High wall shear stress beyond a certain range in the parent artery could predict the risk of anterior communicating artery aneurysm rupture at follow-up

Impinging Flow Mediates Vascular Endothelial Cell Injury through the PKCα/ERK/PPARγ Pathway in vitro

INTRODUCTION

This study aimed to elucidate the mechanisms underlying endothelial injury in the context of intracranial aneurysm formation and development, which are associated with vascular endothelial injury caused by hemodynamic abnormalities. Specifically, we focus on the involvement of PKCα, an intracellular signaling transmitter closely linked to vascular diseases, and its role in activating MAPK. Additionally, we investigate the protective effects of PPARγ, a vasculoprotective factor known to attenuate vascular injury by mitigating the inflammatory response in the vessel wall.

METHODS

The study employs a modified T-chamber to replicate fluid flow conditions at the artery bifurcation, allowing us to assess wall shear stress effects on human umbilical vein endothelial cells in vitro. Through experimental manipulations involving PKCα knockdown and Ca2+ and MAPK inhibitors, we evaluated the phosphorylation status of PKCα, NF-κB, ERK5, ERK1/2, JNK1/2/3, and P38, as well as the expression levels of PPARγ, NF-κB, and MMP2 via Western blot analysis. The cellular localization of phosphorylated NF-κB was determined using immunofluorescence.

RESULTS

Our results showed that impinging flow resulted in the activation of PKCα, followed by the phosphorylation of ERK5, ERK1/2, and JNK1/2/3, leading to a decrease in PPARγ expression, an increase in the expression of NF-κB and MMP2, and the induction of apoptotic injury. Inhibition of PKCα activation or knockdown of PKCα using shRNA leads to a suppression of ERK5, ERK1/2, JNK1/2/3, and P38 phosphorylation, an elevation in PPARγ expression, and a reduction in NF-κB and MMP2 expression, alleviated apoptotic injury. Furthermore, we observe that the regulation of PPARγ, NF-κB, and MMP2 expression is influenced by ERK5 and ERK1/2 phosphorylation, and activation of PPARγ effectively counteracts the elevated expression of NF-κB and MMP2.

CONCLUSION

Our findings suggest that the PKCα/ERK/PPARγ pathway plays a crucial role in mediating endothelial injury under conditions of impinging flow, with potential implications for vascular diseases and intracranial aneurysm development.

Huperzine A ameliorates neurological deficits after spontaneous subarachnoid hemorrhage through endothelial cell pyroptosis inhibition

Spontaneous subarachnoid hemorrhage (SAH) is a kind of hemorrhagic stroke which causes neurological deficits in survivors. Huperzine A has a neuroprotective effect, but its role in SAH is unclear. Therefore, we explore the effect of Huperzine A on neurological deficits induced by SAH and the related mechanism. In this study, Evans blue assay, TUNEL staining, immunofluorescence, western blot analysis, and ELISA are conducted. We find that Huperzine A can improve neurological deficits and inhibit the apoptosis of nerve cells in SAH rats. Huperzine A treatment can improve the upregulation of brain water content, damage of blood-brain barrier, fibrinogen and matrix metalloprotein 9 expressions and the downregulation of ZO-1 and occludin expressions induced by SAH. Huperzine A inhibit the expressions of proteins involved in pyroptosis in endothelial cells in SAH rats. The increase in MDA content and decrease in SOD activity in SAH rats can be partly reversed by Huperzine A. The ROS inducer H 2O 2 can induce pyroptosis and inhibit the expressions of ZO-1 and occludin in endothelial cells, which can be blocked by Huperzine A. In addition, the increase in the entry of p65 into the nucleus in endothelial cells can be partly reversed by Huperzine A. Huperzine A may delay the damage of blood-brain barrier in SAH rats by inhibiting oxidative stress-mediated pyroptosis and tight junction protein expression downregulation through the NF-κB pathway. Overall, Huperzine A may have clinical value for treating SAH.

