Background: Endovascular therapy (EVT) was the standard treatment for acute ischemic stroke with large vessel occlusion. Prognosis after EVT is always a major concern. Here, we aimed to explore a predictive model for patients after EVT. Method: A total of 156 patients were retrospectively enrolled. The primary outcome was functional dependence (defined as a 90-day modified Rankin Scale score ≤ 2). Least absolute shrinkage and selection operator and univariate logistic regression were used to select predictive factors. Various machine learning algorithms, including multivariate logistic regression, linear discriminant analysis, support vector machine, k-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models. Result: Six predictive factors were selected, namely, age, baseline National Institute of Health Stroke Scale (NIHSS) score, Alberta Stroke Program Early CT (ASPECT) score, modified thrombolysis in cerebral infarction score, symptomatic intracerebral hemorrhage (sICH), and complications (pulmonary infection, gastrointestinal bleeding, and cardiovascular events). Based on these variables, various models were constructed and showed good discrimination. Finally, a nomogram was constructed by multivariate logistic regression and showed a good performance. Conclusion: Our nomogram, which was composed of age, baseline NIHSS score, ASPECT score, recanalization status, sICH, and complications, showed a very good performance in predicting outcome after EVT.

Objective: Moderate-to-severe stenosis has been identified as a significant risk factor for stroke recently. This study aims to investigate the relationship between non-traditional lipid parameters and the location and distribution of stenosis, as well as symptomatic events, in patients with moderate-to-severe intracranial atherosclerotic stenosis (ICAS) and extracranial atherosclerotic stenosis (ECAS).Methods: This study analyzed correlation between non-traditional lipid parameters and moderate-to-severe ICAS and ECAS concerning stenosis location, distribution, and the presence or absence of symptoms. Logistic models and restricted spline analysis were utilized to explore the relationship between Castelli's risk index-II (CRI-II) and the occurrence of stroke events.Results: The present study comprised 1,030 participants, of whom 143 were non-stenotic and 887 were patients with moderate-to-severe stenosis. The study focuses on the latter and indicated statistically significant differences in AIP, LCI, RC, AC, CRI-I, and CRI-II among the three groups of ICAS, ECAS, and combined ICAS and ECAS (P = 0.012, 0.005, 0.013, 0.009, 0.009, 0.032, respectively). Lipid parameters for ICAS were generally higher than those for ECAS. Remnant cholesterol (RC) exhibited a discrepancy among the anterior, posterior, and combined anterior and posterior circulation stenosis groups (P = 0.047). Logistic regression analysis revealed that CRI-II (Odds ratio [OR] = 1.20, Confidence interval [CI] 1.03–1.40, P = 0.009) and low-density lipoprotein cholesterol (LDL-c) (OR = 1.21, CI 1.03–1.42, P = 0.011) demonstrated remarkable elevations in symptomatic stenosis patients compared to patients without symptoms. After adjusting for potential confounding factors, CRI-II remained an independent risk factor for symptomatic stenosis. Furthermore, multivariate spline regression modeling elucidated that an augmented risk of stroke events in moderate-to-severe stenosis was associated with an elevated CRI-II. As CRI-II elevated, the risk of stroke events increased progressively.

Pretreatment ischemic location may be an important determinant for functional outcome prediction in acute ischemic stroke. In total, 143 anterior circulation ischemic stroke patients in the THRACE study were included. Ischemic lesions were semi-automatically segmented on pretreatment diffusion-weighted imaging and registered on brain atlases. The percentage of ischemic tissue in each atlas-segmented region was calculated. Statistical models with logistic regression and support vector machine were built to analyze the predictors of functional outcome. The investigated parameters included: age, baseline National Institutes of Health Stroke Scale score, and lesional volume (three-parameter model), together with the ischemic percentage in each atlas-segmented region (four-parameter model). The support vector machine with radial basis functions outperformed logistic regression in prediction accuracy. The support vector machine three-parameter model demonstrated an area under the curve of 0.77, while the four-parameter model achieved a higher area under the curve (0.82). Regions with marked impacts on outcome prediction were the uncinate fasciculus, postcentral gyrus, putamen, middle occipital gyrus, supramarginal gyrus, and posterior corona radiata in the left hemisphere; and the uncinate fasciculus, paracentral lobule, temporal pole, hippocampus, inferior occipital gyrus, middle temporal gyrus, pallidum, and anterior limb of the internal capsule in the right hemisphere. In conclusion, pretreatment ischemic location provided significant prognostic information for functional outcome in ischemic stroke.