Risk Of Aneurysm Rupture Research Articles

Risk Factors and Location of Intracranial Aneurysm Rupture in a Consecutive Chinese Han Population

The aim of the present study was to retrospectively analyze and investigate the clinical data of 704 cases of ruptured intracranial aneurysms (RIAs) and unruptured intracranial aneurysms (UIAs). The risk factors predicting aneurysm rupture were explored from the perspective of the clinical characteristics of intracranial aneurysm (IA). The clinical data of 704 patients with RIAs (494 patients) and UIAs (210 patients) admitted to the Department of Neurosurgery of Tianjin Medical University General Hospital and Tianjin Fifth Central Hospital between January 2016 and May 2022 were analyzed. A detailed analysis of sex, age, history, personal history, drug intake, and site of aneurysm occurrence was performed. Age was analyzed in segments and strata, and parameters with significant differences in the preliminary analysis results were analyzed by logistic regression to predict factors associated with the risk of aneurysm rupture. Among 494 patients with RIA (70.2%) and 210 patients with UIA (29.8%), the logistic regression showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery (ICA), reference; anterior communicating artery,27.864,12.548-61.878; posterior communicating artery,12.408,6.658-23.124; anterior cerebral artery,5.804,2.333-14.440; middle cerebral artery,9.284,4.599-18.744; posterior circulation arteries, 4.224,2.011-8.871). Age was not a significant factor associated with RIA in the model and Hyperlipidemia (OR: 0.365; 95% CI: 0.171-0.779), Atherosclerosis (OR: 0.277; 95% CI: 0.172-0.446) and Multiple aneurysms (OR: 0.275; 95% CI: 0.177-0.425) patients were less likely to have RIA.IA location and age were the best predictors of RIA using the model. The present findings indicated that hyperlipidemia and atherosclerosis have a protective effect on aneurysm rupture, and different anatomical sites of IA may be risk factors for the occurrence of IA rupture. Among the anatomical sites of IA, the anterior communicating artery and posterior communicating artery have a higher fracture risk.

Assessing the impact of aneurysm morphology on the risk of internal carotid artery aneurysm rupture: A statistical and computational analysis of endovascular coiling

Computational modeling and investigation have become increasingly useful in assessing the hemodynamic and structural factors that contribute to aneurysm rupture, particularly when conventional clinical tools are inadequate. To this end, we conduct a study of a realistic internal carotid artery (ICA) under various working conditions using computational fluid dynamics. We analyze nine specific real ICA geometries in terms of coiling porosity and blood hematocrit, using a response surface model to evaluate 25 runs across selected geometrical parameters and treatment factors. The computational domain is created using computed tomography data from an actual patient, and we apply proposed boundary conditions to solve three-dimensional transient Navier–Stokes equations. By analyzing these cases, we try to understand how the sac section area, mean radius of the parent vessel, hematocrit, and coiling porosity interact and affect the risk of ICA rupture. We conduct an extensive study of the effects of sac section area and mean radius of the parent vessel on blood hemodynamics, examining several factors and comparing them at the peak systolic time instant, including wall shear stress (WSS), oscillatory shear index (OSI), pressure distributions, and velocity. Our results show that a 12% increase in the mean radius of the parent vessel reduced the maximum OSI value by approximately 50%, while a 120% increase in the sac section area decreases WSS on the aneurysm wall by up to 300%. These findings demonstrate the potential of computational modeling to provide valuable insight into the complex factors that influence aneurysm rupture, especially in cases where traditional clinical tools may be insufficient.

Mycotic Aortic Arch Aneurysms: Open versus Endovascular Management – An Institutional Experience

Abstract Background: Mycotic aortic aneurysms are rare disorders, affecting patients with co-morbidities, and associated with a high risk of aneurysm expansion, rupture, and sepsis. Aims and Objectives: In this study, we present our experience on the management of patients with mycotic arch aneurysms. Materials and Methods: From January 2020 till July 2023, eight patients were admitted in our hospital for management of mycotic aortic arch aneurysm. On a case-to-case basis, the management was selected for each patient and consent was taken for the same. Results: Four patients underwent open repair and four endovascular repair, out of which three underwent hybrid arch repair. While only one patient in the open group expired in the immediate postoperative period due to sepsis, the remaining patients have been on follow-up for 1month-1year. There was no mortality in the immediate post TEVAR period. However, three of the patients who underwent TEVAR required re-admissions and re-intervention, with one patient developing aorto-esophageal fistula. Two of these patients expired in a span of 3months-1.5years post TEVAR. Conclusion: In patients with mycotic arch aneurysm, open surgical repair should be the modality of choice in fit surgical patients and endovascular management can be used as a bridge therapy, keeping in mind the risks of residual infection in the aorta which can lead to re-interventions and re-admissions.