Image-derived Metrics Quantifying Hemodynamic Instability Predicted Growth of Unruptured Intracranial Aneurysms

Background

While image-derived predictors of intracranial aneurysm (IA) rupture have been well-explored, current understanding of IA growth is limited. Pulsatility index (PI) and wall shear stress pulsatility index (WSSPI) are important metrics measuring temporal hemodynamic instability. However, they have not been investigated in IA growth research. The present study seeks to verify reliable predictors of IA growth with comparative analyses of several important morphological and hemodynamic metrics between stable and growing cases among a group of unruptured IAs.

Methods

Using 3D images, vascular models of 16 stable and 20 growing cases were constructed and verified using Geodesic techniques. With an overall mean follow-up period of 25 months, cases exhibiting a 10% or higher increase in diameter were considered growing. Patient-specific, pulsatile simulations were performed, and hemodynamic calculations were computed at 5 important regions of each aneurysm (inflow artery, aneurysm neck, body, dome, and outflow artery). Index values were compared between growing and stable IAs using ANCOVA controlling for aneurysm diameter. Stepwise multiple logistic regression and ROC analyses were conducted to investigate predictive models of IA growth.

Results

Compared to stable IAs, growing IAs exhibited significantly higher intrasaccular PI, intrasaccular WSSPI, intrasaccular spatial flow rate deviation, and intrasaccular spatial wall shear stress (WSS) deviation. Stepwise logistic regression analysis revealed a significant predictive model involving PI at aneurysm body, WSSPI at inflow artery, and WSSPI at aneurysm body.

Conclusions

Our results showed that high degree of hemodynamic variations within IAs is linked to growth, even after controlling for morphological parameters. Further, evaluation of PI in conjunction with WSSPI yielded a highly accurate predictive model of IA growth. Upon validation in future cohorts, these metrics may aid in early identification of IA growth and current understanding of IA remodeling mechanism.

Long-Lasting Unruptured Large Intracranial Aneurysm in Bilateral Moyamoya Disease: Case Report

Introduction. The rupture risk of intracranial aneurysms in patients with moyamoya disease is higher than that in the general population. We report a confirmed case of moyamoya disease with bilateral middle cerebral artery (MCA) occlusion with a large and long-lasting aneurysm. Case. A 71-year-old woman visited the clinic with a large intracranial aneurysm. The patient was diagnosed with an ischemic stroke 2 months ago. She exhibited weakness in the left upper and lower extremities and dysarthria and was taking aspirin. The brain magnetic resonance imaging showed complete occlusion in the bilateral MCA proximal (M1) and a large 11 × 11 mm nonruptured cerebral aneurysm in the A3 segment of the left anterior cerebral artery. On transfemoral cerebral angiography, the patient was diagnosed with Suzuki grade VI moyamoya disease with bilateral MCA occlusion. After 7 years, the cerebral aneurysm size further increased, but it remained unruptured. Conclusions. Here, the patient had moyamoya disease with a large aneurysm, but aneurysmal rupture did not occur even after 7 years. Our case report might help in understanding the mechanisms of cerebral aneurysm occurrence and rupture in moyamoya patients.

Small ruptured intracranial aneurysms are overrepresented at the anterior and posterior communicating artery: Results of a multiple regression analysis

Background:

Anterior communicating artery (AcomA) represents the most common location for ruptured intracranial aneurysms (rIAs). Approximately 50% of all rIAs are smaller than 7 mm, but factors that lead to rupture are multifactorial. The study investigates whether AcomA location represents an independent risk factor for small size at time of rupture (<7 mm) in a cohort of aneurysmal subarachnoid hemorrhage (aSAH) when controlling for known risk factors.

Methods:

The aSAH cohort was retrospectively searched from our institution charts. The cohort was dichotomized into small aneurysms (<7 mm) or large aneurysms (≥7 mm). Risk factors for rupture were identified according to the unruptured intracranial aneurysm treatment score (UIATS). These were sex, age, location, smoking, hypertension, alcohol abuse, aneurysm morphology, multiplicity, previous SAH, and family history. With size as independent variable, a multiple regression analysis was performed including UIATS risk factors.

