Risk Of Aneurysm Rupture Research Articles

The markers and risk stratification model of intracranial aneurysm instability in a large Chinese cohort

Intracranial aneurysm is the leading cause of nontraumatic subarachnoid hemorrhage. Evaluating the unstable (rupture and growth) risk of aneurysms is helpful to guild decision-making for unruptured intracranial aneurysms (UIA). This study aimed to develop a model for risk stratification of UIA instability. The UIA patients from two prospective, longitudinal multicenter Chinese cohorts recruited from January 2017 to January 2022 were set as the derivation cohort and validation cohort. The primary endpoint was UIA instability, comprising aneurysm rupture, growth, or morphology change, during a 2-year follow-up. Intracranial aneurysm samples and corresponding serums from 20 patients were also collected. Metabolomics and cytokine profiling analysis were performed on the derivation cohort (758 single-UIA patients harboring 676 stable UIAs and 82 unstable UIAs). Oleic acid (OA), arachidonic acid (AA), interleukin 1β (IL-1β), and tumor necrosis factor-α (TNF-α) were significantly dysregulated between stable and unstable UIAs. OA and AA exhibited the same dysregulated trends in serums and aneurysm tissues. The feature selection process demonstrated size ratio, irregular shape, OA, AA, IL-1β, and TNF-α as features of UIA instability. A machine-learning stratification model (instability classifier) was constructed based on radiological features and biomarkers, with high accuracy to evaluate UIA instability risk (area under curve (AUC), 0.94). Within the validation cohort (492 single-UIA patients harboring 414 stable UIAs and 78 unstable UIAs), the instability classifier performed well to evaluate the risk of UIA instability (AUC, 0.89). Supplementation of OA and pharmacological inhibition of IL-1β and TNF-α could prevent intracranial aneurysms from rupturing in rat models. This study revealed the markers of UIA instability and provided a risk stratification model, which may guide treatment decision-making for UIAs.

Reduction of rupture risk in ICA aneurysms by endovascular techniques of coiling and stent: numerical study

The initiation, growth, and rupture of cerebral aneurysms are directly associated with Hemodynamic factors. This report tries to disclose effects of endovascular technique (coiling and stenting) on the quantitative intra-aneurysmal hemodynamic and the rupture of cerebral aneurysms. In this paper, Computational Fluid Dynamic are done to investigate and compare blood hemodynamic inside aneurysm under effects of deformation (due to stent) and coiling of aneurysm. The blood stream inside the sac of aneurysm as well as pressure and OSI distribution on the aneurysm wall are compared in nine cases and results of two distinctive cases are compared and reported. Obtained results specifies that the mean WSS is reduced up to 20% via coiling of the aneurysm while the deformation of the aneurysm (applying stent) could reduce the mean WSS up to 71%. In addition, comparison of the blood hemodynamic shows that the blood bifurcation occurs in the dome of aneurysm when endovascular technique for the treatment is not applied. It is found that the bifurcation occurs at ostium section when ICA aneurysm is deformed by the application of stent. The impacts of coiling are mainly limited since the blood flow entrance is not limited in this technique and WSS is not reduced substantial. However, usage of stent deforms the aneurysm angle with the orientation of parent vessel and this reduces blood velocity at entrance of the ostium and consequently, WSS is decreased when deformation of the aneurysm fully occurs. These qualitative procedures provide a preliminary idea for more profound quantitative examination intended for assigning aneurysm risk of upcoming rupture.

Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms

Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients. This study aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods to accurately describe the mechanical exchanges between the blood flow, the aneurysm, and the flow-deviator and deep reinforcement learning algorithms to identify a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state.