Morphological characteristics of brain aneurysms among age groups.

Patient's age is an important factor in determining the risk of aneurysm rupture. However, there is limited data on how aneurysm morphology differs among age groups. We studied morphological characteristics of brain aneurysms among age groups in a large cohort. Aneurysms from the Stroke Thrombectomy and Aneurysm Registry (STAR) were analyzed. The following parameters were included: location, size, neck, width, height, aspect ratio, and regular versus irregular morphology. The risk of rupture presentation was estimated using logistic regression. A total of 1407 unruptured and 607 ruptured saccular aneurysms were included. The most common locations of ruptured aneurysms in patients younger than 70 years-old were the middle cerebral artery (MCA) and the anterior communicating artery (ACOM). The most common location of ruptured aneurysms in patients older than 70 years-old were the posterior communicating artery (PCOM) and ACOM. The size of unruptured aneurysms increased with age (p < .001). Conversely, the size of ruptured aneurysms was similar among age groups (p = .142). Unruptured and ruptured aneurysms became more irregular at presentation with older age (p < .001 and p .025, respectively). Irregular morphology and location were associated with rupture status across all age groups in multivariate regression. Younger patients have small unruptured and ruptured aneurysms, and ruptured aneurysms are mostly located in the MCA and ACOM. Older patients have larger and more irregular unruptured aneurysms, and ruptured aneurysms are mostly located in the PCOM and ACOM. An irregular morphology increases the risk of rupture in all age groups.

Geometric versus Hemodynamic Indexes for Rupture-Destined Aneurysms: A Retrospective Cohort and a Repeated-Measures Study

Plain Language SummaryThe incidence of intracranial aneurysm (IA) is 2–3% in the general population, and with the widespread use of neurovascular imaging techniques, more IAs are being diagnosed. IA management is crucial as it can lead to fatal subarachnoid hemorrhage, causing high mortality and neurological issues. About 1% of patients with IA experience severe hemorrhage annually, which raises questions about the best method to assess rupture risk. Past studies used geometric indexes and computational fluid dynamics to differentiate between ruptured and unruptured aneurysms. However, these studies mostly focused on cross-sectional data and did not analyze the evolutionary changes of ruptured aneurysms. In this study, we call such aneurysms rupture-destined aneurysms (RDAs) to differentiate from stable unruptured intracranial aneurysm (UIA). In the present study, the key findings could be summarized as below: (1) the RDA group showed a significantly higher hemodynamic instability than the UIA group; (2) the UIA group showed features of hemodynamic stability, even with geometric growth; (3) aneurysmal rupture risk should be assessed using both geometric and hemodynamic information.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

Growth and rupture of an intracranial aneurysm: the role of wall aneurysmal enhancement and CD68.

Intracranial aneurysms occur in 3%-5% of the general population. While the precise biological mechanisms underlying the formation, growth, and sudden rupture of intracranial aneurysms remain partially unknown, recent research has shed light on the potential role of inflammation in aneurysm development and rupture. In addition, there are ongoing investigations exploring the feasibility of employing new drug therapies for controlling the risk factors associated with aneurysms. CD68, a glycosylated glycoprotein and the human homolog of macrosialin, is prominently expressed in monocyte/macrophages within inflamed tissues and has shown potential application in oncology. An observational study was conducted with the aim of comparing the histological characteristics of aneurysm walls with preoperative MRI scans, specifically focusing on CD68 activity. An observational pilot study was conducted to investigate the histological characteristics of the aneurysm wall that could be potentially associated with aneurysm growth and rupture. A total of 22 patients diagnosed with ruptured and unruptured intracranial aneurysms who had undergone conventional clipping between January 2017 and December 2022 were included in the study. A histopathological analysis of the aneurysm wall was performed in all patients, particularly focusing on the presence of CD68. A preoperative MRI with gadolinium was conducted in 10 patients with unruptured aneurysms and six patients with ruptured aneurysms. An emergency clipping was performed in the remaining six patients. The results showed that CD68 positivity and wall enhancement were significantly associated with intracranial aneurysm wall degeneration, growth, and rupture. The histological and radiological inflammatory findings observed in the wall of cerebral aneurysms, as well as the CD68 positivity, are significantly associated with the risk of intracranial aneurysm growth and rupture. This study highlights the crucial importance of considering clinical and medical data when making treatment decisions for intracranial aneurysms. Furthermore, it emphasizes the relevance of evaluating wall enhancement in MRI scans as part of the diagnostic and prognostic process.