Results:

One-hundred and seventy-six patients were included in the study. About 49.4% of the aneurysms were <7 mm. Multiple regression analysis demonstrated that aneurysms located at AcomA and posterior communicating artery (PcomA) was significantly more frequent smaller than 7 mm, compared to middle cerebral artery (P = 0.006), internal carotid artery (other than PcomA) (P = 0.013), and posterior circulation (P = 0.017), when controlling for risk factors.

Conclusion:

Ruptured AcomA and PcomA aneurysms are more frequent smaller than 7 mm compared to other locations. Patients with unruptured UIA at either AcomA or PcomA may be at increased risk of rupture even if the size of the aneurysm is small. Further studies are needed to confirm this finding.

Dexmedetomidine Inhibits Gasdermin D-Induced Pyroptosis via the PI3K/AKT/GSK3β Pathway to Attenuate Neuroinflammation in Early Brain Injury After Subarachnoid Hemorrhage in Rats

Subarachnoid hemorrhage (SAH) is one kind of life-threatening stroke, which leads to severe brain damage. Pyroptosis plays a critical role in early brain injury (EBI) after SAH. Previous reports suggest that SAH-induced brain edema, cell apoptosis, and neuronal injury could be suppressed by dexmedetomidine (Dex). In this study, we used a rat model of SAH to investigate the effect of Dex on pyroptosis in EBI after SAH and to determine the mechanisms involved. Pyroptosis was found in microglia in EBI after SAH. Dex significantly alleviated microglia pyroptosis via reducing pyroptosis executioner GSDMD and inhibited the release of proinflammatory cytokines induced by SAH. Furthermore, the reduction of GSDMD by Dex was abolished by the PI3K inhibitor LY294002. In conclusion, our data demonstrated that Dex reduces microglia pyroptosis in EBI after SAH via the activation of the PI3K/AKT/GSK3β pathway.

Evaluation of aneurysm rupture risk based upon flowrate-independent hemodynamic parameters: a multi-center pilot study

BACKGROUND

Specifying generic flow boundary conditions in aneurysm hemodynamic simulations yields a great degree of uncertainty for the evaluation of aneurysm rupture risk. Herein, we proposed the use of flowrate-independent parameters in discriminating unstable aneurysms and compared their prognostic performance against that of conventional absolute parameters.

METHODS

This retrospective study included 186 aneurysms collected from three international centers, with the stable aneurysms having a minimum follow-up period of 24 months. The flowrate-independent aneurysmal wall shear stress (WSS) and energy loss (EL) were defined as the coefficients of the second-order polynomials characterizing the relationships between the respective parameters and the parent-artery flows. Performance of the flowrate-independent parameters in discriminating unstable aneurysms with the logistic regression, Adaboost, and support-vector machine (SVM) methods was quantified and compared against that of the conventional parameters, in terms of sensitivity, specificity, and area under the curve (AUC).

RESULTS

In discriminating unstable aneurysms, the proposed flowrate-independent EL achieved the highest sensitivity (0.833, 95% CI 0.586 to 0.964) and specificity (0.833, 95% CI 0.672 to 0.936) on the SVM, with the AUC outperforming the conventional EL by 0.133 (95% CI 0.039 to 0.226, p=0.006). Likewise, the flowrate-independent WSS outperformed the conventional WSS in terms of the AUC (difference: 0.137, 95% CI 0.033 to 0.241, p=0.010).

CONCLUSION

The flowrate-independent hemodynamic parameters surpassed their conventional counterparts in predicting the stability of aneurysms, which may serve as a promising set of hemodynamic metrics to be used for the prediction of aneurysm rupture risk when physiologically real vascular boundary conditions are unavailable.

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

Background

The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making.