Long-term outcomes of the Endurant endograft in patients undergoing endovascular abdominal aortic aneurysm repair

Objective/BackgroundTo investigate the long-term outcomes after endovascular aneurysm repair (EVAR) in a real-world setting using the Endurant endograft (EG). Methods184 EVAR candidates treated with the Endurant family EGs in a single vascular center were prospectively enrolled from January 2009 to December 2016. Kaplan-Meir estimates of long-term standardized primary and secondary outcome measures were performed. Per protocol, subgroup comparison analysis was performed in three groups: patients treated within instructions for use (in-IFU) vs patients treated outside IFU (outside-IFU), EVAR in patients receiving the Endurant proximal diameter 32 or 36 mm EG vs those receiving the <32 mm diameter EG and EVAR with various Endurant EG versions. ResultsThe mean follow-up was 75.09 ± 37.9 months (range: 4.1-172 months). The median age of the patients was 72.96 ± 7.03 years (range: 55-88 years). A total of 177 patients were male (96.2%). Compliance with IFU was followed in 107 patients (58.2%). Overall survival was 69.5% and 48% at 5 and 8 years, respectively. Of the 102 all-cause deaths, 7 (6.9%) were aneurysm related. Six of these postimplant deaths occurred in patients presented with aneurysm rupture from type Ia or/and type Ib endoleak. At 5, 8, and 10 years of observation, freedom from aneurysm rupture, open surgical conversion, type I/III endoleak, any type of endoleak, aneurysm-related secondary intervention probabilities, and neck-related events were as follows: 98.1%, 95%, and 89.4%; 95.1%, 91.2%, and 85.7%; 93.6%, 87.3%, and 83.9%; 83.4%, 74%, and 70.9%; 89.8%, 76.7%, and 72%; and 96.3%, 90%, and 87.6%, respectively. Corresponding clinical success was 90%, 77.4%, and 68.4%, respectively. Patients treated outside-IFU had significantly higher risk of aneurysm rupture, open surgical conversion probability, occurrence of type I/III endoleak, and chance of reinterventions and lower clinical success probabilities compared with the in-IFU counterparts at 5 and 8 years. This statistical difference remained when type Ia endoleak or endoleak of any type was considered independently. In addition, it was stronger in patients having extreme anatomic boundaries (>1 hostile anatomic condition), when aneurysm-related death, aneurysm rupture, and clinical success at 5 years were considered. Overall proximal migration and limb occlusion were recorded in 1.1% and 4.9% of the patients, respectively. Overall reintervention rate was 17.4%. An increase in aneurysm sac diameter was observed in 12.5% of patients and was not related to IFU status. The Endurant version or the proximal EG diameter had no significant association with the chance of any complication or adverse event. ConclusionsThe data confirmed the durability of the Endurant EG, achieving promising long-term outcomes in a real-world setting. However, its positive performance must be interpreted with caution in patients treated off-label especially those with extreme anatomic boundaries. In this cohort, some of EVAR advantages might be lost in the late future. Further similar studies are warranted.

An experimental study on effects of interventional stent treatment on hemodynamics in elastic aneurysms

Cerebral hemodynamics plays a significant role in the growth and rupture of intracranial aneurysms (IAs), which pose a threat to human health. Real time visualization experiment is applied to observe fluid flow in the aneurysm using a high-speed camera in this paper with a focus on the influences of interventional stent treatment for elastic aneurysms on hemodynamics. The effects of blood flow rate, wall compliance as well as stent on IA hemodynamics patterns and flow field are explored. It indicated that, compared to the low inflow rate ranging between 12 and 18 l/h, there are less steady flow field variables and poorer stability in the aneurysm compared to that at high inflow rate ranging between 11.5 and 22 l/h. Wall compliance, allowing blood to pass in and out aneurysm more easily, acts as a buffer, especially at an excessive inflow range. In addition, the stent not only attenuates the effect of wall compliance but also acts as a flow diverter in the blood vessel to the reduction in the risk of aneurysm rupture. Consequently, this paper offers a contribution to clinical reference in predicting the risk of IA rupture and holds great promise for optimal treatment.

Risk Factors for Headache Disorder in Patients With Unruptured Intracranial Aneurysms.