Management of Intracranial Aneurysms that Do Not Occlude on Initial Follow-up After Treatment With the Pipeline Embolization Device.

The pipeline embolization device (PED) has become widely accepted as a safe and efficacious treatment for intracranial aneurysms with high rates of complete occlusion at initial follow-up. For aneurysms that are not completely occluded at initial follow-up, further treatment decision-making is varied. Furthermore, the risk of aneurysmal rupture in these incompletely occluded aneurysms after PED is not known. The objective of this study was to determine treatment decision-making that results in increased occlusion status at final follow-up and to evaluate risk of rupture in those aneurysms that do not go onto occlusion. This study is a retrospective review of prospective data for intracranial aneurysms treated with PED at two institutions from 2013 to 2019. Aneurysms with near-complete or incomplete occlusion at initial follow-up were included in the statistical analysis. There were 606 total aneurysms treated at two academic institutions with PED with incomplete occlusion at initial follow-up in 134 aneurysms (22.1%). Of the 134 aneurysms that were nonoccluded at initial follow-up, 76 aneurysms (56.7%) went on to complete or near complete occlusion with final complete or near complete occlusion in 90.4% of all aneurysms treated. The time to final imaging follow-up was 28.2 months (13.8-44.3) Retreatment with a second flow diverter was used in 28 aneurysms (20.9%). No aneurysms that were incompletely occluded at initial follow-up had delayed rupture. Furthermore, older patient age was statistically significant for incomplete occlusion at initial follow-up ( P = .05). Intracranial aneurysms treated with the PED that do not occlude at initial follow-up may go on to complete occlusion with continuous observation, alteration in antiplatelet regimens, or repeat treatment. Delayed aneurysmal rupture was not seen in patients with incomplete occlusion.

Investigation of the Effect of Clot Deposition on the Risk of Abdominal Aortic Aneurysm Rupture by Engineering Methods

Abdominal aort anevrizması (AAA) karın bölgesinde yer alan aort damarının genişlemesine bağlı olarak oluşan bir kardiyovasküler hastalıktır. Tedavi edilmeyen AAA yapıları büyümeye devam etmekte ve yırtılma riski oluşturmaktadır. AAA yırtılması acil bir sağlık durumu olup, yırtılma vakalarının yarıdan fazlası ölümle sonuçlanmaktadır. Bu nedenle erken safhada AAA yapılarını teşhis edebilmek önem teşkil etmektedir. Yüksek seviyede genişleme olduğunda AAA içinde pıhtı yapıları oluşabilmektedir. Bu çalışmada, AAA içindeki pıhtı yapılarının yırtılma riski üzerindeki etkisi irdelenmiştir. Bu amaçla idealize edilmiş bir AAA modeli oluşturulmuştur. AAA modeli katı-sıvı etkileşimi göz önüne alınarak hesaplamalı akışkanlar dinamiği (HAD) analizleri ile incelenmiştir. Gerçekleştirilen analizler sonucunda pıhtı olan ve olmayan durumlar için farklı hemodinamik koşulların oluştuğu gözlenmiştir. AAA oluşumunun damar duvarı üzerinde oluşan mekanik gerilmeleri azalttığı görülmüştür. Yüksek seviyede pıhtı içeren AAA modelinin damar gerilme seviyeleri, pıhtı içermeyen AAA modeline kıyasla %12 daha düşüktür. Elde edilen sonuçlar, pıhtı yapılarının AAA hemodinamiğine ve damar duvarındaki mekanik gerilmelere olan etkisinin anlaşılmasına katkı sağlamıştır.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

MYCOTIC ANEURYSM OF INTERNAL ILIAC ARTERY CAUSED BY SALMONELLA SPP. INFECTION: A CASE REPORT

Mycotic aneurysm (MA) is an extremely rare disease caused by secondary infection, one of the most common causes is Salmonella spp. A male patient with type 2 diabetes mellitus presented with edema on the left lower limb, fever, abdominal pain radiating to the back, and was suspicious of Deep Vein Thrombosis (DVT). Laboratory examination showed leukocytosis and elevated inflammatory markers such as C-reactive protein and procalcitonin, with Salmonella spp. detected in the blood culture, and the image of MA with thrombus presented on the run-off CT scan with contrast. The patient was scheduled to undergo Endovascular Aneurysm Repair (EVAR) procedure. However, he suddenly had a cardiac arrest on the sixth day of treatment before surgery, caused by suspected pulmonary embolism with suspected DVT. MA has a high mortality rate (around 40%) with a high risk of aneurysm rupture, however, pulmonary embolism has been reported as a cause of death in MA patients with DVT. In addition to rapid diagnosis, aggressive yet adequate therapy, namely surgery and long-term intravenous antibiotics, is crucial.

