A Meta-Analysis of Rupture Risk for Intracranial Aneurysms 10 mm or Less in Size Selected for Conservative Management Without Repair

A multiscale discrete fiber model of failure in heterogeneous tissues: Applications to remodeled cerebral aneurysms

Damage-accumulation failure models are broadly used to examine tissue property changes caused by mechanical loading. However, damage accumulation models are purely phenomenological. The underlying justification in using this type of model is often that damage occurs to the extracellular fibers and/or cells which changes the fundamental mechanical behavior of the system. In this work, we seek to align damage accumulation models with microstructural models to predict alterations in the mechanical behavior of biological materials that arise from structural heterogeneity associated with nonuniform remodeling of tissues. Further, we seek to extend this multiscale model toward assessing catastrophic failure events such as cerebral aneurysm rupture. First, we demonstrate that a model made up of linear elastin and actin and nonlinear collagen fibers can replicate bot the pre-failure and failure tissue-scale mechanics of uniaxially-stretched cerebral aneurysms. Next, we investigate how mechanical heterogeneities, like those observed in cerebral aneurysms, influence fiber and tissue failure. Notably, we find that failure occurs and the interface between regions of high and low material stiffness, suggesting that spatial mechanical heterogeneity influences aneurysm failure behavior. This model system is a step toward linking structural changes in growth and remodeling to failure properties.

Pharmaceutical Modulation of Intracranial Aneurysm Development and Rupture

Management of intracranial aneurysms (IAs) is determined by patient age, risk of rupture, and comorbid conditions. While endovascular and microsurgical interventions offer solutions to mitigate the risk of rupture, pharmacological management strategies may complement these approaches or serve as alternatives in appropriate cases. The pathophysiology of IAs allows for the targeting of inflammation to prevent the development and rupture of IAs. The aim of this review is to provide an updated summary of different pharmaceutical management strategies for IAs. Acetylsalicylic acid and renin-angiotensin-aldosterone system (RAAS) inhibitor antihypertensives have some evidence supporting their protective effect. Studies of selective cyclooxygenase-2 (COX-2) inhibitors, statins, ADP inhibitors, and other metabolism-affecting drugs have demonstrated inconclusive findings regarding their association with aneurysm growth or rupture. In this manuscript, we highlight the evidence supporting each drug's effectiveness.

Preoperative Risk Assessment Before Elective Craniotomy: Are Aspirin, Arrhythmias, Deep Venous Thromboses, and Hyperglycemia Contraindications to Surgery?

OBJECTIVE

Perioperative risk assessment and stratification before craniotomy is necessary to identify and optimize modifiable risk factors. Due to the high costs of diagnostic testing and concerns for delaying surgery, some have questioned whether and when surgery delays are warranted and supported by the current body of literature. The objective of this scoping review was to evaluate the available evidence on the prognostic value of preoperative risk assessment before anesthesia for elective craniotomy.

METHODS

In this scoping review, we reviewed 156 papers that assess preoperative risk assessment before elective craniotomy, of which 27 papers were included in the final analysis.

RESULTS

There is little high-quality evidence to suggest significant risk reduction when 4 common preexisting abnormalities are present: preoperative chronic aspirin therapy, cardiac arrhythmias, deep vein thrombosis, or hyperglycemia.

CONCLUSIONS

The risk of delaying craniotomy should ultimately be weighed against the perceived risks associated the patient's comorbid conditions and should be considered on an individualized basis.

Using DFT on ultrasound measurements to determine patient-specific blood flow boundary conditions for computational hemodynamics of intracranial aneurysms

Boundary conditions (BCs) is one pivotal factor influencing the accuracy of hemodynamic predictions on intracranial aneurysms (IAs) using computational fluid dynamics (CFD) modeling. Unfortunately, a standard procedure to secure accurate BCs for hemodynamic modeling does not exist. To bridge such a knowledge gap, two representative patient-specific IA models (Case-I and Case-II) were reconstructed and their blood flow velocity waveforms in the internal carotid artery (ICA) were measured by ultrasonic techniques and modeled by discrete Fourier transform (DFT). Then, numerical investigations were conducted to explore the appropriate number of samples (N) for DFT modeling to secure the accurate BC by comparing a series of hemodynamic parameters using in-vitro validated CFD modeling. Subsequently, a comprehensive comparison in hemodynamic characteristics under patient-specific BCs and a generalized BC based on a one-dimensional (1D) model was conducted to reinforce the understanding that a patient-specific BC is pivotal for accurate hemodynamic risk evaluations on IA pathophysiology. In addition, the influence of the variance of heart rate/cardiac pulsatile period on hemodynamic characteristics in IA models was studied preliminarily. The results showed that N ≥ 16 for DFT model is a decent choice to secure the proper BC profile to calculate time-averaged hemodynamic parameters, while more data points such as N ≥ 36 can ensure the accuracy of instantaneous hemodynamic predictions. In addition, results revealed the generalized BC could overestimate or underestimate the hemodynamic risks on IAs significantly; thus, patient-specific BCs are highly recommended for hemodynamic modeling for IA risk evaluation. Furthermore, this study discovered the variance of heart rate has rare influences on hemodynamic characteristics in both instantaneous and time-averaged parameters under the assumption of an identical blood flow rate.

A multicenter study of the efficacy and safety of treatments (endovascular or conservative) in small intracranial aneurysms in Colombia

BACKGROUND

The registry of cerebral aneurysms <5 mm, known for their low risk of rupture, is significant, given their high incidence globally. Our study aimed to identify, in small aneurysms (<5 mm), the potential morphological characteristics, risk factors that can predict the risk of rupture, and the risk or benefit of treating them with endovascular or conservative treatment in ruptured and unruptured intracranial aneurysms.

METHODS

The medical records of patients with cerebral aneurysms <5 mm were retrospectively reviewed between January 2014 and December 2022 at two neurovascular centers in Colombia. We evaluated clinical and angiographic outcomes using statistical tests.

RESULTS

Two hundred fifty-six patients (425 intracranial aneurysms) were registered in the database. Two hundred and seventy-five IA were treated with endovascular treatment: 70 ruptured aneurysms and 205 unruptured aneurysms. One hundred fifty intracranial aneurysms underwent conservative treatment (follow-up). Women accounted for 82.1% of cases. Most cases were incidentally diagnosed (83.5%). After a year of follow-up, 87.3% of unruptured and 67.1% of ruptured intracranial aneurysms had an mRS 0-2. In the Raymond-Roy occlusion classification, among 101 unruptured intracranial aneurysms embolized were 53 cases class I, and among 66 ruptured intracranial aneurysms embolized, 67.1% were class I.

CONCLUSION

Endovascular therapy for aneurysms <5 mm appears to be a technically feasible treatment, with satisfactory occlusion rates and few re-treatments at the 12-month follow-up. The complication rates were similar to those reported in studies on small aneurysms.

Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.