Headache disorders are a prevalent yet frequently underestimated issue in patients with unruptured intracranial aneurysms (UIAs). The primary aim of this study is to systematically examine the incidence, specific characteristics, and associated risk factors of headache disorders in the context of individuals diagnosed with UIAs. Through this investigation, we hope to contribute valuable insights to the current understanding of this complex relationship and potentially inform future diagnostic and treatment approaches. Data from 146 consecutive patients harboring UIAs were evaluated. The location and morphological characteristics of the aneurysm were analyzed. Factors associated with headache incidence and methods of treatment were investigated. The headache pattern in 48 patients was assessed using self-reported questionnaires. A total of 146 patients were identified. Out of 146 patients, 95 (65%) were in the Headache Group (HG) and 51 (35%) were asymptomatic and in the No Headache Group (NHG). Factors associated with a higher likelihood of headache were past or current tobacco, alcohol, and illicit drug use (p=0.029). On average, patients had 1.49 (SD=1) aneurysms in the HG and 1.43 (SD=.92) in the NHG group, respectively. In our series, the size of aneurysms, the status of the aneurysm (treated vs untreated), and the method of treatment did not significantly differ between the groups. There was a high incidence of headaches in patients with aneurysms of the ophthalmic segment (C6) of the internal carotid artery (ICA) and sphenoidal segment (M1) of the middle cerebral artery (MCA).Of 48 patients that completed headache questionnaires, 25 had headaches on more than 15 days a month. The majority of participants (85.4%) reported the severity of their pain as being greater than 5 on a scale of 10, while one-third (33.3%) experienced the maximum pain level of 10 out of 10. Headache more often occurs in patients with aneurysms of the ophthalmic segment (C6) of the ICA and sphenoidal segment (M1) of the MCA. Its distinctive features are deep pain for more than 15 days a month. Although the treatment of aneurysms reduces the risk of aneurysmal rupture, its efficacy in relieving the headache is still uncertain.

Computational modelling to evaluate abdominal aortic aneurysm rupture risk: state-of-the-art, pitfalls and scientific impact

Computational modelling to evaluate abdominal aortic aneurysm rupture risk: state-of-the-art, pitfalls and scientific impact

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk. This retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models. The AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively). Radiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters. • Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.

163 Integrative Genomics Analysis Implicates Decreased FGD6 Expression Underlying Risk of Intracranial Aneurysm Rupture

INTRODUCTION: The genetic determinants of intracranial aneurysm rupture (rIA) are largely unknown. To identify and characterize genetic risk factors for rIA, we perform a genome-wide association study (GWAS) containing 84,353 individuals (7,843 rIA cases and 76,510 controls). We then use functional genomics approaches to delineate the mechanistic consequences of rIA risk loci. METHODS: We perform a meta-analysis across 24 published GWAS of rIA. Logistic regression was used to identify rIA-associated single nucleotide polymorphisms (SNPs). Gene-burden, expression quantitative trait loci (eQTL) mapping, and analysis of publicly-available single-cell RNA sequencing (scRNA-seq) data from normal human brain endothelial and perivascular cells was used to delineate cell-lineage expression of rIA-associated genes. RESULTS: We identify 5 independent genetic loci reaching genome-wide significance (p &lt; 5.0 x 10-8) for rIA: rs73392700 (CDKN2B, OR = 1.12), rs6841581 (EDNRA, OR = 0.77), rs11661542 (RBBP8, OR=0.84), rs62516550 (RP1, OR = 1.20), and rs12310399 (FGD6, OR = 1.16). FGD6, to our knowledge, has not been implicated in prior GWAS of (r)IA. We then quantified gene-level mutation-burden across ∼20,000 genes, and only FGD6 (containing 21 rIA-associated SNPs) reached transcriptome-wide significance. Functional eQTL mapping indicates that rs12310399 causes decreased FGD6 gene expression in arterial tissue. scRNA-seq identified high expression of FGD6 in 1 of 3 arterial lineages, but absent in perivascular cells, identifying the likely cell-lineage underlying FGD6-mediated rIA risk. CONCLUSIONS: We perform the largest genetic study of rIA to date. We identify and characterize a previously unknown risk loci for rIA containing FGD6. Elucidation of high-risk genetic loci may instruct population-genetic screening and clinical-genetic testing strategies to identify patients predisposed to rIA enabling early treatment and improved patient outcomes.