Purpose

We aim to build an integrated model to improve the assessment of the rupture risk of IAs.

Materials and Methods

A total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared.

Results

Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively.

Conclusion

The integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction

OBJECTIVES

We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.

METHOD

Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons.

RESULT

Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system.

CONCLUSION

Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture.

KEY POINTS

• A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.

The rupture of an anterior communicating artery aneurysm does not associate with an asymmetry in the A1 or A2 arteries: a retrospective study of radiological features

BACKGROUND AND OBJECTIVES

Although the formation and rupture risk of an anterior communicating artery (ACoA) aneurysm has been the subject of many studies, no previous study has primarily searched for the relationship of the parent and daughter vessels and the impact of their size/diameter ratio on the potential rupture risk of an AcoA aneurysm. The objective of this study is to explore this link and to further analyse the surrounding vasculature of the anterior communicating artery aneurysm.

MATERIALS AND METHODS

We conducted a retrospective analysis of 434 patients: 284 patients with an ACoA aneurysm (121 unruptured and 162 ruptured) and 150 control patients without an ΑCoA aneurysm. Radiological angiography investigations were used to assess the diameter ratios of the parent vessels in addition to ACoA aneurysm morphology parameters.

RESULTS

When comparing the ruptured to the unruptured cases, we observed no significant difference in the parent or daughter vessel diameter ratios. Younger patient age (OR 0.96, p = 0.00) and a higher aneurysm size ratio (OR 1.10, p = 0.02) were of prognostic importance concerning the rupture risk of the aneurysm. The A1 diameter ratio and the A2 diameter were not statistically significant (OR 1.00, p = 0.99, and OR 3.38, p = 0.25 respectively).

CONCLUSIONS

In our study, we focused on asymmetry in the parent and daughter vessels as well as traditional ACoA aneurysm morphological characteristics. We were able to label younger patient age and a greater size ratio as independent prognostic factors for ACoA aneurysm rupture. We were unable to label parent and daughter vessel asymmetry as prognostic factors. To validate our findings, parent and daughter vessel asymmetry should be subjected to future prospective studies.

An MRI study of typical anatomical variants of the anterior communicating artery complex

PURPOSE

This magnetic resonance imaging study examined the most frequent anatomical variants of the anterior communicating artery (ACoA) complex of the cerebral arterial circle, and aimed to determine whether they were associated with ACoA complex aneurysm.

METHODS

The study enrolled 669 patients. Using three-dimensional time-of-flight magnetic resonance angiography, 617 patients were classified into the following groups based on the anatomical variation in the ACoA complex: no ACoA complex anomaly; ACoA complex aneurysm; and vascular anomaly distant from the cerebral arterial circle.

RESULTS

Of the 617 classified subjects, the classical anatomical description applied to 48.73% in the no ACoA complex anomaly group and 37.5% in the ACoA complex aneurysm group. One variant (left anterior cerebral artery segment A1 hypoplasia) was significantly more frequent in the ACoA complex aneurysm group. There was no sex difference in the prevalence of any variant.

CONCLUSIONS

Anatomical variants of the ACoA complex of the cerebral arterial circle were found in almost half of the subjects. One variant seemed to be associated with a higher likelihood of an aneurysm, but causality could not be inferred.

Resolvin D1 ameliorates Inflammation-Mediated Blood-Brain Barrier Disruption After Subarachnoid Hemorrhage in rats by Modulating A20 and NLRP3 Inflammasome

