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

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, 15365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RE), 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 RE 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.