA and provides possible patient characteristics (CKD) to help with periprocedural risk stratification.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".