Shifted-windows transformers for the detection of cerebral aneurysms in microsurgery

PurposeMicrosurgical Aneurysm Clipping Surgery (MACS) carries a high risk for intraoperative aneurysm rupture. Automated recognition of instances when the aneurysm is exposed in the surgical video would be a valuable reference point for neuronavigation, indicating phase transitioning and more importantly designating moments of high risk for rupture. This article introduces the MACS dataset containing 16 surgical videos with frame-level expert annotations and proposes a learning methodology for surgical scene understanding identifying video frames with the aneurysm present in the operating microscope’s field-of-view.MethodsDespite the dataset imbalance (80% no presence, 20% presence) and developed without explicit annotations, we demonstrate the applicability of Transformer-based deep learning architectures (MACSSwin-T, vidMACSSwin-T) to detect the aneurysm and classify MACS frames accordingly. We evaluate the proposed models in multiple-fold cross-validation experiments with independent sets and in an unseen set of 15 images against 10 human experts (neurosurgeons).ResultsAverage (across folds) accuracy of 80.8% (range 78.5–82.4%) and 87.1% (range 85.1–91.3%) is obtained for the image- and video-level approach, respectively, demonstrating that the models effectively learn the classification task. Qualitative evaluation of the models’ class activation maps shows these to be localized on the aneurysm’s actual location. Depending on the decision threshold, MACSWin-T achieves 66.7–86.7% accuracy in the unseen images, compared to 82% of human raters, with moderate to strong correlation.ConclusionsProposed architectures show robust performance and with an adjusted threshold promoting detection of the underrepresented (aneurysm presence) class, comparable to human expert accuracy. Our work represents the first step towards landmark detection in MACS with the aim to inform surgical teams to attend to high-risk moments, taking precautionary measures to avoid rupturing.

Structure and function of the aortic and pulmonary outflows in a patient, 12 years after Nikaidoh operation, A cautionary Note

BackgroundNikaidoh translocation operation is increasingly being used for patients with TGA, VSD and LVOTO. The early results of this operation are excellent and possibly better than the Rastelli repair. However, the long-term results remain inadequately defined. Methods and resultsDetailed follow up data of a patient, 12 years after Nikaidoh operation, using computerized image analysis are reported. The patient complained of chest pain on exertion. Imaging showed severe dilatation of the non-coronary sinus of the aortic root, compressing the RVOT and pulmonary artery branches resulting in RV dilatation. The dilated aortic sinus showed marked pulsatility with expansion during systole and diastole. As the child was symptomatic, with a risk of aneurysm rupture, excision was performed. This complication is thought to be due to interruption of the innervation of the root as well as cutting vasa vasorum. ConclusionNikaidoh operation is a viable solution for the repair of TGA, VSD, PS. Dilatation of the neo-aortic root should be carefully followed and causes of the dilatation investigated.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is . Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiologists are labor intensive and have limited use for risk assessment. Therefore, we propose an end-to-end deep-learning method, called TransIAR net, to automatically learn the morphological features from 3D computed tomography angiography (CTA) data and accurately predict the status of IA rupture. We devise a multiscale 3D convolutional neural network (CNN) to extract the structural patterns of the IA and its neighborhood with a dual branch of shared network structures. Moreover, we learn the spatial dependence within the IA neighborhood with a transformer encoder. Our experiments demonstrated that the features learned by TransIAR are more effective and robust than handcrafted features, resulting in a improvement in the accuracy of rupture status prediction.

Computational study of blood flow inside MCA aneurysm with/without endovascular coiling

The simulation of blood hemodynamics inside the MCA aneurysm is done to investigate the potential region for rupture and hemorrhage. The main focus of this work is to disclose the impacts of endovascular coiling on blood hemodynamics and the risk of aneurysm rupture. Navier–stokes equations are solved for the computational study of blood flow while it is assumed that flow remains laminar, unsteady, and non-Newtonian. Influences of blood hematocrits and coiling porosity are also examined in this work. Obtained results show that impacts of blood hematocrit on the maximum OSI are limited in the MCA case.

