Location specific rupture risk of intracranial aneurysms: case of ophthalmic aneurysms.

BACKGROUND: Aneurysm location is a key element in predicting the rupture risk of an intracranial aneurysm. A common impression suggests that pure ophthalmic aneurysms are under-represented in ruptured intracranial aneurysms (RIAs). The purpose of this study was to specifically evaluate the risk of rupture of ophthalmic aneurysms compared with other aneurysm locations. METHODS: This multicenter study compared the frequency of ophthalmic aneurysms in a prospective cohort of RIAs admitted to 13 neuroradiology centers between January 2021 and March 2021, with a retrospective cohort of patients with unruptured intracranial aneurysms (UIAs) who underwent cerebral angiography at the same neuroradiology centers during the same time period. RESULTS: 604 intracranial aneurysms were included in this study (355 UIAs and 249 RIAs; mean age 57 years (IQR 49-65); women 309/486, 64%). Mean aneurysm size was 6.0 mm (5.3 mm for UIAs, 7.0 mm for RIAs; P<0.0001). Aneurysm shape was irregular for 37% UIAs and 73% RIAs (P<0.0001). Ophthalmic aneurysms frequency was 14.9% of UIAs (second most common aneurysm location) and 1.2% of RIAs (second least common aneurysm location; OR 0.07 (95% CI 0.02 to 0.23), P<0.0001). CONCLUSIONS: Ophthalmic aneurysms seem to have a low risk of rupture compared with other intracranial aneurysm locations. This calls for a re-evaluation of the benefit-risk balance when considering preventive treatment for ophthalmic aneurysms.

Risk Prediction of New Intracranial Aneurysms at Follow-Up Screening in People With a Positive Family History.

BACKGROUND: In first-degree relatives of patients with aneurysmal subarachnoid hemorrhage (aSAH), the risk of an intracranial aneurysm can be predicted at initial screening but not at follow-up screening. We aimed to develop a model for predicting the probability of a new intracranial aneurysm after initial screening in people with a positive family history of aSAH. METHODS: In a prospective study, we obtained data from follow-up screening for aneurysms of 499 subjects with ≥2 affected first-degree relatives. Screening took place at the University Medical Center Utrecht, the Netherlands, and the University Hospital of Nantes, France. We studied associations between potential predictors and the presence of aneurysms using Cox regression analysis and the predictive performance at 5, 10, and 15 years after initial screening using C statistics and calibration plots, while correcting for overfitting. RESULTS: In 5050 person-years of follow-up, intracranial aneurysms were found in 52 subjects. The risk of aneurysm at 5 years was 2% to 12%, at 10 years, 4% to 28%, and at 15 years, 7% to 40%. Predictors were female sex, history of intracranial aneurysms/aneurysmal subarachnoid hemorrhage, and older age. The sex, previous history of intracranial aneurysm/aSAH, older age score had a C statistic of 0.70 (95% CI, 0.61-0.78) at 5 years, 0.71 (95% CI, 0.64-0.78) at 10 years, and 0.70 (95% CI, 0.63-0.76) at 15 years and showed good calibration. CONCLUSIONS: The sex, previous history of intracranial aneurysm/aSAH, older age score provides risk estimates for finding new intracranial aneurysms at 5, 10, and 15 years after initial screening, based on 3 easily retrievable predictors; this can help to define a personalized screening strategy after initial screening in people with a positive family history for aSAH.

Number of Affected Relatives, Age, Smoking, and Hypertension Prediction Score for Intracranial Aneurysms in Persons With a Family History for Subarachnoid Hemorrhage.

BACKGROUND: Persons with a positive family history of aneurysmal subarachnoid hemorrhage are at increased risk of aneurysmal subarachnoid hemorrhage. Preventive screening for intracranial aneurysms (IAs) in these persons is cost-effective but not very efficient. We aimed to develop and externally validate a model for predicting the probability of an IA at first screening in persons with a positive family history of aneurysmal subarachnoid hemorrhage. METHODS: For model development, we studied results from initial screening for IA in 660 prospectively collected persons with ≥2 affected first-degree relatives screened at the University Medical Center Utrecht. For validation, we studied results from 258 prospectively collected persons screened in the University Hospital of Nantes. We assessed potential predictors of IA presence in multivariable logistic regression analysis. Predictive performance was assessed with the C statistic and a calibration plot and corrected for overfitting. RESULTS: IA were present in 79 (12%) persons in the development cohort. Predictors were number of affected relatives, age, smoking, and hypertension (NASH). The NASH score had a C statistic of 0.68 (95% CI, 0.62-0.74) and showed good calibration in the development data. Predicted probabilities of an IA at first screening varied from 5% in persons aged 20 to 30 years with two affected relatives, without hypertension who never smoked, up to 36% in persons aged 60 to 70 years with ≥3 affected relatives, who have hypertension and smoke(d). In the external validation data IA were present in 67 (26%) persons, the model had a C statistic of 0.64 (95% CI, 0.57-0.71) and slightly underestimated IAs risk. CONCLUSIONS: For persons with ≥2 affected first-degree relatives, the NASH score improves current predictions and provides risk estimates for an IA at first screening between 5% and 36% based on 4 easily retrievable predictors. With the information such persons can now make a better informed decision about whether or not to undergo preventive screening.

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.