Quantitative evaluation of the subsequent hemorrhage with arteriography-derived hemodynamic features in patients with untreated cerebral arteriovenous malformation.

Patients with untreated cerebral arteriovenous malformations (AVMs) are at risk of intracerebral hemorrhage. However, treatment to prevent AVM hemorrhage carries risks. This study aimed to analyze the AVM nidus-related hemodynamic features and identify the risk factors for subsequent hemorrhage. We retrospectively identified patients with untreated AVMs who were assessed at our institution between March 2010 and March 2021. Patients with ≥6 months of treatment-free and hemorrhage-free follow-up after diagnosed by digital subtraction angiography were included in subsequent examinations. The hemodynamic features were extracted from five contrast flow-related parameter maps. The Kaplan-Meier analyses and Cox proportional hazards regression models were used to find the potential risk factors for subsequent hemorrhage. Overall, 104 patients with a mean follow-up duration of 3.37 years (median, 2.42 years; range, 6-117 months) were included in study, and the annual risk of rupture was 3.7%. Previous rupture (hazard ratio [HR], 4.89; 95% confidence interval [CI], 1.16-20.72), deep AVM location (HR, 4.02; 95% CI, 1.01-15.99), higher cerebral blood volume (HR, 3.35; 95% CI, 1.15-9.74) in the nidus, and higher stasis index (HR, 1.54; 95% CI, 1.06-2.24) in the nidus were associated with subsequent hemorrhage in untreated AVMs. Higher cerebral blood volume and stasis index in the nidus suggest increased blood inflow and stagnant blood drainage. The combination of these factors may cause subsequent hemorrhage of AVMs.

Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications.

An aneurysm is a pathological widening of a blood vessel. Aneurysms of the aorta are often asymptomatic until they rupture, killing approximately 10,000 Americans per year. Fortunately, rupture can be prevented through early detection and surgical repair. However, surgical risk outweighs rupture risk for small aortic aneurysms, necessitating a policy of surveillance. Understanding the growth rate of aneurysms is essential for determining appropriate surveillance windows. Further, identifying risk factors for fast growth can help identify potential interventions. However, studies in the literature have applied many different methods for defining the growth rate of abdominal aortic aneurysms. It is unclear which of these methods is most accurate and clinically meaningful, and whether these heterogeneous methodologies may have contributed to the varied results reported in the literature. To help future researchers best plan their studies and to help clinicians interpret existing studies, we compared five different models of aneurysmal growth rate. We examined their noise tolerance, temporal bias, predictive accuracy, and statistical power to detect risk factors. We found that hierarchical mixed effects models were more noise tolerant than traditional, unpooled models. We also found that linear models were sensitive to temporal bias, assigning lower growth rates to aneurysms that were detected earlier in their course. Our exponential mixed model was noise-tolerant, resistant to temporal bias, and detected the greatest number of clinical risk factors. We conclude that exponential mixed models may be optimal for large studies. Because our results suggest that choice of method can materially impact a study's findings, we recommend that future studies clearly state the method used and demonstrate its appropriateness.

SAD score of intracranial aneurysms for rupture risk assessment based on high-resolution vessel wall imaging

