Risk factors for in-stent stenosis after flow-diverter implantation for intracranial aneurysm: a single center analysis of 161 consecutive patients
Bibliographic record
Abstract
INTRODUCTION: Flow diverters (FD) are used for the treatment of intracranial aneurysms, by redirecting flow and serving as a scaffold for endothelial coverage of the aneurysm ostium. However, in-stent stenosis has been observed in some patients treated with these devices, the cause of which and the epidemiology remaining elusive. In the current study we aimed to elucidate potential factors leading to higher degree of in-stent stenosis including gender, location, and type of FD. METHODS: The authors queried their institutional Electronic Health Record (EHR) for all patients undergoing FD for intracranial saccular aneurysms. We excluded cases where an FD was performed for a dissecting aneurysm, or other indications. We also excluded patients who had no available follow up. RESULTS: We identified 161 patients undergoing FD for aneurysms, with a mean age of 57.4 (SD = 12.94) and141 (87.6%) of which were females. A total of 24 patients (14.9%) had an in-stent stenosis at a median interval of 10 months; 9 (5.6%) had a severe (i.e. symptomatic or requiring treatment) stenosis. When subsetting for females, we found that females with any in-stent stenosis were significantly younger compared to those without stenosis (51.045, SD = 15.7 vs 58.5, SD = 12.31, p = 0.013). Females with severe in-stent stenosis were even younger (42.2, SD = 14.2 vs 58.3, SD = 12.54; p < 0.001) compared to the rest of the females. Patients presenting with ruptured aneurysm had a higher rate of severe in-stent stenosis (16.7%, n = 4/24, p = 0.014). Regarding devices, patients who underwent treatment with a high-braid FD were more likely to have severe in-stent stenosis (18.8%, n = 3/16; p = 0.016). CONCLUSION: Our findings indicate that younger age, presentation with rupture and high-braid FD may be associated with higher risk of severe in-stent stenosis. These findings may provide more insight into the selection of treatment modality and/or device in patients undergoing management of their cerebral aneurysms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".