Inflammation is typically related to dysfunction of the blood-brain barrier (BBB) that leads to early brain injury (EBI) after subarachnoid hemorrhage (SAH). Resolvin D1 (RVD1), a lipid mediator derived from docosahexaenoic acid, possesses anti-inflammatory and neuroprotective properties. This study investigated the effects and mechanisms of RVD1 in SAH. A Sprague-Dawley rat model of SAH was established through endovascular perforation. RVD1was injected through the femoral vein at 1 and 12 h after SAH induction. To further explore the potential neuroprotective mechanism, a formyl peptide receptor two antagonist (WRW4) was intracerebroventricularly administered 1 h after SAH induction. The expression of endogenous RVD1 was decreased whereas A20 and NLRP3 levels were increased after SAH. An exogenous RVD1 administration increased RVD1 concentration in brain tissue, and improved neurological function, neuroinflammation, BBB disruption, and brain edema. RVD1 treatment upregulated the expression of A20, occludin, claudin-5, and zona occludens-1, as well as downregulated nuclear factor-κBp65, NLRP3, matrix metallopeptidase 9, and intercellular cell adhesion molecule-1 expression. Furthermore, RVD1 inhibited microglial activation and neutrophil infiltration and promoted neutrophil apoptosis. However, the neuroprotective effects of RVD1 were abolished by WRW4. In summary, our findings reveal that RVD1 provides beneficial effects against inflammation-triggered BBB dysfunction after SAH by modulating A20 and NLRP3 inflammasome.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838-0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729-0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713-0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

From 2D to 4D Phase-Contrast MRI in the Neurovascular System: Will It Be a Quantum Jump or a Fancy Decoration?

Considering the crosstalk between the flow and vessel wall, hemodynamic assessment of the neurovascular system may offer a well-integrated solution for both diagnosis and management by adding prognostic significance to the standard CT/MR angiography. 4D flow MRI or time-resolved 3D velocity-encoded phase-contrast MRI has long been promising for the hemodynamic evaluation of the great vessels, but challenged in clinical studies for assessing intracranial vessels with small diameter due to long scan times and low spatiotemporal resolution. Current accelerated MRI techniques, including parallel imaging with compressed sensing and radial k-space undersampling acquisitions, have decreased scan times dramatically while preserving spatial resolution. 4D flow MRI visualized and measured 3D complex flow of neurovascular diseases such as aneurysm, arteriovenous shunts, and atherosclerotic stenosis using parameters including flow volume, velocity vector, pressure gradients, and wall shear stress. In addition to the noninvasiveness of the phase contrast technique and retrospective flow measurement through the wanted windows of the analysis plane, 4D flow MRI has shown several advantages over Doppler ultrasound or computational fluid dynamics. The evaluation of the flow status and vessel wall can be performed simultaneously in the same imaging modality. This article is an overview of the recent advances in neurovascular 4D flow MRI techniques and their potential clinical applications in neurovascular disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms

OBJECTIVES

Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture.

METHODS

One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use.

RESULTS

Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876.

CONCLUSIONS

Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance.

KEY POINTS

• Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.

Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture

Background: Intracranial aneurysm wall degradation can be associated with lipid infiltration. However, the relationship between lipid infiltration and aneurysm rupture has not been explored quantitatively. To investigate the correlation between lipid infiltration and aneurysm rupture, we utilized patient-specific simulation of low-density lipoprotein (LDL) transport to analyze lipid infiltration in the cerebral aneurysm wall.

Methods: Sixty-two aneurysms were analyzed. Patient blood pressure, plasma LDL concentration, and three-dimensional angiographic images were obtained to simulate LDL transport in aneurysms. Morphological, hemodynamic, and lipid accumulation parameters were compared between ruptures and unruptured groups. Multivariate logistic regression was also performed to determine parameters that are independently associated with rupture.

Results: Size ratio, wall shear stress, low shear area, relative residence time, area-averaged LDL infiltration rate, and maximum LDL infiltration rate were significant parameters in univariate analysis (P < 0.05). Multivariate analysis revealed that only average LDL infiltration remained as a significant variable (P < 0.05). The prediction model derived showed good performance for rupture prediction (AUC, 0.885; 95% CI, 0.794–0.976).

Conclusions: Ruptured aneurysms showed significantly higher LDL infiltration compared to unruptured ones. Our results suggested that lipid infiltration may promote aneurysm rupture. Lipid infiltration characteristics should be considered when assessing aneurysm rupture risk.