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Record W4283454262 · doi:10.1016/j.jvssci.2022.05.056

Identification of geometric and mechanical factors predictive of bird-beak configuration in thoracic endovascular aortic repair using computational models of stent graft deployment

2022· article· en· W4283454262 on OpenAlexafffund
Negin Shahbazian, David A. Romero, Thomas L. Forbes, Cristina H. Amon

Bibliographic record

VenueJVS Vascular Science · 2022
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBeakThoracic aortic aneurysmAortic repairStentAortic archAortaAnatomyAortic aneurysmRadiologySurgeryMedicineBiology

Abstract

fetched live from OpenAlex

Objective: Formation of a bird-beak configuration in thoracic endovascular aortic repair (TEVAR) has been shown to be influenced by various factors. However, the main cause of bird-beak formation remains poorly understood. The hypothesis has been that the geometric and mechanical properties of both the aorta and the stent graft contribute to the formation and extent of a bird-beak configuration. The goal of the present study was to use parameter-based computational simulations of TEVAR to predict for bird-beak formation and identify its most significant contributing factors. Methods: In the present study, we considered five parameters for the computational simulations of TEVAR, including aortic curvature, aortic arch angle, age as a surrogate for thoracic aortic tissue properties, TEVAR landing zone, and stent graft oversizing. Using an experimental design approach, computational models for 160 TEVAR scenarios were developed by varying the values of the simulation parameters within clinically relevant ranges. The bird-beak length and angle were used as metrics to evaluate the simulation results. Statistical analysis of the simulation data using a random forest model was conducted to identify significant parameters and interactions. Results: The mean ± standard deviation of the bird-beak length and angle across 160 simulations were 4.32 ± 4.87 mm and 9.16° ± 12.21°, respectively. The largest mean bird-beak length and angle were found in the most distal location in zone 0 (10.04 mm) and zone 2 (21.48°), respectively. An inverse correlation was found between the aortic arch angle and the bird-beak length and angle. In ∼75% of the scenarios, increased stent graft oversizing either fully resolved the presence of the bird-beak configuration or had reduced its size. In the remaining 25%, oversizing minimally changed the bird-beak length and enlarged the bird-beak angle, which mainly occurred in cases with a smaller aortic arch angle and landing zones near the arch apex. This was justified by the mechanism of stent graft bending in the arch angulation. The aortic curvature and tissue properties were shown to be statistically insignificant in relation to bird-beak formation. Conclusions: Significant parameters predictive of a bird-beak configuration in TEVAR were identified, and the trends in which each parameter influenced the bird-beak size were determined. The findings from the present study can inform the surgical planning and device selection process with the goal of minimizing bird-beak formation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

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

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 head0.043
GPT teacher head0.297
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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