Association between aortic peak wall stress and rupture index with abdominal aortic aneurysm–related events

ObjectiveThe aim of this study was to assess whether aortic peak wall stress (PWS) and peak wall rupture index (PWRI) were associated with the risk of abdominal aortic aneurysm (AAA) rupture or repair (defined as AAA events) among participants with small AAAs.MethodsPWS and PWRI were estimated from computed tomography angiography (CTA) scans of 210 participants with small AAAs (≥ 30 and ≤ 50 mm) prospectively recruited between 2002 and 2016 from two existing databases. Participants were followed for a median of 2.0 (inter-quartile range 1.9, 2.8) years to record the incidence of AAA events. The associations between PWS and PWRI with AAA events were assessed using Cox proportional hazard analyses. The ability of PWS and PWRI to reclassify the risk of AAA events compared to the initial AAA diameter was examined using net reclassification index (NRI) and classification and regression tree (CART) analysis.ResultsAfter adjusting for other risk factors, one standard deviation increase in PWS (hazard ratio, HR, 1.56, 95% confidence intervals, CI 1.19, 2.06; p = 0.001) and PWRI (HR 1.74, 95% CI 1.29, 2.34; p < 0.001) were associated with significantly higher risks of AAA events. In the CART analysis, PWRI was identified as the best single predictor of AAA events at a cut-off value of > 0.562. PWRI, but not PWS, significantly improved the classification of risk of AAA events compared to the initial AAA diameter alone.ConclusionPWS and PWRI predicted the risk of AAA events but only PWRI significantly improved the risk stratification compared to aortic diameter alone.Key Points• Aortic diameter is an imperfect measure of abdominal aortic aneurysm (AAA) rupture risk.• This observational study of 210 participants found that peak wall stress (PWS) and peak wall rupture index (PWRI) predicted the risk of aortic rupture or AAA repair.• PWRI, but not PWS, significantly improved the risk stratification for AAA events compared to aortic diameter alone.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth.

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Risk of Aneurysm Rupture (ROAR) study: protocol for a long-term, longitudinal, UK multicentre study of unruptured intracranial aneurysms

IntroductionUnruptured intracranial aneurysms (UIA) are common in the adult population, but only a relatively small proportion will rupture. It is therefore essential to have accurate estimates of rupture risk to...

An Analysis of the Incidence and Cost of Intracranial Aneurysm and Subarachnoid Haemorrhage Treatment between 2013 and 2021

The incidence of unruptured intracranial aneurysms (UIAs) amounts to 3.2% among adults. The annual risk of aneurysm rupture is 2-10% and it results in subarachnoid haemorrhage (SAH). The aim of this study is to assess changes in the incidence of unruptured intracranial aneurysms and subarachnoid haemorrhages in Poland between 2013 and 2021 and the cost associated with their in-hospital treatment in the acute phase. The analysis was based on the National Health Fund database. Patients diagnosed with UIA and SAH and hospitalised between 2013 and 2021 were chosen. The statistical analysis was performed with an assumed significance level of α = 0.05. The ratio between the prevalence of SAH and UIA diagnoses was 4:6. The proportion of women in relation to men was higher in both diagnoses. The highest proportions of patients with diagnoses SAH and UIA were found in highly urbanised provinces. The value of medical services in 2021 compared to 2013 increased by 81.8%. The highest values in this period were recorded in Mazowieckie province, and the lowest were recorded in Opolskie province. The overall number of patients hospitalised with diagnosis of UIA or SAH did not decrease, but the risk of aneurysm rupture probably decreased, which resulted in lower incidence of SAH in subsequent years of observation. The recorded changes in the dynamics of the value of medical services per patient or per hospitalisation largely coincided. However, it is difficult to speculate on expected value levels as not all provinces showed linear changes in the value of services provided.

Risk factors for the rupture of anterior communicating artery aneurysms: Coexistence of fetal-type posterior cerebral artery and A1 segment hypoplasia/agenesis

