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Record W4385398463 · doi:10.1136/jnis-2023-snis.89

P-017 Fusiform vs non-fusiform posterior circulation aneurysms treated with flow diversion: a multicenter study

2023· article· en· W4385398463 on OpenAlexaff
Juan Vivanco‐Suarez, Aarón Rodríguez-Calienes, Gustavo M Cortez, Hidehisa Nishi, Vítor Mendes Pereira, Matías Costa, Chaim Feigen, David Altschul, Stavros Matsoukas, Johanna T Fifi, Muhammad Ubaid Hafeez, Peter Kan, Anna Luisa Kühn, Ajit S Puri, Margarita Rabinovic, Ajay K. Wakhloo, Priyank Khandelwal, Yang Lü, Milagros Galecio‐Castillo, Carlos Alva, Mudassir Farooqui, Ricardó A. Hanel, Santiago Ortega‐Gutiérrez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsFusiform AneurysmMedicineAneurysmSurgeryRetrospective cohort studyRadiologyOcclusionFlow diverterInternal medicine

Abstract

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Introduction/Purpose Flow diverters have demonstrated reliable safety and effectiveness for the treatment of selected intracranial aneurysms (IAs) located mainly in the anterior circulation. Posterior circulation aneurysms comprise around 10-15% of all aneurysms. They have an increased risk of rupture compared to equal-sized lesions in the anterior circulation, higher rates of thromboembolic complications during and after treatment, and more complex anatomical configurations. Importantly, the risk for complications may differ substantially based on the aneurysm type. Hence, we aimed to evaluate the safety and effectiveness of FDs in posterior circulation fusiform [FIA] vs. non-fusiform [non-FIA] aneurysms. Materials and Methods We performed a multicenter, retrospective cohort study at 10 centers. All patients treated with FDs for aneurysms located in the posterior circulation (vertebral and basilar arteries) between 2015 and 2022 were included. Patients were divided into two groups according to the morphology of the aneurysm (FIA vs. non-FIA [saccular, blister, dissecting, mycotic]). The effectiveness outcome was measured by the rates of complete aneurysm occlusion using the Raymond-Roy classification at the latest follow-up. Safety outcomes included the incidence of ischemic/hemorrhagic and mortality. Results A total of 97 patients with 97 aneurysms were included. The fusiform group included 28 cases, and the non-FIA 69 (44 saccular, 22 dissecting, 2 blister, 1 mycotic). Median age (FIA, 58 years [46.5-64.5] vs. non-FIA, 61 [47.0-70.0]; p=.579), and female sex (FIA, 56% vs. non-FIA, 63%; p=.565) were not different. Clinical presentation and comorbidities were similar. The rates of premorbid disability (mRS 3-5: FIA, 18% vs. non-FIA, 3%; p=.001) were different. Previous endovascular treatment (p=.793) and location (vertebral artery: FIA, 57% vs. non-FIA, 59%; p=.900) were similar. We found significant differences in median aneurysm size (FIA, 17.0 mm [10.4-27.0] vs. non-FIA, 6.5 [3.4-9.8]; p=.001), and proximal (FIA, 2.8 mm [2.3-3.3] vs. non-FIA, 3.2 [2.4-4.0]; p=.023) and distal landing (FIA, 3.1 mm [2.6-3.8] vs. non-FIA, 3.7 [2.8-4.1]; p=.046) zones were significantly different. The most commonly implanted FD was Pipeline Flex (FIA, 50%, vs. non-FIA, 43%). FIAs had a higher mean FDs per patient (FIA, 1.6±1.3 vs. non-FIA, 1.1±0.1; p=.002) and adjunctive coiling (FIA, 96% vs. non-FIA, 76%; p=.025). There were no differences in the rates of procedural and intrahospital ischemic/hemorrhagic events (p=.534). Follow-up ischemic/hemorrhagic events (FIA, 14.5% vs. non-FIA, 5%; p=.074) were similar. However, there was an increased trend in the rate of mortality events in the FIA group (15%) vs. non-FIA (7%) (p=.246). The rate of complete occlusion was higher in the non-FIA (69% vs. FIA, 54%), but this difference was not statistically significant (p=.116). The overall median follow-up time of 11.6 [1.2-24.8] months. Conclusion We found that the treatment of posterior circulation FIAs with flow diversion had lowers rates of occlusion compared to non-FIA. The safety profile was lower than previously reported, suggesting the need for prospective studies to minimize non-adjudicated self-reporting bias on clinical outcomes. Disclosures J. Vivanco-Suarez: None. A. Rodriguez-Calienes: None. G. Cortez: None. H. Nishi: None. V. Pereira: None. M. Costa: None. C. Feigen: None. D. Altschul: None. S. Matsoukas: None. J. Fifi: None. M. Hafeez: None. P. Kan: None. A. Kühn: None. A. Puri: None. M. Rabinovic: None. A. Wakhloo: None. P. Khandelwal: None. Y. Lu: None. M. Galecio-Castillo: None. C. Alva: None. M. Farooqui: None. R. Hanel: None. S. Ortega-Gutierrez: None.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.258
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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