BACKGROUND AND PURPOSE: Parenchymal hemorrhage is a severe complication following mechanical recanalization in patients with acute ischemic stroke with large-vessel occlusion. This study aimed to assess whether the metallic hyperdensity sign on noncontrast CT performed immediately after mechanical thrombectomy can predict parenchymal hemorrhage at 24 hours. MATERIALS AND METHODS: We included consecutive patients with acute ischemic stroke with large-vessel occlusion who underwent noncontrast CT immediately after mechanical thrombectomy between January 2014 and September 2018. The metallic hyperdensity sign was defined as a nonpetechial intracerebral hyperdense lesion (diameter, ≥1 cm) in the basal ganglia and a maximum CT density of >90 HU. The sensitivity, specificity, and positive and negative predictive values of the metallic hyperdensity sign in predicting parenchymal hemorrhage were calculated. RESULTS: A total of 198 patients were included. The metallic hyperdensity sign was found in 59 (29.7%) patients, and 51 (25.7%) patients had parenchymal hemorrhage at 24 hours. Patients with the metallic hyperdensity sign are more likely to have parenchymal hemorrhage than those without it (76.3% versus 4.3%, P < .001). The sensitivity, specificity, positive predictive value, and negative predictive value of the metallic hyperdensity sign in predicting parenchymal hemorrhage were 88.2%, 90.5%, 76.3%, and 95.7%, respectively. CONCLUSIONS: The presence of the metallic hyperdensity sign on noncontrast CT performed immediately after mechanical thrombectomy in patients with large-vessel occlusion could predict the occurrence of parenchymal hemorrhage at 24 hours, which might be helpful in postinterventional management within 24 hours after mechanical thrombectomy.

Background: Early neurological deterioration occurs in approximately 10% acute ischemic stroke patients after thrombolysis. Over half of the early neurological deterioration occurred without known causes and is called unexplained early neurological deterioration. Aims: We aimed to explore the development of early neurological deterioration at 24 h after thrombolysis, and whether it could be predicted by the presence of baseline hypoperfusion in lenticulostriate arteries territory in acute ischemic stroke patients. Methods: We retrospectively reviewed our prospectively collected database of acute ischemic stroke patients in the unilateral middle cerebral artery territory who had baseline perfusion image and received thrombolysis. Unexplained early neurological deterioration was defined as ≥ 2 points increase of National Institutes of Health Stroke Scale (NIHSS) from baseline to 24 h, without known causes. Hypoperfusion lesions in different territories were identified on perfusion maps. Results: A total of 306 patients were included in analysis. Patients with pure lenticulostriate arteries hypoperfusion (defined as the presence of hypoperfusion in lenticulostriate artery territory, but not in middle cerebral artery terminal branch territory) were more likely to have unexplained early neurological deterioration than others (27.6% vs. 6.1%; OR, 5.974; p = 0.001), after adjusting for age, baseline NIHSS and onset to treatment time. Conclusions: Patients presenting hypoperfusion in pure lenticulostriate arteries territory were easier to experience unexplained early neurological deterioration.