ObjectiveWe aimed to develop a comprehensive model that integrates the radiological, morphological, and clinical factors to assess rupture risk for intracranial aneurysms. MethodsWe prospectively enrolled patients with intracranial saccular aneurysms who underwent high-resolution vessel wall imaging (HR-VWI) preoperatively. Clinical characteristics, aneurysm features and aneurysm wall enhancement scale (AWES) were recorded. AWES was categorized into three grades (no/faint/strong enhancement) by comparing AWE to enhancement of the pituitary infundibulum or choroid plexus on HR-VWI. Univariate and multivariate logistic regression analyses were performed to determine risk factors associated with aneurysmal rupture. ResultsA total of 25 ruptured and 116 unruptured aneurysms were included. Multivariate logistic regression analysis revealed that non-ICA site (OR 6.25, 95% CI 1.35–28.30, P = 0.019), AWES (OR 5.99, 95% CI 2.51–14.29, P < 0.001) and daughter sac or lobulated shape (OR 6.22, 95% CI 1.68–23.16, P = 0.006) were independent factors associated with ruptured aneurysms. The “SAD” model was generated and named after the first letters of each of these factors. SAD scores of 0–4 predicted 0, 2%, 12%, 42% and 100% ruptured aneurysms, respectively. The area under the receiver operating characteristic curve for the SAD model was 0.8822. ConclusionThe SAD model aids in distinguishing aneurysm rupture status and in managing unruptured aneurysms. Larger cohort studies are needed to confirm its applicability in predicting the rupture risk of unruptured aneurysms.

A novel parameter for the prediction of rupture risk of cerebral aneurysms based on morphology.

Neurosurgeons often encounter dilemmas in the clinical management of cerebral aneurysms owing to an uncertainty of their rupture status and rupture risk. This study evaluates the influence of natural frequency of an aneurysm, as a novel morphological parameter to understand and analyze rupture status and risk prediction. In this work, we employ the natural frequency of 20 idealized and 50 patient specific aneurysms. The natural frequency of patient specific aneurysms is then compared against their rupture status. A strong correlation was observed between various morphological indicators and natural frequency for ideal and patient specific geometries. A statistical analysis with both Mann Whitney U test and T-test for rupture status against natural frequency has given a p-value less than 0.01 indicating a strong correlation between them. The correlation of morphological parameters with natural frequency from Pearson correlation coefficient and T-test suggests a holistic reflection of their effects on the natural frequency of an aneurysm. Thus, natural frequency could be a good indicator to discern the rupture potential of an aneurysm. The correlation between rupture status and natural frequency makes it a novel parameter that can differentiate between ruptured and unruptured patient specific aneurysms.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

Renin-Angiotensin-Aldosterone System Inhibitors Are Associated With Favorable Outcomes Compared to Beta Blockers in Reducing Mortality Following Abdominal Aneurysm Repair.

Background The best medical therapy to control hypertension following abdominal aortic aneurysm repair is yet to be determined. We therefore examined whether treatment with renin-angiotensin-aldosterone system inhibitors (RAASIs) versus beta blockers influenced postoperative and 1-year clinical end points following abdominal aortic aneurysm repair in a Medicare-linked database. Methods and Results All patients with hypertension undergoing endovascular aneurysm repair and open aneurysm repair in the Vascular Quality Initiative Vascular Implant Surveillance and Interventional Outcomes Network database between 2003 and 2018 were included. Patients were divided into 2 groups based on their preoperative and discharge medications, either RAASIs or beta blockers. Our cohort included 8789 patients, of whom 3523 (40.1%) were on RAASIs, and 5266 (59.9%) were on beta blockers. After propensity score matching, there were 3053 matched pairs of patients in each group. After matching, RAASI use was associated with lower risk of postoperative mortality (odds ratio [OR], 0.3 [95% CI, 0.1-0.6]), myocardial infarction (OR, 0.1 [95% CI, 0.03-0.6]), and nonhome discharge (OR, 0.6 [95% CI, 0.5-0.7]). Before propensity score matching, RAASI use was associated with lower 1-year mortality (hazard ratio [HR], 0.4 [95% CI, 0.4-0.5]) and lower risk of aneurysmal rupture (HR, 0.7 [95% CI, 0.5-0.9]). These results persisted after propensity score matching for mortality (HR, 0.4 [95% CI, 0.4-0.5]) and aneurysmal rupture (HR, 0.7 [95% CI, 0.5-0.9]). Conclusions In this large contemporary retrospective cohort study, RAASI use was associated with favorable postoperative outcomes compared with beta blockers. It was also associated with lower mortality and aneurysmal rupture at 1 year of follow-up.

Percutaneous treatment of abdominal aortic aneurysm and aortic valve stenosis with ‘staged’ EVAR and TAVR: a case series

Symptomatic aortic valve stenosis (AS) and abdominal aortic aneurysm (AAA) are critical clinical conditions, increasingly more prevalent with aging of the population. Calcific aortic stenosis is the most common structural cardiac disease in the elderly population, and medical management of severe aortic stenosis of the elderly population is associated with poor outcomes as compared to surgical treatment. Transcatheter aortic valve replacement (TAVR) is a treatment of choice in inoperable, often elderly, patients with symptomatic severe AS and in intermediate-to-high surgical risk patients. It is not yet clarified the incidence of AAA and its impact on procedural and clinical outcomes among patients undergoing TAVR. It is known that after AS resolution with aortic valve replacement or TAVR there is an increase in blood pressure that increases the risk of dissection or abdominal aortic aneurysm rupture if AAA repair is delayed. The purpose of this report is to describe the anatomical details and technical and procedural considerations when proposing totally endovascular strategies dedicated to the treatment of patients with AS and AAA.

