[Future prospects for the development of a novel medical therapy for intracranial aneurysm]

Alcohol flushing syndrome is significantly associated with intracranial aneurysm rupture in the Chinese Han population

Objective

Although alcohol flushing syndrome (AFS) has been associated with various diseases, its association with intracranial aneurysm rupture (IAR) is unclear. We aimed to examine this association in the Chinese Han population.

Methods

We retrospectively reviewed Chinese Han patients with intracranial aneurysms who were evaluated and treated at our institution between January 2020 and December 2021. AFS was determined using a semi-structured telephone interview. Clinical data and aneurysm characteristics were assessed. Univariate and multivariate logistic regression were conducted to determine independent factors associated with aneurysmal rupture.

Results

A total of 1,170 patients with 1,059 unruptured and 236 ruptured aneurysms were included. The incidence of aneurysm rupture was significantly higher in patients without AFS (p < 0.001). Meanwhile, there was a significantly difference between the AFS and non-AFS group in habitual alcohol consumption (10.5 vs. 27.2%, p < 0.001). In the univariate analyses, AFS [odds ratio (OR) 0.49; 95% confidence interval (CI), 0.34–0.72] was significantly associated with IAR. In the multivariate analysis, AFS was an independent predictor of IAR (OR 0.50; 95%, CI, 0.35–0.71). Multivariate analysis revealed that AFS was an independent predictor of IAR in both habitual (OR 0.11; 95% CI, 0.03–0.45) and non-habitual drinkers (OR 0.69; 95% CI, 0.49–0.96).

Conclusion

Alcohol flushing syndrome may be a novel clinical marker to assess the risk of IAR. The association between AFS and IAR exists independently of alcohol consumption. Further single nucleotide polymorphism testing and molecular biology studies are warranted.

Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population

BACKGROUND AND PURPOSE

The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks.

METHODS

We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019. Baseline characteristics, familial history of IA, tobacco and alcohol consumption, pharmacological treatments before the IA diagnosis, cardiovascular risk factors and comorbidities, headaches, allergy and atopy, IA location, absolute IA size and adjusted size ratio (aSR) were analysed with a multivariable logistic regression (MLR) model. A random forest (RF) method globally assessed the risk factors and evaluated the predictive capacity of a multivariate model.

RESULTS

Among 994 patients with RIA (39.7%) and 1511 patients with UIA (60.3 %), the MLR showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery, reference; middle cerebral artery, 2.72, 2.02-3.58; anterior cerebral artery, 4.99, 3.61-6.92; posterior circulation arteries, 6.05, 4.41-8.33). Size and aSR were not significant factors associated with RIA in the MLR model and antiplatelet-treatment intake patients were less likely to have RIA (OR: 0.74; 95% CI: 0.55-0.98). IA location, age, following by aSR were the best predictors of RIA using the RF model.

CONCLUSIONS

The location of IA is the most consistent parameter associated with RIA. The use of 'artificial intelligence' RF helps to re-evaluate the contribution and selection of each risk factor in the multivariate model.