PurposeTo investigate the effect of the coexistence of fetal-type posterior communicating artery (fPCA) and anterior cerebral artery (ACA) A1 segment agenesis/hypoplasia on the rupture of an anterior communicating artery (AComA) aneurysm. Materials and MethodsA total of 216 patients consecutively presenting with AComA aneurysms between January 2014 and December 2021 on digital subtraction angiography were evaluated. Patients without three-dimensional rotational angiography images, those aged under 18 years, those with suspected mycotic aneurysms, and those with dissecting and giant aneurysms were excluded from the study. The aneurysms were divided into two groups as ruptured and non-ruptured. Hemodynamic filling patterns were classified into four different types. ResultsThe study included 192 AComA aneurysms, 44.8% (n = 86) ruptured and 55.2% (n = 106) non-ruptured. According to hemodynamic filling patterns, in type 1, the frequency of non-ruptured aneurysms was statistically significantly higher than that of ruptured aneurysms (39.5% vs 18.9%; p = 0.001). In type 4, where fPCA and ACA A1 segment agenesis/hypoplasia coexists, the frequency of ruptured aneurysms was significantly higher than that of non-ruptured aneurysms (10.5% vs 22.7%; p = 0.026). The most common aneurysm size range was 4–7 mm (n = 85; 44.3%). There was no statistically significant difference in size between the ruptured and non-ruptured aneurysms (p = 0.627). ConclusionAccording to the hemodynamic filling classification, we observed that the presence of type 4 filling pattern, i.e., the coexistence of ACA A1 segment agenesis/hypoplasia and fPCA, increased the risk of rupture in AComA aneurysms.

A machine learning-derived gene signature for assessing rupture risk and circulatory immunopathologic landscape in patients with intracranial aneurysms.

Intracranial aneurysm (IA) is an uncommon but severe subtype of cerebrovascular disease, with high mortality after aneurysm rupture. Current risk assessments are mainly based on clinical and imaging data. This study aimed to develop a molecular assay tool for optimizing the IA risk monitoring system. Peripheral blood gene expression datasets obtained from the Gene Expression Omnibus were integrated into a discovery cohort. Weighted gene co-expression network analysis (WGCNA) and machine learning integrative approaches were utilized to construct a risk signature. QRT-PCR assay was performed to validate the model in an in-house cohort. Immunopathological features were estimated using bioinformatics methods. A four-gene machine learning-derived gene signature (MLDGS) was constructed for identifying patients with IA rupture. The AUC of MLDGS was 1.00 and 0.88 in discovery and validation cohorts, respectively. Calibration curve and decision curve analysis also confirmed the good performance of the MLDGS model. MLDGS was remarkably correlated with the circulating immunopathologic landscape. Higher MLDGS scores may represent higher abundance of innate immune cells, lower abundance of adaptive immune cells, and worse vascular stability. The MLDGS provides a promising molecular assay panel for identifying patients with adverse immunopathological features and high risk of aneurysm rupture, contributing to advances in IA precision medicine.

Abstract WP224: Pharmacological Clearance Of Senescent Cells Prevents Intracranial Aneurysm Rupture

Background: Clinical studies consistently show a strong association between aging and increased risk of intracranial aneurysmal rupture. Aging causes the accumulation of senescent cells that promote chronic inflammation through the senescence-associated secretory phenotype. Since inflammation is emerging as a vital component of the pathophysiology of intracranial aneurysms, the accumulation of senescent cells in the aneurysm wall may play a critical role in the rupture of intracranial aneurysm. We hypothesized that the pharmacological clearance of senescent cells reduces the risk of aneurysmal rupture. We tested this hypothesis using a well-established mouse model with a senescent cell-clearing senolytic drug, ABT263. Methods: We used male C57BL/6J mice and induced intracranial aneurysms by combining an elastase injection and hypertension. We treated mice with either ABT263 (50 mg/kg/day, oral gavage) or a vehicle from one day before aneurysm induction and continued for three weeks. The development of aneurysmal rupture was used as the primary endpoint. We also assessed the effects of ABT263 or vehicle on mRNA expression levels of senescent cell markers (p16 and p21) in cerebral arteries. Results: The pharmacological clearance of senescent cells with ABT263 significantly decreased the rupture rate in mice (P&lt;0.05, Figure A). Furthermore, ABT263 treatment significantly reduced the mRNA expression of p16 and p21 in cerebral arteries (P&lt;0.05, Figure B and C). Conclusion: We demonstrated that pharmacological clearance of senescent cells decreased intracranial aneurysmal rupture. The reduction of rupture rate was associated with the reduction of senescence markers. These findings suggest a link between cellular senescence and aneurysmal rupture. Cellular senescence may be responsible for the age-dependent increase in aneurysm rupture. Senolytic drugs may serve as a novel therapy for preventing aneurysm rupture.