BACKGROUND AND PURPOSE: Thrombus permeability assessed on conventional CTA is associated with neurologic outcome in patients with acute ischemic stroke. We aimed to investigate whether dynamic CTA can improve the accuracy of thrombus permeability assessment and its predictive value for outcome. MATERIALS AND METHODS: We reviewed consecutive patients with acute ischemic stroke who had occlusion of the M1 segment of the middle artery cerebral artery and underwent pretreatment perfusion CT. Thrombus permeability, determined by thrombus attenuation increase (TAI), was assessed on 26-phase dynamic CTA derived from perfusion CT. TAI_max was defined as the maximum TAI among phases; TAI_peak, as TAI of peak arterial phase; TAI_con, as TAI on phase 13. Good outcome was defined as a 3-month mRS score of ≤2. RESULTS: One hundred four patients were enrolled in the final analysis. The median TAI_max, TAI_peak, and TAI_con were 30.1 HU (interquartile range, 13.0–50.2 HU), 9.5 HU (interquartile range, −1.6–28.7 HU), and 6.6 HU (interquartile range, −5.1–24.4 HU), respectively. Multivariable regression analyses showed that TAI_max (OR = 1.027; 95% CI, 1.007–1.048; P = .008), TAI_peak (OR = 1.029; 95% CI, 1.005–1.054; P = .020), and TAI_con (OR = 1.026; 95% CI, 1.002–1.051; P = .037) were independently associated with good outcome. The areas under the ROC curve of TAI_max, TAI_peak, and TAI_con in predicting good outcome were 0.734, 0.701, and 0.658, respectively. CONCLUSIONS: Thrombus permeability assessed on dynamic CTA could be a better predictor of outcome after reperfusion therapy than that assessed on conventional single-phase CTA.

Background: The benefit of alteplase in minor non-disabling acute ischemic stroke (AIS) is unknown. We aimed to explore the clinical efficacy of alteplase-treatment in minor non-disabling stroke in clinical practice. Methods: We used a prospectively collected database of AIS patients who were being assessed for thrombolysis with alteplase. Minor non-disabling AIS was identified as patients with baseline National Institutes of Health Stroke Scale (NIHSS) score ≤ 5 and a score 0 or 1 on each baseline NIHSS score item (items 1a to 1c being 0). Results: A total of 461 patients with minor non-disabling AIS were included and among them 240 (52.1%) patients were treated with alteplase and 113 (24.5%) patients had severe stenosis/occlusion of large vessels. No significant association of 90-day excellent outcome was found with alteplase-treatment (77.1% vs. 80.5%, p 1 = 0.425; OR 0.911, 95% CI 0.428 to 1.940; p 2 = 0.808). However, among patients with severe stenosis/occlusion of large vessels, alteplase-treatment was independently associated with excellent outcome (74.4% vs. 45.7%, p 1 = 0.005; OR 4.709, 95% CI 1.391 to 11.962; p 2 = 0.010). Conclusion: Although alteplase-treatment did not result in an excellent outcome in general minor non-disabling stroke patients, it may work in those specific patients who had severe stenosis/occlusion of large vessels.

Background: Prognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging.Objective: This study aimed to develop a model to identify the cause of stroke using machine learning (ML) methods and test its accuracy.Methods: We retrospectively reviewed the data of patients who had determined etiology defined by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) from CASE-II (NCT04487340) to train and evaluate six ML models, namely, Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Ada Boosting, Gradient Boosting Machine (GBM), for the detection of cardioembolism (CE), large-artery atherosclerosis (LAA), and small-artery occlusion (SAO). Between October 2016 and April 2020, patients were enrolled consecutively for algorithm development (phase one). Between June 2020 and December 2020, patients were enrolled consecutively in a test set for algorithm test (phase two). Area under the curve (AUC), precision, recall, accuracy, and F1 score were calculated for the prediction model.Results: Finally, a total of 18,209 patients were enrolled in phase one, including 13,590 patients (i.e., 6,089 CE, 4,539 LAA, and 2,962 SAO) in the model, and a total of 3,688 patients were enrolled in phase two, including 3,070 patients (i.e., 1,103 CE, 1,269 LAA, and 698 SAO) in the model. Among the six models, the best models were RF, XGBoost, and GBM, and we chose the RF model as our final model. Based on the test set, the AUC values of the RF model to predict CE, LAA, and SAO were 0.981 (95%CI, 0.978–0.986), 0.919 (95%CI, 0.911–0.928), and 0.918 (95%CI, 0.908–0.927), respectively. The most important items to identify CE, LAA, and SAO were atrial fibrillation and degree of stenosis of intracranial arteries.Conclusion: The proposed RF model could be a useful diagnostic tool to help neurologists categorize etiologies of stroke.Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT01274117].