Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests

Jianan Wang, Jungen Zhang, Xiaoxian Gong, Wenhua Zhang, Ying Zhou, Min Lou, Jianan Wang, Jungen Zhang, Xiaoxian Gong, Wenhua Zhang, Ying Zhou, Min Lou

Abstract

Backgrounds: The timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine learning (ML) model to predict LVO using prehospital accessible data.

Methods: Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included. We developed eight ML models to integrate National Institutes of Health Stroke Scale (NIHSS) items with demographics, medical history and vascular risk factors to identify LVO and validate its efficiency.

Results: Finally, 15 365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RF), gradient boosting machine and extreme gradient boosting presented area under the curve (AUC) of 0.831 (95% CI 0.819 to 0.843), which were higher than other models, and RF presented the highest specificity (0.827). In addition, the AUC of RF was higher than other scales, and the accuracy of the model was improved by 6.4% compared with NIHSS. We also found the top five items of identifying LVO were total NIHSS score, gaze deviation, level of consciousness (LOC), LOC commands and motor left leg.

Conclusions: Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data.

Trial registration number: NCT04487340.

Keywords: arteries; stroke.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Flow chart of the study population and process. AIS, acute ischaemic stroke; CTA, CT angiography; NIHSS, National Institutes of Health Stroke Scale; TOF-MRA, time of flight MR angiography.
Figure 2
Figure 2
Illustration of features contributing to identification of LVO by Gini importance values. Gini importance is a measurement of the feature importance in the model, the higher the value of Gini importance is, the more important the feature is. LOC, level of consciousness; NIHSS, National Institutes of Health Stroke Scale.

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