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

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion

OBJECTIVE

Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency.

METHODS

Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection.

RESULTS

We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89-0.91).

INTERPRETATION

Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.

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.