ObjectiveTo develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.MethodsA total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.ResultsA total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.ConclusionPresurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.

The recent global pandemic of coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although respiratory symptoms are the primary manifestation of the majority of COVID-19 patients, an increasing number of neurological symptoms and manifestations of COVID-19 have been observed. In this review, we detail the neurological complications of COVID-19, such as gustatory and olfactory dysfunctions, stroke, memory decline, muscle injury, and seizures. Furthermore, we introduce neural invasion mechanism underlying SARS-CoV-2 infection and, further, explain the occurrence of these complications. This review offers insights into the neurological signs and symptoms of COVID-19, which may help improve the prognosis of the infected patients.