Dataset Information

Analysis of the wall thickness of intracranial aneurysms: Can computational fluid dynamics detect the translucent areas of saccular intracranial aneurysms and predict the rupture risk preoperatively?

ABSTRACT:

Background and purpose

The translucent area on the surface of intracranial aneurysms (IAs) is associated with rupture risk. In the present study, the Polyflow module of the Ansys software was used to simulate and analyze the thickness of the aneurysm wall to detect whether it was "translucent" and to assess the rupture risk.

Methods

Forty-five patients with 48 IAs who underwent microsurgery were retrospectively reviewed. The medical records, radiographic data, and intraoperative images of the patients were collected. The image data were analyzed using computational fluid dynamics (CFD) simulations to explore the relationship between the simulated thickness of the aneurysm wall, the translucent area, and the rupture point of the real aneurysm's surface to predict the rupture risk and provide a certain reference basis for clinical treatment.

Results

The Polyflow simulation revealed that the location of the minimum extreme point of the simulated aneurysm wall thickness was consistent with the translucent area or rupture point on the surface of the real aneurysm. There was a downward trend in the correlation between the change rate (I_S) in the wall area and volume during aneurysm growth and rupture. Ruptured aneurysms have a greater inhomogeneity coefficient I_δ than the unruptured ones. In the unruptured group, translucent aneurysms also had greater inhomogeneity coefficients I_δ and more significant thickness changes (multiple I_BA) than non-translucent ones.

Conclusions

The Ansys software Polyflow module could detect whether the unruptured aneurysms were translucent and predict the rupture risk and rupture point.

Clinical trial registration

https://clinicaltrials.gov/, Identifier, NCT03133624.

SUBMITTER: Fan XX

PROVIDER: S-EPMC9869126 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Analysis of the wall thickness of intracranial aneurysms: Can computational fluid dynamics detect the translucent areas of saccular intracranial aneurysms and predict the rupture risk preoperatively?

Fan Xin-Xin XX Geng Jie-Wen JW He Chuan C Hu Peng P Sun Li-Yong LY Zhang Hong-Qi HQ

Frontiers in neurology 20230109

<h4>Background and purpose</h4>The translucent area on the surface of intracranial aneurysms (IAs) is associated with rupture risk. In the present study, the Polyflow module of the Ansys software was used to simulate and analyze the thickness of the aneurysm wall to detect whether it was "translucent" and to assess the rupture risk.<h4>Methods</h4>Forty-five patients with 48 IAs who underwent microsurgery were retrospectively reviewed. The medical records, radiographic data, and intraoperative i ...[more]

PMID: 36698880

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Project description:BackgroundIntravenous thrombolysis seems safe in acute ischemic stroke patients with saccular, unruptured intracranial aneurysms (UIAs), but little is known about the differences in cardiovascular risk factors and outcomes between intravenous thrombolysis-treated stroke patients with and without UIAs. We hypothesized that UIA patients would have a higher burden of cardiovascular risk factors and, therefore, a higher risk of an unfavorable outcome.MethodsIn this prospective cohort study conducted in Helsinki University Hospital, we identified intravenous thrombolysis-treated patients with concurrent saccular UIAs admitted to a comprehensive stroke center between 2005 and 2019 using 2 overlapping methods. For each UIA patient, a control patient was identified and matched (1:1) for age, sex, admission year, and stroke severity. The primary outcome was an unfavorable outcome at 3 months, defined as a modified Rankin Scale (mRS) score 3 to 6. The secondary outcomes were an excellent outcome (mRS score 0-1) at 3 months and mRS difference in shift analysis.ResultsIn total, 118 UIA patients and 118 matched control patients were identified. The UIA patients were more often current smokers, and their admission systolic blood pressure was higher. The rate of hemorrhagic complications did not differ between the groups. UIAs were not associated with an unfavorable outcome in the conditional logistic regression analysis (odds ratio, 1.41 [95% CI, 0.79-2.54]; P=0.25). However, the UIA patients were less likely to have excellent outcomes (odds ratio for non-excellent outcome, 2.09 [95% CI, 1.13-3.85]; P=0.02). In shift analysis, UIAs were associated with higher mRS (odds ratio, 1.61 [95% CI, 1.03-2.49]; P=0.04).ConclusionsThe intravenous thrombolysis-treated stroke patients with UIAs were more often current smokers and had higher systolic blood pressure than the matched patients without UIAs. They were as likely to have unfavorable outcomes at 3 months but seemed less likely to achieve excellent outcomes and were more likely to have higher mRS in shift analysis.

Project description:BACKGROUND AND PURPOSE:Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture. METHODS:We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs). RESULTS:Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (?5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model. CONCLUSIONS:Small (<5 mm) and large (?5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Dataset Information

Analysis of the wall thickness of intracranial aneurysms: Can computational fluid dynamics detect the translucent areas of saccular intracranial aneurysms and predict the rupture risk preoperatively?

Background and purpose

Methods

Results

Conclusions

Clinical trial registration

Publications

Analysis of the wall thickness of intracranial aneurysms: Can computational fluid dynamics detect the translucent areas of saccular intracranial aneurysms and predict the rupture risk preoperatively?

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