Aneurysm Prevalence and Quality of Life During Screening in Relatives of Patients With Unruptured Intracranial Aneurysms: A Prospective Study.

Screening for unruptured intracranial aneurysms (UIAs) is effective for first-degree relatives (FDRs) of patients with aneurysmal subarachnoid hemorrhage (aSAH). Whether screening is also effective for FDRs of patients with UIA is unknown. We determined the yield of screening in such FDRs, assessed rupture risk and treatment decisions of aneurysms that were found, identified potential high-risk subgroups, and studied the effects of screening on quality of life (QoL). In this prospective cohort study, we included FDRs, aged 20-70 years, of patients with UIA without a family history of aSAH who visited the Neurology outpatient clinic in 1 of 3 participating tertiary referral centers in the Netherlands. FDRs were screened for UIA with magnetic resonance angiography between 2017 and 2021. We determined UIA prevalence and developed a prediction model for UIA risk at screening using multivariable logistic regression. QoL was evaluated with questionnaires 6 times during the first year after screening and assessed with a linear mixed-effects model. We detected 24 UIAs in 23 of 461 screened FDRs, resulting in a 5.0% prevalence (95% CI 3.2-7.4). The median aneurysm size was 3 mm (interquartile range [IQR] 2-4 mm), and the median 5-year rupture risk assessed with the PHASES score was 0.7% (IQR 0.4%-0.9%). All UIAs received follow-up imaging, and none were treated preventively. After a median follow-up of 24 months (IQR 13-38 months), no UIA had changed. Predicted UIA risk at screening ranged between 2.3% and 14.7% with the highest risk in FDRs who smoke and have excessive alcohol consumption (c-statistic: 0.76; 95% CI 0.65-0.88). At all survey moments, health-related QoL and emotional functioning were comparable with those in a reference group from the general population. One FDR with a positive screening result expressed regret about screening. Based on the current data, we do not advise screening FDRs of patients with UIA because all identified UIAs had a low rupture risk. We observed no negative effect of screening on QoL. A longer follow-up should determine the risk of aneurysm growth requiring preventive treatment.

DeepInfusion: A dynamic infusion based-neuro-symbolic AI model for segmentation of intracranial aneurysms

The detection and segmentation of cerebral aneurysms is a crucial step in the development of a clinical decision support system for estimating aneurysm rupture risk. However, accurately identifying and segmenting regions of interest in two-dimensional (2D) medical images is often challenging, particularly when using deep learning (DL) methods on small datasets with limited annotated data. The accuracy of DL approaches is often affected by the availability of large, annotated training datasets that are required for effective deep learning. Additionally, when using DL to differentiate aneurysms from arterial loops in 2D DSA images, DL can fail to detect aneurysms in areas where dye concentration is low. To address these issues and enhance the reliability and accuracy of aneurysm detection and segmentation methods, incorporating medical expert-advised, hand-crafted features can provide a clinical perspective to DL methods. This approach can help to improve the performance of DL methods by providing additional information that is not captured in the data. To this end, a novel Neuro-symbolic AI-based DeepInfusion model is proposed which allows for the infusion of human intellect through hand-crafted features into deep neural networks (DNNs), thus combining the strengths of DL with the knowledge and expertise of medical professionals. The proposed approach includes a novel technique for dynamic layer selection and feature weight adjustment during the model infusion process. The performance of the DeepInfusion model is evaluated on an in-house prepared dataset of 409 DSA images, and experimental results demonstrate the effectiveness of the proposed method for the segmentation of cerebral aneurysms. The model achieves an IOU score of 96.76% and an F1-score of 94.15% on unseen DSA images. The model is also tested on two publicly available datasets of Kvasir-SEG polyp and DRIVE for vessel segmentation of retinal images. The results show a significant improvement compared to existing methods, which indicates the generalizability of the approach in medical segmentation. The complete code for DeepInfusion is available on our GitHub repository at https://github.com/smileslab/deep-infusion/blob/main/deepinfusion.ipynb.