Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke

Clinical predictors of prognosis in stroke patients after endovascular therapy

Endovascular therapy (EVT) is effective in the treatment of large vascular occlusive stroke. However, many factors are associated with the outcomes of acute ischemic stroke (AIS) after EVT. This study aimed to identify the main factors related to the prognosis of AIS patients after EVT. We analyzed the clinical data of AIS patients in the neurology department of our medical center from June 2017 to August 2021 following treatment with EVT. The data included the patients' blood pressure upon admission, blood glucose concentration, National Institutes of Health Stroke Scale (NIHSS) score, 90-day modified Rankin scale (mRs) score follow-up data, and time from LKN to the successful groin puncture (GP). A good outcome was defined as a 90-day mRs score of 0-2, and a poor outcome was defined as a 90-day mRs score of 3-6. A total of 144 patients were included in the study. Admission, smoking, and LKN-to-GP time, NIHSS score of 6-12 was found to be relevant to the prognosis. The results of multivariate analysis showed that prognosis was significantly influenced by baseline NIHSS (odds ratio = 3.02; 95% confidence interval, 2.878-4.252; P = 0.001), LKN-to-GP time (odds ratio = 2.17; 95% confidence interval, 1.341-2.625; P = 0.003), and time stratification (6-12 h) (odds ratio = 4.22; 95% confidence interval, 2.519-5.561; P = 0.001). Our study indicated that smoking, baseline NIHSS score, and LKN-to-GP time were the risk factors for a poor outcome in stroke patients following an EVT. Quitting smoking and shortening LKN time to GP should improve the outcome of AIS after EVT.

Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

The quick advancement of digital technology through artificial intelligence has made it possible to deploy machine learning to predict stroke outcomes. Our aim is to examine the trend of machine learning applications in stroke-related research over the past 50 years. We used search terms stroke and machine learning to search for English versions of original and review articles and conference proceedings published over the past 50 years in Scopus and Web of Science databases. The Biblioshiny web application was utilized for the analysis. The trend of publication and prominent authors and journals were analyzed and identified. The collaborative network between countries was mapped, and a thematic map was used to monitor the authors' trending keywords. In total, 10,535 publications authored by 44,990 authors from 2,212 sources were retrieved. Two distinct clusters of collaborative network nodes were observed, with the United States serving as a connecting node. Three terms - deep learning, algorithms, and neural networks - are observed in the early stages of the emerging theme. Overall, international research collaborations, the establishment of global research initiatives, the development of computational science, and the availability of big data have facilitated the pervasive use of machine learning techniques in stroke research.