Effect of intracranial venous collaterals on neurological outcomes in cerebral venous thrombosis.

This study identifies predictors of favourable intracranial venous collaterals and the effect of intracranial venous collaterals on outcomes and recanalization in patients with cerebral venous thrombosis (CVT). Data of 61 patients with CVT were retrospectively reviewed. Venous collateralization was defined as expanded cortical vein formation through different drainage pathways. Recanalization grades were classified into complete or partial recanalization based on images obtained during hospitalisation and follow-up. Independent predictors of collateral formation and poor prognosis were investigated via univariate and binary logistic regression analyses. The effects of different intracranial venous collaterals on recanalization in patients with CVT were assessed. A risk prediction nomogram for prognosis was constructed. Age ≤ 35 years (odds ratio (OR) = 7.067; 95% confidence interval (CI) = 1.776-28.277; P = 0.006) and male sex (OR = 5.490; 95% CI = 1.205-25.004; P = 0.028) were independent predictors of favourable venous collaterals. Venous collaterals were associated with early recanalization (P = 0.017) and not with long-term recanalization (P = 0.252). Male sex (OR = 0.047; 95% CI = 0.003-0.651; P = 0.023), subacute onset (OR = 0.026; 95% CI = 0.002-0.367; P = 0.007), and good collateral grade (OR = 0.168; 95% CI = 0.029-0.985; P = 0.048) were independent factors of favourable neurological outcomes at discharge. Haemorrhage on computed tomography at admission (OR = 10.868; 95% CI = 2.082-56.733; P = 0.005) was inversely correlated with prognosis. These findings suggested that male patients under 35 years of age are more likely to have favourable venous collaterals and good outcomes. Venous collaterals are significantly associated with early recanalization. These findings highlight the importance of venous collateral evaluation in patients with CVT.

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion.

Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission. Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Subtypes of anterior circulation large artery occlusions with acute brain ischemic stroke.

Anterior circulation large artery occlusion (AC-LAO) related acute ischemic stroke (AIS) is particularly common in clinics in China. We retrospectively analyzed 787 consecutively hospitalized AIS patients with AC-LAO in Hebei Province, China. AC-LAO was defined as a complete occlusion of at least one intracranial internal carotid artery (ICA) or middle cerebral artery (MCA) based on computed tomography or magnetic resonance angiography. Among eight subtypes of AC-LAO, unilateral MCA occlusion is the most common one (49.8%, n = 392), while bilateral ICA/unilateral MCA occlusion is the least (0.3%, n = 2). Compared with unilateral MCA and unilateral ICA occlusion, patients with tandem ICA/MCA and bilateral ICA/MCA occlusion had poor outcomes after suffering AIS. Age (OR 1.022; 95%CI, 1.007 to 1.036) was an independent risk factor for single artery progressed to multiple artery occlusion, while ApoA1 (OR 0.453; 95% CI, 0.235 to 0.953) was a protective factor. Patients with unilateral MCA occlusion were prone to artery-to-artery embolism infarction subtype, unilateral ICA occlusion group were the most vulnerable to hypoperfusion/impaired emboli clearance subtype. Our results suggested various AC-LAO subtypes have different clinical characteristics and prognosis and were prone to different subtypes of infarction. Customized preventive measures based on AC-LAO subtypes may be more targeted preventions of stroke recurrences for AIS patients and could improve their prognoses.