Background and purpose Asymptomatic intracranial hemorrhage (aICH) is a common occurrence after endovascular treatment (EVT) for acute ischemic stroke (AIS). The aims of this study were to address its impact on 3‐month functional outcome and to identify risk factors for aICH after EVT. Methods Patients with AIS attributable to anterior circulation large vessel occlusion who underwent EVT were enrolled in a multicenter prospective registry. Based on imaging performed 22–36 h post‐EVT, we included patients with no intracranial hemorrhage (ICH) or aICH. Poor outcome defined as a 3‐month modified Rankin Scale (mRS) score 4–6 and overall 3‐month mRS score distribution were compared according to presence/absence of aICH, and aICH subtype using logistic regression. We assessed the risk factors of aICH using a multivariate logistic regression model. Results Of the 1526 patients included in the study, 653 (42.7%) had aICH. Patients with aICH had a higher rate of poor outcome: odds ratio (OR) 1.88 (95% confidence interval [CI] 1.44–2.44). Shift analysis of mRS score found a fully adjusted OR of 1.79 (95% CI 1.47–2.18). Hemorrhagic infarction (OR 1.63 [95% CI 1.22–2.18]) and parenchymal hematoma (OR 2.99 [95% CI 1.77–5.02]) were associated with higher risk of poor outcome. Male sex, diabetes, coronary artery disease, baseline National Institutes of Health Stroke Scale score and Alberta Stroke Program Early Computed Tomography Score, number of passes and onset to groin puncture time were independently associated with aICH. Conclusions Patients with aICH, irrespective of the radiological pattern, have a worse functional outcome at 3 months compared with those without ICH after EVT for AIS. The number of EVT passes and the time from onset to groin puncture are factors that could be modified to reduce deleterious ICH.

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.

Background and purposeThe 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.MethodsWe 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.ResultsAmong 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.ConclusionsThe 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.

No abstract